Human periodontal ligament stem cells (hPDLSCs) are ideal seed cells for periodontal regeneration. A greater understanding of the dynamic protein profiles during osteogenic differentiation contributed to the improvement of periodontal regeneration tissue engineering.Tandem Mass Tag quantitative proteomics was utilized to reveal the temporal protein expression pattern during osteogenic differentiation of hPDLSCs on days 0, 3, 7 and 14. Differentially expressed proteins (DEPs) were clustered and functional annotated by Gene Ontology (GO) terms. Pathway enrichment analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes database, followed by the predicted activation using Ingenuity Pathway Analysis software. Interaction networks of redox-sensitive signalling pathways and oxidative phosphorylation (OXPHOS) were conducted and the hub protein SOD2 was validated with western blotting.A total of 1024 DEPs were identified and clustered in 5 distinctive clusters representing dynamic tendencies. The GO enrichment results indicated that proteins with different tendencies show different functions. Pathway enrichment analysis found that OXPHOS was significantly involved, which further predicted continuous activation. Redox-sensitive signalling pathways with dynamic activation status showed associations with OXPHOS to various degrees, especially the sirtuin signalling pathway. SOD2, an important component of the sirtuin pathway, displays a persistent increase during osteogenesis. Data are available via ProteomeXchange with identifier PXD020908.This is the first in-depth dynamic proteomic analysis of osteogenic differentiation of hPDLSCs. It demonstrated a dynamic regulatory mechanism of hPDLSC osteogenesis and might provide a new perspective for research on periodontal regeneration.The online version contains supplementary material available at 10.1186/s13287-020-02123-6. Since they were first defined, human periodontal ligament stem cells (hPDLSCs) have been identified as the optimal seed cells for periodontal regeneration , 2. TheyPrevious studies have conducted high-throughput techniques to reveal gene expression alterations in hPDLSCs at the transcriptional level. Regrettably, they focused only on regulatory non-coding RNAs rather than functionally translational genes, or focused only on a single time point (day 7 or 14 of osteogenesis duration) and the final phenotype . OsteogeTo date, only two reports have examined the proteomic profile during the osteogenic differentiation of hPDLSCs. As early as 2009, Xiao et al. compared the protein profile between osteogenic-induced and non-induced hPDLSCs using gel-based mass spectrometry . They foTo address this question comprehensively, we conducted the first dynamic proteome analysis to reveal the temporal protein profile of hPDLSCs during the osteogenic differentiation. In the present study, we found that proteins involved in this process presented dynamic expression patterns and functioned distinctively. Oxidative phosphorylation (OXPHOS), the most significant pathway, was demonstrated to be continuously activated during the process. It was associated with several redox-sensitive signalling pathways (such as sirtuin and AMPK), which also showed altered activation/inhibition status. Furthermore, SOD2 might play a vital role in the osteogenesis of hPDLSCs.2 per piece and scattered on the culture flask and maintained in low-glucose Dulbecco’s modified Eagle’s medium  with 10% foetal bovine serum  and 100 U/mL penicillin/streptomycin . After reaching 80% confluency, hPDLSCs were obtained utilizing the limiting dilution method, featuring clone formation capacity. Passages 4–6 of hPDLSCs were used in the subsequent experiments. We applied the chemically osteogenic differentiation protocol to induce osteogenesis of hPDLSCs. Briefly, hPDLSCs at a confluency of 80–90% were induced to differentiate using growth culture media supplemented with 10 mM beta-glycerophosphate , 50 μg/mL l-ascorbic acid-2-phosphate  and 100 nM dexamethasone . The osteogenic differentiation culture medium was changed every 3 days. Alkaline phosphatase (ALP) and alizarin red S (ARS) staining were performed to verify the establishment of the osteogenesis phenotype .hPDLCs were obtained from three periodontally healthy donors  who requested tooth extraction for orthodontic treatment at the Department of Stomatology, Nanfang Hospital, Southern Medical University. This study was approved by the Ethics Committee of Nanfang Hospital, Southern Medical University (NFEC-2020-253), and informed consent was obtained from donors. The middle third of the periodontium was carefully scraped off with a surgical blade. After washing with PBS containing 100 U/mL penicillin/streptomycin, the periodontium was minced to approximately 1 mmThe detailed materials and methods are described in supplementary materials.p value < 0.05 determined by one-way ANOVA followed by post hoc Tukey HSD test were defined as differentially expressed proteins (DEPs). Correlations among different time points were calculated in Prism using Pearson correlation. To address the temporal dynamics of osteogenic differentiation of hPDLSCs, we subjected the DEPs to the k-means clustering algorithm assigning the optimal cluster number based on the reckon of sum of the squared errors. Functional enrichment analyses were subsequently performed for each cluster. Gene Ontology (GO) annotations were retrieved from the GO database (http://www.geneontology.org/), and hypergeometric test was used to find significantly enriched GO terms based on GO::TermFinder [http://metascape.org/gp/index.html) with the ontology source of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway using the p value set at 0.9 [https://string-db.org/) and visualized using Cytoscape. The minimum required score for PPI network construction was set to medium confidence (0.400) and the interaction derived from all provided sources including textmining, experiments, database, co-expression, neighbourhood, gene fusion and co-occurrence.Proteins in each differentiated time point were compared to any other time point, and those with fold change > 1.2 or < 0.83 and rmFinder . Pathwayt at 0.9 . Ingenuit at 0.9 . The proTotal RNA and proteins of osteogenic hPDLSC samples at days 0, 3, 7 and 14 were extracted and tested. Please see details in supplementary materials.p < 0.05. Data are presented as the mean ± SD. Band intensity in western blot images was quantified with ImageJ Software, and values are expressed as the means ± SD of at least three independent experiments. All experiments were performed at least in triplicate.GraphPad Prism was used to perform one-way ANOVA with post hoc Tukey HSD for comparisons among more than two groups. Significance was determined at To describe the proteome dynamics during osteogenic differentiation of hPDLSCs, we used the classical induction protocol to induce osteogenic-differentiated hPDLSCs Fig. a. DuringA total of 1024 DEPs were identified, and the number of DEPs across all time points is illustrated in Fig. To better monitor the temporal changes in protein expression, we subjected the 1024 DEPs to the k-means clustering algorithm. The optimal number of clusters was 5 (data not shown). Therefore, the quantitative temporal profiles partitioned into 5 clusters Fig. a. The laConsidering that proteins with different expression patterns may perform different functions, we tested the respective enrichment of GO terms. Generally, the GO enrichment results indicated that proteins with different tendencies show different functions. The results of GO enrichment are illustrated in the form of heatmaps Fig. . ClusterCluster 2, with proteins that continuously declined upon initiation of osteogenic differentiation, was enriched in DNA replication categories Fig. b. This bWhen referring to molecular function GO analysis Fig. c, functiIn Cellular Component GO analysis Fig. d, proteiIn summary, GO enrichment demonstrated that proteins associated with ECM organization/interaction, secretory function and mitochondrial activity played functions in a complex regulatory manner during osteogenesis of hPDLSCs.http://metascape.org/gp/index.html), and their functional annotations were classified . The sirtuin signalling pathway was the most associated signalling pathway.Additional file 9: Figure S9. Sample set of TMT proteomic analysis. Three six-plex TMT sets were used for the four time point analysis  with triplicate biological replication. A sample combined with all 12 sample was labeled with Label 130 and used as a reference sample for normalization between different sets. D0, 3, 7, 14 means samples undifferented, differentiated for 3, 7 and 14 days.Additional file 10: Table S1. Details of differentially expressed proteins.Additional file 11: Table S2. Details of GO enrichments of each cluster of differentially expressed proteins.Additional file 12: Table S3. Details of KEGG pathway enrichments of differentially expressed proteins.Additional file 13: Table S4. Details of activation prediction of canonical pathways from Ingenuity Pathway Analysis.Additional file 14. Supplemental materials and methods.


