Prostate cancer (PCa) diagnosis with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment, highlighting the need for novel biomarkers. The aim of this review is to provide a summary of available metabolomics PCa biomarkers, particularly for clinically significant disease. A systematic search was conducted on PubMed for publications from July 2008 to July 2018 in accordance with PRISMA guidelines to report biomarkers with respect to their application in PCa diagnosis, progression, aggressiveness, recurrence, and treatment response. The vast majority of studies report biomarkers with the ability to distinguish malignant from benign prostate tissue with a few studies investigating biomarkers associated with disease progression, treatment response or tumour recurrence. In general, these studies report high dimensional datasets and the number of analysed metabolites often significantly exceeded the number of available samples. Hence, observed multivariate differences between case and control samples in the datasets might potentially also be associated with pre-analytical, technical, statistical and confounding factors. Giving the technical and methodological hurdles, there are nevertheless a number of metabolites and pathways repeatedly reported across various technical approaches, cohorts and sample types that appear to play a predominant role in PCa tumour biology, progression and recurrence. Prostate cancer (PCa) is the most common cancer in males aged over 70 years and the second most common cause of cancer death in men . AnnuallWhile multiparametric magnetic resonance imaging (MRI) holds some potential with continued evaluation to improve prognostic information , currentThe majority of PCa detected by screening have PSA levels between 4–10 ng/mL and moderate Gleason sum scores. These patients are considered to have low to moderate risk disease, and their treatment decisions should ideally be tailored to their anticipated tumour behaviour. “Active surveillance” that defers initial treatment in favour of close monitoring and reassessment of non-aggressive low-Gleason score PCa was introduced. However, it still requires repeated PSA-testing coupled to re-biopsies (often at multiple time points) with additional risks and costs.Depending on the findings and the psychological distress, clinicians and patients often tend to opt for intense therapy in the absence of any decisive indication for the presence of aggressive disease, which significantly contributes to widespread overtreatment. According to current estimates, up to 50% of PCa patients are subjected eventually to intense therapy, while only 20% are suffering from aggressive disease . Thus, POnce a clinical decision for intense therapy has been made, patients will initially receive surgery or various forms of radiation therapy, which is associated with severe side effects, impacting negatively on quality of life ,3. Up toAll these facts underline the urgent need for novel biomarkers  to improThis systematic review follows the PRISMA guidelines and is reported in accordance with the PRISMA statement.A systematic search was conducted on PubMed for all publications with relation to metabolomics biomarkers of prostate cancer reported from July 2008 to July 2018, using the following combinations of MeSH terms: prostate cancer (tiab) intervention with (metabolite OR metabolomic OR metabolomics) intervention with (marker OR biomarker). Initially, titles and abstracts of all identified studies were screened and reviewed on the basis of the established selection criteria.English articles were selected based on their titles and abstracts for full-text review according to their relevance to the issue of interest. The following inclusion criteria were applied with no restriction to the bio-specimen used: identification of human PCa specific metabolites; indication on PCa diagnosis, prognostics, aggressiveness or recurrence; evidence for the clinical utility of the biomarkers; level of standardization of the analytical platforms used and their limitations. Only metabolomics studies were included; other “omics” results were excluded. In addition, reviews and studies made on animal models of PCa or on cell model systems were excluded. Finally, a screening of the reference lists of included articles resulted in reviewing additional titles and abstracts for potential inclusion.The selected studies were thoroughly examined and the following information were extracted from each article: name of first author, year of publication, sample size (specifying the number of cases and controls), analytical platform used, use case, relevant biomarkers candidates, validation of biomarkers, statistical details and relevant comments about the study. Data were independently extracted by two different reviewers  and disagreements regarding the selected information were solved by further review and discussion among them.A total of 169 articles were identified in the literature search . The fulOf all the 59 selected reports, a total of 43 studies were conducted on only one bio specimen as follows: 14 studies were conducted on blood samples, 10 on urine samples and 19 on tissue samples. Eight studies have used more than one of the aforementioned bio specimen in their analysis and thus were reported in more than one table. nine reports analysed other types of biospecimens, such as seminal fluid, or did in vivo magnetic resonance spectroscopy imaging (MRSI), i.e., spectroscopic analysis of the prostate in situ using MRI scanners.In total, seventeen metabolomics studies were conducted on blood samples . Six pubp < 0.000119) inverse association of 1 stearoylglycerol with overall prostate cancer risk . Additional biomarker candidates identified included glycerol and alpha-ketoglutarate, but their associations did not reach statistical significance when corrected for multiple comparisons.The ATBC study was a cancer prevention trial that enrolled 29,133 Caucasian male smokers from Finland. These men were aged between 50–69 years at baseline, smoked at least 5 cigarettes per day, and were assigned to one of four distinct intervention groups. The aim of the study was to examine whether the vitamin supplementation with alpha-tocopherol and beta-carotene, either alone or in combination, would prevent lung and other cancers compared to Placebo. All metabolomics studies within the ATBC cohort were conducted by Demetrius Albanes and co-workers with the aim to identify metabolomic profiles associated with the risk of prostate cancer up to 20 years prior to diagnosis. The group applied ultrahigh performance liquid chromatography/mass spectrometry (LC-MS) and gas chromatography/mass spectrometry (GC-MS) for metabolic profiling of fasted serum samples collected at baseline. In their first study, Mondul et al. analysed sera from 148 participants [p = 0.00008). Nevertheless, strong risk associations between molecules involved in energy and lipid metabolism and risk of aggressive cancer were observed. In addition, the previously reported association between alpha-ketoglutarate and risk of aggressive prostate cancer  was confirmed with a significance level of p = 0.00008 . In addition, the authors identified citrate, a large number of glycerophospholipids, including oleoyl-linoleoyl-glycerophosphoinositol, and long chain fatty acids (LCFA) among the top metabolites associated with risk of aggressive Pca. Most of these biomarkers were inversely related to aggressive PCa risk, with inositol-1-phosphate, a precursor of myo-inositol, showing the strongest association . When stratified by median time from blood collection to diagnosis, distinct metabolites were found to be associated with risk of aggressive PCa. Furthermore, high risk of aggressive Pca was associated with elevated levels of thyroxine and trimethylamine N-oxide (TMAO), a liver metabolite biosynthesised from trimethylamine which in turn is produced from dietary phosphatidylcholines and carnitine by gut bacteria. In contrast, the pyrimidine-nucleoside 2′-deoxyuridine and adenosine 5′-monophosphate, a constituent of ribonucleic acid, appeared related to more indolent forms of PCa.In their second study within the ATBC cohort , the autp = 0.000017), but surprisingly not with later diagnosis of T4 disease. Apart from that, non-significantly elevated serum levels of metabolites in histidine metabolism, including the nucleoside 2’-deoxyuridine also found in the primary analysis, and an inverse association with certain glycerophospholipids were found in men with T2 tumours. Men with T3 prostate cancers showed increased sphingolipids and as well as lower glycerophospholipid signals. Men with a later diagnosis of T4 tumour exhibited elevated signals for secondary bile acid lipids, sex steroids and caffeine-related metabolites, while the carboxylic acids fumarate and citrate were decreased in this group compared to controls. Moreover, the authors found consistent lower levels of glycerophospholipids stearoyl-arachidonoyl-glycerophospho-ethanolamine (GPE) and stearoyl-linoleoyl-GPE as well as a positive signal for euricoyl sphingomyelin in T2 and T3, but not in T4 cases. Serum levels of the histidine metabolite 4-imidazoleacetate and the secondary bile acid glycolithocholate sulfate were found to be elevated across all tumour stages.Using the same sample set, the authors performed a secondary analysis in order to test, whether men diagnosed with ≥ T2 tumours exhibit different metabolite profiles up to 20 years prior to clinical diagnosis . CompareThe same group performed metabolomics profiling using serum samples from the PLCO trial , which wp = 0.03) or glycine (p = 0.07) levels have modestly reduced PCa risk, whereas serum betaine, dimethylglycine, and serine were not associated with prostate cancer risk. However, the association of sarcosine and glycine only held true when folate concentration was above 13.7 nmol/L. In addition, a high glycine/serine ratio was related to a decreased PCa risk (p = 0.001), while other metabolite ratios containing betaine, dimethylglycine, sarcosine or glycine were not.The JANUS study conducted by de Vogel and colleagues investigated the association of sarcosine and metabolites along the choline oxidation pathway, i.e., from betaine down to serine, with risk of incident PCa . For thip < 0.05) were seen for three acylcarnitines, two amino acids and a glycerophospholipid with overall risk of prostate cancer, for 29 metabolites including 20 different glycerophospholipids with risk of advanced disease, for 13 metabolites with risk of aggressive PCa (T4 and/or N1-3 and /or M1 or Gleason ≥ 8), and for seven metabolites with death from prostate cancer. However, none of these markers have been validated in an independent sample set.Schmidt et al. reported various metabolites from several metabolite classes associated with risk of PCa, advanced stage disease and death from PCa using blood samples from the EPIC study . This trn = 59) and a validation set (n = 31), with 25 patients suffering from benign prostatic hyperplasia (BPH) and 34 patients diagnosed with PCa in the training set and 17 patients with BPH and 14 PCa patients in the validation group. High-performance liquid chromatography coupled to electrospray ionization quadropole Time-of-Flight mass spectrometry (HPLC-ESI-QTOF MS) was performed for a targeted analysis of 18 amino acids and 33 metabolites. No amino acid was significantly different between PCa and BPH patients in the training cohort (p < 0.05). However, several of the 33 metabolites were significantly altered, including glycerol-3-phosphate, glycerophosphocholine, distinct lysophosphatidylcholines, retinoic acid, and prostaglandin. Due to a high grade of multicollinearity between metabolites, six metabolites with p < 0.02 and the lowest correlations with other compounds were selected for partial least square regression. The resulting score incorporating lysophosphatidylcholine 18:2, homocysteine-inosine, methyladenosine, lipoic acid, hydroxymelatonin and decanoilcarnitine as markers showed an area under the curve (AUC) value of 0.779 (p < 0.001) with a sensitivity of 74% and a specificity of 76% for discriminating PCa from BPH in the training set. In the validation set, the score distinguished PCa from BPH with a sensitivity of 88% and specificity of 60%. Including PSA to the metabolomic score did not significantly increase its AUC-value.Andras and colleagues published a study that aimed to identify a metabolomic score for PCa using 90 serum samples from patients who were suspected to have