Occupational exposure to free crystalline silica and tobacco smoking are associated with an increased risk rheumatoid arthritis, with the evidence of an interaction in seropositive subjects.• Further studies in the field are needed to support such association• We carried out a systematic search for all published epidemiological studies concerning the association between occupational exposure to free crystalline silica (FCS) and subsequent development of rheumatoid arthritis (RA). A meta-analysis was conducted on relevant studies. We searched PubMed and Embase, search engines, for original articles published (from 1960 to November 2019) in any language. In addition, we also searched reference lists of included studies manually for additional relevant articles. Finally, twelve studies were included in the meta-analysis (seven case-control cases and five cohort studies). The odds risks and 95% confidence interval (CI) were calculated using a random effect meta-analysis. A primary meta-analysis (using a random effect model)—regarding RA risk in subjects exposed to FCS—yelled to an overall OR of 1.94 (95% CI 1.46–2.58). We also conducted three further meta-analysis, taking into account the presence of autoantibodies (anti-RF or anti-ACPA) and smoking habits and found a significant association between FCS and RA in both seropositive and seronegative subjects  and in seropositive subjects which were smokers . The studies that have investigated the association between RA and occupatational exposure to FCS are still scarce and the heterogeneity between the studies remains high. Some critical limitations have been identified within studies, among which, the methods for assessing exposure stand out. Although with due caution, our results confirm the hypothesis of an association between occupational exposure to FCS and RA development. There was an interaction between FCS and tobacco smoking in RA seropositive workers.The online version contains supplementary material available at 10.1007/s12016-021-08846-5. The aim of this review and meta-analysis is to investigate possible associations between occupational exposure to free crystalline silica (FCS) and rheumatoid arthritis (RA). Although in the last decades this association has been discreetly studied by epidemiological studies, to the best of our knowledge, this is the first systematic review and meta-analysis to date. The studies conducted to date have led to variable results, motivating our study to carefully seek consistent evidence on a possible association.RA is a chronic inflammatory disorder that typically affects small and medium-sized joints. The primary lesion is synovitis, with the invasion of immune cells in a normally acellular synovium, leading to the formation of inflammatory “pannus.” This hyperplastic tissue causes cartilage breakdown, bony erosion, and, ultimately, loss of function.While the main target is represented by the joints, RA can also present itself with a great variety of systemic and extra-articular manifestations: assecondary Sjogren’s syndrome , rheumatoid nodules, haematologic involvement, pericardial and pleural effusions, interstitial lung disease, ocular inflammation, skin vasculitis, etc..The prevalence in the general population is estimated to be approximately 0.5–1%, and women are two or three times more likely to be affected than men .In the majority of patients with RA, circulating autoantibodies are detectable; the most represented are the rheumatoid factor (RF) and the antibodies to citrullinated protein antigens (ACPA) commonly identified by antibodies to cyclic citrullinated peptides (anti-CCP) , 4. AutoWhen one or more of these autoantibodies are detected in serum, RA is defined seropositive (RA+) and the disease might manifest with more severe symptoms such as erosive joint degeneration and rapid radiographic progression , 6.Several studies and multiple scientific evidences have focused in particular on ACPA autoantibodies considered more sensitive and specific for diagnostic and prognostic purposes as early indicators of joint destruction, persistent RA and extra-articular involvement , 8.The main pathogenetic hypothesis about RA development is that of a multifactorial disease, that arises from gene-environment interactions. Among environmental risk factors, the main suspects are for cigarette smoke, FCS, and viral infections . AlthougThis assumption has been hypothesized in particular in ACPA-positive RA , 10 thatIt has beenhypothesized that FCS is able tostimulate the immune response by inducing the expression of citrullinatedproteins in lungs. Such modified proteins are presented by MHC complexesexpressed on the surface of antigen presenting cells (APCs), that activate Tcells, which in turn will stimulate the maturation of B cells responsible forthe production of ACPAs , 11.The aim of this study was to evaluate all published literature investigating the association between occupational exposure to FCS and RA development. protocol was defined, in which we established the criteria for inclusion and exclusion of epidemiological studies. We searched PubMed and Embase for original articles published between 1960 and November 2019. The bibliographic search has been extended also using the links “related article” of PubMed and the reference lists of some key studies. There were no restrictions regarding the language of the articles.PRISMA checklist  can be fWe performed the following literature search on PubMed and Embase Table :Table 1LPubMed search strategy was developed through the strings proposed by Professor S. Mattioli   studies examining the association between occupational exposure to FCS and RA; (ii) studies in which a measure of association such as relative risk (RR), odds ratio (OR), standardized mortality ratios (SMR), or standardized incidence ratios (SIR) was either reported or could be derived from data reported in the article. Exclusion criteria were as follows: (i) by study design: experimental studies, case reports, and reviews; (ii) data incompleteness, e.g., presence of prevalence data in only one of the two samples; (iii) use of data from samples of subjects already used in previous studies (in order to avoid duplication of results); (iv) use of non-standardized methods for diagnosis and/or very high risk of inaccuracy in the assessment; and (v) Inadequacy of results for meta-analysis purposes, e.g., studies with insufficient relative risk and CI information.Two authors  independently reviewed the studies retrieved from the databases, paying particular attention to the inclusion and exclusion criteria. Doubtful cases or disagreement situations were resolved involving a third author (SC).The final selection of the articles included in the study was based on a careful reading and analysis of the entire texts.The whole process followed during the systematic review is shown in Fig. Following the title revision, we eliminated 52 articles and further 11 after abstract revision. Additional articles  were excluded based on the reading of the full text and non-compliance with the inclusion criteria.Finally, the studies included in qualitative synthesis were twelve, and all of them were eligible for the meta-analysis.In order to perform the meta-analysis, we extracted the most relevant data from each study Table , includiThe quality of the studies was evaluated by applying the Newcastle Ottawa Scale (NOS), that isComprehensive Meta-analysis v3.0 software from Biostat Inc. ,,19], 8 , 7 22],,22], 5 , 7 26],,26], 4 , 3 21],,21], 4 , 5 25],,25], 7 , and 2 .A few study in particular , 27, 29 We found that the exposure assessment in all the studies has some critical limitations; this can be attributed to the use of self-completed questionnaires or a face-to-face interview in which the patient is asked to remember events dating many years in the past.A widely used tool is the Job Exposure Matrix (JEM)  to estimIt should also be taken into account that personal Interviews might be subjected to recall biases. The exposure assessment in the case-control mortality studies , 23 is pCases of the different studies can be defined as dusty trade workers affected by RA and defined according to the American College of Rheumatology criteria (ACR). In this regard, it is important to note that over years, the classification criteria have undergone different revisions: in 1987 –20, 22 aThe 1987 revised ACR classification criteria have been criticized for including late manifestations  failing to identify patients with early inflammatory arthritis. Nevertheless, they are able to classify established RA with high sensitivity and specificity.With regard to cohort studies and mortality studies, the diagnosis of RA was defined according to the International Classification of Diseases (ICD) 10th , 27 and A non-negligible limit of the studies that use the ICD classification is to define RA+ patients based on the ICD-M05 code without distinction between antibodies for rheumatoid factor or citrullinated peptide.In one study , the diaAll the studies controlled for the main and potential confounding agents  with a few exception , 29.Several studies –34, repoSome studies have also investigated the correlation between ever/never smokers exposed to FCS and RA , 25, 27.selection bias.As usual, in these kinds of studies, participants were selected on a voluntary basis and this may introduce a Some main biases affected the identification of cases and controls; specifically, in the Schmajuk et al. study , patientIn the studies , 24 enroI2 = 95% (pronounced heterogeneity). This result was statistically significant (p ≤ 0.05).he meta-analysis of eleven studies , 25–29, We performed an additional meta-analysis on seven studies, 24–27, RA+ patients: We obtained the following result: OR 1.74 (95% CI 1.35–2.25 (I2 = 59%)). This result was statistically significant (p ≤ 0.05)  (I2 = 0%)). Significant association has been observed (p ≤ 0.05) ). This result was again statistically significant (p ≤ 0.05).Using a random effect, we obtained the following result: OR 3.30  for publication bias, in all the performed meta-analyses.The visible asymmetry in the Funnel Plots confirms the presence of heterogeneity in the majority of the meta-analyses. The To the best of our knowledge, the present study represents the first meta-analysis that analyzes the correlation between development of RA and occupational exposure to FCS.Since the status of seropositive/seronegative RA subjects has assumed increasingly prognostic importance, we performed two additional meta-analysis, one for each subgroup.Furthermore, some epidemiological studies –37 have Six out of twelve studies included in this systematic review were performed in Sweden. Three , 20, 31 The remaining studies were performed in the USA , 25, 28,dusty trades, including also iron foundries [All studies have long-running data sets conducted on workers mainly involved in oundries  and coaloundries , 26.I2 = 95%). Such heterogeneity can be explained by different study designs, studied populations, and exposure assessment.The results of our study support the pathogenetic hypothesis that occupational exposure to FCS is a possible risk factor for RA. The final magnitude of the association obtained from the primary meta-analysis is statistically significant but we have found a high heterogeneity among studies ((OR 1.94 95% CI 1.46–2.58) It is important to highlight that four studies , 28, 29 We specify that in the study Brown et al.  for the With this further analysis, we investigated the association between exposure to FCS and seropositive/seronegative RA.Several studies have shown that autoantibodies may be originated in the lungs by autoimmune responses to citrullinated peptides. Moreover, it is well known that anti-CCP is more sensitive than RF and may appear earlier in the course of RA. Only feThese results indicate that the association between occupational exposure to FCS and RA is higher for seropositive RA subjects. Nevertheless, such results are yet based on few studies and with limited numbers of cases to consider them as conclusive.Tobacco smoking is considered a main risk factor for ACPA+ RA development. The pathogenic mechanism is still unknown, and it is not clear how the breakdown of immunological tolerance towards citrullinated proteins and the production of ACPAs occurs .ever smokers/RA+ cases who had been occupationally exposed to FCS. The OR we obtained was 3.30 (95% CI 2.40–4.50). Such result reinforces the hypothesis of a synergistic interaction between cigarette smoking and occupational exposure to FCS. Nevertheless, the limited number of studies included in this meta-analysis as well as the presence of further unknown confounding agents implies caution in the interpretation of the resultsSince both FCS and tobacco smoke are inhaled through the airways and are deposited at the lung level, it has been speculated that both can act synergistically in the establishment of RA. To perfA major limitation of meta-analysis, and this one is not exempt, is that the investigated populations, the FCS exposure, and the outcome definitions are not the same across the studies.As evidenced by the use of the Newcastle and Ottawa scale, the quality of the studies is comparable and attested with acceptable values. However, the adjustment of the main confounding factors and the response rate of cases and controls, obtained different grades in each study Below is the link to the electronic supplementary material.