The present study was conducted to evaluate the prevalence of the signaling pathways mutation rate in the Gastrointestinal (GI) tract cancers in a systematic review and meta-analysis study. The study was performed based on the PRISMA criteria. Random models by confidence interval (CI: 95%) were used to calculate the pooled estimate of prevalence via Metaprop command. The pooled prevalence indices of signal transduction pathway mutations in gastric cancer, liver cancer, colorectal cancer, and pancreatic cancer were 5% (95% CI: 3–8%), 12% (95% CI: 8–18%), 17% (95% CI: 14–20%), and 20% (95% CI: 5–41%), respectively. Also, the mutation rates for Wnt pathway and MAPK pathway were calculated to be 23%  and 20% , respectively. Moreover, the most popular genes were APC (in Wnt pathway), KRAS (in MAPK pathway) and PIK3CA (in PI3K pathway) in the colorectal cancer, pancreatic cancer, and gastric cancer while they were beta-catenin and CTNNB1 in liver cancer. The most altered pathway was Wnt pathway followed by the MAPK pathway. In addition, pancreatic cancer was found to be higher under the pressure of mutation compared with others based on pooled prevalence analysis. Finally, APC mutations in colorectal cancer, KRAS in gastric cancer, and pancreatic cancer were mostly associated gene alterations. The Smad signaling is critical in TGF-β signaling, which controls the transcription. MAPK signaling pathway makes use of three different downstream effectors, including Extracellular-signal-regulated kinase pathway, c-Jun N-terminal kinase (JNK) pathway, and p38 pathway. Also, the Wnt signaling pathway is important in cell differentiation and proliferation. In Wnt signaling, the Wnt/β-catenin signaling pathway is the only canonical pathway2. The p53 signaling is not a canonical signaling pathway but due to the p53 non-transcriptional functions, the role of this pathway in generating cancer and its interaction with other signaling pathways, p53 can be considered as an individual pathway3.Cell signaling is a communication process of cell activities mediated by downstream genes and proteins. Distraction of signaling process induce disturbance in cellular mechanisms and may cause diseases, such as cancer, autoimmunity, and diabetes. In the major category, the signaling pathways are divided into intracellular activating signaling pathways, such as Hippo signaling and Notch signaling pathways or the extracellular activating pathways, for instance, Mitogen-activated protein kinase (MAPK) signaling, Nuclear factor κB (NF-κB), Janus kinase4. Mutations in signaling pathways, such as signal transduction systems, are the basic triggering mechanisms in different types of cancers5. The role of MAPK signaling pathway, Wnt, TGF beta, and JAK-STAT signaling pathways are more common in cancer induction. The Wnt signaling pathway, which include genes like PTEN (phosphatase and tensin homolog deleted on chromosome 10), WISP3 , APC (Adenomatous polyposis coli), β-catenin, AXIN, and TCF4 (T-cell factor 4), has significant role in carcinogenesis. Thus, its microsatellite instability (MSI), among other carcinogenesis processes, has been a hot topic, especially in the studies of colorectal cancer8. APC mutation and promoter hypermethylation are two important mechanisms in carcinogenesis and colorectal cancer (CRC) progression11. Two AXIN genes, AXIN1 and AXIN2, could be prone to mutation in some CRC cases13. PIK3CA  and PTEN are two important genes in the PI3K/AKT signal pathway and previous studies have put emphasis on them as important genes in the CRC development by altering the proliferation and cell death patterns15. Moreover, CTNNB1 (catenin beta 1) transformation via β-catenin alteration as another mediators of the Wnt/β-catenin pathway have been found in some of the liver tumors16. Liver carcinogenesis process is related to the interactions of three major pathways: the p53/p21, the p16/cyclin D1/pRB, and the Wnt/wingless18. Also, numerous factors such as TNFα , TGFβ (transforming growth factor beta), c-myc, IGF2R (insulin like growth factor 2 receptor), SMAD2, SMAD4, DLC-1, and HIC1  could initiate liver tumorogenesis18.Gastrointestinal (GI) cancers are a group of cancers that affect the digestive system and its accessory organs. The most prevalent cancers related to GI tract are colorectal, gastric, and esophageal cancers, respectively19. Two vital intracellular pathways affected by EGFR are the RAS/RAF/MAPK and the PIK3CA/PTEN/AKT signaling pathways. These pathways mediate activation of transcription factors like ERK (extracellular regulated MAP kinase) and p38 and lead to cell transformation reactions like the up-regulation of proliferation, relocation, mesenchymal separation induction, and apoptosis reduction. As EGFR has been a target for anti-tumor drugs, its mutations and related downstream signaling pathway mutations have become important20.Mutation analysis of signaling pathway mediators could have prognostic impact on tumor development. Transformation of the epidermal growth factor receptor (EGFR) and its downstream pathway mediators could lead to development of human tumors21, we made an attempt to evaluate the prevalence of the signaling pathways mutation rate in the GI tract cancers in a systematic review and meta-analysis setting.Indeed, interaction of various signaling pathway mediator mutations and their behavior in cancer development has been a hot topic. These alterations could include susceptibility, resistant or non-sense for treatment management or tumorogenesis in different individuals geographically. By considering the PRISMA  criteria1, and 15 on other gastrointestinal cancers, namely esophagus, bile duct, rectal cancer, gall bladder, and ampullary adenocarcinomas. The details of screened data based on PRISMA guideline are provided in Fig. A total of 10,808 records were detected using the search strategy keywords. After screening by the title and abstracts, 414 articles were included for further analysis. Next, the full-text assessment resulted in selecting 121 eligible records including 65 studies on colorectal cancer (CRC), 21 on liver cancer (LC), 16 on Gastric Cancer (GC), 9 on pancreatic cancerThe risk of bias assessment is given in Table From among 16 studies on GC, mostly the MAPK and PI3 pathways were analyzed in 2489 participants. The most evaluated gene in MAPK was KRAS and mutations ranged from 0 to 20%. Also, the PI3K mutations in the PI3 pathway were 3 to 8.7% and CTNNB1 mutations ranged from 1.7% to 7%. The detailed data are listed in Table P = 0.001) . The pooled prevalence funnel plot in GC signal transduction pathway mutations is illustrated in Fig. The results of meta-analysis revealed that pooled prevalence index of signal transduction pathway mutations in GC was 5% 95% CI: 3–8%) and there was high heterogeneity between these studies in estimating the prevalence  . The pooled prevalence funnel plot in CRC signal transduction pathway mutations is illustrated in Fig. The results of meta-analysis revealed that pooled prevalence of signal transduction pathway mutations in CRC was 17% 95% CI: 14%, 20%) and there was a high heterogeneity between these studies in estimating the prevalence  and 174 hepatoblastoma participants in 21 studies. There were different ranges of mutations in these studies, which are listed in supplementary Table P = 0.0001) . The pooled prevalence funnel plot in LC signal transduction pathway mutations is illustrated in Fig. The results of meta-analysis showed that pooled prevalence of signal transduction pathway mutations in LC was 12% 95% CI: 8–18%) and there was a high heterogeneity between these studies in estimating the prevalence  . The pooled prevalence funnel plot in PC signal transduction pathway mutations is illustrated in Fig. The results of meta-analysis showed that pooled prevalence of signal transduction pathway mutations in pancreatic cancer was 20% (95% CI: 5–41%) and there was a high heterogeneity between these studies in estimating the prevalence , 17% (95% CI: 14–20%), 12% (95% CI: 8–18%), and 20% (95% CI: 5–41%), respectively. The higher rates in pooled prevalence could suggest more association between the signal transduction mutations and GI cancers incidence. The subgroup analysis for CRC shows that KRAS and APC are the most mutant genes with 32%  and 44%  mutation rates, respectively. Also, the most altered pathway was Wnt (23%) , followed by MAPK (20%)  pathway.The aim of the current study was to evaluate the prevalence of the signaling pathway mutation rate in GI tract cancers in a systematic review and meta-analysis setting. It should note that, the signaling pathway mutations were comprehensively studied by The Cancer Genome Atlas (TCGA)144. Recently, different studies have been reported focusing on the cost-effectiveness of the CRC screening programs indicating the importance of the CRC diagnosis146. Signaling pathways have crucial impacts on the development of different cancers5. Although the nucleotide alterations have critical impacts on cancer initiation, the environmental factors are predisposing elements in cancer induction and are affecting the signaling pathways mutations148. As an example, smoking affects CRC cancers generation and mortality151. In this regard, lung cancer investigations revealed that smoking could increase the EGFR and its downstream elements, such as KRAS and BRAF mutations148. Moreover, studies on CRC and smoking showed that TGFβ signaling pathway mutations have significant roles in carcinogenesis147. Inflammation is another key player in generation of cancer153. TLR2 alterations associated with inflammation could lead to the signaling pathways related ERK (extracellular-regulated kinase) and PI3K/AKT mutations. The importance of the inflammation in the CRC were illustrated by Liu and et al.154. These substitutions might be due to the microbiome disturbance, too155.The CRC carcinogenesis is firstly initiated by the mild colon polyps and gradually progresses to the cancerous lesions. The adenocarcinoma is globally the most prevalent type of the CRC156 who found KRAS mutation to be 24% in 86 CRC patients. Meanwhile, Tong et al.113 reported the highest rate (75%) of the KRAS mutations in CRC patients in codon 12 in 1506 individuals. Tong’s study showed different mutation rates between the separate codons of the KRAS gene with the highest in codon 12 and the lowest (2.5%) in codon 61. Also, in the study conducted by Kawazoe et al.127 on 264 metastatic colorectal cancers (mCRC), the KRAS exon 2 mutation was calculated to be 34%, as the highest mutation rate. In this study, BRAF mutation rate was reported to be 5.4%. The highest prevalence for the BRAF mutation reported in other studies was 78%88. This huge difference in the BRAF mutation rate could be due to the differences in the sample size and the method used for analysis.The MAPK/ERK signaling was analyzed in the study reported by