PCa and underwent prostate biopsy . In contp < 0.01) and discriminated between HC and PCa samples with an AUC of 0.966 in receiver operating characteristic (ROC) analysis following calculation of discriminant predicted probability scores. While the combination of alanine, glycine, and sarcosine provided the highest AUC (0.970) for differentiating HC and low grade tumours, glycine and sarcosine showed best discriminative power when comparing HC to high-grade tumours (AUC 0.997). Alanine, pyruvate, and glycine were able to distinguish low grade from high grade cancers with an AUC value of 0.978. Unfortunately, it is unclear whether the reported performances in ROC analyses refer to the whole data set or the test set only.Kumar et al. conducted two metabolomics studies using NMR spectroscopy to identify diagnostic PCa biomarkers in serum ,22. BothIn their second study , the samp < 0.05), none reached statistical significance after correction for multiple testing . Using OPLS-DA, only data acquired by NMR and MS resulted in significant classification models. A combination of 14 and 12 metabolites from NMR and MS gave best classification results and their discriminative power was assessed in ROC analyses. Among these 26 markers, the carnitine derivatives decanoylcarnitine (C10:0), tetradecenoylcarnitine (C14:1), octanoylcarnitine (C8) as well as the sulfur compound dimethylsulfone, and the amino acids phenylalanine and lysine were all increased in PCa and of high importance for classification. The phosphatidylcholine diacyl C34:4, and lipid signals from (CH2)n -CH2-CH2-CO were increased BPH and also contributed substantially to separation of BPH and PCa groups.Tessem and co-workers explored the value of combined serum and plasma metabolomics analysis to differentiate 29 PCa patients from 21 control patients with BPH using various techniques . Serum sp < 0.05). AUC values calculated using random forests with 10-fold cross-validation were 0.876 and 0.532 for distinguishing BPH from PCa and Gleason 5 from Gleason 7, respectively. However, the authors did not mention the most important NMR metabolites for these classifications.Fan et al. applied random forests to identify metabolite alterations in serum for the detection and the staging of PCa . They reOsl et al. developed a novel feature selection algorithm termed associative voting for identifying biomarker candidates in PCa . Flow inZang et al. developed a metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) using serum samples from a cohort of 64 PCa patients and 50 age-matched healthy controls . Metabolp < 0.05) between serum samples from PCa and those from control patients with 14 amino acids being reduced in PCa patients. These included methionine, ethanolamine, glutamine, isoleucine, arginine, and leucine , all of which had AUC values above 0.75 in univariate ROC analyses. Sarcosine (p = 0.006), 3-methylhistidine (p = 0.008), p = 0.013), and aspartate (0.034) were detected at higher levels in PCa patients but showed higher p-values and poorer performance in ROC analyses. Methionine, sarcosine, and 3-methylhistidine, together with serine and proline, were the most significant metabolites in multivariate PLS-DA according to their VIP scores. The same metabolites were also most frequently found in models built on two-thirds of the dataset and validated in the remaining third of samples in multivariate ROC analyses based on a Monte Carlo cross-validation approach. In a stepwise discriminant function analysis, sensitivity of 68% and specificity of 100% were observed for classifying PCa and control patients. Among the most important metabolites in serum, ethanolamine and arginine were also reported as potential biomarkers in urine (details see urine section below).Dereziński and colleagues performed a targeted analysis of amino acids (proteinogenic and non-proteinogenic) in serum and urine samples to identify potential biomarkers of PCa . By usinn = 7) and without bone metastases  and from patients with benign prostates (n = 17) were analysed as well. Using OPLS-DA, ANOVA and Mann–Whitney U-test, seven of 27 identified metabolites were significantly altered, including pseudouridine (an isomer of the nucleoside uridine), creatinine, glucose, glutamate, taurine, phenylalanine, and stearate, the last four of which were also identified as markers of metastatic PCa in bone tissue. In addition, sarcosine was specifically measured using a targeted approach, but no significant differences in sarcosine levels were observed when comparing patients with PCa to patients with benign prostate biopsies or metastatic PCa to non-metastatic PCa.In addition to the above mentioned publications, we found two studies that analysed blood samples but mainly focused on data generated using other biospecimens. In 2009, Sreekumar and colleagues profiled tissue, blood, and urine samples using GC-MS and LC-MS . Howeverp-values > 1.5 and p< 0.05) between healthy controls and PCa groups and between poor and good responders to ADT, respectively. When comparing these 160 features, 20 ions were common in the two OPLS models and had less than 20% relative standard deviations in each group. Tandem MS identified two bile acids deoxycholic acid and glycochenodeoxycholate, the omega-3 fatty acid docosapentaenoic acid , tryptophan, the omega-6 fatty acid arachidonic acid, the nucleotide deoxycytidine triphosphate, and pyridinoline (released from cross-linked collagen fibers during bone loss) as potential biomarkers for predicting response to ADT. All these markers were altered in PCa compared to healthy controls. However, their serum levels in patients who responded to ADT reverted close to the levels observed for the control group. This suggests that serum levels of these metabolites are potential markers for the early response to endocrine therapy.The only study focusing on blood biomarkers for predicting treatment response was conducted by Huang and colleagues and published in 2014 . They anp < 0.001) between the two populations in Wilcoxon rank sum tests. Each of these metabolites was able to rapidly separate recurrent from recurrence-free patients in Kaplan–Meier curves with cysteine being the most discriminating marker. The same three markers were also independent predictors of recurrence-free survival in Cox proportional hazard regression models. Likewise, these metabolites were the top predictors for recurrence in multiple logistic regression models. The addition of serum homocysteine to a basic model with serum PSA and Gleason score resulted in the greatest improvement in discriminating between recurrence-free and recurrent patients (p = 0.0007). The AUC values for models including one of the markers were similar (AUC = 0.86) and superior to the basic model with an AUC of 0.81. Interestingly, when urine samples were analysed, only cysteine along with sarcosine (p = 0.03), was found to be significantly different (p = 0.007) between the two groups (see below), while homocysteine and cystathionine were not.Like in the field of therapy prediction, only one of the identified studies dealt with prognostic biomarkers in blood . In theiNine studies used urine samples for metabolomics profiling . Eight pp < 0.05) altered and four of them were identified as ureido isobutyric acid (an intermediate in the thymine catabolism), indolylacryloyglycine (a compound derived from dietary tryptophan or indole compounds), acetylvanilalinine (a catecholamine metabolite) and 2-oxoglutarate . The four identified biomarkers had an AUC value of 0.90 which was comparable to the use of the PSA testing (AUC at 0.94). Gkostos et al. measured sarcosine, uracil and kynurenic acid in urine samples of 32 PCa patients prior to radical prostatectomy, 101 patients with increased PSA prior to ultrasonographically guided biopsy. Samples were collected before and after prostatic massage (PM), and 15 healthy volunteers as controls [p = 0.066). Moreover, none of the metabolites detected in pre-PM showed any diagnostic potential in patients undergoing biopsy. In post-PM urine samples, however, kynurenic acid had a significant diagnostic value (AUC = 0.62). ROC curves were used to also investigate the role of the aforementioned metabolites in the monitoring of PCa progression using Gleason score as a cut-off point between high and low aggression. Results from urine samples of all participants did not show any predictive value for sarcosine, kynurenic acid and uracil .Zhang et al. reported urinary alterations associated with PCa validated in a partially independent cohort . They incontrols . The objn = 68) having no evidence of malignancy (NEM) [p > 0.005) when comparing PCa to NEM as well as BCa to PCa and NEM. Furthermore, their urine levels were associated neither with tumour grade nor with tumour stage. However, the biomarkers were highly correlated with urinary creatinine levels, suggesting that their occurrence is mainly regulated by renal excretion. The levels of biomarkers in both cancer and normal samples were not correlated to age or PSA.In a validation study conducted by Gamagedara et al. levels of proline, kynurenine, uracil, and glycerol-3-phosphate were analysed in 126 patients with genitourinary malignancies (PCa and BCa) and were compared to healthy controls (cy (NEM) . The fout-test) and used to develop a partial least squares discriminant analysis model characterized by 88% sensitivity and 93% specificity. The stability of the model was assessed in a validation set comprising 30% of the entire samples set not used in the prior training step. Sensitivity and specificity were 63% and 79%, respectively. Several metabolites were found to contribute to clustering of the PCa patients, such as urea and the purine 7-methylguanine. In contrast, amino acids, including tyrosine, citrulline and histidine, together with acetylated and methylated amino acids such as acetyllysine, acetylhistidine, dimethyllysine and trimethyllysine were relevant markers in the control group of individuals with a negative biopsy. In another principal component analysis, imidazole lactate, acetylputrescine and dimethylarginine characterized the healthy group, while a heterogeneous group of metabolites including 5-methyldeoxycytidine-5-phosphate, 7-methylguanosine, acetylcitrulline, acetylaspartatylglutamic acid and acetyltaurine supported clustering of the PCa patients group. Dereziński et al. focused on determining urinary amino acids (proteinogenic and non-proteinogenic) profiles as potential biomarkers [p-values  and AUC values above 0.75 in univariate ROC analyses. A stepwise discriminant function analysis using two-thirds of the samples for training showed a sensitivity and specificity in a test set (remaining third of the samples) of 89% and 73%. No significant differences in urinary amino acid profiles between patients with different Gleason scores were observed.Fernández-Peralbo et al. performed an untargeted metabolomics analysis of urine from 62 patients with clinically significant PCa and 42 healthy individuals (both groups confirmed by biopsy) . Twenty-omarkers . Among tn = 59) from non-cancer ones (n = 43) using random forest (RF) and linear discrimination analysis (LDA) [Tanzeela et al. investigated volatile organic compounds (VOCs) emanating from urine samples and their ability to discriminate PCa samples (is (LDA) . The diais (LDA) . For thiis (LDA) . Althougis (LDA) . They rep = 0.01). There was an inverse association between the levels of urinary 16-KE2 and 17-epiE3 and prostate cancer risk (p trend = 0.02), after adjustment for age, race, smoking status, presence of BPH and time of urine collection. Estrogen concentrations were not affected by body mass index, use of non-steroidal anti-inflammatory drugs, presence of diabetes, family history of prostate cancer or presence of BPH. However, smoking is a modifier of urinary estrogen levels. Men in the lowest quartile of 16-KE2 had a 4.6-fold risk of prostate cancer  compared with those in the highest quartile. However, larger studies are needed to confirm these findings.Kosti et al. reported the only pilot study of urinary changes associated with PCa risk prediction . They usWe identified 21 studies that used tissues as biospecimen for the discovery of metabolomic biomarkers of prostate cancer . Almost A relatively stringent validation approach using three independent sample sets was employed by Huan and colleagues, although sample sizes were small . For biop < 0.05) correlated with disease progression but unaltered in normal tissue or BPH. Their strongest