Esophageal squamous carcinoma (ESCC) has a high morbidity and mortality rate. Identifying risk metabolites associated with its progression is essential for the early prevention and treatment of ESCC. A total of 373 ESCC, 40 esophageal squamous dysplasia (ESD), and 218 healthy controls (HC) subjects were enrolled in this study. Gas chromatography-mass spectrometry (GC/MS) was used to acquire plasma metabolic profiles. Receiver operating characteristic curve (ROC) and adjusted odds ratio (OR) were calculated to evaluate the potential diagnosis and prediction ability markers. The levels of alpha-tocopherol and cysteine were progressively decreased, while the levels of aminomalonic acid were progressively increased during the various stages (from precancerous lesions to advanced-stage) of exacerbation in ESCC patients. Alpha-tocopherol performed well for the differential diagnosis of HC and ESD/ESCC (AUROC>0.90). OR calculations showed that a high level of aminomalonic acid was not only a risk factor for further development of ESD to ESCC (OR>13.0) but also a risk factor for lymphatic metastasis in ESCC patients (OR>3.0). A low level of alpha-tocopherol was a distinguished independent risk factor of ESCC (OR< 0.5). The panel constructed by glycolic acid, oxalic acid, glyceric acid, malate and alpha-tocopherol performed well in distinguishing between ESD/ESCC from HC in the training and validation set (AUROC>0.95). In conclusion, the oxidative stress function was impaired in ESCC patients, and improving the body’s antioxidant function may help reduce the early occurrence of ESCC. Esophageal cancer (EC) is the seventh most common cancer and the sixth most common cause of cancer death globally, causing about 572 000 new cases and 509 000 deaths worldwide . EsophagWith its powerful screening and identification of small molecule metabolites, Metabolomics has become a powerful tool to identify metabolic changes in cancer progression and discover non-invasive biomarkers for cancer prediction and diagnosis –12. CurrIn this work, a two-phase development strategy  was applied in 631 subjects, including clinically relevant controls covering the whole progression of ESCC. Based on the GC/MS metabolomics platform, we propose establishing a suitable non-invasive diagnostic approach and screening for risk factors associated with ESCC progression. This work could help discover new biomarkers for risk prediction and early detection of ESCC.13C2-Myristic acid and methyl myristate were used as internal standard (IS) and external standard (ES), respectively. 1, 2-13C2-Myristic acid, methyl myristate, methoxyamine, MSTFA (N-methyl-N-trimethylsilyltrifluoroacetamid) plus 1% TMCS, n-heptane, and pyridine (silylation grade) were obtained from Sigma-Aldrich. HPLC-grade (>99.5%) methanol was obtained from Merck.1, 2-Plasma samples were processed, extracted, and derived following our previously developed methods , 20. An To diminish the opportunity for systematic variation, all the samples were randomly selected for analysis by GC/MS. A 0.5 μL portion of the derived samples was injected into Shimadzu GC/MS QP2010Ultra/SE . It is equipped with a 30 m × 0.25 mm ID, fused silica capillary column, which was chemically bonded with 0.25 m DB1-MS stationary phase .The column temperature was initially kept at 80°C for 3.0 min, then increased from 80 to 300°C at 20°C/min, where it was held for 5.0 min. The transfer line temperature was set at 220°C and the ion source temperature at 200°C. Ions were generated by a 70-eV electron beam at a current of 3.2 mA. The mass spectra were acquired over the mass range of 50-700 m/z at a rate of 25 spectra/s after a solvent delay of 160 s.The metabolites were by comparing the mass spectrum and retention indexes for the analyte with the corresponding values from the literature and various libraries .10-transformed. The transformed data were imported into SIMCA-P 14.1 software  and pre-processed for multivariate statistical analysis using unit variance scaling (UV). Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) models were built and plotted to show the clustering or separation of samples from different groups. The goodness of fit for the OPLS-DA models was evaluated using three quantitative parameters: R2X, R2Y and Q2. R2X and R2Y are the explained variations, and Q2 is the predicted variation, with a higher level of R2Y and Q2Y indicating the model’s better fit and predictive performance  curve. The Youden index was the best threshold to select the optimal cut-point that maximized its value .Metabolite variability analysis, logistic regression analysis, ROC curve analysis and (adjusted) OR calculations were performed using SPSS 26.0 , bar graphs were produced using GraphPad Prism 8.0, and heatmap and pathway analysis were performed using the online software MetaboAnalyst . The permutation test results showed that the intercept of Q2 was negative in all groups  Table 4.Relative to the group without lymphatic metastases, there were five differential metabolites in ESCC patients with lymphatic metastases, with decreased succinate and glyceric acid levels and increased aminomalonic acid, pyrophosphoric acid, and uric acid Table 4.To construct effective diagnostic models, we applied logistic regression analysis using the data from the training set. First, binary logistic regression analysis and an optimized algorithm of the stepwise forward method  method were applied to establish the best model using the above differential metabolites. Eventually, the combination of five metabolites was defined as the ideal biomarker panel to discriminate ESCC and ESD from HC. These five metabolites are glycolic acid, oxalic acid, glyceric acid, malate and alpha-tocopherol.The diagnostic potential of these five metabolites was evaluated in both the training set and the validation set. To discriminate ESD from HC, the AUC value of the training set was 0.99 , whereas that of the validation set was 0.96  , whereas that of the validation set was 0.70   continued to change abnormally in ESCC patients at different stages. The development of esophageal cancer is associated with abnormal levels of oxidative stress. The use of antioxidants and regulating oxidative stress levels in the body may help prevent and control early-stage esophageal cancer –27.Traditionally, alpha-tocopherol is considered the most active form of vitamin E in humans and is a powerful biological antioxidant. In the present study, lower plasma concentrations of alpha-tocopherol were associated with a significantly increased risk of ESCC. Previous large-scale intervention studies have shown that alpha-tocopherol deficiency is associated with the development of ESCC . Hui Yan2S), stimulating cellular bioenergy; and as a carbon source for biomass and energy production. Gwen Murphy et al. found that higher serum concentrations of cysteine were associated with a significantly reduced risk of oesophageal squamous cell carcinomas  and ESCC . Its sensitivity was poor. To improve the diagnostic performance of alpha-tocopherol, we used a combined biosignature of glycolic acid, oxalic acid, glyceric acid, malate and alpha-tocopherol. This combination greatly improved the ability to differentiate between HC and ESD/ESCC (AUC>0.95) and had good sensitivity and specificity. Unfortunately, we did not find a good combination of metabolites and metabolites to distinguish ESD from ESCC in this study.We collected patients with ESCC and performed a comprehensive analysis of their metabolic phenotypes and metabolic characteristics, but there are still some limitations. The major limitation of the present study is that it is a single-center study, and it is unclear whether the findings apply to other regions and populations. Although many metabolic changes associated with ESCC were identified in this study, further mechanistic studies are lacking. In the future, we will combine multiple centers, expand the sample size to validate our experimental results, and conduct mechanistic studies on the metabolic characteristics of ESCC.The development of ESCC is accompanied by persistent abnormal changes in oxidative stress in patients. Improving the body’s antioxidant capacity may help prevent the development of ESCC.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by The ethics committee of the First Affiliated Hospital of Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study.WZ and JA were responsible for the concept of the study. GW and JA provided the GC/MS platform. WW collected the blood samples, recorded the medical history of the volunteers, and prepared the plasma samples. MY performed the untargeted metabolomics. MY and XY analyzed the data. MY and WW wrote the manuscript. All authors contributed to the article and approved the submitted version.This study was funded by the leading technology foundation research project of Jiangsu Province (BK20192005) and the National Natural Science Foundation of the People’s Republic of China (82173890&81773814). We thank all the investigators, study nurses, patients, and their family members.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Dialister and [Eubacterium] rectale were significantly lower in both blood and tissue samples of BT patients than in those of HCs. In vivo tests showed that BT risk was decreased through the addition of sorghum, brown rice oil, and garlic but conversely increased by the addition of bellflower and pear. In conclusion, serum EV metagenomics shows promise as a rich data source for highly accurate detection of BT risk, and several foods have potential for mitigating BT risk.The human microbiome has been recently associated with human health and disease. Brain tumors (BTs) are a particularly difficult condition to directly link to the microbiome, as microorganisms cannot generally cross the blood–brain barrier (BBB). However, some nanosized extracellular vesicles (EVs) released from microorganisms can cross the BBB and enter the brain. Therefore, we conducted metagenomic analysis of microbial EVs in both serum ) and brain tissue (5 BT patients and 5 HC) samples based on the V3–V4 regions of 16S rDNA. We then developed diagnostic models through logistic regression and machine learning algorithms using serum EV metagenomic data to assess the ability of various dietary supplements to reduce BT risk in vivo. Models incorporating the stepwise method and the linear discriminant analysis effect size (LEfSe) method yielded 12 and 29 significant genera as potential biomarkers, respectively. Models using the selected biomarkers yielded areas under the curves (AUCs) >0.93, and the model using machine learning resulted in an AUC of 0.99. In addition,  The gut microbiome affects brain health via tiny packets of microbial metabolites called extracellular vesicles (EVs) that are small enough to pass through the blood–brain barrier. The brain was thought to be sheltered from the microbiome’s effect on health by this barrier, which blocks microbes from entering the brain via the blood. Yoon-Keun Kim at MD Healthcare Inc. and Sun Ha Paek at Seoul National University, both in Seoul, South Korea, and co-workers compared the EVs in the blood of brain cancer patients and healthy individuals, and found the two groups harbored different types of microbes. Feeding mice brown rice oil, sorghum, and garlic markedly shifted their EV profiles towards those of the healthy microbiome. These results provide a foundation for investigating new methods for brain cancer risk assessment and dietary interventions to reduce that risk. Recent interest has been particularly paid to understanding the emerging relationship between our microbiome and