Sameer et al.26 showed that 37.5% of the 73 CRC patients had mutations in the exon 3 of the beta-catenin gene. Also, Shitoh et al.32 reported the rate of 3% for beta-catenin mutation in exon 3, and 27% in the high-frequency microsatellite instability (MSI-H). Furthermore, the APC gene mutations were assessed in 10 different studies with the lowest reported to be 33% in the study by Chen88 study and the highest as 73% reported by Lee et al.107. The previous studies showed that the MSI could be associated with the in/del substitutions in genome hot spots which can initiate CRC tumorogenesis by increasing the mismatches indiscriminately159. Investigation on Wnt/beta-catenin signaling was firstly introduced by the association between APC gene and beta-catenin161. Other studies found the interactions of these genes with beta-catenin-Tcf (T-cell factor) complex suggesting the association of these genes with CRC omplication 162. The role of APC gene in causing cancer was initially introduced in the familial adenomatous polyposis (FAP)163. This gene facilitates beta-catenin distorting. APC gene mutations influence beta-catenin and AXIN protein binding sites165. Moreover, they could maximize the protein stability and life cycle166. Thus, the carcinogenesis process is accelerated by altered signal transduction and cell cycle167.The Wnt/beta-catenin signaling and PI3K/AKT signal have been assessed in a variety of studies. The Wnt/beta-catenin was assessed in 18 different studies and the most evaluated genes were APC, beta-catenin, and CTNNB1. Fujimori et al.49 found p53 gene mutation rate to be about 18% in CRC patients.From among the studies which assessed the PI3 signal transduction pathway, the mutation of PIK3CA gene was reported in 20 studies ranging between 0 and 34%. Meanwhile, Thorstensen et al.156 the clinic-pathological assessment indicated that, the SMAD4 mutations are more frequent in colon tumors and statistically associated with tumor grade and lymph nodes involvement. Tong and colleagues113 reports the KRAS mutations are in association with gender and tumor site. Also, Kawazoe et al.127 points out the BRAF mutations are associated with tumor location, site of metastasis and differentiation pattern. Meanwhile, Yang and colleagues168 reports the association of the KRAS mutations with tumor location, type of tumor, differentiation pattern and gender of the patients. Furthermore, there were limited data about the association of the mutations in signaling pathways with survival rate in patients. Some studies suggested BRAF mutations169 and SMAD4 mutations140 are association with poor prognosis and survival rate. Highly variable and limited data about clinic-pathological features, survival and prognosis in association with signaling pathway mutations were extracted. The clinic-pathological features and patients survival association with signaling pathway mutations is one of the current study limitations and needs further investigation.There are variable reports in the matter of clinic-pathological association with mutations in the current study. In the conducted study by Sameer et al.171. In LC patients, the Wnt signaling was the top research interest and the CTNNB1 was the most assessed gene. The CTNNB1 mutation was also investigated in HCC patients in different studies131. Purcell et al.73 reported CTNNB1 mutations in 15% of hepatoblastoma patients while the reported prevalence in Ueda’s study was 75%74. Our study subgroup analysis for liver cancer145 studies showed that beta-catenin has higher mutation rate ) and the most altered pathway was Wnt ). It has been indicated that the CTNNB1 and P53 genes are the most involved genes in the HCC173. Moreover, the conducted studies showed that the P53 mutations were mostly associated with the Asian and African countries, while the CTNNB1 mutations were mostly associated with HCC in the Western countries173.HCC is the fifth cause of death worldwide and is mostly inducted by the chronic liver disorders, such as viral hepatitis174. Witkiewicz et al.130 assessed different genes in MAPK/ERK, PI3K/AKT, and Wnt/beta-catenin signaling pathways in pancreatic ductal adenocarcinoma patients. They showed that the AXIN1, KRAS, and PI3CA mutations rate were 5%, 92%, and 4%, respectively. Moreover, the high rate of KRAS mutations in pancreatic cancer patients was confirmed by the other studies175. Our study showed that in the subgroup analysis for pancreatic cancer, the KRAS was the most mutated gene (58% (95% CI: 31–83%)) and MAPK was the most altered pathway (31% (95% CI: 5–66%)). In GC, mutations were 14% (95% CI: 2–34%) for KRAS, 7% (95% CI: 1–17%) for MAPK, and 6% (95% CI: 2–12%) for PI3 pathways. In the pancreatic and gastric cancers, the most evaluated pathways were PI3 and MAPK. The KRAS gene generates a GTPase protein which is critical in regulating the cell proliferation and metabolism176. The mutations in KRAS leads to impaired cells activity enhancement and malignancy progression177.The pancreatic cancer is known as the forth cause of cancer mortality in the US with only 10% of the cases living more than 5 years178. Zhang et al.104 studied 100 advanced primary GC cases for the purpose of evaluating PI3K/AKT signaling pathway mutations. They suggested that the MAPK/ERK and PI3K/AKT pathways could be potential therapeutic targets for GC treatment180. The AKT gene produced a protein in the PI3K/Akt pathway which could play a role in tumorogenesis80. The mutations in the PIK3CA and AKT in PI3K/AKT pathway could affect downstream signaling pathway genes, like mTOR (mechanistic target of rapamycin kinase) and caspase 9, which are important in GC progression182. Wang et al.99 investigated hedgehog pathway in GC patients and showed that the PTCH1 (patched 1) and SMO (smoothened) genes were mutated in 51.2% and 25.6% of the cases. Alterations in PTCH1 gene were associated with the basal cell carcinoma and basal cell nevus syndrome184.Gastric Cancer (GC), as another invasive GI cancer, has significant mortality rate worldwide185. But the Sanger sequencing is an accurate and sensitive method for mutation analysis and it has been suggested for the confirmation of the NGS results186. Also, in the subgroup analysis for the GC, the method of detection could be mentioned as a potent source of the heterogeneity in the current study  to detect mutation. The method used the least was the NGS (next generation sequencing) as a preferred method in the recent years. The NGS can be used to analyze numerous samples at the same time and thus reduce the cost and the time requireddy Table .3.The major limitation in the current study was the extent of subject; it is suggested that further investigations use more narrowing strategies. Also, we aimed at minimizing the author bias in data extraction and screening biases using different authors and double check strategies. Also, it should be mentioned that the p53 signaling is not a canonical signaling pathway but due to the p53 non-transcriptional functions, the importance of this pathway in cancer generation, and its interaction with other signaling pathways, in the present study, we assessed p53 as individual pathwayIn conclusion, progression of GI cancers is affected by signaling pathway mediators. Different studies have shown diverse results based on their population, method, and target gene. Our study concluded that the most important genes that are under mutation pressure include KRAS and PI3CA in the CRC, PC, and GC while beta-catenin and CTNNB1 are genes under mutation pressure for liver malignancies. Subgroup analysis and heterogeneity of the studies could illustrate more valid data between different studies for screening strategies. In this regard, signal transduction pathway mutations pooled prevalence was higher in PC and lower in GC (20% vs. 5%). Thus, PC is the most common cancer involved by signal transduction mediator’s mutations. Among studied genes, KRAS in GC and pancreatic cancer and APC in CRC had the most association with cancer outcome. Moreover, MAPK had higher mutation rate among the studied pathways. Furthermore, PCR-SS method had the highest popularity among different methods. Future studies should be carried out to focus on cancer progression and patient’s survival assessments.In the present comprehensive study, we assessed all relevant original research studies via the electronic literature search in Web of Science (SCIE), PubMed (Including MEDLINE), Science Direct, Scopus, EMBASE, and Google Scholar databases using the keywords including Polymorphism, Mutation, Mutation Rate, Mutation Prevalence, Silent Mutation, Point Mutation, Missense Mutation, INDEL Mutation, Frameshift Mutation, Synonymous Mutation, Non-synonymous Mutation, Transversion Mutation, Transition Mutation, Insertion Mutation, Deletion Mutation, Digestive System Diseases, Gastrointestinal Neoplasms, Digestive System Abnormalities, Biliary Tract Diseases, Biliary Tract Neoplasms, Gallbladder Diseases, Anorectal Malformations, Colorectal Neoplasms, Pancreatic Neoplasms, Hepatocellular carcinoma, Esophageal cancer, Intestinal Diseases, Stomach Diseases, Stomach cancer, Gastric cancer, Liver Diseases, Liver Neoplasms, Pancreatic Diseases, Signaling Pathways, Signal Transduction, Wnt Signaling Pathway, and MAP Kinase Signaling System between January 1998 and September 28, 2019. Also, the reference lists of the screened studies were reviewed so as to find relevant studies  and were reviewed by two authors of the study independently; disagreements between them were settled by a third expert. All the relevant studies were screened considering the inclusion criteria and the data were extracted. The extracted data included the first author’s name, the publication date (based on year), country, design of the study, type of the cancer, sample size, mutation pathway, gene name, mean age, gender, mutation positive population, and method of detection.187.The risk bias for the non-randomized controlled trials (RCT) was assessed making use of the 13 items in the Research Triangle Institute (RTI), Evidence-based Practice Center2 statistics. In addition, for the assessment of the source of heterogeneity among studies we used subgroup analysis. Also, funnel plot and Egger test used for the publication bias assessment. For the statistical analysis in this study STATA 16.0  were used by setting the statistical significant value at p < 0.05.In this study, to compute of the pooled estimate of prevalence we used the Metaprop command and random models with confidence interval of CI = 95%. The prevalence estimation performed by random effects models in some analyses due to statistically significant of the heterogeneity test. In the present study, for the evaluation of statistical heterogeneity between studies we used Cochran’s Q test and ISupplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.Supplementary Table 5.Supplementary Table 6.