finding was significantly elevated sphingosine levels in both discovery and validation cohorts (p < 0.001). Sphingosine increased with disease progression and showed an AUC of 0.81 in the validation cohort. Besides sphingosine, citicoline , choline, pantothenic acid, carnitine C4-OH, GPC, NAD , phenylacetyl-glutamine and carnitine C14:2 (both decreased in PCa) showed significant correlation with PCa. In a study comparing different biospecimens, Sreekumar and colleagues performed metabolomic analysis on tissue and matched urine and plasma samples [p < 0.05). They identified 37 and 91 known metabolites that were altered in localized PCa compared to benign tissue and in metastatic PCa compared to localized PCa, respectively. The authors highlighted six metabolites, namely sarcosine, uracil, kynurenine, glycerol-3-phosphate, leucine, and proline, which were reported to be significantly and consistently increased from benign tissue over localized to metastatic PCa. When using a support vector machine classification algorithm to discriminate between these groups these markers exhibited p-values <0.001 in 100% of the leave-one-out cross-validated datasets.In another study that had a validation set available, transcriptomics and metabolomics were combined to identify altered metabolic pathways in prostate cancer . By this samples . They usp < 0.05) changed levels for 83 metabolites in cancer-containing tissue. Highest increases in cancer biopsy extracts were observed for cysteine, the unsatturated fatty acids dihomo-linoleate and docosapentaenoate, N-acetylaspartate, N-acetylglucosamine , uracil, xanthine, and the glycerophospholipid 1-stearoylglycerophophoinositol. In addition, they evaluated the behavior of the set of six metabolites described by Sreekumar et al. (except for sarcosine which was below the detection limit) and were able to recapitulate the previous findings, i.e., higher levels in cancer compared to benign tissue extracts and in T3 PCa compared to T2 PCa. Moreover, they assessed the levels of known biomarkers reported from in vivo studies using MRSI for assessing the metabolomic profile of PCa in situ, i.e., within living patients. Consistent with the in vivo data, choline, lactate, and alanine were increased in cancer compared benign tissue and at least choline was higher in T3 cancer than in T2 cancer. Citrate and the polyamines putrescine, spermidine, and spermine exhibited more complex behavior, as these metabolites were detected at increased levels in T2 but at lower concentrations in T3 PCa when compared to benign tissue samples.These metabolites (except for sarcosine) were recapitulated by two subsequent studies (see below), one that focused on biomarkers for PCa aggressiveness , and onep-value < 0.05), 12 known metabolites overlapped with differentiating metabolites discovered in both the studies of Sreekumar et al. and Shuster et al. (see above ), namely 2-aminoadipate (an intermediate in lysine metabolism), alanine, cysteine, glutamate, glycine, histidine, leucine, malate, proline, threonine, uracil, uridine. Based on four distinct criteria, the authors selected eight metabolites exclusively found in their study as well as 2-aminoadipate as common metabolite for further evaluation. Among these, the long-chain fatty acid tricosanoic acid showed the highest AUC value for discriminating malignant from non-malignant tissue in ROC analysis (AUC of 0.86), which was only slightly below the AUC value of 0.88 when all nine metabolites were combined. AUC values > 0.8 were also reported for other long chain fatty acids namely cerebronic acid and 2-hydroxybehenic acid. None of the biomarkers were able to distinguish between Gleason score <7 and ≥7, and only gluconic acid showed moderate discriminating power to differentiate between pT2 and pT3 tumours (AUC of 0.64).In another study lacking a validation cohort, a German group around Glen Kristiansen used GC-MS and LC-MS/MS to analyse tissue extracts of PCa and matched non-cancerous adjacent tissue obtained via punch-biopsy from cryo-sections of 95 prostatectomies . Among tp < 0.01), arachidonic acid (p< 0.05), and docosahexaenoic acid (p< 0.05) were found significantly increased in MYC-driven tumours. The main goal of their second study was to evaluate whether formalin fixed paraffin embedded (FFPE) tissue specimens can be used for metabolic profiling and the results were compared to metabolomics performed on fresh frozen tissue [p< 0.05) between PCa and benign samples in both frozen and FFPE tissue. OSC-PLS modelling was performed and correctly discriminated between normal and cancer tissue in the validation set (four matched tissue pairs). The relevant metabolites were however not disclosed.The group around Massimo Loda and colleagues conducted two metabolic studies using GC-MS and LC-MS/MS. In their first study, they investigated whether prostate cancer subtypes driven by distinct oncogenes, namely AKT1 or MYC, exhibit distinct metabolic profiles. Untargeted metabolomic profiling was done in cell line and prostate samples from mice and humans, while selected metabolites, including the fatty acids oleic acid, arachidonic acid and docosahexaenoic acids as well as 2-aminoadipate and creatine, were validated in human tissue . While tn tissue . Besidest-test [t-test and hierarchical clustering [In addition to the above mentioned studies, there were two studies with small sample sizes. Wang and colleagues used a MALDI-FTICR-MS approach to profile three matched, intact tissue pairs and identified differential metabolites using student’s t-test . Howeverustering .p < 0.001). Relevant metabolites were citrate, taurine and creatine  and glycerophosphocholine, phosphocholine, choline, and glycine . Correct classification of normal, high grade and low grade PCa by PLS-DA was achieved with overall accuracies of 86%, 77%, and 66%, respectively. Absolute quantification of metabolite levels revealed significant differences between low and high-grade cancer for spermine and citrate , while choline metabolite levels did not change significantly with Gleason score. Thus, spermine and citrate contribute mainly to the observed higher total choline+creatine+polyamines over citrate (CCP/C) ratio in high grade than in low grade tumours. Interestingly, none of the metabolites distinguished Gleason score 7 cancers from cancers with higher scores.Among the identified metabolomic studies focusing on metabolite biomarkers for staging PCa, four studies were conducted by M.B. Tessem’s group from Norway using HR-MAS-MRS for metabolomic analysis of intact tissue. In one of their first studies, the group aimed to discover metabolite biomarkers for aggressive PCa by metabolomics profiling . They inp < 0.001). When comparing non-cancer, low risk (Gleason < 3 + 4), and high risk (Gleason > 4 + 3) groups, significant differences were also observed in the CCS/C ratio (p < 0.05). In a more recent study [p < 0.05). Levels of taurine  and phophoethanolamine (precursor molecule for glycerophospholipid and sphingomyelin derivatives) were significantly altered between low and intermediate score groups (p < 0.05). Since the NCWP-EMT score was reported to be associated with biological recurrence and metastasis, spermine and citrate may therefore be considered indirectly as biomarkers for aggressive PCa. In 2016, Tessem et al. used the same sample set to assess the metabolic profile of PCa driven by the TMPRSS2-ERG gene fusion [p < 0.05) decreased levels of citrate, spermine, putrescine and glucose, while glycine levels were significantly increased. These trends held true only for spermine and citrate in the validation cohort. However, after correction for multiple testing (Benjamini–Hochberg correction), the difference in spermine and citrate concentrations remained statistically significant only in the main cohort (p < 0.001).In another study published in 2013, Selnæs et al. assessed 40 tissue samples from prostatectomies of 13 patients that underwent MRI prior to surgery and compared the ex vivo spectra to the in vivo spectra recorded during MRI at the same location . They cant study , the groe fusion , which ip < 0.05) higher in PCa than in benign tissue, no significant differences were observed between tumour stage (pT2 vs. pT3) or grade (neither Gleason <7 vs. Gleason ≥7 nor Gleason ≤7 vs. ≥8). Consistently, sarcosine showed a moderate AUC value of 0.59 for discriminating PCa and benign tissue, but inferior performance for predicting tumour grade or stage (AUC 0.54). Moreover, no association of sarcosine with biochemical recurrence was found, leading the authors to argue against sarcosine as a suitable marker for tumour aggressiveness or biochemical recurrence. In a more recent study based on an untargeted approach, Meller et al. profiled tissue metabolite extracts from matched tumour and normal adjacent tissue taken from 106 prostate specimens after prostatectomy using GC-MS and LC-MS/MS [p < 0.05) confirmed the previously found association of gluconic acid with Gleason score [p < 0.05 corrected using the Benjamini–Hochberg method), including the progression-associated metabolites glycerol-3-phosphate, kynurenine, proline, threonine, and uracil. Notably, sarcosine was only significantly elevated in tissue samples with Gleason score of 8 or higher. Besides these findings, the authors mainly focused on metabolites that are correlated with aggressive PCa with extracapsular extension, Gleason pattern progression and tumour spread into seminal vesicles and regional lymph nodes as clinically defined endpoints. Calculation of the odds ratios for the distinct endpoints revealed NAD+, N-acetylaspartate, putrescine, glucose as top markers (highest and lowest odds ratios) for extracapsular extension, while choline phosphate, glycerol-3-phosphate, putrescine, 6-sialyl-N-acetyllactosamine (oligosaccharide involved in immune response regulation and colon cancer progression) showed highest associations with tumour spread. Proline, malate, ADP-ribose  and 6-sialyl-N-acetyllactosamine were top ranked metabolites associated with Gleason pattern progression. Interestingly, when looking for metabolites for improving clinical performance of nomograms, different sets of metabolites turned out to be important for predicting organ confinement when added to the Partin table ) and for predicting 5-year recurrence when added to the Han table (7-ahydroxy-3-oxo-4-cholestenoate (involved in primary bile acid biosynthesis), pregnen-diol disulfate (steroid sulfate oxoanion), and mannosyl tryptophan(glycopeptide)).Besides their publication on diagnostic biomarkers, Kristiansen and co-workers focused on PCa staging in subsequent studies ,57. In 2on score . Besideson score , they adon score . Tissue p < 0.05). Moreover, PE and GPE increased especially from benign to low grade PCa, while concentrations of PC and GPC showed the highest increase between low grade and high grade PCa. Interestingly, these findings contrast to the results published by Tessem et al. [In 2011, Keshari et al. published a targeted analysis of phospholipid metabolites in prostate cancer . 