cancer. Through integration of the holobiont paradigm in oncological research, recent studies have demonstrated a complex relationship between the microorganisms occupying our intestinal tract and carcinogenesis. The gut microbiota has been reported to impact the tumor macroenvironment through modulation of host immune and neuroendocrine factors4. The microbiome exerts tremendous influence on immune function, development and response, with over 70% of the immune cells in our body residing in our gut6. Furthermore, certain microorganisms have been identified to have cancer-promoting effects, while others exert inhibitory effects on cancer growth by boosting the body’s immune response and diminishing immune evasion of cancer cells7. Therefore, the complex dynamics between the gut microbiome and cancer micro- and macroenvironments must be elucidated to better understand the multifaceted manner in which cancer develops and progresses.The human microbiome is the collection of genes contributed by the total microbial community in our body and has been associated with human health and disease. Despite the majority of the bacteria in our bodies residing in the gastrointestinal (GI) tract, the characteristics and activity of the microbiome have far reaching effects on metabolism, immune function, and carcinogenic activity8. The biogenesis of EVs is closely regulated in both Gram-negative and Gram-positive commensal bacteria, and EVs are generated throughout cell proliferation as ectosomes and cell death as apoptotic bodies9. Microbial EVs have been shown to travel distally throughout the body and are detectable in a variety of samples, including stool, urine, serum, and tissue, and elicit a variety of immunological effects in vitro and in vivo10. Previously, we reported the effective use of microbial EVs for cancer diagnostics using human serum11. Furthermore, Lactobacillus rhamnosus EVs have been shown to exert a cytotoxic effect on hepatic cancer cells12. A previous meta-analysis of 22 cohort and case–control studies revealed that obesity is a risk factor for meningiomas, gliomas, and brain and central nervous system (CNS) tumors in females and meningiomas in males13. Our group recently reported that dietary intervention with several different grains was capable of ameliorating high-fat diet-induced microbiome-associated colorectal cancer risk in vivo14. Therefore, we sought to apply this methodology to determine the role of the EV microbiome in brain cancer by analyzing microbial EVs circulating in the serum and brain tissue of brain tumor (BT) patients and healthy control (HC) subjects.While the field of microbiome research has made tremendous strides in the 21st century, the nanosized extracellular vesicles (EVs) released by bacteria and archaea remain a relatively underexplored area of study. Microbial EVs are typically 20–200 nm in diameter and composed of a lipid bilayer encapsulating various proteins, nucleic acids and metabolites derived from the parent microbial cell15. However, previous studies recently revealed that nanosized EVs released from commensal microorganisms are capable of crossing the BBB and entering the brain17.As nanosized microbial vesicles are able to enter circulation and interact with a variety of host sites via distal intercellular communication, microbial EVs play an immunomodulatory role in the cancer macroenvironment in addition to the cancer microenvironment. BTs are a particularly difficult area to directly link to the microbiome, as microorganisms themselves are generally incapable of crossing the blood–brain barrier (BBB). The BBB also presents a major hurdle for clinical applications of chemotherapy medications such as doxorubicin and paclitaxel for glioma treatment due to the p-glycoprotein drug efflux mechanisms associated with the BBBTherefore, as the microbiome is known to affect the body’s susceptibility to cancer and has a direct link to the brain via the gut–brain axis, we sought to elucidate the relationship between the EV microbiome and BTs. We conducted metagenomic analysis of microbial EVs isolated from both serum and brain tissue collected from BT patients and HC subjects to determine significant microbiome alterations in the cancer macroenvironment and microenvironment. We then developed intelligent diagnostic models at the genus level using serum EV metagenomic data and machine learning algorithms. The resulting diagnostic models were used to assess the ability of a variety of grain, protein, lipid and vegetable dietary supplements to reduce BT risk induced by consumption of a high-fat diet (HFD). Our findings support the role of microbial EVs and diet in the tumor micro- and macroenvironments and the need for further assessment for future diagnostic and therapeutic development.In total, serum samples of 152 BT patients and 198 HC subjects were obtained from Seoul National University Hospital and Inje University Haeundae Hospital, respectively. In addition, tissue samples were obtained from 5 BT patients and 5 HC subjects enrolled from Seoul National University Hospital. Each BT clinical subject presented symptoms leading them to visit the hospital for treatment. Healthy control subjects were screened through a general health examination. The present study was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. H-1009-025-331) and Inje University Haeundae Hospital (IRB No. 1297992-2015-064). All methods in this study were conducted in accordance with the approved guidelines, and informed consent was obtained from all clinical subjects.All collected human serum samples were transferred to serum separator tubes (SSTs) and then centrifuged at 3000 rpm for 15 min at 4 °C. All brain tissue samples were frozen in liquid nitrogen and stored at −80 °C for analysis.All mice used in this study were female C57BL/6 mice at 6 weeks of age . Mice were housed and maintained under standard laboratory conditions of 22 ± 2 °C and 50 ± 5% humidity under 12-h day and night cycles throughout the course of the in vivo study. The animal study was approved by the Institutional Animal Care and Use Committee of Chung-Ang University . All methods in this animal study were conducted in accordance with the approved guidelines.n = 60): a group fed a regular chow diet (RCD) and a group fed a HFD. Second, mice were randomly divided into 30 groups (n = 5) including an RCD group, HFD group, and HFD + group fed an HFD supplemented with adlay, glutinous rice, nonglutinous rice sorghum, buckwheat, brown rice, acorn, mung bean, roasted bean, fermented bean, mealworm, perilla (Perilla frutescens var. japonica Hara), perilla oil, brown rice oil, sesame oil, broccoli, garlic, ginger, turmeric (Curcuma longa Linne), lotus root, cabbage, bellflower (Platycodon grandiflorum), onion, pumpkin, pear, grape, kelp, or shiitake mushroom. Mice within the RCD control group were fed regular chow containing 18% dietary fat  for 4 weeks. Mice in the HFD group were fed a 60% fat diet , and diet powder (100 µg) or oil (100 µL) was orally administered once every day for 4 weeks. At the conclusion of the 4-week study period, all mice were sacrificed, and serum was collected for EV microbiome compositional assessment.To analyze dietary effects, in vivo sampling of mouse serum was conducted before and after a dietary intervention. First, mice were randomly divided into two groups . Finally, the extracted EV DNA in each sample was quantified using QIAxpert (QIAGEN). Isolated EV microbial genomic DNA was amplified by targeting the 16S V3–V4 hypervariable regions. The libraries were prepared using PCR products, and all amplicons were sequenced using a MiSeq instrument .To extract EVs from serum and tissue samples, centrifugation, filtering, and boiling methods were performed as described in our previous studyTaxonomic assignment was performed by the profiling program MDx-Pro ver. 2 . Briefly, paired-end reads were filtered according to the barcode, and primer sequences were trimmed using Cutadapt (version 1.1.6) and then merged with CASPER. To obtain high-quality sequencing reads, sequences with read lengths under 350 bp or over 550 bp and with Phred quality scores below 20 were discarded. The VSEARCH de novo clustering method was used to assign operational taxonomic units (OTUs) to the genus level with a 97% similarity threshold. OTUs containing 1 sequence in only one sample were excluded from further analysis. Subsequently, taxonomic assignment was conducted to the species level using UCLUST and QIIME 1.9.1 against the Silva 132 database under default parameters. If clusters could not be assigned at the genus level due to insufficient taxonomic information in the database, the taxon was assigned to the next highest level indicated in parentheses. Brackets around the taxon name represent an unverified, suggested taxonomic assignment based primarily on whole genome phylogeny within the genomic database.p-values below 0.01, fold-changes >2, and average relative abundances >0.1%. Biomarkers included as model variables were selected by one of four methods that were then compared to determine the model with the highest area under the curve (AUC), sensitivity, specificity, and accuracy. The first method (M1) used stepwise selection for which the Akaike information criterion (AIC) was used for comparison among predictive diagnostic models with differing variables. The second method (M2) incorporated age and sex as covariates in addition to stepwise selection methodology. The third selection method (M3) used linear discriminant analysis (LDA) and LDA effect size (LEfSe) algorithms for biomarker discovery, while the fourth method (M4) included age and sex as covariates in addition to incorporating biomarkers selected using LEfSe. In addition, the fifth model (M5) was calculated by a machine learning algorithm based on the gradient boosting machine (GBM) ensemble method. The GBM was incorporated in the modeling process using the Gradient Boosting Regressor of scikit-learn (version 0.21.3) in python (version 3.6.9). After variable selection, the predictive diagnostic model was calculated using logistic regression with training and test sets established at an 80:20 ratio for model validation.For the development of a BT predictive diagnostic model, we considered the relative abundances of OTUs at the genus level as model variables. First, we selected candidate biomarkers with t-test and Wilcoxon rank-sum test, respectively. A chi-square test was performed to determine any significant difference between the groups based on sex. To analyze alpha diversity, Chao1, Shannon index, and Simpson index were assessed. Chao1 corrects for observed richness, and the Shannon index considers species evenness and relative abundance in each sample. The Simpson index evaluates both evenness and richness by considering the total number of species and their abundance in a community18. Principal coordinate analysis (PCoA) was conducted to determine individual taxa-level clustering of groups based on Bray–Curtis dissimilarity distance. To analyze the difference in microbiome composition between the HC and BT groups, Student’s t-test was performed. LEfSe was also used to determine significant, differentially abundant genera between the clinical groups for the selection of biomarkers with statistical and biological significance. The LEfSe algorithm utilized the Wilcoxon rank-sum test and linear discriminant analysis (LDA) with the cut-off LDA score (log10) set as 2. The results were considered significant when p-values were <0.05 (p < 0.05), and all analyses were conducted using R version 3.6.1.Significant differences in age between the BT and control groups were determined