TM, a Cortellis Solution software, to exhibit the Gene Ontology (GO) and enriched pathways between the datasets. Our study identified 4 upregulated and 13 downregulated genes. Analysis of GO and functional enrichment using ClueGO revealed the pathways that were statistically significant, including pathways involving T-cell costimulation, lymphocyte costimulation, negative regulation of vascular permeability, and B-cell receptor signaling. The DEGs were mainly enriched in metabolic networks such as the phosphatidylinositol-3,4,5-triphosphate pathway and the carnitine pathway. Additionally, potentially enriched pathways, such as the signaling pathways induced by oxidative stress and reactive oxygen species (ROS), chemotaxis and lysophosphatidic acid signaling induced via G protein-coupled receptors (GPCRs), and the androgen receptor activation pathway, were identified from the DEGs that were mainly associated with the immune system. Four genes  were identified to be strongly associated with SLE. Our integrative analysis using a multitude of bioinformatics tools might promote an understanding of the dysregulated pathways that are associated with SLE development and progression. The four DEGs in SLE patients might shed light on the pathogenesis of SLE and might serve as potential biomarkers in early diagnosis and as therapeutic targets for SLE.Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disorder that is clinically complex and has increased production of autoantibodies. Via emerging technologies, researchers have identified genetic variants, expression profiling of genes, animal models, and epigenetic findings that have paved the way for a better understanding of the molecular and genetic mechanisms of SLE. Our current study aimed to illustrate the essential genes and molecular pathways that are potentially involved in the pathogenesis of SLE. This study incorporates the gene expression profiling data of the microarray dataset GSE30153 from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between the B-cell transcriptomes of SLE patients and healthy controls were screened using the GEO2R web tool. The identified DEGs were subjected to STRING analysis and Cytoscape to explore the protein–protein interaction (PPI) networks between them. The MCODE (Molecular Complex Detection) plugin of Cytoscape was used to screen the cluster subnetworks that are highly interlinked between the DEGs. Subsequently, the clustered DEGs were subjected to functional annotation with ClueGO/CluePedia to identify the significant pathways that were enriched. For integrative analysis, we used GeneGo Metacore Systemic lupus erythematosus (SLE), also known as lupus, is a rare systemic autoimmune disease that mostly affects middle-aged women, mainly of Asian, African, American, and Hispanic origin . SLE affSLE is characterized by its clinical heterogeneity, with a wide range of clinical manifestations reflecting its complex etiopathogenesis . The cliWith the extensive use of gene detection methods, high-throughput sequencing and extensive microarray data profiling studies on SLE have been conducted, and several differentially expressed genes (DEGs) and cellular pathways in SLE have been identified . NeverthIntegrating and reanalyzing the data using bioinformatics methods may help in identifying gene regulatory pathways, essential genes, and their associated networks in SLE disease, which can provide new and valuable ideas for understanding the molecular mechanisms and identifying reliable diagnostic and therapeutic targets of SLE. Therefore, in this study, we first conducted a comprehensive collection of genes associated with SLE from the GEO dataset with ID GSE30153. Then, we performed a bioinformatics analysis of these genes with the MCODE (Molecular Complex Detection), GeneGo, and ClueGO tools. To further explore the pathogenesis of SLE in a more specific manner, functions and pathways identified by the modules were used to indicate the biological processes and biochemical pathways related to the immune system. Finally, the genes potentially associated with arthritis, pleurisy, and myocarditis, which are the common complications of SLE, were compared with SLE-related genes to identify common genes that participated in the development of SLE. To interpret the biological relevance of these changes in gene expression, we analyzed the microarray data via an integrated bioinformatic analysis expanding on traditional microarray analysis methods, namely, Gene Ontology (GO) and pathway analysis, thereby allowing the construction of interaction networks that might identify novel prognostic markers and therapeutic targets.1. The database accommodates gene expression datasets from a variety of experiments, such as DNA-seq, ChIPs, RNA-seq, microarray, and high-throughput hybridization array on array . GSE3015on array . The dowT-tests to compute the false discovery rate (FDR) and p-values to identify the DEGs between SLE patients and healthy control human sorted B-cells  network by using Search Tool for the Retrieval of Interacting Genes (STRING v11.0)3 , 2019. Tant DEGs . The MCOant DEGs . The topant DEGs .5, was used to perform curated pathway enrichment analysis and GO analysis. GeneGo facilitates the rapid assessment of metabolic pathways, protein biological networks, and pathway maps from high-throughput experimental data . Based on a significance threshold of p < 0.05, a pictorial representation of the molecular interactions of DEGs from the study groups is generated. Determination of a hypergeometric p-value enables the estimation of the chance that an intersection between DEGs and ontological elements is random. An FDR < 0.05 was used as a criterion to calculate if statistically significant DEGs constituted a processor pathway.Metacore, a Cortellis Solution software p-values and | log2FC | values, the top 250 DEGs were identified. A volcano plot was constructed using the Rstudio web server ShinyVolcanoPlot to identify DEGs by comparing the SLE and control groups from the dataset. The volcano plot in p-value) between the two groups. With cutoffs of p < 0.05 and log2FC ≥ 1.0 or ≤−1, we found 4 and 13 genes that were upregulated and downregulated, respectively, between the two groups , lymphocyte costimulation (GO: 0031294), negative regulation of vascular permeability (GO: 0043116), the metaphase/anaphase transition of the mitotic cell cycle (GO: 0007091), regulation of the transcription involved in the G1/S transition of the mitotic cell cycle (GO: 0000083), negative regulation of signal transduction in the absence of ligand (GO: 1901099), and KEGG pathways such as hematopoietic cell lineage (KEGG: 04640), B-cell receptor signaling pathway (KEGG: 04662), ErbB signaling pathway (KEGG: 04012), and AGE-RAGE (advanced glycation end products and receptor for AGE) signaling pathway in diabetic complications  ribose hydrolase CD38 is an enzyme involved in the activation of the genes Semaphorin 4D, CD19, and c-Cbl through physical interactions. Caveolin 1 (CAV1) is a binding protein shown to interact with the ErbB2, MDR1, HTR2A, and androgen receptor genes, and inhibition or activation is followed by specific binding to its corresponding proteins. RAC-alpha serine/threonine-protein kinase (AKT1) is a protein kinase that interacts with the FKHR, mTOR, Bcl-10, FOXO3A, HNF3-beta, and GSK3 beta genes via phosphorylation, resulting in inhibition or activation. These genes were differentially expressed between sorted B-cells from controls and sorted B-cells from SLE patients and result in transcriptional regulation and inhibition of genes/proteins within the top-scored pathway maps.From the Metacoreway maps . Early gTM analysis for GO and pathway annotation, and constructed the interacting networks of PPI and used for cluster analysis. In the network, the nodes were considered proteins, and the edges were their interactions. Using network topology features, the PPI network can be analyzed to distinguish the core proteins that are involved in the pathways (DNA microarrays and next generation sequencing (NGS) approaches are high-throughput technologies that have resulted in the emergence of new biomedical discoveries. Data from microarray and gene expression profiles have enabled a deeper understanding of the intrinsic molecular pathways of complex mechanisms of biological systems and their responses . It is tpathways . The idepathways . We perfpathways . Screenipathways .AKT1, VEGFA, CDK6, and MAPK9) that were involved in the risk of developing pancreatic cancer in SLE patients was due to chronic inflammation, suggesting that these genes might be involved in the pathogenesis of SLE. Our findings are therefore consistent with the roles of genes that are differentially expressed in SLE-causing pathways  from the cluster 2 subnetwork are highly related to and consistent with the members of the signaling pathways associated with the immune system, apoptosis, the cell cycle, and vasculopathy.Furthermore, to identify the functional enrichment of these subnetworks from MCODE, we implemented the ClueGO plugin for analysis. This revealed that the DEGs were enriched in most essential pathways, which are highly associated with the immune system. The GO and KEGG enrichment analyses of the DEGs from cluster 1 showed that they were mostly enriched in T-cell costimulation, lymphocyte costimulation, negative regulation of vascular permeability, the metaphase/anaphase transition of the mitotic cell cycle, regulation of the transcription involved in the G1/S transition of mitotic cell cycle, the hematopoietic cell lineage, the B-cell receptor signaling pathway, the ErbB signaling pathway, the AGE-RAGE signaling pathway in diabetic complications, and pancreatic cancer. Interestingly, the costimulation of T-cell and