1D and m et al. , which dp < 0.05) with Ki-67 staining, which is increased in more aggressive cancer, was observed for phosphocholine (PC), glycerophosphocholine (GPC), and free choline. In untreated cancer, levels of choline, PC, GPC, glutamate, alanine, and lactate were significantly (p < 0.05) increased between indolent and aggressive cancer. The same was true for benign vs. indolent cancer tissue. In contrast, the concentrations of citrate and polyamines were significantly lower in cancer compared to benign tissue. Although signal intensities were reduced compared to samples from untreated prostates, there was an increase in choline, PC, and GPC from benign tissue over indolent to aggressive cancer in tissue from treated prostates. Moreover, these metabolites were significantly higher in aggressive cancer tissue than in indolent cancer (or benign) tissue. Lactate levels were also significantly elevated in post-radiation cancers but were unable to distinguish between indolent and aggressive cancer. The ratio of total choline, i.e., choline + PC + GPC, to creatine ratio was significantly higher in aggressive vs. indolent cancer and predicted aggressive recurrent prostate cancer after radiation therapy with an AUC value of 0.95 in ROC analysis.The aim of a more recent study of the same group published in 2014 was to identify metabolomic biomarkers that are correlated with Ki-67 staining and pathologic grade in histopathology and are able to discriminate between aggressive and indolent cancer recurrences in patients who received radiation therapy . To thisn = 14 samples) and adjacent normal appearing bone tissue (n = 10), they used OPLS-DA, ANOVA and Mann–Whitney U-test to discriminate between the groups and 34 known of 71 significantly differential metabolites (VIP > 0.9 (OPLS-DA) or p < 0.05 (Mann–Whitney-U)) were identified. The resulting OPLS-DA model was able to discriminate PCa bone metastasis samples (n = 6) from normal bone samples (n = 11) in an independent cohort. When modelling was repeated using the test set, most metabolites identified in the discovery set were replicated as significantly separating biomarkers. Moreover, bone metastases from PCa were distinguishable from bone metastases of other cancer in OPLS-DA. Besides several amino acids, myo-inositol-1-phosphate, citrate, fumarate, glycerol-3-phosphate, and fatty acids, especially cholesterol was found to be the most important differential marker. The authors also applied metabolomic analysis to primary high-risk tumour samples with and without bone metastases  and benign prostate samples (n = 17). Eight of 13 known metabolites were found to be significantly altered both between M1 and M0 and between M1 and benign, namely malate, dehydroascorbic acid (oxidized derivative of ascorbic acid o vitamin C), urea, hypoxanthine, asparagine, threonine, fumarate, and linoleic acid . Since sarcosine was not detected by profiling, this metabolite was specifically measured using a targeted approach. Sarcosine was increased in PCa bone metastases compared to normal bone tissue, but seemed to be unaltered between bone metastases of different origins and between benign prostate and primary PCa tissue.In 2010, Thysell and colleagues published a metabolomics study that aimed to identify metabolite biomarkers for metastatic PCa using primarily tissue from bone metastases but also primary tumour and plasma samples . FollowiOnly one study performed metabolomic profiling in tissue with the main goal to find metabolite biomarkers that predict the risk of biochemical cancer recurrence (detectable serum PSA > 0.2 ng/mL). However, Kristiansen and colleagues at least addressed this issue in subgroup analyses in their two studies on PCa diagnosis  and stagp < 0.05 corrected by Benjamini–Hochberg method). However, when combined with tumour stage and Gleason score in multivariate Cox regression analysis, only aminoadipic acid was an independent marker for recurrence risk prediction. This metabolite set pre-selected for diagnostic purposes did not include any of the biomarkers found by Maxeiner et al., and replication of results was thus not possible. In their second study focusing on biomarkers for PCa staging [p < 0.05 corrected by Benjamini–Hochberg method). However, none of the markers found by Maxeiner et al. could be replicated. Tryptophan and tyrosine showed the highest prognostic potential for predicting biochemical relapse (with p < 0.0001). In their targeted analysis of sarcosine as PCa marker, they found no evidence for sarcosine levels being correlated with biochemical recurrence [In their studies on metabolite biomarkers for PCa diagnosis and staging (see above), Kristiansen and colleagues also evaluated their biomarkers for prognostic potential to predict biological recurrence. In the diagnostic study , they an staging , the samcurrence .Besides the work on PCa biomarkers in blood, urine, and tissue, literature search revealed nine additional publications that were either performed on clinically rarely used bio specimens or were conducted in vivo using MRI-based spectroscopy of prostates in situ . Two stup = 0.031, 0.049, 0.040, 0.052 and 0.028, respectively) between T2 and T3 subgroups. Finally, they also tried to identify metabolites associated with disease prognosis. EV samples with perineural invasion (Pn1) had significantly lower abundance of cyclic AMP (cAMP) and higher abundance of the combination of isomers of the steroid hormone metabolites androsterone sulphate and etiocholanolone sulphate. However, sample size for this purpose was low and the unsupervised multivariate analysis was unable to separate any of the above-mentioned groups.Extracellular vesicles (EVs) are small particles with a lipid bilayer. EVs are released from cells by outward budding from the plasma membrane or by fusion of multivesicular bodies with the plasma membrane or after apoptosis. They typically contain a mixture of cytoplasmatic proteins, lipids, and RNA representative to their cellular origin. Clos-Garcia et al. aimed at the identification of metabolic alterations detectable in urine EVs associated with PCa cancer pathogenesis and progression. To this end, they compared metabolic profiles of EVs in PCa vs. BPH, PCa pathological TNM stage 3 vs. 2 and finally perineural invasion Pn1 vs. Pn0 within the T2 group . EVs werp < 0.01) was also observed. Normalization to choline revealed low levels of other metabolites in the pre-prostatectomy samples among which ratios of guanidoacetic acid (intermediate of the urea cycle), taurine and isovalerylcarnitine to choline were statistically significant. Since robust discrimination of pre-prostatectomy uEV samples from the other study groups was not possible after normalisation to urine volume or creatinine levels, the authors concluded that normalisation to EV-derived parameters or analysis of metabolite ratios should be the preferred for identifying cancer-related alterations in uEV metabolites profiles.In a similar context, Puhka et al. also conducted a proof-of-concept study of PCa-derived changes in urinary EV (uEV) . Their fn = 1) and intermediate (n = 10) risk tumours, confirmed by histology were analysed as well. The single low-risk sample was separated from the intermediate risk samples due to reduced lactate, pyruvate and lipids/ lipoproteins and increased citrate, myo-inositol, spermine and fructose. Within these samples, a discrimination was observed due to primary Gleason pattern 4 (present against absent) and was associated with higher levels of lipids/ lipoproteins, lactate, and pyruvate as well as lower levels of citrate, spermine, and myoinositol. These relationships were also observed when the analysis was expanded to all low and intermediate risk patients (determined by biopsy or RP). The models were non predictive when benign samples were considered with risk group combinations and primary Gleason pattern 4 presence.Roberts et al. conducted a study using seminal plasma for selection and monitoring of active surveillance candidates using NMR based metabolomics . One hunMRSI allows NMR spectroscopic analysis of prostate tissue in situ, i.e., without taking a biopsy or tissue resection, guided by magnetic resonance imaging. In a first study, Weis et al. aimed at estimating the concentrations of prostate choline (Cho), spermine (Spm) and citrate (Cit) in the benign peripheral zone (PZ) and the benign central gland zone (CG) . SubsequIn a pilot study by Nagarajan et al., the validation of two different nonlinear reconstruction methods, namely maximum entropy (MaxEnt) and total variation (TV), for PCa detection were intended . For thaKumar et al. attempted to evaluate the incidence of prostate cancer in men with increased PSA level of 4 to 10 ng/mL and a negative MRSI study . Thirty-A plethora of PCa biomarker candidates have been evaluated. Candidate biomarkers often differed by specimen type, analytical platform and statistical analysis methods applied, there are nevertheless a number of metabolites reported repeatedly and/or independently validated . These mUrine-based studies reported multiple metabolites to be associated with PCa. Two studies validated their findings using an independent validation set. Fernández-Peralbo et al. validated urea, 7-methylguanine, tyrosine, citrulline and histidine, acetyllysine, acetylhistidine, dimethyllysine and trimethyllysine . HoweverIn tissue, numerous metabolites were found by the different studies. Some metabolites were independently reported by different groups. In regard to PCa diagnosis, uracil ,42,45,47Currently, the paradigm of PCa management would benefit from robustly validated novel biomarkers not only for optimising widely used PSA testing and MRI scans, but also for phenotyping tumour biology before and after treatment. During the last decade, metabolomics is an emerging and promising tool in PCa biomarker development. A plethora of multivariate biomarker sets for different use cases has been studied. The vast majority of these studies report the identification of biomarker candidates with the ability to distinguish malignant from benign prostate tissue with a few studies investigating disease progression and treatment response or tumour recurrence. In general, the reported metabolomic analyses yield high dimensional datasets and the number of analysed metabolites often significantly exceeded the number of available samples. Hence, observed multivariate differences between case and control samples in the datasets might potentially not only be associated with identified biomarkers but also by pre-analytical, technical or statistical artefacts and confounding factors. As powerful machine learning and data mining procedures are particularly suited to find even subtle differences in datasets to enable data discrimination, these techniques may be prone to false-positive associations of arbitrary metabolite combinations with the outcome data, an effect often referred to as “over fitting”. The general heterogeneity of the datasets prevents a precise validation of a biomarker or combination of biomarkers based on the group of samples used for discovery even when utilizing cross-validation procedures. Therefore, as best practice, metabolomics studies should include the validation of the findings using an appropriate set of independent samples useful for the assessment of any clinical effectiveness as a mandatory step.For the vast majority of reported biomarkers, correcting for multiple testing was rarely applied and replication through use of a clinical independent validation was mostly lacking. Therefore, currently it remains unclear to which extent the reported candidates may represent false positive statistical artefacts. This statistical correction is exemplified by the most commonly cited metabolomics PCa marker, sarcosine. In contrast to the very promising initial reports on sarcosine ,31,44,71Therefore, clinical metabolomics can only overcome its infancy and solve clinical problems when studies for biomarker discovery and validation are carefully designed with appropriate controls in place at the pre-analytical, the analytical and the clinical stage. Sample size is a very crucial element to the analysis. Indeed, the choice of the statistical tests and the performance of the data processing and modelling are dependent on the sample size of datasets. Unfortunately, sample size calculations for these types of studies is not easy. This highlights the importance of collaboration between research groups in terms of biostatistics, standardizing analytical strategies, choosing appropriate clinical references to maximise reproducibility, reliability and robustness of results. Given all these technical and methodological hurdles, there are nevertheless a number of metabolites and pathways repeatedly reported across various technical approaches, cohorts and sample types that appear to play a predominant role in PCa tumour biology, progression and recurrence. Although encouraging, it also demonstrates the enormous challenges faced by metabolomics, which the field is just started to address. Therefore, caution must be taken until a more holistic picture of the strength and weaknesses of novel candidates is available to ensure that they simply are not the most abundant, easy to measure or stable analytes. When clinical metabolomics learns these critical lessons and the field matures, it will benefit patients. However, expectations should not be raised erroneously high, and so key evidence-based standards are essential. When a critical mass of evidence supports a metabolite as having clinical efficacy in initial validation studies, the next stage in the development should be its application in larger prospective studies to demonstrate its clinical utility. In conclusion, the study of the prostate cancer metabolome is still at an early stage, but it may be an important tool for understanding the development and progression of prostate cancer and for developing biomarkers to support its treatment.