through Student’s p < 0.001). The HC subjects ranged from 40 to 78 years of age with a mean age of 59.7 (SD 10.5) years, while the BT subjects yielded a mean age of 51.5 (SD 14.2) ranging from 16 to 81 years   over 4.0 .A total of 4, 9, 12, 18, and 29 taxa showed a proportion higher than 0.5% in either group and significant difference between the control and patient groups using a Stenotrophomonas, Knoellia, Sphingomonas, Solanum melongena, Parabacteroides, Actinomyces, Ruminiclostridium, Lactococcus, Turicibacter, Faecalibacterium, Streptococcus, and Bifidobacterium , Acinetobacter, Staphylococcus, Pseudomonas, Ruminococcaceae UCG-013, Klebsiella, Bifidobacterium, Ruminococcus 1, Streptococcus, Ruminiclostridium 6, Peptoclostridium, Sphingomonas, Clostridiales vadinBB60 (f), Turicibacter, Ruminococcaceae (f), Ruminococcus 2, Peptococcaceae (f), Diaphorobacter, Corynebacterium 1, Lactococcus, Propionibacterium, Solanum melongena, Actinomyces, Knoellia, Stenotrophomonas, and Veillonella , Lachnospiraceae NK4A136 group, Prevotella 9, and Candidatus Saccharimonas were significantly higher  and Dialister were significantly decreased in both serum and tissue samples obtained from BT patients. In addition, Lachnospiraceae NK4A136 was significantly lower in BT patient tissue, whereas it was significantly increased in patient serum compared to its levels in control tissue and serum samples  were fitted to the M1 and M3 models. Serum bacterial EV composition differed significantly between the different dietary groups, and the fitted value obtained through each model was also subsequently altered. However, the difference between the dietary effect of the regular chow diet and the HFD on brain tumor risk was not significant 17, while Dialister has been shown to be diminished in individuals with autism spectrum disorder, Alzheimer’s disease, and depressive disorder patients compared to the respective levels in healthy subjects27. In addition, an in vivo study demonstrated that Lachnospiraceae NK4A136 is reduced in Alzheimer’s disease patients in comparison to the levels in healthy controls28. Bacterial EVs are secreted from bacteria as shedding vesicles, ectosomes, and apoptotic bodies9. Decreased bacterial populations could lead to a reduction in proliferation activity, leading to fewer secreted EVs; therefore, communication components might also be decreased. However, as bacterial populations are reduced, apoptosis induces the secretion of apoptotic bodies that may cross the intestinal epithelial cell layer and circulate throughout the blood. Therefore, we suggest that the interplay of gut microbiota abundance and the blood EV microbiome has a very complex and potentially nonlinear relationship. Further studies on the role of circulating bacterial EVs in communication between the gut microbiota and various organs should be performed. Additional investigations should focus on verifying the efficacy of bacterial EVs in the treatment and diagnosis of a variety of neurological diseases.In this study, we analyzed bacterial EVs in blood and tissue as BT biomarkers for the first time. We found that the distribution of bacterial EVs in both blood and tissue samples was significantly different between HC and BT patients. At the genus level, the number of significantly different bacterial EVs between HCs and BT patients was higher in blood than in tissue samples. Through this analysis, we revealed that the 11. Following those findings, in this study, we analyzed bacterial EVs in blood as biomarkers for BT diagnosis for the first time. Furthermore, we applied machine learning to analyze blood EV microbiome data for highly accurate BT diagnosis. The results of this study are significant as a novel and accurate diagnostic method for BTs and yielded an AUC over 0.93 in all diagnostic models. Other studies exploring diagnostic applications for BTs used a computer-aided detection method based on magnetic resonance imaging (MRI) with an accuracy of 99%29 in addition to a core of iron oxide as a nanoparticle based on MRI contrast agents15. Our results showed that bacterial EVs in blood also have strong potential as biomarkers aiding in the detection of BTs that may not be readily detected through conventional imaging methods.In a previous study, we demonstrated that bacterial EVs in serum had strong potential as diagnostic biomarkers for hepatocellular carcinoma30. Here, we found that different diets impacted the composition serum EV microbiome components associated with BTs in mice. Of the dietary supplements tested in this study, sorghum, brown rice oil, garlic, cabbage, and onion were the primary foods that decreased serum EV microbiome-associated BT risk, whereas bellflower increased the risk. Only a few published epidemiological studies have reported the impact of vegetable consumption on glioma risk32. Specifically, different colored vegetables showed different abilities to decrease glioma risk33. Leafy green vegetables and yellow-orange vegetables were significantly related to decreased BT risk, especially glioma, whereas eggs, grains, noncured meat, and citrus fruits were significantly related to increased risk34. These previous studies targeted combined diets, such as vegetables, grains, and fruits, rather than specific foods, as these studies were primarily epidemiological studies assessing diet on a large scale. However, this study showed the relation between BT risk and individual foods based on differential serum EV microbiome composition in an in vivo model. Furthermore, the BT risk associated with a certain food can vary according to the applied cooking method. The results of this study suggest that roasting might increase risk and extracted oil (lipid) might decrease risk of BT. However, further study is necessary using other foods and comparisons of precise cooking methods such as cooking time, concentration of ingredients, and cooking environment.Aside from determining the diagnostic potential of serum EVs in BTs, we also sought to further determine dietary interventions that could reduce BT risk associated with the serum EV microbiome. Dietary habits have significant impacts on gut microbiota activity and community structure that are known to influence health35. Furthermore, consumption of medicinal food containing phosphatide precursors and various cofactors for 12 weeks was demonstrated to improve memory in patients with mild cases of Alzheimer’s disease36. Several taxa of bacterial EVs derived from serum that were shown to be significantly decreased in BT patients in this study, Dialister, Bifidobacterium, and E. rectale, were demonstrated to be significantly increased by certain dietary treatments, including brown rice and whole grain barley, in a previous study37. Strategic dietary management could improve the symptoms of brain diseases and enhance treatment efficacy. Dietary management affects human health through various routes, including chemical, nutrient, immunology, and microbiome modulation. Therefore, dietary treatment has vast potential to supplement and enhance neurological disease therapy and medicinal approaches. The dietary intervention findings reported in this study provide a strong foundation for further investigation of dietary treatment to reduce BT risk.Recently, studies on medicinal food and dietary treatments for neurological diseases have been increasing. For example, supplementation of arginine-rich food with conventional therapeutics has been shown to improve several aspects of stable angina patients’ quality of life and vascular function38; however, the relation between age and serum microbiome composition has yet to be clearly demonstrated. Further study should determine if the factor of age is a potentially complicating effect and minimize such factors accordingly. In addition, the samples used in this study were drawn solely from a hospital population, which could limit the representativeness and generalization of these results. The diagnostic models developed in this study should be further validated using a larger sample size from a more diverse patient population, and more clinical information should be collected. Another limitation of this study that could act as a confounding factor in our diagnostic models was the lack of control for medication usage, sampling time, diet, and physical activity40. In the future, we should incorporate drug-naive or drug-free patients, dietary information, and physical activity of patients for greater insight. In addition, more precise exploration of the influence of diet on BT risk is necessary. To thoroughly determine the effect of diet on BT patients, clinical testing should be conducted as well as larger animal brain tumor models. In addition, the cooking method should be more precisely characterized. Alterations in cooking methods such as roasting, fermentation, oil extraction, temperature, time, humidity, and concentration could have different effects. Therefore, future studies should more tightly control these variables and compare various cooking methods of the same food to determine the optimal dietary intervention for BTs. Finally, the exact mechanism through which serum bacterial EVs influence BT development should be verified in the future.This study has several limitations, including a relatively small sample size and limited clinical information that included only age and sex. In addition, there was a significant difference in age between the BT and HC groups, which could potentially act as a confounding factor in this study. Age may affect the microbiome composition of serumIn conclusion, this study provides further evidence to support our expanding understanding of the complex, underexplored role of circulating EVs as biomarkers of chronic disease. By incorporating machine learning and serum EV metagenomics, we developed high-strength BT diagnostic models in a clinical cohort that were then used to determine the efficacy of individual foods to reduce serum microbial EV-BT risk in vivo. Future studies including larger patient cohorts, more detailed clinical information, and efforts to determine the mechanism through which microbial EVs impact brain health should be conducted to validate the results of this study.Supplementary Figures