lymphocyte receptors is recognized to be important in SLE pathogenesis by enabling communicating with B-cells for the production of autoantibodies . In SLE,pathways . The enrpathways . The DEGpathways . p53 sigpathways . Apoptospathways . Thus, op-values. Among the top 10 metabolic networks, the phosphatidylinositol-3,4,5-triphosphate pathway, O-hexadecanoyl-(L)-carnitine pathway, 1,2-didocosapentaenoyl-sn-glycerol 3-phosphate pathway, and 1-linoleoyl-glycerol-3-phosphate pathway were profoundly enriched and significant in the SLE DEGs  signaling are essential pathways in autoimmune inflammatory disorders, and GPCRs are responsible for regulating immune cells via LPA receptors . G2A genent mice . Variousent mice . The andent mice . AR alsoctivated . The enrEGR1, CD38, CAV1, and AKT1) from the pathway maps constructed from the DEGs. Among them, EGR1 (early growth response 1) is a transcription factor shown to interact with the IGF-2 (insulin-like growth factor 2), APEX (apurinic/apyrimidinic endodeoxyribonuclease 1), SRD5A1 , CD44 (cell surface glycoprotein CD44), and EGFR  genes and transcriptionally regulate them by activating or promoting their expression in sorted B-cells from patients with SLE (CD38 result in the activation of B-lymphocytes and increase immune responses in SLE patients. The protein caveolin 1 (CAV1) has been shown to interact with the ErbB2 (Erb-B2 receptor tyrosine kinase 2), MDR1 (multidrug resistance protein 1), HTR2A (5-hydroxytryptamine receptor 2A), and AR (androgen receptor) genes. Several studies have suggested that caveolin 1 physically interacts with HER2, p-gp, HTR2A, and AR and activates/inhibits them by binding to their specific caveolin-binding motif (FKHR (Forkhead box protein O1), mTOR (mechanistic target of rapamycin), Bcl-10 (B-cell lymphoma/leukemia 10), FOXO3A (Forkhead box O3), HNF3-beta (hepatocyte nuclear factor 3-beta), and GSK3 beta (glycogen synthase kinase three beta). The protein kinase AKT1 inhibits FKHR via phosphorylation and decreases its activity , from the pathway enrichment analysis. Indeed, the identified pathways from the enrichment analysis were strongly related to the immune system, vasculopathy, cardiovascular functions, and inflammatory responses, which are processes that can lead to the development of SLE. The broad understanding of SLE pathophysiology from this study will allow us to identify and develop therapies targeting SLE and contribute to personalized treatment strategies. Collectively, the study findings could aid in enhancing our understanding of the fundamental molecular processes of SLE and provide possible strategies for early diagnosis in SLE; in addition, combinatorial therapeutic strategies using oxidative stress and ROS cellular signaling and lysophosphatidic acid signaling via GPCRs might have symbiotic effects on the molecular events in SLE.Taken together, the results of our comprehensive bioinformatics analysis showed that the DEGs identified between sorted B-cells from patients with SLE sorted B-cells from controls could play a significant role in the growth, progression, and development of SLE. This study identified 4 upregulated and 13 downregulated genes, including essential genes (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30153.Publicly available datasets were analyzed in this study. This data can be found here: SU, DT, HZ, and CG were involved in the design of the study and the acquisition, analysis, and interpretation of the data. SU, DT, CG, SY, NY, MS, and HZ were involved in the interpretation of the data and drafting the manuscript. CG, RS, and HZ supervised the entire study and were involved in study design, the acquisition, analysis, and interpretation of the data, and drafting the manuscript. The manuscript was reviewed and approved by all the authors.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.


Ligularia fischeri (Ledeb) Turcz (LFT) is a well-known expectorant and active anti-inflammatory agent in Chinese traditional medicine. LFT’s expectorant effect is closely related to its anti-inflammatory effects. This study aimed to evaluate the differential composition and anti-inflammatory mechanisms of the volatile components in LFT from different production areas.Headspace solid-phase microextraction-gas chromatography-mass spectrometry analysis of volatile components, as well as chemometric methods, including similarity analysis, hierarchical clustering analysis, and principal component analysis, were performed to identify LFT produced in different areas. The molecular mechanism underlying the anti-inflammatory effects of these components was determined by network pharmacology analysis.We observed significant differences in the chemical constituents and percentage contents in samples with different origins. Eighteen volatile components were identified in four different producing areas, among which the highest content of olefinic components was the main component of the aroma of LFT. The mechanisms of these pharmacological effects involved multiple targets and pathways. Twenty-seven potential target proteins and 65 signaling pathways were screened, and a “component-target-disease” interaction network map was constructed. The volatile components of the LFT function mainly by inhibiting the production of inflammatory factors.This study provides a theoretical framework for further development and application of LFT used in traditional Chinese medicine. Radix et Rhizoma Ligularia fischeri (Ledeb) Turcz (LFT) is a perennial herbaceous plant that grows at 1400–3300 m above sea level by rivers, on hillsides, and in forests [ forests . The che forests . The pla forests –10. Howe forests . Volatil forests .The ethyl acetate extract and ethanol extract of LFT show potent anti-inflammatory activity , 14. InfHere, volatile oils were extracted from LFT root collected from Guizhou Province by HS-SPME. The volatile components of LFT were identified by gas chromatography-mass spectrometry (GC-MS). The percentage content of volatile components was calculated using the peak area normalization method, and the content was analyzed by chemometric methods, including similarity analysis, hierarchical clustering analysis, and principal component analysis. We constructed a complex network of “component-target-disease” based on molecular docking and network pharmacology. The anti-inflammatory targets and signaling pathways of the volatile components of LFT were preliminarily determined. We explored the effect of the production area on LFT quality and provided a reference for determining the molecular mechanism of the anti-inflammatory effects of volatile components in LFT.Ligularia fischeri (Ledeb) Turcz samples of different areas in Guizhou Province (China) were obtained from the wild, Table The SPME fibers, including 85 μm Carboxen (CAR)/polydimethysiloxane (PDMS), 65 μm PDMS/divinylbenzene (DVB), 85 μm polyacrylate and 50/30 μm DVB/CAR/PDMS, were purchased from Supelco . The TRIPLUS (RSH) headspace sampler was purchased from Thermo Fisher Scientific . The muffle furnace (P330) was purchased from Nabore Co., Ltd. .All samples were crushed and sifted through 40 mesh; 1.0 g sample was packed into a 5 mL extraction bottle. Next, 50/30 μm DVB/CAR/PDMS extraction fiber head, which had been activated for 5 min was inserted into the bottle and incubated for 10 min at 100 °C. The extraction head was extracted by headspace extraction for 40 min, then extraction head was immediately inserted into the injection port of the gas chromatograph (temperature 220 °C) and desorbed for 5 min.Analyses were carried out with a Thermo Fisher Scientific GC system (TRACE1310) coupled to an ISQLT mass spectrometer. The volatile compounds were separated on a TG-5SilMS capillary column . The carrier gas was helium with a column-head pressure of 10 psi. The oven temperature was held at 50 °C and for 5 min, increased to 300 °C at a rate of 5 °C/min, and then held for 5 min. The flow rate was 1 mL/min. The injection temperature was 220 °C at splitless injection. A full scan mode (m/z 35–350) was applied to identify all target compounds. The ion source temperature was 280 °C with an ionizing energy of 70 eV. Peaks were identified by comparison of their mass spectra with those of the Nist2005 and Wiley275 library, and the percentage of each compound was calculated by a normalization method.https://pubchem.ncbi.nlm.nih.gov/), and the 3D structures were downloaded in .sdf format. The target proteins of these components were predicted using the BATMAN-TCM  database (http://bionet.ncpsb.org/batman-tcm/index.php/Home/Index/index). The target proteins corresponding to volatile components were screened according to a cutoff score of ≥48 and a P-value of ≤0.01. Inflammation-related target proteins were retrieved from a comprehensive database of human genes and gene phenotypes . Finally, the screened targets were converted into UniProt ID format with the UniProt database.The molecular structures of the volatile compounds were obtained from the PubChem database (http://www.genome.jp/kegg/). The network was visually analyzed using Cytoscape 3.6.1 software. The topology parameters of degree, betweenness centrality, and closeness centrality for each node were evaluated with CentiScaPe1.2. Proteins showing values greater than the median value for the above three topological parameters at all nodes was regarded as a potential target of the anti-inflammatory response. The anti-inflammatory affinity of these targets was evaluated by molecular docking analysis. Protein interaction analysis of the selected targets was carried out using the STRING database (https://string-db.org/).These components, target proteins, and interactive proteins were connected as a “component-target-disease” network by protein-protein interaction (https://david.ncifcrf.gov/) database. P-values (P < 0.05) were considered as statistically significant, with a smaller P-value indicating a more significant correlation. The potential targets were marked on signaling pathways closely related to inflammation using the KEGG Mapper function in the KEGG (https://www.genome.jp/kegg/) signaling pathway database.Potential targets were screened by the KEGG pathway and GO biological process analysis using the DAVID . Principal components analysis (PCA) was performed using SIMCA 14. 