MLH1, MSH2 and MSH6 carriers, with conflicting results. We aimed to determine the role of GWAS SNPs in PMS2 mutation carriers. A cohort study was performed in 507 PMS2 carriers (124 CRC cases), genotyped for 24 GWAS SNPs, including SNPs at 11q23.1 and 8q23.3. Hazard ratios (HRs) were calculated using a weighted Cox regression analysis to correct for ascertainment bias. Discrimination was assessed with a concordance statistic in a bootstrap cross-validation procedure. Individual SNPs only had non-significant associations with CRC occurrence with HRs lower than 2, although male carriers of allele A at rs1321311 (6p21.31) may have increased risk of CRC . A polygenic risk score (PRS) based on 24 HRs had an HR of 2.6 (95% CI 1.5–4.6) for the highest compared to the lowest quartile, but had no discriminative ability (c statistic 0.52). Previously suggested SNPs do not modify CRC risk in PMS2 carriers. Future large studies are needed for improved risk stratification among Lynch syndrome patients.Lynch syndrome (LS) patients are at high risk of developing colorectal cancer (CRC). Phenotypic variability might in part be explained by common susceptibility loci identified in Genome Wide Association Studies (GWAS). Previous studies focused mostly on The online version of this article (10.1007/s10689-017-0061-3) contains supplementary material, which is available to authorized users. MLH1, MSH2 (EPCAM), MSH6 or PMS2. Mutations in all MMR genes are associated with a significantly increased cancer risk compared to the general population, although MSH6 and PMS2 carriers show lower penetrance compared to MLH1 and MSH2 carriers  and/or when the family complied with the Bethesda Criteria . ParticiPMS2 genotyping in this cohort was carried out as previously described [escribed . SNP genPMS2 carriers were analyzed as a birth cohort. A Cox-proportional hazards regression model was fitted to estimate hazard ratios (HRs), with age at CRC as endpoint and SNP genotype as independent variable. Patients without CRC were censored at the last age known to be alive. The HR was calculated separately for heterozygous and homozygous carriers of the risk allele, with homozygotes of the non-risk allele as reference category. We also calculated the per allele HR (additive model). Cox-regression analyses were also stratified for gender. These sub-analyses only includes a per allele (additive) model, due to multiple testing risks. Missing age at CRC diagnosis (n = 3) was imputed using median age of CRC in the general population  or set at 1 year before death (n = 1). The proportional hazards assumption was investigated by examining the scaled Schoenfeld residuals with a formal statistical test and by visual inspection.Previous studies have described the oversampling of cases in clinic-based cohorts. Moreover, affected family members are more likely to be tested for the mutation and this too results in oversampling of cases. To adjust for this non-random sampling, we used a weighted cohort approach as previously described . StandarWe also calculated two polygenic risk scores (PRS)  based onPMS2 mutation carriers in the participating centers . We had at least 80% power to find an HR of 1.5 for the majority of SNPs. A more detailed description of the statistical analysis is available in the supplementary methods . Statistical analyses were performed using STATA version 14  and R software .Lastly, a post hoc power analysis was performed to assess the chance of finding significantly increased risks using the collected cohort, which contains all currently known PMS2 mutation were genotyped, derived from 152 families. Fourteen patients were excluded because (1) they were younger than 25 years at censoring and were therefore not yet at risk of developing CRC (n = 11) or (2) insufficient clinical data was available . The analyzed cohort consisted of 124 cases (PMS2 carriers with CRC) and 383 controls (PMS2 carriers without CRC), with attributed person years of 6527 and 19,549, respectively. Person-years were calculated until age of CRC for cases , and age at polypectomy, age of death, or last known age alive (whichever occurred first) for controls . The mean age was 52.5 for CRC cases and 51.0 for non-cases  and rs4925386 (20q13), were not in Hardy Weinberg equilibrium (HWE). Violation of the HWE was present in both cases and controls and as this might be the result of a genotyping error, these SNPs were removed from the analysis. Ultimately, 24 SNPs were included in the final analysis.PMS2 carriers for rs1321311 (6p21.31), with an HR for the each additional A allele of 2.1  and 0.83  for males and females, respectively . For carriers of more than three risk alleles the HR was 1.58 (95% CI 0.55–3.39) compared to patients with no risk alleles, see Fig. A previous meta-analysis reported a significant pairwise effect on CRC risk of rs3802842 (11q23.1) and rs16892766 (8q23.3) in PMS2 carriers with complete genotyping. The medians for PRS1  were − 0.12 [interquartile range (IQR): − 0.48 to 0.30) for controls and − 0.03 (IQR: − 0.39 to 0.40) for CRC cases. The HRs for group 2 (second and third quartile) and 3 (fourth quartile) were 1.33 (95% CI 0.76–2.33) and 1.50 (95% CI 0.82–2.72) respectively  and rs16892766 (8q23.1), two SNPs previously shown to be associated with enhanced risk in MLH1 mutation carriers [MLH1 mutation carriers reported that a higher number of risk alleles in a carrier is associated with a younger onset of disease  [PMS2 carriers.criteria , 15–17. MLH1 and MSH2, or MSH6 carriers, while PMS2 is seldom analyzed. The only study to include PMS2 carriers (n = 40) found that carriers of the G-alleles of rs10795668 (10p14) and rs9929218 (16q22.1) were at lower risk of CRC, a notable finding in that this is the opposite effect compared to sporadic CRC [Many studies on (genetic) modifiers in LS patients focus on BRCA1 or BRCA2 mutation carriers have resulted in the identification of SNPs that clearly modify breast cancer risk. However, BRCA2 carriers appear unaffected by SNPs that confer an increased breast cancer risk in BRCA1 carriers, even though both genes play a role in homologous recombination [MLH1 carriers, they do not appear to have an effect in MSH2 or PMS2 carriers [A relevant question is why our study did not confirm reported findings of previous studies of MMR carriers. One explanation might be that although patients have germline mutations in genes with similar functions, carriers are affected by genetic modifiers in different ways. Indeed, comparable studies in bination . This cocarriers , 6. ReseCDKN1A gene that encodes the p21 protein. p21 is involved in several (p53-independent) pathways as a tumor suppressor, although it also has oncogenic characteristics [http://www.gtexportal.org/home/) and did not find a statistically significant effect of rs1321311 on CDKN1A expression in sigmoid and transverse colon tissue (p = 0.84 and p = 1.00 respectively) [PMS2 carriers. Although no gender difference was noted by the meta-analysis that identified the SNP [Gender stratification in our cohort led to the notable finding that male carriers of allele A at locus rs1321311 (6p21.31) show a per allele HR of 2.07 (95% CI 1.21–2.96), while the HR for females was 0.83 (95% CI 0.63–1.28). This SNP has been linked to the eristics , 20. Intctively) , 23. It  the SNP , it is p the SNP , 25. Ind the SNP . Another the SNP , 28. Unf the SNP –33. SimiPMS2 cohort by means of a polygenic risk score (PRS). While there did not appear to be a significant effect of the PRS based on ORs from sporadic CRC cohorts, there was a difference in the cumulative incidence of CRC for PMS2 carriers with a PRS2 (based on HRs in this study) in the highest quartile. Bootstrap validation however refuted this promising observation. Further studies are hence needed in other large cohorts.We also investigated the effect of the 24 SNPs on CRC risk in the PMS2 carriers and thus had a relatively homogeneous genetic makeup, implying that differences between our results and previous studies might be due to population-specific effects.There were some limitations to this study. Our study consisted exclusively of Dutch PMS2 mutation is rare or even unique. A previous study by our group did not identify such a correlation with CRC risk in PMS2 carriers  [A second limitation might be that we did not correct for the specific mutation present in each family, mainly because in the majority of families the segregating details) .PMS2 cohorts currently collected, and bootstrap validation is a strong approach to assess discriminative ability of a prediction model [MLH1 mutation carriers, we had 60–80% power to detect an HR of 1.5, which we would consider clinically relevant. The previously reported HR in MLH1 carriers for rs3802842 was 2.7, an HR for which we have ample power to detect .Unfortunately, we were not able to validate our findings in an external cohort. To our best knowledge this is one of the largest on model . StratifPMS2 mutation show a high degree of phenotypic variability. We were not able to confirm the risk modifying effect of rs3802842 (11q23.1) and s16892766 (8q23.3), which were previously found to increase the risk in MLH1-associated LS. This, together with the established lower penetrance, raises the question of whether PMS2-associated LS should be considered a separate Lynch disease entity. Additional explanations for phenotypic variability that warrant greater exploration include gene-environment interactions and risk modification by other genetic variants.Families with a segregating Below is the link to the electronic supplementary material.Supplemental Table 1: Overview of all tested SNPs, including gender stratification Threshold for multiple testing: p < 0.0005. HR=Hazard Ratio. OR=Odds Ratio. 95%CI= 95% confidence interval. MAF=minor allele frequency. (w)HR: (weighted) Hazard Ratio. #This meta-analysis uses homozygotes for the major allele as a reference category. Supplemental Table 2a: Overview of germline PMS2 mutations in the cohort. Each row describes 1 family, Supplemental Table 2b: Most frequent germline PMS2 mutations in the cohort. (DOCX 93 KB)Supplemental File 1: Supplemental method – statistical analysis (DOCX 31 KB)Supplemental Figure 1: Post hoc power analysis. Purple dashed lines indicate the hazard ratios previously reported for rs3802842 (11q23.1) and rs16892766 (8q23.3). (TIF 62335 KB)Supplemental Figure 2: Kaplan Meier survival curve. Note: 0=homozygous for non-risk allele. 1=Heterozygous/homozygous for the risk allele. p=0.0043. (TIF 2126 KB)Supplemental Figure 3: Kaplan Meier survival curve for PRS1. Note: This plot compares curves for the lowest, the two middle and the highest quartile of the PRS. PRS1 is based on meta-analysis derived ORs in sporadic colorectal cancer cohorts. PRS: polygenic risk score. (TIF 2126 KB)