No evidence of an association emerged in subgroup analyses of menopausal status, cancer stage, fat content of milk, life stage of milk intake, or study design. Consistent results were observed in the meta-analyses using total dairy intake. In conclusion, milk intake during childhood and adolescence might not be associated with risks of breast, prostate, and colorectal cancer later in life. Given the small number of studies that were included in our meta-analysis, and the high heterogeneity, more studies are warranted for a definitive conclusion.Dairy consumption in adulthood has been demonstrated to influence cancer risk. Although childhood and adolescence represent critical periods of rapid growth, the relationship between milk intake in early life and later cancer risk is unclear. Thus, we examined this relationship by conducting a meta-analysis of the observational studies. PubMed and Embase were searched for relevant articles that were published throughout December 2021. The summary relative risk (RR) and 95% confidence interval (CI) were estimated using the DerSimonian-Laird random-effects model. The summary RR for the highest vs. lowest milk intake was 0.83 (95% CI = 0.69–1.00;  Dairy products are widely consumed worldwide, and they contain diverse components that are shown to influence cancer risk. The most notable one is calcium, as a high calcium intake is shown to suppress prostate cancer proliferation ,2, whileMilk and other dairy products are also widely consumed in early life, with dietary guidelines for Americans recommending that children and adolescents consume the equivalent of 2–3 cups of dairy products per day depending on their age . Of noteThe design, analysis and reporting of this meta-analysis was performed in accordance with the Meta-analysis Of Observational Studies in Epidemiology (MOOSE)  28]..28].PubMed and Embase were searched for relevant articles published throughout December 2021. The search was limited to human studies and the English language, but no other restrictions were imposed. The detailed search terms are provided in To be included, studies had to be an observational study  that examined the relationship between adolescent dairy intake  and cancer risk later in life , with the results reported as relative risks (RR)  and at a 95% confidence interval (CI). We excluded abstracts, unpublished results, and review articles. When there were multiple publications from the same cohort ,21,22,29The following information was extracted from each article: name of the first author, publication year, study design, type , fat content , and life stage (in age) of dairy intake; type and subtype  of cancer, relative risk and corresponding 95% CI, important characteristics of the study population , and variables which were adjusted for.For the study selection and data extraction, two authors (HG and Q-YC) independently participated, and any disagreement was resolved through discussion with NK.Across studies, milk was the principal type of dairy intake, and the total dairy intake was defined heterogeneously. Thus, we set milk as the primary exposure and ran sensitivity analyses with dairy intake. The summary RR and the 95% CI were calculated using the DerSimonian-Laird random effects model, which estimates the weighted mean of true effects, and accounts for both between-study variations and within-study variations . Potenti2 [2 value of 25%, 50%, and 75% was considered to represent low, moderate, and high heterogeneity, respectively [Heterogeneity in the relationship across trials was assessed by I2 , which r2 . An I2 vectively . Potentip values of < 0.05 were considered statistically significant. Analyses were performed using STATA 17 .All the statistical tests were two-sided and p = 0.050), with moderate heterogeneity (I2 = 60%) (Egger = 0.04). Several sensitivity analyses were performed. First, when meta-analysis was run after omitting one study that showed the most extreme result  [2 = 31%) with no evidence of small study effects (PEgger = 0.06). Second, the use of the contour-enhanced funnel plot  A. Small 2 = 60%)  and Eggenel plot  potentiaWhen heterogeneity was explored, there was no evidence of a significant relationship between early life milk intake and breast cancer risk, regardless of menopausal status B, cancerWhen sensitivity analyses were conducted upon dairy intake in early life rather than milk intake, the aforementioned results remained consistent .2 = 51%) (Egger = 0.52).A total of four studies were included in this meta-analysis ,13,14,152 = 51%) A. There When heterogeneity was explored, there was no evidence of a significant relationship between early life milk intake and prostate cancer risk, with respect to cancer stage B, life sIn sensitivity analyses based on early life dairy intake, the aforementioned results remained consistent .2 = 83%) (Egger = 0.93). In sensitivity analysis based on early life dairy intake, no significant association was observed   . Small s2 = 77%) .Despite the high heterogeneity observed, due to a small number of studies, heterogeneity of the a priori selected factors was not explored.In this meta-analysis of observational studies, we observed that childhood and adolescent dairy intake, as represented by milk intake, was not associated with the risks of breast, prostate, and colorectal cancers in later life. Furthermore, the relationships did not vary by menopausal status, cancer stage, fat content of milk, life stage of milk intake, and study design. Although there was suggestive evidence of a reduced breast cancer risk associated with a higher milk intake in early life, the association was not statistically significant and was affected by publication bias. Thus, our study suggests that dairy intake during childhood and adolescence might neither benefit nor harm one’s cancer risk during adulthood.With regard to breast cancer risk, out of the seven studies included in our meta-analysis, six studies ,23,25,26It has been suggested that several compounds in milk and other dairy products confer protection against breast cancer. Dairy products are the main food source of calcium in many populations. Intracellular calcium regulates the synthesis of components related to cell proliferation and differentiation ,40,41, aFor colorectal cancer, given that the evidence for dairy products and calcium supplements protecting against colorectal cancer in adults is considered to be strong by the Continuous Update Project (CUP) panel , calciumConcerning prostate cancer, it is notable that calcium, unlike its anti-carcinogenic effect on breast and colorectal cancers, may rather increase prostate cancer risk by inhibiting the synthesis of 1,25-dihydroxyvitamin D3 2D3, also known as calcitriol), and the active metabolite of vitamin D3 that suppresses cell proliferation in the prostate . SaturatThere are several strengths to our meta-analysis. To our knowledge, this is the first meta-analysis on the relationship between milk intake during childhood and adolescence, and cancer risk later in life. By conducting subgroup analyses by menopause, cancer stage, or life stage of milk intake, when possible, our study explored the potential for important heterogeneity in the relationship.n = 3). Thus, despite the high heterogeneity observed for colorectal cancer, we could not explore the sources of heterogeneity. Second, the summary estimate for breast cancer was affected by small study effects, which reduces the validity of our finding. However, we conducted several sensitivity analyses to check the consistency of the results. Third, as a meta-analysis, our results are also affected by biases that individual studies included in our meta-analysis are prone to, including confounding. Finally, we could not address important questions such as the interaction between milk intake in early life, the intake in adulthood, and heterogeneity in breast cancer by hormone receptor status, due to lack of data from individual studies included in our meta-analysis.However, our study has limitations to acknowledge. First, the number of studies included in our meta-analysis was small, particularly for colorectal cancer (In conclusion, unlike well-known associations between adult dairy intake and cancer risk, intake of milk and other dairy products during childhood and adolescence might not be associated with a cancer risk for the breast, prostate, and colorectum later in life. However, due to a limited number of studies included in our meta-analyses, our results need to be interpreted with caution until they are confirmed by future studies. Future studies are warranted to address the potential interaction between dairy intake in early life and that in adulthood in relation to cancer risk.


Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson’s disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits. MetABF is a Bayesian GWAS meta-analysis framework but the efficiency is restricted by the number of studies included. In this article, we propose SMetABF by introducing SSS, an improved edition of traditional MCMC, to speed the MetABF algorithm. We develop an R package and a web tool based on R Shiny to make SMetABF practical for biomedical research. Comparing with the exhaustive approach and MCMC, we validate the effectiveness of SSS in terms of speed and accuracy through simulations. We applied SMetABF to identify several important variants associated with Parkinson’s disease and other autoimmune diseases, and explore the relationship between them. We hope this method can benefit future GWAS meta-analyses, help to identify more risk variants associated with complex traits, and improve the prediction of diseases. Genome-wide association study (GWAS), a powerful tool to find out the associations between genetic variations and phenotypes, has received more and more attention in the field of statistical genetics and epidemiology . Numeroup-value is dependent on the sample size and minor allele frequency (MAF) of the variant. Therefore, it is improper to use a single threshold [n subsets represented by n-dimensional vectors to compute the optimal ABF, the considerable time and memory consumption required makes the computation almost impossible as the number of studies n increases.Either the fixed-effect model (FEM)  or the rhreshold . Besideshreshold . On the hreshold . Recentlhreshold . With GWhttps://github.com/sjl-sjtu/GWAS_meta.In this article, we propose SMetABF, a method based on the Markov chain Monte Carlo (MCMC) method and its extension named shotgun stochastic search (SSS)  to speedp-value for statistical inference, the Bayes factor (BF) is used in Bayesian statistical framework. BF is defined as the relative size of the likelihood to observe data under the null hypothesis (H0) or the alternative one (H1),D stands for the data observed, β is the effect parameter we are interested in, γ is the parameter vector of confounders, and π(⋅) stands for the prior of β and γ. In general, BF > 1 means more inclined to accept H1, and on the contrary, 0 < BF < 1 means more inclined to accept H0. Since BF is difficult to calculate directly in many studies, an asymptotic Bayes factor (ABF) is proposed as an alternative [P is replaced by the asymptotic distribution β instead of the joint prior π is considered, the probability of obtaining the parameter β under a certain hypothesis could replace the probability of observing data Y, written asDifferent from the traditional statistical framework based on the ernative . If P is the probability density of normal distribution N at x.For a study aimed to measure the association between several risk factors and specific outcomes, like GWAS, let n studies in the meta-analysis, let β stands for the true effect vector, and SEi is the standard error of i-th study, and ri,j is the correlation between the i-th and j-th studies. For each study, the prior effect size is σi, and the prior correlation coefficient between two studies is ρi,j, then the prior matrix Σ isEach study included in the meta-analysis provides an estimated effect size, With the estimated effect vector and covariance matrix of the estimated standard errors, ABF for meta-analysis could be calculated asH0 is defined that Σ equals to the zero matrix.Similarly, σ and ρ remain the same for all studies included in the meta-analysis.The assumption on the heterogeneity among studies is critical in prior selection. H0 is harder to be accepted and the probability of the type II error decreases. In other words, it increases the statistical power, which equals one minus the probability of the type II error.Since both the null model and the complete model can be regarded as special cases of the subset model, the subset model is adopted in the meta-analysis. But it also brings a tricky issue to determine the optimal subsets. It is preferred to get a higher ABF score in the meta-analysis, for it means that n studies, there are in total 2n different subsets. It requires taking all these subsets as a prior one by one to calculate ABF, and then find the optimal one. The time consumed will explode exponentially as the number of