0 software from Umetrics, Inc. .The total ion chromatogram (TIC) was obtained by evaluating the volatile components in LFT from different producing areas. The results are shown in Fig. The percentage content of volatile components in all sample was standardized to form an 18 × 4 order original data matrix. CA was carried with SPSS 20.0 software. The intergroup connection method was used, and the Euclidean distance was used as the measure of the sample. According to the combination of correlation coefficients, the samples were divided into two groups; Bijie, Duyun, and Anshun were clustered into I, while Kali was clustered into II. The results are shown in Fig. O-cymene (1.28), − (−)-isoledene (1.20), alpha-pinene (1.16), beta-pinene (1.15), sabinene (1.03), ocimene (1.03), atractyline (discontinued) (SH) (1.03), caryophyllene (1.02), and eremophilene (1.01) were all greater than 1. Therefore, these chemical components were the main cause of the differences in the LFT.To evaluate and synthetically analyze the volatile components of LFT in different growth environments, the percentage of volatile components in all samples were analyzed by PCA. Three principal components  were obtained. According to the variance contribution rate, the model’s cumulative explanatory power parameter R2X, and predictive power parameter Q2 were 1.000 and 0.989, respectively. These results show that the model has a good degree of discrimination and prediction. Therefore, the first three principal component analysis can reflect the main characteristics of the LFT. Using the principal components to establish a coordinate system, the PCA score diagram, a three-dimensional score diagram and load diagram of the samples were obtained. Each point on the load graph represents a variable; as distance from the origin increases, the content of the component makes a greater contribution to the classification. The results are shown in Fig. We aimed to evaluate the standard chemical composition of the four different producing areas of LFT. A total of 296 target proteins of the typical chemical constituents of LFT were obtained from the BATMAN-TCM database Fig. . Two hunA “component-target-disease” network of the anti-inflammatory effects of the LFT was constructed using disease-specific drug target analysis software. The network of visual analysis was prepared with Cytoscape 3.6.1 software. The results are shown in Fig. The median of the three topological parameters  of all nodes in the network was considered as the calculation result and had values of 3.000, 0.0049, and 0.1991, respectively. The topological parameter values of the 27 target proteins were all greater than the median mean. These targets were considered as potential target proteins of the anti-inflammatory effects of the volatile component. The results are shown in Table P-value ≤0.01. The results indicate that these targets are involved in various biological processes, including the positive regulation of I-κB kinase/NF-κB signaling, positive regulation of transcription from RNA polymerase II promoter, TRIF-dependent Toll-like receptor signaling pathway, cellular response to mechanical stimulus, I-κB kinase/NF-κB signaling, regulation of TNF-mediated signaling pathway, inflammatory response, activation of cysteine-type endopeptidase activity involved in apoptotic signaling pathway, and positive regulation of NF-κB transcription factor activity. These biological processes may be closely related to the occurrence and development of inflammation and that the onset of inflammation involves up-and-down regulation of multiple biological processes in the human body. The results also suggest that the volatile components in LFT exert anti-inflammatory effects by regulating these biological processes.These potential target proteins underwent enrichment analysis of GO biological processes and KEGG pathways using the DAVID database Fig. . GO enriHelicobacter pylori infection, toxoplasmosis, HTLV-I infection, NOD-like receptor signaling pathway, and T cell receptor signaling pathway. These signaling pathways may directly or indirectly participate in the anti-inflammatory effects of LFT and may be closely related to the pathogenesis of inflammation. These results also reveal the therapeutic effect of traditional Chinese medicine on multi-components and multi-targets.A total of 65 signal pathways were obtained by KEGG pathway enrichment analysis, of which 50 signal pathways showed a P-value ≤0.01. Thus, volatile components in LFT may be involved many important signaling pathways, such as apoptosis, TNF signaling pathway, herpes simplex infection, RIG-I-like receptor signaling pathway, Toll-like receptor signaling pathway, pathways in cancer, MAPK signaling pathway, Epstein-Barr virus infection, chronic myeloid leukemia, small cell lung cancer, NF-κB signaling pathway, epithelial cell signaling in In this study, molecular docking was performed using the potential target proteins as research objects and was carried out using the SystemsDock website to verify the affinity of these target proteins with small-molecule compounds. A system docking score higher than 4.25 indicates binding activity between the docking molecule and target protein, greater than 5.00 indicates better binding activity, and greater than 7.00 indicates strong binding activity . The molThe contents of volatile components in LFT from different producing areas show apparent differences. The quality of traditional Chinese medicine depends not only on its genetic characteristics but also on its ecological environment and cultivation methods . TherefoThe compounds identified the LFT were mainly alkenes, followed by phenols, aldehydes, and alcohols, which are characteristic components of the LFT. Some volatile components had distinct biological activities and pharmacological functions. For example, Li , 24 showCA showed that the volatile components of the sample from Kaili greatly differed from samples from the other three locations. These four producing areas differ in geographical location, altitude, and climate. Therefore, the differences in samples may be closely related to the growth environment  and genetic characteristics.In the present work, except for the samples from Kaili, the samples were concentrated in the principal component space. The volatile components of the samples from Kaili and other regions significantly differed. This may be because of differences in the geographical environment and climatic conditions, resulting in the accumulation of different chemical components and contents. Populations from geographically close regions showed similar distributions of principal components, suggesting that attention should be given to the influence of environmental modification on the quality of LFT.The results of GO enrichment analysis indicated that the LFT potential targets are mainly regulated by positive regulation of I-κB kinase/NF-κB signaling, positive regulation of transcription from RNA polymerase II promoter, TRIF-dependent Toll-like receptor signaling pathway, cellular response to mechanical stimulus, I-κB kinase/NF-κB signaling, regulation of TNF-mediated signaling pathway, and inflammatory response. Biological processes have an anti-inflammatory role. These biological processes are closely related to the occurrence and development of inflammation. KEGG signaling pathway enrichment analysis showed that LFT mainly exerts synergistic anti-inflammatory effects via TNF signaling pathway, herpes simplex infection, RIG-I-like receptor signaling pathway, Toll-like receptor signaling pathway, pathways in cancer, MAPK signaling pathway. Molecular docking showed that these chemical components have high binding activity to the predicted target protein and that the anti-inflammatory effects of volatile components of LFT are closely related to these targets. Studies have also confirmed that these signaling pathways are closely related to inflammation. Constructing an interactive network diagram of “component-target-disease” by network pharmacology analysis, it will provide a foundation for further studies of the anti-inflammatory effects of the LFT.In summary, our study found significant variability in the volatile components of the different habitats of LFT analysed, confirmed by the similarity analysis, the hierarchical clustering analysis, and the principal component analysis. The obtained results by SPME-GC/MS indicated that different chromatographic profiles for the four LFT samples, leading to the conclusion that factors such as the climatic conditions, the storage conditions and the different geographic origin of the samples may contribute to such variability. The present study revealed potential mechanism regarding the anti-inflammatory effect of volatile components in LFT by network pharmacology and molecular docking analyses. The results proved that inhibiting the production of inflammatory factors might play a key role in the anti-inflammatory effects of volatile component of LFT. This study not only explored the molecular mechanisms of the anti-inflammatory effect of volatile components in LFT, which laid the foundation for further investigation, but also presented novel clues for the development and utilization of LFT resources.