A previous meta-analysis concluded that TNF-α 238A/G and TNF-α 308A/G polymorphisms were not associated with the risk of juvenile idiopathic arthritis (JIA) in the overall population or Caucasian subjects. With the publication of a fair number of studies on the association between TNF-α polymorphisms and JIA in recent years, we conducted this updated meta-analysis to make a more accurate evaluation of such relationship.We adopted PubMed, EMBASE, ISI Web of Science and CNKI to identify observational studies that addressed the association between TNF-α polymorphisms and risk for JIA. The allelic effect of variant A for the risk of JIA was expressed as odds ratio (OR) along with the associated 95% confidence interval (95% CI). Meta-analyses were performed by pooling ORs and 95%CI from included studies using RevMan 5.3 software. The stratified-analysis based on ethnicity was performed to confirm the ethnicity-dependent effect on the relationship.P = .0002). The study in Iranian showed similar results (P = .0002) whereas the studies in other ethnicities failed to replicate this finding: Han (P = .29), Mexican (P = .64) and Turkish population (P = .32). TNF-α 308A/G was not statistically associated with JIA in overall subjects or Caucasians.A total of 15 case-control studies including 2845 patients in JIA groups and 4771 patients in control groups were included in our study. The findings indicated a statistically significant association between the A allele of the TNF-alpha 238A/G polymorphism and the decreased JIA risk in Caucasians (Our study confirmed the protective role of the A allele in TNF-α 238A/G but not TNF-α 308A/G against the occurrence of JIA in the Caucasian population. To exactly validate the correlation between TNF-α polymorphisms and JIA in other ethnic backgrounds, additional studies are required. Despite the heterogeneity of JIA, genetic susceptibility, balancing tolerance and inflammation are all considered to be closely related to the subtypes of JIA.–5Juvenile idiopathic arthritis (JIA) is 1 of the most common childhood rheumatic diseases, which is characterized by arthritis for more than 6 weeks with onset before age of 16 years, and in severe cases may be debilitating, even life-threatening. According to the International League of Associations for Rheumatology (ILAR), JIA is a term which encompasses all forms of arthritis, including systemic arthritis, oligo-arthritis, polyarthritis rheumatoid factor (RF) negative, polyarthritis RF positive, psoriatic arthritis, enthesitis-related arthritis (ERA), and undifferentiated arthritis, but not a single disease. This pro-inflammatory cytokine is involved in the regulation of a wide spectrum of biological processes including cell proliferation, differentiation, apoptosis, lipid metabolism, and coagulation, through binding to its receptors TNFRSF1A/TNFR1 and TNFRSF1B/TNFBR. As 1 of the members of the TNF superfamily, it has been implicated in a variety of pathological processes including autoimmune diseases, insulin resistance, inflammatory diseases and cancer. Currently, the combination of methotrexate and tumor necrosis factor α blockade is the primary treatment program for JIA, in spite of the exception of a possible small increased risk of lymphoma, other types of cancer and serious infections in children.,14 Additionally, a large number of studies have confirmed elevated levels of TNF-α in the serum and synovial fluid of JIA patients, which indicates the potential relationship between elevated level of TNF-α and occurrence of JIA.TNF-α is a multifunctional pro-inflammatory cytokine that belongs to the TNF superfamily, which is mainly secreted by macrophages. However, the sample size of that meta-analysis was not large enough, and they included an article that did not fit the inclusion criteria. Therefore, with the publication of a fair number of studies on the association between TNF-α polymorphisms and JIA in recent years, we conducted this updated meta-analysis to critically evaluate all the currently available evidence and to determine whether there is any association between SNPs of TNF-α promoter and risk of JIA.Single nucleotide polymorphisms (SNPs) are single nucleotide variations that can occur at any site in the DNA, which distribute widely across the human genome and may influence transcription of protein. Despite lots of research that has been conducted to study the association of SNPs of TNF promoter −308A/G and −238A/G and JIA susceptibility, the outcomes have been inconsistent. As far as we know, only 1 meta-analysis has been carried out to assess the correlation between TNF-α polymorphisms and JIA, the authors concluded that there was no association between the A allele  of TNF-α 238A/G or TNF-α 308A/G and JIA in European populations.2 This study does not need an ethical approval because it is a meta-analysis and does not include any data directly linked to individual patient information.This meta-analysis was performed in accordance with the PRISMA guideline.2.1A comprehensive literature search was performed in PubMed, EMBASE, ISI Web of Science, and CNKI to identify potentially eligible studies that were published before July 1, 2017. No language restriction was imposed. We used a structural literature search strategy to retrieve observational studies that analyzed the association between TNF-α polymorphisms and JIA, the literature search strategy used in English databases was as follow:  and  and (“Tumor Necrosis Factor-α”[Mesh] or TNF-α or Tumor Necrosis Factor-α). In CNKI, we searched relevant Chinese publications using “TNF-α” and “Duo Tai Xing” and “Guan Jie Yan”. We used “Guan Jie Yan” which means “arthritis” to increase the sensitivity of our literature search in CNKI. Additionally, we screened the reference lists of relevant reviews and meta-analyses to avoid missing eligible studies.2.2Available data were extracted from published articles, thus abstracts in meetings and conferences were not included in our study. The PICOS principle was used to establish the inclusion and exclusion criteria:(1) ILAR classification  or Durban criteria;subjects in JIA groups should be diagnosed as JIA according to well-established criteria such as American College of Rheumatology criteria,(2)subjects enrolled in control groups should be healthy individuals without a history of JIA and other autoimmune diseases;(3)the type of included studies was required to be observational studies (case–control or cohort studies) that featured an assessment of the association between TNF-α polymorphisms and risk of JIA;(4)the association between TNF-α SNP and JIA was expressed as OR and 95% confidence interval (95% CI), consequently all included studies should report the OR along with the 95% CI, or the OR and 95% CI could be calculated based upon the genotype frequencies. In the cases in which several studies reported an overlapping population, the most complete 1 was deemed eligible for inclusion.2.3Two independent reviewers (X Li and C Liang) did the literature search separately and were blinded to the results of the other investigator. A stringent screening according to the predetermined eligibility criteria was performed to determine potentially eligible article, the results of the literature search were compared afterwards. Data was extracted independently by 2 reviewers from these eligible articles using a standardized data collection sheet, which included first author, country, year of the publication, ethnicity, sample sizes, genotyping method, diagnostic criteria for JIA, frequencies of A allele in JIA groups and control groups, main results of each included study and HWE of control groups. Any discrepancy between the 2 investigators was resolved through discussion until a consensus was reached. The third review author (Z Lv) was consulted if a consensus could not be reached.2.4 Three broad perspectives including selection of cases and controls, comparability of the groups and ascertainment of outcome of interest were assessed using the Star system . Two reviewers (X Li and C Liang) independently evaluated the methodological quality of included studies. The results of risk of bias assessment were compared afterwards. In cases of discrepancies between 2 reviewers, the third reviewer (Z Lv) was consulted.The Newcastle–Ottawa Scale (NOS) for the assessment of non-randomized studies was adopted to assess the risk of bias in case–control studies and cohort studies.2.5Q test and the Higgins I2 test, where P > .1 and I2 < 50% indicate acceptable heterogeneity. I2 values of 0%, 25%, 50%, and 75% were defined as no, low, moderate and high heterogeneity, respectively. We combined the OR of each study using the fixed-effect model if there was no high between-study heterogeneity across the eligible comparisons. Otherwise, a random-effect model was employed.,24To test the association between TNF-α 238A/G or TNF-α 308A/G and JIA, the allelic effect of A  versus G  was expressed as OR and 95% CI using the genotype frequency data derived from the included studies. Heterogeneity among included studies was estimated using a  Publication bias was considered present with P < .05. The forest plots and funnel plots were generated via RevMan 5.3 software .As indicated by previous meta-analysis, there was a strong ethnicity-specific effect on the association between TNF-α polymorphisms and JIA risk, thus we performed subgroup analysis by ethnicity. The leave-1-out sensitivity analysis was conducted by removing each included study at a time and reevaluating the resulting effect on pooled results. Begg rank correlation test and Egger linear regression test using Stata version 12.0 (Stata Corp LP) were combined to assess the publication bias if the number of included studies were larger than 5.33.1–40 were included in our meta-analysis according to our strict inclusion criteria.A total of 200 and 42 studies were identified through literature searches including 59 from PubMed, 90 from EMBASE, 92 from ISI Web of Science, and 1from CNKI  criteria, International League of