studies included increases. At the same time, the memory required to store all subsets  has also expanded dramatically. Therefore, it becomes quite essential to introduce a method to get higher ABF quickly.The former study  performs3 algorithm [A commonly used method to quickly find the best subset is the Markov chain Monte Carlo method (MCMC). Here MClgorithm , 16 is uAlgorithm 1 MCMC PseudocodeRequire: Ω: Universe of subsets.Ensure: x: Sample with stable distribution.x0 ← random sample from Ω fort = 0…Tdo nbd(xt)                   ⊳ Neighborhood  Update   y based on Π(xt)                   ⊳ Alternative ModelGenerate   u from uniform distribution UGenerate ifu < hthen  xt+1 = y   else  xt+1 = xt   end if  end for x0, and calculate the ABF.Randomly select a subset as the initial prior model xt, define the neighborhood as a set constituted of subsets formed by adding or deleting an element from the current subset, as well as the current model itself. The proposal distribution is defined by equalizing the sampling probability of all models in the neighborhood. In other words, the sampling probability of all models in the neighborhood remains equal, and the transition probability of all models outside the neighborhood is 0. Since each model has the same size of the neighborhood, the proposal distribution is symmetric.For the current model y according to the transition probability, and then calculate the ABF. The discriminant function is defined as ABF(x) represents the ABF value with subset x as the prior.Generate the alternative prior model u that follows the uniform distribution U. If u < h, accept y as a new step of xt+1, and otherwise, xt+1 = xt.Generate a random number Repeat steps 2-4 until the maximum number of iterations or stable distribution is reached.The first half of the entire iterative sequence is used for the warm-up and the second for the final sampling.Here we introduce an extension of MCMC for variable selection named shotgun stochastic search (SSS) . It can B optimal models. Randomly select the initial model x0, set Γ = {x0}, and calculate the score of the model S(x) = ABF(x).Let Γ donate a set containing up to xt, define models that add or delete or replace an element from the current subset to constitute the sets Γ+, Γ−, and Γ∘, respectively, and then define the neighborhoodFor the current model nbd(xt). If |Γ| > B, remove (|Γ| − B) models with the lowest scores.Then update Γ = Γ ∪ x+, x−, and x∘ from Γ+, Γ−, and Γ∘, with the score S(x) as sampling weight, respectively.Sample x+, x−, and x∘, with the score S(x) as sampling weight, and let the sample be the new model xt+1.Then take a sample from Repeat steps 2-4 until the maximum number of iterations is reached.Algorithm 2 SSS PseudocodeRequire: Ω: Universe of subsets.Ensure: x: Sample with stable distribution.x0 ← random sample from Ω x0} Γ = {S(x)←ABF(x) fort = 0…Tdo +, Γ−, Γ∘  Constitute Γnbd(xt) = Γ+ ∪ Γ− ∪ Γ∘  nbd(xt)  Update Γ = Γ ∪ if |Γ| > Bthen  B) models with lowest S.   Remove   Sample x− from Γ−, weight = S(x)  Sample x∘ from Γ∘, weight = S(x)  Sample xt+1 from x+, x−, x∘, weight = S(x)  Sample ifxt+1 satisfies stable distribution then     breakend if  end for The former study  has provp), the frequency of the major allele of the studied variant , the effect size , and the sample size of both case and control groups . For example, suppose A is the risk allele while G is the non-risk allele. If the dominant model is applied, both AA and AG can be considered as equivalent risk genotypes while GG is non-risk. Suppose baseline effect is α, the increased effect on prevalence by risk genotype is θ, thenD represents the outcome (disease). Then we can getSeveral parameters are given to build the simulated datasets, including the incidence of the disease in the population . The sample size ni in each study is generated as a random integer in a given range, and p and f remain the same for all studies. Through the process above, Suppose there are p-value obtained by the traditional method. When the true OR approaches 1, the p-value increases, while the ABF value decreases to 0. However, when the sample size of the study included is small  and SSS (500 and 1000 iterations), as shown in Here an application is performed to measure the risk variants associated with Parkinson’s disease (PD), a common chronic neurodegenerative disease among the elderly population. Its common clinical manifestations include tremors, slow movement, and disorders in balance and movement posture. PD has been reported to be associated with both genetic variations  and envihttp://distild.jensenlab.org), Open Targets Genetics [https://genetics.opentargets.org/) and GWAS Catalog [https://www.ebi.ac.uk/gwas/), 59 studies in which the summary statistics  and the mixed pattern (∼10127), and the subsets indicates that BTNL2 is associated with all four disorders. For the top 4 variants on BTNL2 in the mixed pattern, on average 66.7% studies on PD, 65.7% studies on inflammatory bowel disease, 100% studies on multiple sclerosis, and 62.5% studies on systemic sclerosis are included in the final subsets to calculate ABF. However, although variants on SCNA also have a high ABF value in both pure pattern and mixed pattern, we found most of the studies in the final subsets to calculate ABF are from those studies on PD and the ABF values remain similar (∼10135) in both patterns. In other words, SCNA has weaker associations with other autoimmune disorders. The same is also true for those variants on MAPT. These two loci may relate more to the diseases in nervous system instead of autoimmune disorders. On the contrary, variants on KANSL1, reported as a factor in the immune system [The results supports the previous reports on several essential loci related to PD, such as d KANSL1 . Additioe system , show ase system .https://sunjianle-sjtu.shinyapps.io/analycode.We implement all the algorithms as an R package named GWASmeta. Besides, to help researchers to use SMetABF to quickly find key SNPs, we develop a web tool based on R Shiny as well. The requirements of the file uploaded can be found in the website. Multiple variants can be analyzed at once. This tool is accessible at p-value method used in meta-analysis is facing increasing criticisms. For example, it is not proper to use a single threshold since p-value is dependent on the MAF and sample size [P(H0|Y) and P(H1|Y), and therefore is a better alternative to the p-value. Based on the Bayesian framework, a useful statistical model named MetABF has been proposed, which could easily measure the associations between multiple phenotypes and variants at the same time using GWAS summary statistics but confronts challenges in computation.Meta-analysis has been widely conducted on GWAS data to discover essential loci associated with some complex genetic diseases during recent years –75, satiple size . Moreoveple size . Insteadσi represents the prior variation of the i-th variant on the outcome, and ρi,j stands for the linkages between different variants. Furthermore, since many traits related to some complex diseases are correlated, it is necessary to consider the effect of multiple loci on the outcome across a large number of studies simultaneously [Σ will transform to a three-dimensional array, which will bring more challenges in computation.In this article, we propose SMetABF, an improved tool to attain the optimal ABF in a large-scale meta-analysis efficiently. Through simulation, we confirm that SSS is superior to MCMC in terms of speed, accuracy, and stability. To a certain extent, our improvements effectively overcome the calculation problems due to the increase in the number of studies included. We performe an application to PD and other autoimmune disorders, illustrating the effectiveness of SMetABF. With more research conducted on various traits among a larger population and the increasing accumulations of GWAS summary statistics, the large-scale multi-phenotypic meta-analyses will be possible through SMetABF. Another possible application is to analyze the effect size across different variants in one study, where aneously . In thisσ and ρ will affect the ABF values but seem not to change the relative effect size between different variants. Besides, the considerable size of human genome still brings challenges in computation.The method still confronts many challenges. The choice of prior parameters is an example. Sensitive analysis reveals that different values of The pooled statistics derived through meta-analysis can be further used for other post-GWAS analysis, for example, to identify causal genes through statistical fine-mapping  or to in


There has been no study systematically assessing the causal effects of putative modifiable risk factors on lung cancer. In this study, we aimed to construct a modifiable risk factors atlas of lung cancer by using the two‐sample Mendelian randomization framework.p‐value smaller than 0.05 were considered as suggestive risk factors. While the Bonferroni corrected p‐value for significant risk factors was set to be 8.33 × 10−4.We included 46 modifiable risk factors identified in previous studies. Traits with In this two‐sample Mendelian randomization analysis, we found that higher socioeconomic status was significantly correlated with lower risk of lung cancer, including years of schooling, college or university degree, and household income. While cigarettes smoked per day, time spent watching TV, polyunsaturated fatty acids, docosapentaenoic acid, eicosapentaenoic acid, and arachidonic acid in blood were significantly associated with higher risk of lung cancer. Suggestive risk factors for lung cancer were found to be serum vitamin A1, copper in blood, docosahexaenoic acid in blood, and body fat percentage.This study provided the first Mendelian randomization assessment of the causality between previously reported risk factors and lung cancer risk. Several modifiable targets, concerning socioeconomic status, lifestyle, dietary, and obesity, should be taken into consideration for the development of primary prevention strategies for lung cancer. With the utilization of Mendelian randomization analysis, we provided the evidence for the relationship between previously reported risk factors and lung cancer from the aspect of causation. We identified several modifiable targets for primary prevention of lung cancer, concerning socioeconomic status, lifestyle, dietary and obesity. In the situation when R2 was not provided by the GWAS, we calculated it using data from MRbase.2 of the SNPs, power and F‐statistic were shown in Table 2, power and F‐statistic as the characteristics of the GWAS of traits, which was useful in the sensitivity analysis for weak instrument bias. Finally, we got 60 traits corresponding to the 46 included risk factors, because some risk factors had more than 1 traits  of exposure as proxies. The MR method was based on the following three key assumptions: (A) The IVs is associated with the risk factor (Relevance); (B) The IVs affects the outcome only through the risk factor (Exclusion restriction); and (C) The IVs is not associated with any confounders (Independent).2.5Wald ratio estimate was performed if there was only 1 SNP for the trait, in which SNP‐outcome association was divided by its SNP‐exposure association to obtain the causal relationship.p‐value. Association was considered significant, when p‐value was less than 0.0008 , and considered suggestive, when p‐value was larger than 0.05 / 60 but less than 0.05.Results of the evaluation of causal association were displayed as odds ratio (OR) between the exposure and outcome, as well as its 95% confidence interval (CI) and 2.6Sensitivity analysis