The mitogen-activated protein kinase (MAPK) pathways are crucial regulators of the cellular processes that fuel the malignant transformation of normal cells. The molecular aberrations which lead to cancer involve mutations in, and transcription variations of, various MAPK pathway genes. Here, we examine the genome sequences of 40,848 patient-derived tumours representing 101 distinct human cancers to identify cancer-associated mutations in MAPK signalling pathway genes. We show that patients with tumours that have mutations within genes of the ERK-1/2 pathway, the p38 pathways, or multiple MAPK pathway modules, tend to have worse disease outcomes than patients with tumours that have no mutations within the MAPK pathways genes. Furthermore, by integrating information extracted from various large-scale molecular datasets, we expose the relationship between the fitness of cancer cells after CRISPR mediated gene knockout of MAPK pathway genes, and their dose-responses to MAPK pathway inhibitors. Besides providing new insights into MAPK pathways, we unearth vulnerabilities in specific pathway genes that are reflected in the re sponses of cancer cells to MAPK targeting drugs: a revelation with great potential for guiding the development of innovative therapies. et al. report that collectively, specific mutations of genes in 5 MAPK pathways are associated with worse patient survival. They identify gene components that are pharmacologically vulnerable to pathway inhibitors in cancer cell models, suggesting potential clinical applications of these findings.Musalula Sinkala  The MAPK pathways involve four main modules: the extracellular-signal-regulated kinase 1 and 2 (ERK1/2) pathway , the c-Jun N-terminal kinase (JNK) pathway, the p38 pathway and the ERK5 pathway3. Each of these modules is initiated by specific extracellular signals that lead to the sequential activation of a MAP kinase kinase kinase (MAPKKK), then a MAP kinase kinase (MAPKK) which phosphorylates a MAP kinase (MAPK)3. Subsequent phosphorylation of the MAP kinases results in the activation of multiple substrates, including the transcription factors that are effectors of cellular responses to MAPK pathway activation4.The mitogen-activated protein kinase (MAPK) pathways are crucial cell signal transduction pathways that regulate molecular processes such as cell proliferation, cell differentiation, cell survival, cancer dissemination, and resistance to drug therapy9. We now have many anti-cancer drugs that target the components of the MAPK pathways, most of which have been successful in treating cancers of, among other tissues, the skin and kidney13.Over the last few decades, our understanding of the individual MAPK signalling modules and their role in oncogenesis has grown significantly and, along with this increased interest, concerted efforts to treat various tumours that display altered MAPK signalling17. However, we still do not have a complete understanding of the extent to which MAPK pathways are altered across the entire spectrum of human cancers, and whether these alterations impact either disease outcomes or the responses of tumours to anti-MAPK pathway drugs.Several genetic aberrations, including mutations in, and copy number variations of, MAPK pathway genes have been identified in human cancers, and several of the proteins encoded by these genes are promising drug targetsThere is, therefore, a pressing need to identify cancer types that harbour mutations in genes that encode MAPK pathway proteins. By extension, a better understanding of how genetic alterations and gene dependencies within cancer cells impact the responses of these cells to anti-cancer drugs will aid in rationally selecting the best available drugs for treating particular cancers. Furthermore, a better appreciation of the specific MAPK pathway aberrations in different human cancers would likely translate to improved disease outcome predictions because some of the genetic alterations of cancer cells are likely to be directly associated with disease aggressiveness and clinical outcomes.18) project and other consortia and compiled by the cBioPortal project19, has yielded vast amounts of publicly accessible data that can be leveraged to understand the complexity of MAPK signalling in human cancers. Further, the Achilles project is processing and releasing data on gene dependencies in hundreds of cancer cell lines, as determined using high-resolution fitness screens of cells following the knockout of particular genes using CRISPR 20. In addition, the Genomics of Drug Sensitivity in Cancer (GDSC21) project and the Cancer Cell Line Encyclopaedia (CCLE23) project has screened, and continue to screen, the sensitivity of thousands of human cancer cell lines to hundreds of diverse small molecule inhibitors.The large-scale molecular profiling of human cancers such as has been carried out by The Cancer Genome Atlas  project which aspires to illuminate the responses of complex cellular systemsThe large scales and the public accessibility of the datasets that these projects are producing provide new prospects for us to link cancer phenotypes to molecular features, clinical outcomes, and the drug responses of tumours. Here, by integrating information extracted from these datasets, we provide a comprehensive analysis of MAPK pathways across different cancer types. Besides providing novel biological insights into the mutational landscape of MAPK pathway genes, and how these affect disease outcomes in cancer patients, we show both the specific MAPK pathway genes that impact the fitness of cancer cells and how vulnerabilities exposed by mutations and transcriptional changes in these genes are reflected in the responses of cancer cells to MAPK pathway inhibitors.27, we first identified a list of all genes encoding members of the ERK1/2, JNK, p38 and ERK5 pathways. This list consisted of 142 genes that function mainly through the MAPK pathways as core genes of the ERK5 pathway (14 genes), the JNK pathway (52 genes), the p38 MAPK pathway (45 genes) and the ERK1/2 pathway (73 genes) , BRAF (6%) and NF1  is the most frequently altered gene, followed by BRAF (7%)28.We then calculated the somatic mutation frequencies in these 142 genes as determined in 192 cancer studies focusing on 101 different human cancer types and involving 40,848 patients  and the p38 pathway (40.3%) showed the highest frequencies of MAPK pathway gene mutations, followed by the ERK1/2 pathway (33.7%) and the ERK5 pathway (6.1%); Fig. .Fig. 2MuFurthermore, we found mutations in genes that encode multiple classes of MAPK proteins in 15% (6481) of the patent samples Fig. . Among tRemarkably, we found that for most cancer types patient tumours tend to have high mutation frequencies in genes involved in either the ERK1/2 pathway or the p38 and JNK pathways, but rarely in the genes involved in both the ERK1/2 pathway and p38 and/or JNK pathways Fig. .Among the 101 cancer types that we analysed, we found that the extent to which the MAPK pathway genes were mutated varied  than that of patients with no mutations in MAPK pathway genes  for cancer patients with mutations in MAPK pathway genes (undefined median DFS period in that >50% of patients survived beyond the study duration) and those with no mutations in these genes  for patients with mutations in MAPK pathway genes (OS = 73.0 months) was significantly shorter ; the ERK1/2 pathways genes versus those of the p38 pathway ; and the ERK5 pathway genes versus those of the p38 pathway  mutations in genes of only one of the four MAPK pathway modules , (2) in genes of more than one of the MAPK pathway modules (3) or with no mutations in any MAPK pathway genes. We found that patients of these different subgroups exhibited dissimilar OS  that are classifiable as “common essential” (see the “Methods” section) among all cancer cell lines  on having functional ERK1/2 pathway genes than they are dependent on having functional ERK5, p38, and JNK pathway genes Fig. . We founMAPK1, BRAF and MAPK14) that are classifiable as “strongly selective” (see the “Methods” section) across various cancer cell lines. Here, unlike some genes which were found to be non-essential , all MAPK pathway genes impact the fitness of the 688 cell lines tested.We also identified another 40 MAPK pathway genes  on the functionality of known oncogenes (both those of the MAPK pathway and those of other pathways) than on the functionality of known tumour suppressor genes  were significantly more dependent on signalling through the ERK1/2 pathway than were other cancer cell types  between genes of each of the four MAPK pathway modules against all other genes expressed in these cells. Here, we found that ten cancer types  are significantly more dependent on GTPase encoding genes than they are in other classes of genes  are dependent for their fitness, has self-mRNA transcript levels that are negatively correlated across the tested cell lines with CRISPR-derived dependency scores . Here, also, we found that skin cancer cell lines were significantly more dependent on MAPK1 , BRAF , and NRAS  also displayed weak negative correlations between self-mRNA levels and their CRISPR-derived gene dependency scores  showed the strongest positive correlation among the known TSGs . We then compared the mean dose-response of the two cell-line groups using the drug response data from the GDSC see Fig. . FurtherTSC1 was associated with significantly increased sensitivity to the most (64%) MAPK pathway inhibitors, followed by dependence on MAPK1 (48%) BRAF (48%), and NRAS  and DUSP13 (8%), DUSP19 (8%), MAP2K3 (8%) and MAP3K3 , followed by mutations in  NRAS (20%),  MAP3K5 (20%), CRK (20%) and RASGRF1 ,  PRKACA (12%), and PTPN11 , trametinib  and selumetinib , all of which target MEK1 and MEK2 . We then compared drug IC50 values between these two cell line groups for the 28 MAPK pathway inhibitors. Remarkedly, we found that the cell lines with higher MAPK gene dependency were significantly more sensitive to eight out of the 28 MAPK pathway inhibitors , selumetinib  and AZ628 . Again, we compared the mean dose-responses of the 28 MAPK pathway inhibitors between these two groups of cancer types. Here, we found that 14 MAPK pathway inhibitors were significantly more effective at killing cancers that had higher dependencies on MAPK pathway genes than they were at killing cancers with lower dependencies on these genes  and two MAPK pathway inhibitors , that are common between the LINCS datasets, and the GDSC or CCLE datasets.Since we found that the responses of cell lines to MAPK pathway inhibitors is related to their dependence on the functionality of specific MAPK pathway genes, we hypothesised that it should be possible to determine the exact cellular changes that are associated with drug responses. Here, the mRNA transcription patterns displayed by cells following their exposure to MAPK pathway inhibitors should provide a clear representation of these cellular changes. To evaluate this hypothesis, we used the publicly accessible LINCS dataset which details the mRNA transcription patterns of ~1000 genes in cell lines50 = −1.481 μm) exhibits a greater degree of sensitivity than MCF7 (IC50 = 8.0 μm) to PD-0325901  is more sensitive than MCF7 (IC50 = 8.0 μm) to selumetinib  and MCF7  cells , such sensitivity differences are very likely attributable to the extent to which the inhibitors reduce the expression and/or functioning of key cell fitness associated MAPK pathway genes.Our findings here are consistent with previous studies which have shown that differences in the