Associations for Rheumatology (ILAR) classification criteria or Durban classification criteria. The majority of included studies failed to report the HWE of the genotypes distribution of SNP TNF-α-238A/G and TNF-α-308A/G in control groups. The main characteristics and genotypes distribution of TNF-α polymorphisms of included studies were listed in Tables A total of 15 case-control studies published from 2002 to 2016 were identified, including 2845 patients in JIA groups and 4771 patients in control groups. These studies focused on different populations, 12 studies 3.3The NOS was used to assess the methodological quality of included case control studies and the results were summarized in Table 3.4P = .009). The study in Iranian showed similar results  whereas the studies in other ethnicities failed to replicate this finding: Han , Mexican , and Turkish populations .The association between A allele of TNF-α-238A/G and risk of JIA was presented in Figure P = .34). When stratified by ethnicity, the pooled ORs in Caucasian populations revealed that A allele of TNF-α 308A/G was not associated with risk of JIA . Only the Iranian study indicated a statistically significant association between A allele of TNF-α 308A/G polymorphism and decreased risk of JIA , whereas the Mexican population showed a significant association between the A allele of TNF-α 308A/G polymorphism and increased risk of JIA . In comparison to other included studies, Ozen et al reported a much higher A allele frequency in both JIA group (Ozen: 23.3% vs mean: 8.3%) and control groups (Ozen: 17.0% vs 10.2%). As a result, Ozens’ study was excluded in our meta-analysis . However, the heterogeneity across studies was absent after the removal of Ozens’ study  and Egger test  also reveal no statistically significant publication bias.The funnel plots were symmetric and thus suggesting no obvious publication bias Figs.  and 7. T4In our meta-analysis, a total of 15 studies (2845 patients in JIA groups and 4771 patients in control groups) were identified and included in our present study, which were composed of 5 ethnicities from 12 countries. Meanwhile, the stratification by ethnicity was performed to confirm the ethnicity-dependent effect on the relationship between the SNP of TNF-α and the risk of JIA. The results of the meta-analyses indicated that there was a significant association between the A allele of the TNF-α 238A/G but not TNF-α 308A/G polymorphism and a decreased JIA risk in Caucasian patients. The pooled ORs for A allele of the TNF-α 238A/G polymorphism indicated that it conferred a protective role against JIA in Caucasian groups. In terms of polymorphism of TNF-α 308A/G, no significant association for the risk of JIA was found in Caucasians. However, significant findings in the Mexican populations and Iranian were hampered by the limited number of included studies. However, the sample size of that meta-analysis was not large enough (7 studies for TNF-α 238A/G and 9 studies for TNF-α 308A/G), and they included an article that did not fit the inclusion criteria for the diagnosis of JIA. In Hohlers’ study, subjects in JIA and control groups were all adults and the “juvenile onset” was defined as onset before age of 40 in their study. In our study, we incorporated sufficient European samples assessing the association between TNF-α 238A/G or TNF-α 308A/G polymorphisms and JIA with the consideration of between-study heterogeneity. But our results did not support the findings of the previous meta-analysis, a statistically significant association between TNF-α 238A/G and decreased risk of JIA was found in Caucasian subjects . We then tried to determine whether the exclusion of Hohlers’ study and the addition of newly identified studies led to the positive association between TNF-α 238A/G and JIA. Based on the forest plot of previous meta-analysis , the conclusion remained statistically non-significant even after the removal of Hohlers’ study , suggesting that the inclusion of more additional studies contributed to the positive finding in our present study. The positive finding was further confirmed by sensitivity analysis and statistically low between-study heterogeneity (I2 = 0%). In terms of the association between TNF-α 308A/G and JIA, despite the heterogeneity across studies of the European sample was up to 67%, the stability and reliability of conclusion was validated by sensitivity analysis.A previous meta-analysis for the association between TNF polymorphisms and JIA has been published, which showed no significant relationship between either TNF-α 308A/G or TNF-α 238A/G polymorphisms and JIA in European population.,42 A large number of literatures have demonstrated the fluctuating TNF-α concentration in both the plasma and synovial fluid of juvenile with chronic arthritis.–45 The fluctuation in levels of TNF-α would affect the levels of multiple cytokines, including IL-1β, IL-6, IL-8, MCP-1, GM-CSF, and CCL5  as well as several matrix metalloproteinase. These cytokines were further involved in the progression of joint destruction in JIA. Miyamaet et al reported that IL-6 suppressed early differentiation of ATDC5 chondrocyte, which may result in the growth impairment of systemic JIA patients. Meanwhile, MacRae VE and his team demonstrated that the multiple suppressive effects would be exerted on chondrogenic progenitor cells when they were exposed to IL-1β, including the retardation of chondrocyte proliferation and secretion of Aggrecan and Collagen types II and X. TNF-α can also promote the apoptosis of the chondrocyte population and restrain the formation of cartilaginous nodule and the secretion of cartilage-specific proteoglycan.,51 It has been shown that TNF-α regulated the cartilage metabolism by complicated signaling pathways, involving the induction of IGF-1 resistance, suppressor of cytokine signaling proteins (SOCS)-3, activation of the intracellular tyrosine kinase JAK2, as well as several cellular proteins.–55 For the promotor of TNF-α, several polymorphisms have been studied to explore whether there was any relationship between the polymorphisms and the function of TNF-α.,57 Among the polymorphisms, 2 of the most important were SNP TNF-α 238A/G and TNF-α 308A/G, which are associated with enhanced TNF-α production and TNF- α secretion.,59 And it has been demonstrated that the TNF-238 polymorphism could induce the CpG methylation site to regulate the gene transcriptional rate.TNF-α, a gene located in the HLA class 3 region, plays an essential role in the pathology process of inflammation and autoimmune disorders. Haplotype-tagging SNPs were determined for transmitted and non-transmitted haplotype groups separately since findings of non-transmitted haplotypes could represent SNP patterns observed in the normal population whereas data derived from the tests of transmitted haplotypes reflect findings in patients with JIA. Maddah et al  observed a significant positive association for TNF-α GG haplotypes  in patients with JIA compared to healthy volunteers (P < .01), a notable negative haplotypic association was found for TNF-α AG and GA haplotypes at the same positions in case group when comparing with control group. However, only 2 of our included studies evaluated the association between TNF-α polymorphisms and risk of JIA at the haplotypic level, definite conclusion could not be drawn owing to limited number of included studies.Zeggini and coworkers for the first time identified 4 haplotypes  that could be associated with risk of JIA. However, a meta-analysis based on JIA subtype could not be conducted because of the limited quantity of literature. Third, despite of complex genetic diseases, the phenotype and organ specificity of JIA were associate with other confounding factors, involving in age at disease onset, gender, the level of ANA auto-antibodies and so on.,62 Therefore, more clinical materials are required to exactly validate the correlation between TNF-α polymorphisms and JIA subtypes.Several potential limitations of this meta-analysis should be acknowledged. First and the most important is the insufficient number of sample sizes. Though the sample size of European is plenty, the number of other ethnicity is not large enough, which may lead to the inaccurate assessment for the association between TNF-α polymorphisms and susceptibility to JIA in other ethnic backgrounds. Although we found a protective effect of the A allele of TNF-α 238A/G against the occurrence of JIA, this conclusion could not be generalized amongst other ethnicities. Secondly, the disease categories of JIA were classified into 7 subtypes according to the classification of ILAR.In conclusion, our study illuminates the association between TNF-α 238A/G and TNF-α 308A/G polymorphisms and JIA in different ethnicities. The findings indicate that the A allele of the TNF-α 238A/G polymorphism would decrease JIA risk in Caucasian subjects, but A allele of TNF-α 308A/G cannot confer a protective role for JIA. The potential role of TNF-α polymorphisms in JIA could not be confirmed in other ethnicities due to insufficiency of available data. Despite potential limitations, we provided a high-quality and reliable result about the role of TNF-α polymorphisms in the occurrence and progression of JIA.Zheng-tao Lv produced the idea of this study and was responsible for making the final version of this paper. Xing-yan Li and Chun-hua Liang did the literature search, screened the potentially eligible studies and evaluated the data from each included study, they both contributed equally to this work. Virginia Parkman was involved in revising the manuscript, including some important intellectual content and English editing. Zheng-tao Lv had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Conceptualization: Zheng-tao Lv.Data curation: Xing-yan Li, Chu-hua Liang, Virginia Parkman, Zheng-tao Lv.Formal analysis: Xing-yan Li, Chu-hua Liang, Zheng-tao Lv.Funding acquisition: Xing-yan Li.Investigation: Xing-yan Li, Chu-hua Liang.Methodology: Xing-yan Li, Chu-hua Liang, Virginia Parkman.Resources: Xing-yan Li, Chu-hua Liang.Software: Xing-yan Li, Chu-hua Liang, Virginia Parkman, Zheng-tao Lv.Supervision: Zheng-tao Lv.Validation: Xing-yan Li, Chu-hua Liang.Visualization: Xing-yan Li, Chu-hua Liang.Writing – original draft: Xing-yan Li, Chu-hua Liang.Writing – review & editing: Xing-yan Li, Chu-hua Liang, Virginia Parkman, Zheng-tao Lv.Zheng-tao Lv orcid: 0000-0002-8238-7194.