was performed to examine if there was any violation of the assumptions of MR or any other potential biases. Specifically, single SNP analysis, leave‐one‐out analysis, MR Egger, funnel plot, WME, and MR‐PRESSO were utilized. Single SNP analysis and leave‐one‐out analysis were conducted to find whether the estimate was driven by single SNP solely. MR‐Egger was used to assess whether there was any directional pleiotropy, to confirm that the genetic IV only affected the outcome through the exposure.2) and sample size were used to calculate the F‐statistic and power.Proportion of variance explained by the genetic IV  in R (version 3.6.1).3We retained 46 putative modifiable risk factors of lung cancer, which were classified into 6 categories, 5 in factors of socioeconomic status (SES), 4 in factors of lifestyle, 29 in factors of dietary, 6 in cardiometabolic factors, 1 in developmental and growth factor, and 1 in inflammatory factor.MR analysis between the 46 putative modifiable risk factors (60 traits included) and lung cancer, LUAD, and LUSC were conducted. MR estimates were presented in Figure 3.1p‐value <0.001], college or university degree , and household income  against lung cancer .MR provided significant evidence for the protective effect of years of schooling  were identified as significant risk factors of lung cancer , 1.34 1.28–1.41); 8–1.41; pr Figure . In WME,r Figure . MR‐Egger Figure . The funr Figure . Rs64935r Figure .It is worth noting that only 1 SNP was available for cigarettes smoked per day, and thus only wald ratio was performed. Single SNP analysis, leave‐one‐out analysis, MR‐Egger, funnel plot, and MR‐PRESSO were not conducted in terms of cigarettes smoked per day. Power of cigarettes smoked per day did not exceed 80%, while F‐statistics and power for time spent watching television were sufficient Table . We did 3.3p‐value <0.001], docosapentaenoic acid (DPA) (22:5n‐3) in blood , eicosapentaenoic acid (EPA) (20:5n‐3) in blood , and arachidonic acid (AA) (20:4n‐6) in blood  and lung cancer , 1.44 (1.01–2.06); p‐value, 0.046], copper in blood , and docosahexaenoic acid (DHA) (22:6n‐3) in blood  were suggestive risk factors of lung cancer. Other PUFA than 18:2 in blood was still a significant risk factor of lung cancer in WME. While in MR‐Egger, the association turned to be suggestive  than 18:2 in blood  Figure . Directi] Figure . While f] Figure . Horizon] Figure . The dis3.5p‐value, <0.001], cigarettes smoked per day , DPA , and AA  , 1.81 (1.02–3.21); p‐value, 0.04], fat intake , serum vitamin B12 , other PUFA than 18:2 , DHA , and EPA .We also analyzed the causal relationship between modifiable risk factors and LUAD and LUSC. The significant relationships with LUAD were discovered in college or university degree  Table . Suggestp‐value, <0.001], college or university degree , household income , cigarettes smoked per day , time spent watching television , and DPA  remained significant risk factors of LUSC after the Bonferroni correction , 1.16 (1.03–1.30); p‐value, 0.01], EPA , AA , BMI , and body fat percentage  were identified as suggestive risk factors of LUSC.Years of schooling [OR (95% CI), 0.40 (0.26–0.62); on Table . Biochemp‐value, 0.01) and circulating adiponectin  and LUAD , two traits of lifestyle (cigarettes smoked per day and time spent watching television), and four traits related to PUFA .p‐value, 0.04). Considering the directional pleiotropy detected, the claimed positive effect of time spent watching television on cigarettes smoked was based on the MR‐Egger estimate. According to the result of MR‐PRESSO, the p‐value of the distortion test between time spent watching television and AA in blood was 0.043. But both the original MR estimate  and the outlier‐corrected MR estimate  suggested no association between them.We found that all the three significant traits of SES were positively correlated with each other in both directions Figure . Years o41, copper and, DHA in blood as well as body fat percentage and increased risk of lung cancer. The bidirectional MR among the significant traits above indicated that they may be intermediate factors of each other.In this study, we analyzed 46 putative modifiable risk factors of lung cancer utilizing MR analysis. We noted that years of schooling, college or university degree, and household income had significant protective effects on lung cancer. This study also provided significant evidence for the positive association of lung cancer with cigarettes smoked per day, time spent watching television, other PUFA than 18:2, DPA, EPA, and AA in blood. We also noted suggestive associations between raised serum vitamin AOur findings show that higher educational attainment and household income decreased lung cancer risk were concordant with the findings of previous conventional observational studies.Among the lifestyle factors analyzed in this study, cigarettes smoked per day and time spent watching television were identified as significant risk factors of lung cancer. The effect of smoking on lung cancer has been well established.Diet and nutrition factors have been attached with great importance. According to the WCRF/AICR report, there is limited evidence to support the role of diet and nutrition in the development of lung cancer, except for arsenic in drinking water and high‐dose beta‐carotene supplements which have convincing evidence.1 and vitamin B12 even had suggestive tendencies to promote the development of lung cancer.The anticancer effect of vitamin supplements is another issue worthy of attention. Our results in combination with previous research did not provide evidence that circulating vitamins had a protective role in lung cancer.The MR analysis suggested a distinct causal effect of BMI and body fat percentage on LUSC and LUAD, with evidence of an increased risk of LUSC and a null relationship with LUAD. This finding is consistent with previous MR studies, and highlighted the histologic‐specific impact of BMI.Our study has several important strengths. We conducted the first two‐sample MR study to systematically draft the modifiable risk factors atlas of lung cancer by using data from large GWAS studies. All 46 risk factors included in this analysis were selected based on our systematic review of previous meta‐analyses and the WCRF/AICR report. The risk factors selected were potentially implicated in lung cancer development with varying degrees of evidence. Many factors have not previously been included in MR analyses of lung cancer, including those proven to be significant risk factors of lung cancer in this study . The use of MR framework can prevent the residual effect of confounders and reverse causality that are commonly present in conventional observational epidemiological studies. Moreover, we displayed the network among significant risk factors and lung cancer for the first time. We also found no significant difference in risk factors between LUAD and LUSC, except for BMI and body fat percentage.2 was not available in the original article, leaving the weak instrument bias unestimated.However, there were still some limitations in this study. First, false negative results may exist in this study, because of the weak instrument bias of some traits, regarding the limited number of SNPs available and R2 and the insufficient power and F‐statistics.5With the utilization of MR analysis, we provided the evidence for the relationship between previously reported risk factors and lung cancer from the aspect of causation. We identified several modifiable targets for primary prevention of lung cancer, concerning socioeconomic status, lifestyle, dietary, and obesity.All authors have no conflicts of interested to declare.Study concept and design: Jiayi Shen, Huaqiang Zhou, Jiaqing Liu, Yan Huang, Li Zhang. Acquisition of data: Jiayi Shen, Huaqiang Zhou, Jiaqing Liu. Analysis of data: Jiayi Shen, Huaqiang Zhou, Jiaqing Liu. Drafting of the manuscript: Huaqiang Zhou, Jiayi Shen, Jiaqing Liu with input of all authors. Critical revision of the manuscript for important intellectual content: All authors.N/A, the need for approval was waived because the present MR analysis was based on anonymous summary data from previous studies.All authors of this paper have read and approved the final version submitted.Fig S1‐5Click here for additional data file.Table S1‐10Click here for additional data file.


In this study, we have furthered the study to assess the exact association of TNF-α-308G/A gene polymorphism with liver cirrhosis susceptibility by integrating all available data. Eligible case-control studies were carried out from the establishment of the project to September 2021. Published literature from multiple databases was retrieved, collected, and analyzed by two investigators independently. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for every study. Review Manager 5.2 and Stata 15.0 software were used for meta-analysis and stability was assessed by both subgroup analysis and sensitivity analysis. Begg's funnel plot and Egger's regression across the studies were also explored. We examined 22 case-control studies with 2683 cirrhosis patients and 2905 normal controls in four genetic models ; AA vs. GG: OR = 1.11, 95% CI: , GA + AA vs. GG: OR = 1.00, 95% CI: ; AA vs. GA + GG: OR = 1.07, 95%CI: ). TNF-α-308G/A gene polymorphism was relatively independent, and the results did not show a significant difference between the two groups. In the subgroup analysis by etiological classification of liver cirrhosis, cirrhosis after HCV infection was positively associated with the risk of TNF-α-308G/A polymorphism , AA vs. GA + GG: OR = 2.68, 95% CI: ). The meta-analysis showed TNF-α-308G/A gene polymorphism might not have affected susceptibility for liver cirrhosis. Nevertheless, further and well-designed studies were needed to confirm the findings.Cirrhosis is an active hepatic inflammation process of the liver considered as the serious phase of different liver injuries. Epidemiological studies have evaluated the possible association between TNF- Cirrhosis is an active hepatic inflammation process of the liver that is considered as the serious phase of different liver injuries . It is cRisk factors, such as age, gender, alcohol, and obesity, failed to explain all the clinically evident differences and also varied in patients. The advent of new genomic technologies and the decreasing cost of genotyping, together with multiple studies, enabled us to put much emphasis on the importance of genetic predisposition in different liver diseases. We realized that single nucleotide polymorphisms (SNPs) could not be neglected in determining fibrosis risk .α is a kind of cytokine with important immunoregulatory function and is involved in the inflammatory response. The TNF-α gene lies in the class III region of the major histocompatibility complex (MHC). According to the gene location inference, the location of the gene polymorphism might be related to immune infections [α-308G, or TNF1, and the uncommon allele as TNF-α-308A, or TNF2. The TNF2 promoter fragment functions were used as a powerful transcriptional activator [TNF-fections . The comctivator .α promoter appeared to be associated with variability in the histological severity of chronic hepatitis C infection [α reduced AMP-activated protein kinase (AMPK) activity, which could contribute to the development of NAFLD [α production induced by HBV and HCV leads to hypoalbuminemia [α is the first cytokine produced by the inflammatory response mechanism, which jointly promotes the proliferation and fiber proliferation of hepatic stellate cells, inhibits the growth of hepatocytes, prevents the regeneration of hepatocytes, and participates in the formation and development of cirrhosis [α-308G/A polymorphisms and the risk of liver cirrhosis.A series of studies have been conducted to research the possible relationship between TNF gene statistical analysis polymorphisms and liver cirrhosis. Several TNF alleles and haplotypes which were defined by TNF−308G/A dimorphic sequences were observed . Polymornfection . TNF-α ruminemia . Gordon uminemia . TNF- α irrhosis . The resThis systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Reviews Results and Meta-analysis (PRISMA) guidelines.α-308G/A” or “gene polymorphism” or “TNF” or “tumor necrosis factor” or “polymorphism” or “variant”). Moreover, we also collected literature through references lists of the relevant.Comprehensive systematic electronic search studies were published from building databases to September 2021. Published literature from PubMed, Embase, Web of Science, Google Scholar, Chinese National Knowledge Infrastructure (CNKI), and Wanfang Data were retrieved, collected, and analyzed by two investigators independently. The search keywords were as follows: (“liver cirrhosis” or “cirrhosis” or “hepatic cirrhosis” or “primary biliary cirrhosis (PBC),” “hepatitis C virus (HCV) liver cirrhosis,” “alcoholic liver cirrhosis (ALC),” “hepatitis B virus (HBV) liver cirrhosis,” “hereditary hemochromatosis”) and  the data from the article about genotype frequency of cases and controls were calculated; and (4) in the case-control group, there were the total number and genotype distribution and frequency with the Hardy–Weinberg balance .The inclusion criteria of this meta-analysis were as follows: (1) a case-control study or nested case-control study expressed in published journals; (2) comparing the risk of TNF-The exclusion criteria were as follows: (1) reviews and meta-analyses; (2) duplicate studies; (3) irrelevant literature; (4) studies without controls, or studies that did not show genotype frequency were also excluded from this meta-analysis; (5) liver cirrhosis was accompanied by serious complications or liver cancer; and (6) the control group was not healthy people.Two authors extracted the following data independently using the inclusion and exclusion criteria set by our group. The following information was collected from each study: the first name of the author, published time, country in which the study was launched, ethnicity, the number of cases and controls, allele frequency, types of study design, sample size, and types of cirrhosis. In case-control, the genotype frequency was consistent with the Hardy-Weinberg balance. We advocated solving the dispute by discussion, instead of our own decisions.The New Castle Ottawa scale was used to evaluate the risk of bias. Through the selection of research objects, comparability between groups, and exposure factors, 8 items were included in the study, with a full score of 9 points, 0–4 points for low-quality studies, and 5–9 points for high-quality studies.α-308 polymorphisms and liver cirrhosis for each study. In view of the heterogeneity of the results, heterogeneity was assessed by the I2 statistic, where in I2<50% indicates minor heterogeneity, for which a fixed effect model was used, and I2 > 50 indicates large heterogeneity, for which a random effect model was used. The results of heterogeneity were affected by many factors, such as different types of cirrhosis, a limited number of patients, and so on. Subgroup analysis was performed according to the racial population and different types of cirrhosis. The funnel plot, Begg's funnel plot, and Egger's regression tests were used to analyzing publication bias. Analyses were performed by the software Review Manager 5.2 and STATA 15.0 Pvalues. P < 0.5 was considered statistically significant.The odds ratio (OR) with 95% CI statistical methods could be used as the important method to calculate the association between the TNF-A comprehensive search of retrieved websites generated a total of 542 references based on the search strategy, including 65 relevant Chinese articles and 477 relevant English articles. 453 studies were excluded according to the first screening of titles, abstract diagrams, and full-text reading. 89 studies were retrieved for more details and excluded the overview, meta-analysis, and basic research. At this stage, 28 research literature studies were reviewed again. Based on the preset inclusion and exclusion criteria, 6 articles were excluded for lack of original data or case-control data. Finally, a total of 22 studies with 2683 cirrhosis patients and 2905 normal controls were included in the quantitative analysis , 12–31 . 7 Chineα-308G/A polymorphism and the risk of liver cirrhosis are presented in χ2 was about P ≤ 0.1 in a random-effect model. There was statistical heterogeneity in four genetic models . For GA vs. GG : OR = 0.95, 95% CI = 0.70–1.30, and test for overall effect: Z = 0.31(P = 0.76). For AA vs. GG : OR = 1.11, 95% CI = 0.66–1.85, and test for overall effect: Z = 0.38 (P = 0.70). For GA + AA vs. GG : OR = 1.00, 95% CI = 0.73–1.37, and test for overall effect: Z = 0.00 (P = 1.00). For AA vs. GA + GG : OR = 1.07, 95% CI = 0.70–1.63, and test for overall effect: Z = 0.29 (P = 0.77).TNF-α-308G/A gene polymorphism was relatively independent of liver cirrhosis risk including Asians, Africans, Americans, and Europeans in all genetic models. However, the results of subgroup analysis by etiological classification of liver cirrhosis were different. Cirrhosis after HCV infection was positively associated with the risk of TNF-α-308G/A polymorphism. For AA vs. GG : OR = 3.02, 95% CI = 1.15–7.88. For AA vs. GA + GG : OR = 2.68, 95% CI = 1.04–6.95. Other subgroup analyses by classification of liver cirrhosis did not reveal a significant association with TNF -308G/A gene polymorphism . The partial forest plots are shown in Figures In the further subgroup analysis based on ethnology, TNF-α-308G/A polymorphism and liver cirrhosis risk under all models in both the overall analysis and subgroup analysis showed the results did not alter substantially under any genetic models. Since the estimated pooled ORs were not affected by the data, this aspect suggested that our results were stable. TNF-α-308G/A gene polymorphism (GA vs. GG) sensitivity analysis is shown in Sensitivity analysis was carried out by repeating the meta-analysis sequentially and excluding one study at a time to evaluate the degree to which each individual study affected the overall OR. Our previous sensitivity analysis about the TNF-α-308G/A gene polymorphism . P < 0.05 suggested the presence of publication bias. As expected, no significant publication bias was detected by Begg's funnel plot for the association between TNF- α-308G/A gene polymorphism and liver cirrhosis expected   .α is closely related to the occurrence, development, and outcome of liver diseases. TNF- α is the first cytokine produced by the inflammatory response mechanism, which jointly promotes the proliferation and fiber proliferation of hepatic stellate cells, inhibits the growth of hepatocytes, prevents the regeneration of hepatocytes, and participates in the formation and development of cirrhosis [α, there are probably huge quantities of other immune genes around [α gene regulation has a considerable challenge, as it can be used as innate immunity and inflammatory pathology agent. In recent years, there has been an increasing number of studies on the relationship between TNF- α and cirrhosis, such as PBC, hepatitis B, alcoholic hepatitis, and hepatitis C. The research literature between on liver cirrhosis caused by HBV, PBC, and TNF- α-308 gene were various. In our included literature, TNF- α promoter polymorphism (G-308A and G-238A) and alleles of HLA class II (HLA-DRB1) might be associated with the occurrence and severity of PBC in Italians. We realized that single nucleotide polymorphisms (SNPs) could not be neglected in determining fibrosis risk. TNF- α is the host defense key mediator of various kinds of viruses and inflammatory response. Opinions of meta-analysis report were inconsistent. One meta-analysis published in 2011 on the association between TNF- α -308 polymorphism and cirrhosis risk failed to present as many cases as possible. The results were consistent with our study [α polymorphisms with PBC risk also presented no significant associations [α-308 gene polymorphisms and hepatitis C virus infection meta-analysis might have no effect on susceptibility to HCV infection and virus clearance [α -308G/A polymorphism and liver cirrhosis risk. Finally, this meta-analysis covered 22 case-control studies, 2683 cirrhosis patients, and 2905 normal controls. HBV-related studies were mostly found in the Chinese population, and PBC-related studies were limited to the Caucasian population, without a unified review of the population. Based on our notion, our study was more about investigation of the worldwide evidence about TNF-α-308 polymorphism and cirrhosis risk.Cirrhosis is an active hepatic inflammation process of the liver. It is characterized by diffuse fibrosis, pseudolobule formation, and proliferation of blood vessels inside and outside the liver. TNF- irrhosis . Locateds around . TNF- α ur study . Other sciations . The litlearance . Our metα -308 gene polymorphism on liver cirrhosis. No significant correlation was found between TNF-α -308G/A polymorphism and liver cirrhosis risk in all comparisons of GA vs. GG; GA + AA vs. GG; AA vs. GA + GG; AA vs. GG. Cirrhosis after HCV infection was positively associated with the risk of TNF-α-308G/A polymorphism. However, the results were related to genetic polymorphism and the small sample size of the HCV subgroup, so it did not change the overall result. A single study method was deleted each time to reﬂect the influence of the individual data-set to the pooled ORs. All this proves the fact that the results would not be altered by the change of individual studies.Based on previous studies, this study expanded the sample size, and the meta-analysis method was used in the comprehensive evaluation and analysis of TNF- The part of the presence of heterogeneity in all genetic models might be obtained through sensitivity analysis and subgroup, eliminating any document for the reason of the heterogeneity. Finally, the significance of these results was the same and did not vary with the abovementioned statement. We analyzed the causes of heterogeneity and found that it mainly lay in different countries, races, gene types, and the etiology of liver cirrhosis. In this paper, we used Egger's test for the qualitative and quantitative assessment of publication bias tips. Heterogeneity did not affect the results of publication bias, which increased the statistical power.There were some limitations to the research which are as follows: (1) only the published Chinese and English literature was included, which might lead to selection bias; (2) all the literature were case-control studies, which were highly heterogeneous and lacked the support of higher quality clinical trial research; (3) the original literature only involved European, Asian, African, and American populations, but not the Oceania population, and there were only fewer data in Africa and America population; and (4) more PBC and HCV studies, less HCV/HHC/ALC population. In spite of the above limitations, all 22 articles conformed to HWE's law of equilibrium. The result of the meta-analysis was reliable, which means there was no significant publication bias indicated by Begg's funnel diagram. Nevertheless, larger and well-designed studies were needed to confirm the findings. Our future research is to further study these limitations.