Introduction

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 kinase1/signal transducer and activator of transcription (STAT) signaling pathway, Wnt signaling pathways, Hedgehog, Smad signaling pathway, and PtdIns 3-kinase (PI3) signaling pathways. 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.

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, respectively4. 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 (Wnt1-inducible signaling protein 3), 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 cancer6,7,8. APC mutation and promoter hypermethylation are two important mechanisms in carcinogenesis and colorectal cancer (CRC) progression9,10,11. Two AXIN genes, AXIN1 and AXIN2, could be prone to mutation in some CRC cases12,13. PIK3CA (phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit alpha) 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 patterns14,15. 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/wingless17,18. Also, numerous factors such as TNFα (tumour necrosis factor alpha), TGFβ (transforming growth factor beta), c-myc, IGF2R (insulin like growth factor 2 receptor), SMAD2, SMAD4, DLC-1, and HIC1 (HIC ZBTB transcriptional repressor 1) could initiate liver tumorogenesis17,18.

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 tumors19. 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.

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 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria21, 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.

Results

Search results

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 cancer1, 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. 1. The numbers of participants for the assessment of the GI cancer mutations induced 17,269, 1056, 2500, 378, 1080 individuals for CRC, LC, GC, PC, and other GI cancers, respectively.

Figure 1
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PRISMA Flow Diagram of our study population, the diagram indicates the primary search item frequencies, duplicates, Studies included in qualitative synthesis and Studies included in quantitative synthesis.

Bias assessment

The risk of bias assessment is given in Table 1. Also, the RTI tool for the risk of bias determined one study with high risk of Selection Bias. Also, the Selection Bias, Performance Bias, Detection Bias, and Selective Outcome bias indicated 25, 3, 4, and 33 studies with unclear risk of bias, respectively. Furthermore, high risks of Selection Bias and Selective Outcome Bias were evaluated in 3 and 2 references, respectively.

Table 1 Key: + : Low risk of bias, − High risk of bias ?, Unclear risk of bias, *: Non-applicable in non RCT by RTI.
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Signaling pathways mutations in gastric cancer

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 2 and supplementary Table 2.

Table 2 GI tract cancer signaling pathway mutations based on genes and exon (n = 121).
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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 (I-squared = 91.25%, P = 0.001) (Fig. 2). Also, since the CI of the test (Egger’s test) does not include zero, there is no bias in our results (Egger's test = 3.51, P = 0.0001, 95% CI: 2.49 to 4.53). The pooled prevalence funnel plot in GC signal transduction pathway mutations is illustrated in Fig. 2. Furthermore, the Subgroup analyses of pooled prevalence Signal Transduction Pathway Mutations in GC are summarized in Table 3.

Figure 2
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Heterogeneity and pooled prevalence funnel plot of the included studies for GC signal transduction pathway mutations.

Table 3 Subgroup analysis of pooled prevalence of Signal Transduction Pathway Mutations in GC, CRC, HCC, and PC based on gene, pathway, and method of diagnosis.
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Signaling pathways mutations in CRC

CRC related signaling pathway mutation was found in 65 studies. A majority of study samples had the mean age > 60 years and male/female ratios of CRC incidence in most of the evaluated studies were reported more than 2:1. The most prevalent mutation analysis was taken from KRAS exon 2, BRAF exon 15, PIK3CA exon 9 and 20, and APC and beta-Catenin exon 3. Most of the studies were cross-sectional and total CRC patients included 17,269 cases. These studies reported different mutation rates based on the sample size, selected gene, and method of use. The results showed a wide range of mutation in different pathways and related genes as listed in supplementary Table 3. The KRAS mutations in the MAPK pathway were 2.5 to 75% and the BRAF (B-Raf proto-oncogene, serine/threonine kinase) mutations ranged from 0 to 78.6%. The Wnt signaling mediator mutations, such as beta-catenin, were reported from 3 to 37.5% and APC mutations ranged from 28.4 to 73%. The p53 was assessed in 5 studies and its mutation rate was reported 18–65% (Table 2).

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 (I-squared = 97.63%, P = 0.0001) (Fig. 3). Also, the subgroup analysis for heterogeneity was performed in CRC included studies based on the different pathways (heterogeneity plot in Fig. 4), detection method (heterogeneity plot in Fig. 5), and involved genes (heterogeneity plot in Fig. 6). The CI of test (Egger’s test) included zero, thus there was no significant bias in the results (Egger's test = − 0.692, P = 0.109, 95% CI: − 1.54 to 0.156). The pooled prevalence funnel plot in CRC signal transduction pathway mutations is illustrated in Fig. 7 and the Subgroup analyses of pooled prevalence signal transduction pathway mutations in CRC are summarized in Table 3.

Figure 3
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Heterogeneity plot of the included studies for CRC signal transduction pathway mutations.

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Subgroup analysis for heterogeneity based on the different pathways for CRC signal transduction pathway mutations.

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Subgroup analysis for heterogeneity based on the detection method for CRC signal transduction pathway mutations.

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Subgroup analysis for heterogeneity based on involved genes for CRC signal transduction pathway mutations.

Figure 7
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Pooled prevalence funnel plot in CRC signal transduction pathway mutations.

Signaling pathway mutations in liver cancer (LC)

The search on liver cancer resulted in a total of 1056 hepatocellular carcinoma (HCC) and 174 hepatoblastoma participants in 21 studies. There were different ranges of mutations in these studies, which are listed in supplementary Table 4. The Wnt signaling was the most evaluated pathway in which the CTNNB1 gene mutation ranges were evaluated to be 12–75% and the beta-catenin genes had the mutation ranges of 2.8–41%. In addition, the mutation ranges in p53 were 1.2 to 61% and the JAKs in the JAK signaling pathway were observed to be 1.2 to 16%.

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 (I-squared = 85.34%, P = 0.0001) (Fig. 8). Also, since the CI of the test (Egger’s test) included zero, there was no significant bias in the results (Egger's test = − 0.442, P = 0.411, 95% CI: − 0.65 to 1.53). The pooled prevalence funnel plot in LC signal transduction pathway mutations is illustrated in Fig. 8. Furthermore, the Subgroup analyses of pooled prevalence signal transduction pathway mutations in LC are summarized in Table 3.

Figure 8
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Heterogeneity and pooled prevalence funnel plot of the included studies for liver cancer signal transduction pathway mutations.

Signaling pathways mutations in pancreatic cancer1

In a total of 9 studies, 392 PC patients were studied with the KRAS and PIK3CA mutations reported 42 to 92% and 2.7 to 12%, respectively. More data are shown in supplementary Table 5.

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 (I-squared = 97.14%, P = 0.0001) (Fig. 9). Also, the CI of the test (Egger’s test) included zero, s no significant bias was present in the results (Egger's test = − 1.351, P = 0.568, 95% CI: − 6.37 to 3.66). The pooled prevalence funnel plot in PC signal transduction pathway mutations is illustrated in Fig. 9. Furthermore, the Subgroup analyses of pooled prevalence signal transduction pathway mutations in pancreatic cancer are summarized in Table 3.

Figure 9
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Heterogeneity and pooled prevalence funnel plot of the included studies for pancreatic cancer (PC) signal transduction pathway mutations.

Signaling pathways mutations in other GI cancers

The other GI cancers included gastro-esophageal cancer, rectal cancer, esophageal squamous cell carcinoma, gallbladder carcinoma, and cholangiocarcinoma. Different signaling pathways in these GI cancers are listed in supplementary Table 6. Briefly, KRAS was the popular gene for mutation analysis ranging from 0% mutation in squamous cell anal carcinoma to 57% in small intestinal adenocarcinoma. BRAF was analyzed in 6 studies with its mutation reported to be 0–45%. Moreover, APC mutations were reported between 9.5 and 47% in different malignancies.

Signaling pathway mutation association with clinic-pathological features and patients survival

The extracted data about clinic-pathological features and patients survival were listed in supplement Tables 2 to 6. As glimpse, the clinic-pathological features statistically significant in association with signaling pathway mutations that they were mentioned in 2 individual studies for gastric cancer and 30, 6, 1 and 2 individual studies for CRC, LC, PC and other GI cancers, respectively.

Survival rate assessment in association with signaling pathway mutations were listed in supplement Tables 2 to 6. The survival rate or prognostic feature in association with signaling pathway mutations were mentioned in 1, 6, 1, 1, 0 and 1 included studies for CRC, LC, PC and other GI cancers, respectively.

Discussion

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)1. Furthermore, the inclusion criteria for the current study were different with TCGA assessments. Also, this study could be a lead for further investigations in the field of the signaling pathway mutations prevalence and might be useful for further TCGA comprehensive updates. Appropriate keywords were used for search strategy in popular academic databases. Data were screened and eligibility of the studies was evaluated according to the inclusion criteria. PRISMA guideline was used as the study protocol. Through the search strategy, we found that GI malignancies included CRC, LC, PC, GC, esophageal cancer, rectal cancer, and bile duct neoplasm or cholangiocarcinoma. The results obtained in the current study showed that most alterations in CRC patients were in the KRAS gene in MAPK pathway within the range of 3.8 to 54.5%. These differences could be due to the study population or the methodology in different studies although the cancer stage and other risk factors could also play major roles. Furthermore, the pooled prevalence indices of signal transduction pathway mutations in GC, CRC, LC, and PC were 5% (95% CI: 3–8%), 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% (95% CI, 29–36%) and 44% (95% CI, 33–55%) mutation rates, respectively. Also, the most altered pathway was Wnt (23%) (95% CI, 14–33%), followed by MAPK (20%) (95% CI, 17–24%) pathway.

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 CRC143,144. Recently, different studies have been reported focusing on the cost-effectiveness of the CRC screening programs indicating the importance of the CRC diagnosis145,146. 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 mutations147,148. As an example, smoking affects CRC cancers generation and mortality149,150,151. 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 cancer152,153. 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 MAPK/ERK signaling was analyzed in the study reported by Sameer et al.156 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 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.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 indiscriminately157,158,159. Investigation on Wnt/beta-catenin signaling was firstly introduced by the association between APC gene and beta-catenin160,161. 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 sites164,165. Moreover, they could maximize the protein stability and life cycle166. Thus, the carcinogenesis process is accelerated by altered signal transduction and cell cycle167.

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.49 found p53 gene mutation rate to be about 18% in CRC patients.

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.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.

HCC is the fifth cause of death worldwide and is mostly inducted by the chronic liver disorders, such as viral hepatitis170,171. 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 studies118,129,131. 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 (20% (95% CI, 10–31%)) and the most altered pathway was Wnt (17% (95% CI, 11–23%)). It has been indicated that the CTNNB1 and P53 genes are the most involved genes in the HCC172,173. 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 countries172,173.

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 years174. 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 studies55,119,175. 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.

Gastric Cancer (GC), as another invasive GI cancer, has significant mortality rate worldwide178. 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 treatment179,180. 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 progression104,181,182. 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 syndrome183,184.

Moreover, most of the studies included used PCR followed by the Sanger sequencing, as the method of choice. However, some studies used SSCP-PCR (single-strand conformational polymorphism PCR) 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 required185. 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 (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 pathway3.

In 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.

Methods

Search strategy

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 (the exact search strategy is available in the supplement data of supplementary Table 1).

Inclusion and exclusion criteria

The studies were screened by two independent authors and all the studies meeting the inclusion criteria were included. Any discrepancy between the two reviewer authors were sorted out by a third expert. Inclusion criteria were the English language writing, publication up to the date of the search, the study setting of cross-sectional or cohort, and the data eligibility for the study. Furthermore, the meta-analysis, conference seminars, and review articles were excluded from the search results.

Data extraction

Selected studies were listed in EndNote software (EndNote X7, Thomson Reuters) 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.

Risk bias assessment

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 Center187.

Meta-analysis

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 I2 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 (Stata Corp, College Station, TX, USA) were used by setting the statistical significant value at p < 0.05.