The association between plasma interleukin-6 (IL-6) levels and the development of febrile seizures (FS) has been reported in multiple previous studies, which showed significantly higher serum IL-6 levels in FS patients than in control patients. However, the mechanism underlying this association remains unclear. One previous study indicated an increased frequency of the −174 GG and −597 GG genotypes in FS patients. Although IL-6 gene polymorphisms may be associated with FS risk, this association remains a matter of debate.Considering the lack of meta-analyses addressing the possible association between IL-6 gene polymorphisms and the risk of FS, we aimed to perform a meta-analysis to determine the association of IL-6 gene polymorphisms  with the risk of FS.We conducted a systematic literature search in the PubMed, EMBASE, and WANFANG databases to collect eligible articles. The associations of IL-6 gene polymorphisms with FS risk were evaluated by calculating the pooled odds ratios and 95% confidence intervals. The dominant, recessive, heterozygous, homozygous, and allele genetic models were used to calculate the combined ORs.Our meta-analysis showed that IL-6  polymorphisms were significantly associated with susceptibility to FS.This study provided knowledge regarding the association of IL-6  polymorphisms with susceptibility to FS. The T allele and TT genotype may be associated with an increased risk for FS. FS are defined as seizures accompanied by fever without intracranial infection, hypoglycemia, or acute electrolyte imbalance; they can be classified as simple FS (which have a duration of less than 15 minutes) and complex febrile FS (which last for more than 15 minutes). Interleukin-6 (IL-6) is one such proinflammatory cytokine that shows biological activity in inflammation and is associated with fever in acute-stage FS. The association of IL-6 level in the plasma and cerebrospinal fluid with the development of FS has been reported in previous studies for years. Activation and release of IL-6 are believed to cause fever in FS patients. Azab et al reported significantly higher serum IL-6 levels in FS patients in comparison with those in control patients in their study, which was partly supported by the results of other studies.–6 Kalueff et al indicated that exogenously administered IL-6 has strong proconvulsive effects in a seizure model in rats.One previous investigation showed that neuronal excitability can be enhanced by certain cytokines, which may play an important role in FS. However, Nur et al reported significant associations between FS risk and IL-6 −174 GG and −572 GG genotypes. Thus, the results in different studies have been debatable, and even conflicting. In addition, the associations of IL-6 gene polymorphisms may differ across patients of different ethnicities. Nevertheless, no previous meta-analysis has assessed these aspects. Therefore, we performed a meta-analysis to determine the association of IL-6 gene polymorphisms with the risk of FS.The mechanism underlying the presence of higher serum IL-6 levels in FS remains unclear. However, considering the importance of IL-6, previous studies have evaluated the role of IL-6 single-nucleotide polymorphisms (SNPs) in groups of FS patients. Although IL-6 gene polymorphisms may be associated with FS risk, the presence of such an association remains a matter of debate. Previous studies exploring the associations of FS with IL-6 gene polymorphisms showed an increased frequency of the −174 GG and −597 GG genotypes in FS patients, indicating that the −174 and −597 polymorphisms in the IL-6 gene were significantly associated with FS, whereas the −572 polymorphism was not significantly associated with the risk of FS.2Ethical approval was not necessary, and all analyses in the present study were based on previous published studies, thus no ethical approval and patient consent are required.2.1This meta-analysis was performed according to the PRISMA guideline. We conducted a systematic literature search in the PubMed, EMBASE, and WANFANG database to collect eligible articles using the following combinations of PubMed keywords from inception to November 1, 2018:  AND (“interleukin-6” OR “interleukin 6” OR “IL-6” OR “IL 6”) AND (“polymorphism” OR “genetic polymorphism” OR “SNP” OR “variant”). The reference lists of the included studies were also screened to identify additional articles. We restricted our search to case-control trials for meta-analysis. Only English and Chinese articles were included.2.2The studies included in the meta-analysis fulfilled the following inclusion criteria: case-control design; studies evaluating the associations of IL-6 gene polymorphisms with FS risk; and FS diagnosed on the basis of standard criteria; and studies reporting genotype or allele frequencies in both cases and controls. Accordingly, the exclusion criteria were as follows: duplicates; case reports, reviews, letters, or meta-analysis; studies with inadequate information; and studies with overlapping data. We first screened the titles and abstracts to exclude studies that obviously did not fulfil the inclusion criteria. The remaining studies were assessed for eligibility by examining the full text. Two reviewers (Zhong and Chen) independently checked the articles and resolved disagreements by discussion. Only the most relevant articles were included in the final analysis.2.3Two reviewers independently assessed the quality of each included study on the basis of the Newcastle–Ottawa scale. Trials with a score of at least 6 stars were considered to be of good quality, and those rated below 6 stars were classified as poor-quality trials. Disagreements were resolved by discussion or by the third reviewer to ensure a consistent outcome.2.4Two reviewers independently extracted primary data from the selected studies. The following information was extracted from each study: first author, year of publication, country, ethnicity, study design, number of cases and controls, gene polymorphism data, allele frequencies and genotype distributions of cases and controls, and Hardy–Weinberg equilibrium (HWE) among controls. When this information was incomplete, we checked supplementary data and contacted the corresponding authors for the required data.2.5P values of the chi-square and I2 statistics, and heterogeneity was significant if the I2 statistic was greater than 50% or the P value was less than .1. We used a randomized-effects model to pool the results with significant heterogeneity in our meta-analysis; otherwise, a fixed-effects model was applied. To explore the potential source of heterogeneity, subgroup analysis was carried out on the basis of ethnicity (Caucasians vs Asians). In addition, sensitivity analysis was conducted by removing 1 study at a time to evaluate the stability of the results. We performed Begg test and Egger test to assess publication bias . All statistical analyses were conducted with the software STATA 12.0. P values <.05 indicated statistical significance.The associations of IL-6 gene polymorphisms with FS risk were evaluated by calculating the pooled odds ratios (ORs) and 95% confidence intervals (CIs). The dominant, recessive, heterozygous, homozygous, and allele genetic models were used to calculate the combined ORs. Heterogeneity was assessed on the basis of the 33.1–16 that could be used for the meta-analysis. A flowchart of the process used for identification of studies is presented in Fig. The search strategy yielded 26 references, of 18 articles remained after removing 8 duplicates. We excluded 2 articles that were obviously irrelevant, and the full text of the remaining 16 articles was assessed in detail. After full-text assessment, we eventually identified 9 studiesWe eventually included 9 case-control trials with 2091 individuals (1017 FS cases and 1074 controls) in the meta-analysis, and the median number of patients per study was 232 (range 40–237). The 9 studies were performed in 5 countries (number of studies): China ; Japan (2); Iran (1); Italy (1); and Turkey (1). The ethnicities of the participants in these studies were varied, including Asian (7) and Caucasian (2). Eight studies reported the genotype distributions of the IL-6 (−572) polymorphism in FS cases and controls. Six of these studies were performed in Asian participants and 2 were performed in Caucasians. Five studies investigated the genotype distributions of the IL-6 (−174) polymorphism and 1 study was excluded due to insufficient data. Three studies reported the genotype distributions of the IL-6 (−592) polymorphism. The characteristics of the included studies are listed in Table 3.23.2.13.2.1.1P > .05).The association between the IL-6 −572) polymorphism and FS risk was assessed in 8 studies. The random-effect model was used due to the significant heterogeneity. The pooled results indicated that the frequency of G allele was significantly higher in FS group than controls in the allele model. In addition, individuals carrying GG genotype had an increased risk for the development of FS when compared with (CG + CC) genotype in recessive models , but we did not observe a positive association in the other genetic models. We conducted subgroup analyses for ethnicity in all genetic models to explore the potential sources of heterogeneity. The heterogeneity still persisted in the Asian group, but it significantly decreased in the Caucasian group after the analysis. The recessive model showed a significant association of the IL-6 (−572) polymorphism with FS in Asians . However, this association was not present in Caucasians. The results are presented in Table  polymorp3.2.1.2We identified 4 studies assessing the association between the IL-6 (−174) polymorphism and FS susceptibility, and a total of 471 FS cases and 503 controls were included. Our meta-analysis by polling the results showed a significant association of the IL-6 (−174) polymorphism with FS risk in the allele and recessive models   polymorphism can influence the gene expression level. In another study, the presence of the IL-6 (−174) G allele was associated with higher IL-6 protein levels in comparison with those obtained with the C allele. The IL-6 (−572) SNP was proven to be associated with several diseases by significantly increasing IL-6 expression, such as chronic kidney disease, acute coronary syndrome, and cerebral infarction.The relationship between high plasma IL-6 levels and FS has been well-established. Our study aimed to provide more evidence for the associations between specific IL alleles and the risk of FS. Our meta-analysis found that the IL-6  polymorphisms may be significantly associated with susceptibility to FS. We observed a significant association between FS risk and the G allele and the recessive homozygote GG in the −572 polymorphism, suggesting that this gene polymorphism may contribute to FS development. The frequency of G allele was significantly higher in FS group than controls in the allele model. In addition, individuals carrying GG genotype had an increased risk for the development of FS when compared with (CG + CC) genotype in recessive models. In subgroup analysis, to address the differences in allele frequency attributable to ethnicity, a significant association was observed only in Asians, but not in Caucasian patients. The findings of our meta-analysis revealed a significant relationship between the −572 polymorphism and FS susceptibility in the allele and recessive models. However, we observed significant heterogeneity among studies, and after subgroup analyses, the heterogeneity persisted among patients. For the IL-6 (−174) polymorphism, individuals carrying the G allele showed a greater risk for FS than those carrying the C allele, and individuals with the GG genotype were more susceptible to FS than those with the CC + CG genotype. Similarly, for the IL-6 (−597) polymorphism, we observed a positive association between the G allele and FS susceptibility, and the GG genotype was associated with a high risk of FS.The role of specific IL alleles associated with FS has been extensively studied. The findings of a recent meta-analysis showed a possible association of the IL-1 rs16944 polymorphism with the risk of FS.,15 However, the results obtained by Nur et al contradicted those obtained previously, and their study revealed that the frequency of the GG genotype was positively associated with FS risk, which was supported by the results of studies from China.,14 These inconsistencies in the findings of previous studies may be attributable to several factors, including inadequate sample sizes and differences in allele frequencies across different ethnic backgrounds. Our comprehensive meta-analysis with different genetic models aimed to better testify the association between IL-6 polymorphisms and FS susceptibility. Our meta-analysis included 9 studies and may have more adequate statistical power to yield a precise conclusion. Furthermore, we also conducted subgroup analyses based on ethnicity to address the differences in allele frequency attributable to ethnicity. The findings of our meta-analysis revealed a significant relationship between the −572 polymorphism and FS susceptibility in the allele and recessive models. However, we observed significant heterogeneity among studies, and after subgroup analyses, the heterogeneity persisted in the Asian group, but significantly decreased in the Caucasian group. This heterogeneity may have led to the different allele and genotype frequencies in different studies. We performed sensitivity analysis to evaluate the influence of each study on the overall OR, indicating the stability of overall ORs.The association of the IL-6 polymorphisms with susceptibility to FS has been studied extensively, especially with respect to the IL-6 (−572) polymorphism. Previous studies have tried to clarify the role of this polymorphism in the development of FS. Ishizaki et al first reported the IL-6 (−572) polymorphism, but they did not find any association between this polymorphism and FS risk. Several subsequent studies obtained similar findings.To the best of our knowledge, our meta-analysis is the first study to pool the results of case-control trials to assess the association of IL-6  polymorphisms with susceptibility to FS. Our work further certified the association between IL-6  polymorphisms and FS risk. However, the results should be interpreted with caution, because they were not sufficient to draw a conclusion. Additional molecular studies are required to confirm our findings. In addition, several limitations in the meta-analysis should be acknowledged. First, a meta-analysis may be biased when the literature search fails to identify all relevant studies. However, access to unpublished articles remains difficult, which might be a potential limitation of our study. Nevertheless, assessment of the publication bias did not yield any significant publication bias in our study. Second, the number of included studies was limited, and the sample size was small. Thus, there is a need to conduct more trials to confirm the findings of this meta-analysis. Third, we did not evaluate gene–gene and gene–environment interactions. Fourth, subgroup analysis based on the age, sex, or subtype of FS was not performed. Thus, future studies with large sample sizes and detailed characteristics are required. In addition, we did not branch out other state analysis methods such as deep learning-based studies in our meta-analysis.5In conclusion, this study provided knowledge regarding the association of IL-6  polymorphisms with susceptibility to FS. The T allele and TT genotype may be associated with an increased risk for FS. Further large-scale studies with consideration of gene–gene and gene–environment interactions are required to evaluate the causal mechanism underlying this association.Conceptualization: Rui Zhong.Data curation: Rui Zhong.Formal analysis: Xin Zhang.Investigation: Rui Zhong, Qingling Chen, Weihong Lin.Methodology: Rui Zhong, Qingling Chen, Mengmeng Li.Resources: Mengmeng Li, Xin Zhang.Software: Rui Zhong, Qingling Chen, Mengmeng Li, Xinyue Zhang.Supervision: Rui Zhong, Qingling Chen, Xinyue Zhang, Weihong Lin.Validation: Rui Zhong, Qingling Chen, Mengmeng Li, Weihong Lin.Visualization: Rui Zhong, Xin Zhang, Xinyue Zhang.Writing – original draft: Rui Zhong, Qingling Chen, Xin Zhang, Xinyue Zhang.Writing – review & editing: Rui Zhong, Qingling Chen, Weihong Lin.