survival of different tumour cells after drug perturbation can be at least partially explained by differences between the transcriptional signatures of the tumour cellsMYC oncogene and its proximal network (28%), and the Hippo pathway (10%)58. We have, therefore revealed that, after metabolic gene alterations, alterations of MAPK pathway genes are the most frequently observed category of genetic changes associated with the onset of human cancers.We have conducted the most comprehensive analysis of the MAPK pathways in 101 different human cancer types. Here, we found at least one non-synonymous mutation to genes involved directly in MAPK pathways in 42% (58% when TP53 mutations are included) of all analysed samples. Previous studies have examined the frequencies of alterations to various other categories of genes in tumours including those involved in metabolic pathways , the transforming growth factor pathway (39%), the PI3K-mTOR pathway (33%), the cell cycle pathway (33%), the p53 pathway (29%), the 63.Our findings emphasize the significance of the MAPK pathways across most cancer types in promoting and coordinating the proliferative capacity and immortality of cancer cells. Despite the significance during oncogenesis of alterations in MAPK pathway genes, it must be stressed that MAPK pathway gene alterations are not found in 42% of tumours. These mutations are infrequent in cancers such as small cell carcinoma of the ovary (occurring in none of the 15 examined tumours), Ewing sarcoma (occurring in 3% of the 122 examined tumours) and myeloproliferative neoplasms (occurring in 5% of the 151 examined tumours). These exceptions reinforce the notion that there exist multiple MAPK pathway independent routes to oncogenesis64. We find, however, that this is likely only the case for three of the four main MAPK pathway modules in that patients with tumours that have mutations in only JNK pathway genes on average tended to exhibit significantly better disease outcomes (as adjudged by both OS and DFS) than patients with tumours that have mutations in other MAPK pathway modules , the apparently enhanced survival of patients with tumours that have JNK pathway gene mutations suggests that, whenever these mutations have any impact on cancers at all, they may most commonly promote anti-tumour activity.We showed that, on average, patients with tumours that harbour mutations in MAPK pathway genes tend to have significantly worse OS outcomes than those without such mutations. Since the MAPK pathway promotes tumour cell growth, proliferation, resistance to drug therapy, and tissue invasion, it is unsurprising that hyperactivation of this pathway can lead to more aggressive diseaseles Fig. . Given tKRAS mutations in pancreatic cancer  MAPK pathway mutational landscapes; (2) the mRNA expression levels of vital oncogenic drivers; and (3) the degrees of the dependence of cancer cells on these oncogenes. Here, our speculation is further corroborated by our discovery, using the LINCS project datasets, that reduced transcription of crucial oncogenes and/or transcription factor genes are somewhat predictive of whether cancer cells will be sensitive or refractory to a particular anti-cancer drug. It should be interesting to unravel the signalling pathway mechanisms that have knock-down effects on the expression of genes that the Achilles project has identified as “common essential” or “strongly selective” since these mechanisms would also impact the responses of cancer cells to drug perturbation.We also showed that cancer cells with mutations in particular MAPK pathway genes respond more favourably to MAPK pathway inhibitors than do those without mutations into these genes Fig. . SimilarAltogether, we have revealed both the extent of mutations in the MAPK pathway genes across more than 100 human cancer types and the subset of these mutations that are most likely to impact disease outcomes. Our integrative analysis of the CRISPR-derived gene dependencies of cancer cell lines, together with the drug responses of these same cell lines, indicates that the mutations in, and expression signatures of, MAPK pathway genes are associated with the responses of the cell lines to various MAPK pathway inhibitors. It is apparent; therefore, that it should be relatively straightforward to extend such an integrative analysis approach to identify high-confidence drug targets for a broad array of human cancers.19 version 3.1.9 , the JNK pathway (52 genes), the p38 pathway (45 genes) and the ERK1/2 pathway (73 genes)  for each of these genes across 1) all the samples and (2) each of the human cancer types represented among the 40,848 samples : firstly, across each of the cancer types and all the patient’s samples  and the durations of DFS between groups of patients that have tumours with versus without mutations in the MAPK pathway genes. Here, we also compared the OS and DFS durations for groups of patients with tumours that had: (1) mutations in genes of only one signalling module of the MAPK pathway; (2) mutations in genes of multiple MAPK signalling modules; and (3) with no mutations in genes of the MAPK pathways ”.Essential genes are those which are associated with cell fitness in only one or a few cell lines, but whose dependency is <100 times more likely to have been sampled from a skewed distribution than a normal distribution .Non-essential genes are those which show no effect on cell fitness in any of the 688 tested cell lines.Within the database, the genes are grouped into four primary categories based on observed cell line fitness after CRISPR-mediated gene knockouts as follows:Here, we sought to evaluate the extents to which different cell lines are dependent on MAPK signalling genes for their fitness. First, to unearth the cell line dependencies on genes from each MAPK signalling module, we calculated the percentage of genes within each MAPK pathway module that are “strongly selective” or “common essential” across the cancer types that are represented by the cell lines, and across all the cell lines (Supplementary File 74 (699 oncogenes and TSGs); (2) the UniProt Knowledgebase75 (304 oncogenes and 741 TSGs); (3) the TSGene database76 (1220 TSGs); and (4) the ONGene database77 (725 oncogenes). We collated datasets from these four sources to yield a list of 3688 known oncogenes and TSGs, representing 2932 unique genes (1021 Oncogenes and 1911 TSGs). We then used the list of oncogenes and TSGs to extract a list of: (1) MAPK pathway genes that are oncogenes; (2) MAPK pathway genes that are TSGs; (3) oncogenes that are not MAPK pathway genes; and (4) TSGs that are not MAPK pathway genes. Next, we compared the mean CRISPR-derived gene dependence scores for these four groups of genes using a one-way analysis of variance  the Sanger Consensus Cancer Gene DatabaseWe counted the number of instances in which the CRISPR-derived dependence score of each gene within each cancer cell line was <−0.5 (the cut-off point that was devised by the Achilles project to denote reduced cell fitness after CRISPR-mediated gene knockouts) to find the number MAPK pathway genes that are associated with a reduction in cell fitness across all the cell lines.We sought to identify precisely which human cancer types (as represented by the cell lines) are significantly more dependent on oncogenes of each MAPK pathway module compared to all other oncogenes. Here, we grouped the cell lines into categories based on their primary tissue of origin. Then, focusing only on the oncogenes of each MAPK signalling module, for each cancer type we compared the mean CRISPR-derived dependence score of the oncogenes that were members of each of the MAPK signalling modules to the pooled mean dependence scores of the MAPK pathway genes across all the cancer cell lines Fig. . FurtherAdditionally, we compared mean CRISPR-derived dependence scores between genes that encode classes of MAPK pathway proteins for each cancer type to the mean pooled dependence scores of all other MAPK pathway oncogenes for all other cancers  the mRNA transcription profiles of the cell lines; (2) the DNA methylation profiles of the cell lines; and (3) the copy number variation profiles of the cell lines. Since the CCLE has performed comprehensive molecular profiling of the cell lines that are represented within the Achilles datasets, we retrieved the mRNA transcription, DNA methylation and copy number variation data from this database. We then arranged the genes represented in these datasets so that their order corresponds with the pattern of clustering that was produced using the CRISPR-derived dependence scores for each gene Fig. .18; (3) and the transcription levels of these genes in 53 normal human tissues measured in over 15,000 healthy individuals from GTEx consortium78. We then arranged the genes in columns according to the clustering pattern of these genes based on their CRISPR-derived dependency scores to visualize the relationships between the mRNA gene expression levels of normal tissues, primary tumours and cancer cell lines  we grouped the cell lines into two groups: those with a high CRISPR determined dependence on that gene  and those with low dependence on that gene . We then compared the IC50 values for each of the 28 MAPK pathway inhibitors between the two groups of cancer cell lines. Furthermore, for each of the MAPK pathway genes, we grouped the cell lines into another two groups: those with mutations in a particular gene  and those without mutations in that gene . Then we compared the IC50 values for each of the 28 MAPK pathway inhibitors between the two groups  of cell lines.For each MAPK pathway gene .79.We performed gene set enrichment analysis for specific Gene Ontology Biological Processes terms by querying Enrichr with the genes that showed a Pearson’s correlation coefficient between self-mRNA and the CRISPR-derived dependence score of either > 0.3 or <−0.3. , we obtained mRNA transcription responses profiled by the LINCS project80 for six cancer cell lines after small molecule inhibitor perturbations. The elements of these data include the names and concentrations of the anti-cancer drugs used to treat the six cell lines and the mRNA transcription responses following drug treatment. Next, we used the Connective Map toolbox81 in MATLAB to retrieve only the cancer cell lines with corresponding dose-response profiles in the GDSC and CCLE database to the seven MAPK pathway inhibitors that are represented within the LINCS dataset that we retrieved. We found that only the cell lines, MCF7 and A549 when treated with the MEK inhibitors, selumetinib and PD-0325901 were common between the databases. We, therefore, evaluated the MAPK pathway mRNA transcription signatures that occur after selumetinib and PD-0325901 treatment in these two cell lines cognisant of the fact these cell lines have different dose-response profiles to selumetinib and PD-0325901  for each group/comparison using the Benjamini & Hochberg procedure.We performed all statistical analyses in MATLAB 2019b. Where appropriate, we used the independent sample Student The study protocol was approved by The University of Cape Town; Health Sciences Research Ethics Committee IRB00001938. The publicly available datasets were collected by the cBioPortal, TCGA, CCLE, Achilles, GDSC, and LINCS projects and made available via their respective project databases. The methods used here were performed following the relevant policies, regulations and guidelines provided by the TCGA, CCLE, DepMap, GDSC, and LINCS projects.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary