Skip to main content

SYSTEMATIC REVIEW article

Front. Oncol., 29 November 2019
Sec. Gastrointestinal Cancers
This article is part of the Research Topic Emerging Diagnostic and Therapeutic Approaches for Gastric Cancer View all 21 articles

Prognostic Biomarkers for Gastric Cancer: An Umbrella Review of the Evidence

  • Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical University, Shenyang, China

Introduction: Biomarkers are biological molecules entirely or partially participating in cancerous processes that function as measurable indicators of abnormal changes in the human body microenvironment. Aiming to provide an overview of associations between prognostic biomarkers and gastric cancer (GC), we performed this umbrella review analyzing currently available meta-analyses and grading the evidence depending on the credibility of their associations.

Methods: A systematic literature search was conducted by two independent investigators of the PubMed, Embase, Web of Science, and Cochrane Databases to identify meta-analyses investigating associations between prognostic biomarkers and GC. The strength of evidence for prognostic biomarkers for GC were categorized into four grades: strong, highly suggestive, suggestive, and weak.

Results: Among 120 associations between prognostic biomarkers and GC survival outcomes, only one association, namely the association between platelet count and GC OS, was supported by strong evidence. Associations between FITC, CEA, NLR, foxp3+ Treg lymphocytes (both 1- and 3-year OS), CA 19-9, or VEGF and GC OS were supported by highly suggestive evidence. Four associations were considered suggestive and the remaining 108 associations were supported by weak or not suggestive evidence.

Discussion: The association between platelet count and GC OS was supported by strong evidence. Associations between FITC, CEA, NLR, foxp3+ Treg lymphocytes (both 1- and 3-year OS), CA 19-9, or VEGF and GC OS were supported by highly suggestive evidence, however, the results should be interpreted cautiously due to inadequate methodological quality as deemed by AMSTAR 2.0.

Introduction

Gastric cancer (GC) was the most common cancer worldwide less than a century ago (1). Despite a decreasing incidence in recent decades, GC remains the most commonly diagnosed cancer in Eastern Asia (2). According to the National Central Cancer Registry in China, GC is the second most common cancer in china, with 298,800 cases in 2013 alone, which means that approximately 42 individuals suffer GC in every 100,000 people (3). The best options to reduce mortality are treatments aimed at early detection, systematic prevention and personalized therapy. Meanwhile, traditional treatment strategies such as surgery have potentially reached a ceiling regarding locoregional control and mortality reduction, reflecting the dilemma that GC remains unsatisfactorily incurable worldwide (4).

Biomarkers are biological molecules entirely or partially participating in cancerous processes that function as measurable indicators of abnormal changes in the human body microenvironment (5, 6). Many studies have reported the importance of biomarkers in clinical GC applications including diagnosis, treatments and prognosis. There are currently three main types of cancer biomarkers distinguished by clinical use: predictive, prognostic, and pharmacodynamic markers (710). Countries with high GC incidence, such as Japan, have established adequate tumor monitoring systems to detect and diagnose GC at early stages, greatly improving survival (4). Prognostic biomarkers play essential roles in distinguishing between benign and malignant tumors, monitoring progress of advanced GCs, and predicting survival outcomes. Several protein cancer biomarkers are widely used and have become routine in clinical practice, especially α-fetoprotein (AFP) which has been proven to improve early diagnosis of hepatocellular cancer, resulting in more superior survival outcomes (11, 12). Many cohort and case-control studies have explored biomarkers associated with GC, and several meta-analyses have been published to systematically analyze these results. Aiming to provide an overview of associations between prognostic biomarkers and GC, we performed this umbrella review analyzing currently available meta-analyses and grading the evidence depending on the credibility of their associations.

Methods

Search Strategy and Eligibility Criteria

A systematic literature search was conducted by two independent investigators of the PubMed, Embase, Web of Science, and Cochrane Databases to identify meta-analyses investigating associations between prognostic biomarkers and GC published from inception through April 11, 2019. The following relevant keywords were used to conduct our electronic database search: (risk factors OR Helicobacter pylori OR H. pylori OR peptic ulcer disease OR gastritis OR inflammation OR IL-7 OR IL-10 OR gastric ulcer OR gastroesophageal reflux disease OR GERD OR esophagogastric junction OR dysplastic intestinal metaplasia OR cardia OR smoking OR smoker OR alcohol OR chemical exposure OR occupational exposure OR high temperature OR particulates OR metal OR chromium OR asbestos OR talc OR crystalline silica OR diet OR salt OR preserved meat OR red meat OR coffee OR caffeinated intake OR caffeine intake OR caffeine OR decaffeinated OR decaffeinated intake OR fruits OR vegetables OR obesity OR obese OR BMI OR body mass index OR anemia OR gastric surgery OR radiation OR Epstein-Barr virus OR EBV OR socioeconomic status OR poverty OR wealth OR education OR level of education OR educational level OR schooling OR blood group OR blood type OR sex OR gender OR sexuality OR man OR male OR woman OR female OR anti-estrogen drugs OR tamoxifen OR hormone replacement therapy OR HRT OR parity OR pregnancy OR menopause OR premenopausal OR post-menopausal OR ethnic origin OR ethnicity OR race OR screening programs OR radiography OR endoscopy OR serum pepsinogen level OR exercise OR physical activity OR family history OR familial OR radiation OR radiotherapy OR cohabiting OR living together OR partner OR partnered OR insulin OR metformin OR aspirin OR aspirin containing medications OR drugs OR medicine) AND (gastric cancer OR gastric carcinoma OR gastric neoplasia OR gastric tumor OR gastric neoplasm OR gastric maligna* OR GC OR stomach carcinoma OR stomach neoplasia OR stomach tumor OR stomach neoplasm OR stomach maligna*) AND (systematic review OR meta-analysis OR metaanalysis). Only meta-analyses were included in this umbrella review, irrespective of publication year or language; case reports, commentaries, editorials, conference abstracts and letters were excluded. We also manually reviewed the reference lists in the retrieved meta-analyses to include any related studies.

A detailed eligibility criterion was formulated for study inclusion: (1) we included studies clearly examining associations between prognostic biomarkers, rather than predictive or pharmacodynamic markers, and GC survival outcomes including but not limited to overall survival (OS), disease-free survival (DFS), progression-free survival (PFS) and cancer-specific survival (CSS). (2) We excluded studies investigating genetic polymorphism and GC incidence. Studies focusing on benign gastric tumors such as leiomyoma, neurofibroma and gastrointestinal stromal tumors were also excluded (3). We excluded meta-analyses containing less than three original studies or not providing sufficient data from each individual study. When two or more meta-analyses focused on one specific association, we included the meta-analysis with largest sample size.

Data Extraction

Two investigators independently performed data extraction from included meta-analyses and resolved differences through discussion. The following values were retrieved from each included study: first author name, publication year; country, name and classification of biomarker and its associations with GC, relative risk estimates, including risk ratio (RR), odds ratio (OR), hazard ratio (HR) and the corresponding 95% confidence interval (CI), number of include studies, number of cases, and population size.

Quality Assessment

The methodological quality of included meta-analyses was evaluated through AMSTAR (A MeaSurement Tool to Assess systematic Reviews) version 2.0 (2017) (13), a vital appraisal tool for umbrella reviews to assess involved randomized trials with high efficiency. The revised version simplifies response categories and contains 16 items in all which provide a more comprehensive appraisal compared with the original AMSTAR. Rather than outputting an overall score, AMSTAR 2 evaluates single study quality by calculating scores in specific items and then describes results as either high, moderate, low, or critically low grade.

Statistical Analysis

Statistical analyses were conducted using STATA version 12.0 (StataCorp. LLC, College Station, TX, USA). Random-effect models were used to estimate summary effects for included studies considering the inevitable heterogeneity caused by multiple sources. Relative risk estimates, 95% confidence interval (CI) and corresponding P-values were calculated. The significance level was set to P < 0.05 (14).

Interstudy heterogeneity was analyzed through Cochran's Q test and the I2 statistic was calculated. Ranging from 0 to 100%, I2 quantitatively demonstrates variability among risk estimates, with I2 > 50% indicating great heterogeneity (15). Interstudy heterogeneity was also analyzed using 95% prediction intervals (PI), assessing the impact of uncertainty in individual studies and prone to be more conservative (16, 17).

Several methods were used to evaluate bias in associations between prognostic biomarkers and GC. Egger's regression asymmetry test was performed to assess whether small-study effects existed (17), with a P < 0.01 considered statistically significant with more conservative results in the largest study.

Excess significance bias was applied to avoid potential biases such as selective reporting biases or publication biases. To assess whether the number of expected studies (E) was in accordance with the observed number (O) with nominally significant results or less, chi-square statistics were performed (18) with a two-tailed P < 0.10 as the statistical significance threshold. The number of studies expected to be statistically significant was calculated by summing up statistical power estimates extracted from each component using an algorithm from a non-central t distribution. The observed number was extracted from the relative risk estimate of the largest study. In cases where O > E and P < 0.10, excess significance was considered positive.

Credibility ceiling sensitivity analyses were performed for weak evidence to skeptically analyze precise results provided by included meta-analyses. The credibility ceiling was set at 10% for this study, based on the assumption that the likelihood of a specific effect always has a limitation, in other words, no matter how well-designed a study was, its effect in this particular aspect is restricted and impossible to exceed maximum value (19).

Strength of Existing Evidence

The strength of evidence for prognostic biomarkers for GC were categorized into four grades in accordance with previous studies (20, 21): strong, highly suggestive, suggestive, and weak. Categorization criteria are as follow: (1) a study was considered as strong evidence if it presented a P < 10−6, I2 < 50%, calculated 95% PI excluding the null value, a sample size >1,000 cases, was absent evidence of small-study effects and excess significance and survived the 10% credibility ceiling (P > 0.05); (2) a study would be rated as highly suggestive evidence if it presented a P < 10−6 with a sample size >1,000 cases; (3) a study would be categorized as suggestive evidence if it presented a P < 10−3 with a sample size >1,000 cases; (4) a study would be assessed as weak evidence if it presented a P < 0.05.

Results

Characteristics of the Included Systematic Reviews and Meta-Analyses

A total of 2,484 records were identified from the literature search and manual screening of references, of which 2,283 were excluded after title and abstract screening. Ultimately, 74 of the remaining 201 studies met the inclusion criteria after full-text review (2297). The search flowchart is shown in Figure 1, and the full list of the 201 studies and exclusion reasons for 127 of them are shown in Supplementary Table S1. Of note, we selected the most recent systematic review and meta-analysis investigating the association between HER2 and GC mortality (96) rather than the study with the largest number of primary studies (98) for inclusion because the latter searched for studies published in 2015 while the former was published in 2017 and the included studies needed to be updated. The included studies covered 120 different associations between prognostic biomarkers and GC survival outcomes, more than 79,000 subjects, and over 1,000 studies. Characteristics of the 120 associations in the included systematic reviews and meta-analyses are shown in Table 1. Data on the primary studies included in the 74 systematic reviews and meta-analyses were also extracted, processed, and coded to perform various analyses.

FIGURE 1
www.frontiersin.org

Figure 1. Flow diagram.

TABLE 1
www.frontiersin.org

Table 1. Characteristics of the 120 associations in the included systematic reviews and meta-analyses.

Methodological Quality Assessment Using AMSTAR 2.0

The methodological quality of all included systematic reviews and meta-analyses was deemed critically low using the 16-item AMSTAR 2.0. Detailed results, scoring criteria, and rating criteria are shown in Supplementary Table S2. All included studies had more than two critical flaws [usually in items 2 (74/74, 100%), 7 (74/74, 100%), and 13 (74/74, 100%)] and several non-critical flaws [usually in items 3 (74/74, 100%), 10 (74/74, 100%), and 12 (74/74, 100%)]. Of note, studies with at least two critical flaws with or without non-critical flaws were considered as having critically low methodological quality.

Summary Effect Size

The quantitative syntheses of the 120 associations were re-performed using a random-effect model to provide more conservative estimates. Forty-seven associations reached P < 10−6 (Table 2 and Supplementary Table S3). Twenty-one associations had moderate statistical significance (P < 10−3). The remaining 52 associations presented either P < 0.05 or no statistical significance. Most associations that reached statistical significance reported an increased risk of mortality of GC, indicating the potential prognostic effect of biomarkers for GC. Associations between Foxp3+ Treg lymphocytes and 1-, 3-, and 5-year survival of GC, between intraperitoneal free cancer cell (IFCC) and OS of GC, between Forkhead Box M1 (FOXM1) and 1- and 5-year survival of GC, between Silent information regulator 1 (Sirt1) and 3-year survival of GC, and between CC chemokine receptor type 7 (CCR7) and 5-year survival of GC all reported a decreased risk of mortality of GC.

TABLE 2
www.frontiersin.org

Table 2. Evidence-rating results based on the results of statistical analyses of the 120 associations.

Heterogeneity

Seventy-six of the 120 (63.3%) associations demonstrated significant heterogeneity (P < 0.1), of which 54 showed high heterogeneity and 23 presented moderate to high heterogeneity. The 95% PI was also calculated to further assess inter-study heterogeneity. The 95% PIs of 38 associations excluded the null value (Table 2 and Supplementary Table S3).

Small-Study Effects

Small study effects were found in fifteen associations: FITCs and GC OS, tissue VEGF and GC OS, β-catenin and GC OS, p53 and GC OS, MAPF and GC OS, uPAR and GC OS, MET and GC OS, CD133 and 5-year-survival of GC, PTEN and 5-year-survival of GC, FOXM1 and 5-year-survival of GC, Sirt1 and 3-year-survival of GC, MMP9 and 5-year-survival of GC, SOX2 and GC OS, S100A4 and GC OS, NME1 and GC OS all had P < 0.1 for Egger's test (Table 2 and Supplementary Table S3). Only one of the 120 associations contained an inadequate number of studies (<10) and failed to empower Egger's test to identify small-study effects: CD44v6 and GC OS.

Excess Significance

Excess significance was significant (O>E and P < 0.1) in 45 associations (Table 2 and Supplementary Table S3).

10% Credibility Ceiling

Seventy-seven of the 120 associations survived the 10% credibility ceiling, including all associations graded as strong, highly suggestive, or suggestive and most of the associations classified as weak evidence. Details can be found in Table 2 and Supplementary Table S3.

Robustness of Evidence

None of the 120 associations between prognostic biomarkers and GC survival outcomes were considered strong evidence. Only one association, namely the association between platelet count and GC OS, was supported by strong evidence. Seven associations were supported by highly suggestive evidence, including associations between free intraperitoneal tumor cells (FITCs) and GC OS, between CEA and GC OS, between neutrophils to lymphocytes ratio (NLR) and GC OS, between foxp3+ Treg lymphocytes and 1- and 3-year-OS of GC, between serum carbohydrate antigen 19-9 (CA 19-9) and GC OS, and between tissue vascular endothelial growth factor (VEGF) and GC OS (Table 2). Evidence supporting associations between p53 and OS or disease-specific survival of GC, between matrix metalloproteinase 2 (MMP2) and GC OS, and between β-catenin and GC OS were considered suggestive. The remaining 108 associations were supported by weak or not suggestive evidence. Detailed results of these analyses are shown in Supplementary Table S3.

Discussion

Principal Finding

Biomarkers play essential role in clinical applications during several procedures in cancers including diagnosis, treatment, and prognosis. Cancer diagnosis based on biomarkers may improve the accuracy of early diagnosis and facilitate efficient subsequent treatment. Quite a few biomarkers have been identified in clinical trials, which show promises in the benefit of cancer patients, yet limitations exist. Some appear to be predictive biomarkers and their potential of indicating cancer developments remains to be seen. Others are restricted in clinical application due to the poor efficiency of traditional detection methods such as enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR). As novel biosensing approaches sprang up, the predictive and prognostic value of the biomarker has been widely tested in clinical trials. Since clinical practitioners can hardly perform intervention in cancer patients before diagnosis, we focused more on prognostic biomarkers instead of predictive biomarkers. To evaluate the prognostic potential of existing biomarkers and to facilitate the clinical application of more robust prognostic biomarkers, we performed this umbrella review.

This umbrella review was the first to comprehensively collect existing meta-analyses and systematically appraise the robustness of evidence to provide an overview of associations between prognostic biomarkers and GC. Overall, 74 meta-analyses comprising 80 different kinds of biomarkers were included in our umbrella review, only one association (the association between platelet count and GC OS) was supported by strong evidence. Several associations were supported by highly suggestive evidence, namely associations between GC OS and free intraperitoneal tumor cells (FITC), CEA, neutrophils to lymphocytes ratio (NLR), foxp3+ Treg lymphocytes (1- and 3-year OS), serum carbohydrate antigen 19-9 (CA 19-9), and tissue vascular endothelial growth factor (VEGF). Associations between p53, matrix metalloproteinase 2 (MMP2), β-catenin and GC OS were graded as suggestive and the remaining were graded as weak evidence. These results should be interpreted cautiously considering the poor methodological quality of the included meta-analyses as ascribed by AMSTAR 2.0.

Comparison With Other Studies and Possible Explanations

Classical Biomarkers and GC

CEA and CA 19-9 are two classical biomarkers detected in the last century and their predictive value for several cancers have been clinically confirmed (99, 100). However, the prognostic value of these two blood group antigens remains controversial. After systematically assessing the methodological quality and robustness of the pooled meta-analysis of 41 studies covering 14,651 participants, we found that CEA overexpression may relate to reduced OS for GC patients. However, associations between elevated CEA and GC DFS and GC disease specific survival (DSS) were found to be supported by weak evidence. These results might be explained by the low numbers of included studies and subjects: 29/3,491 for OS, 6/295 for DFS, and 7/542 for DSS. Another possible explanation is that elevated CEA is often detected in patients with GC of later stage, meaning the cause of death is not necessarily GC itself, considering severe complications. Of note, this pattern also holds for associations between CA19-9 and GC survival outcomes.

Novel Biomarkers and GC

Blood contains rich sources of tumor-associated biomarkers and is one of the human fluids that are easily accessible and can be analyzed in anytime and anywhere. These biomolecules are considered to be part of primary tumors, products of passive release during apoptosis and necrosis of tumor cells or biomolecules affected by tumor microenvironment (101).

In our research, we found that several biomolecules in blood may be considered as candidate prognostic biomarkers for GC patients. The association between platelet count and GC OS was the only one association that was supported by strong evidence. Platelet was previously reported to extensively interact with tumor cells, promoting tumor chemotaxis, adhesion, proliferation, and metastasis, which reasonably accounts for the robust indicative role of platelet in GC prognosis (102). High platelet count has proven to be associated with increased mortality in several cancers such as gynecologic malignancies, breast cancer, and lung cancer (103105). Platelet count may also serve as an indicator of worse prognosis in GC based on the meta-analysis covering 5,515 subjects.

The prognosis indicative role of another inflammatory marker, NLR, is supported by highly suggestive evidence. Convincing evidence have been found between systematic inflammatory and tumor development. On one hand, myeloid growth factors secreted by cancer cells can upregulate production of neutrophils, on the other hand, immune cytokines provided by cancer cells downregulate function of lymphocyte (106). Elevated neutrophil stimulates angiogenesis and aids tumor progression while relative lymphocytopenia depresses innate anti-tumor cellular immunity, which explains why elevated NLR indicates poor OS in GC patients (107).

The other two highly suggestive evidences are that Foxp3+ Treg lymphocytes contribute to significantly poorer 1- and 3-year OS, while inconsistent result was found in 5-year OS. As a subgroup of CD4+ T help cells, Foxp3+ Treg lymphocytes play a critical role in suppressed T-cell immunity. Foxp3+ Treg lymphocytes turned out to be an unfavorable indicator of poor prognosis in GC.

Peritoneal dissemination is one of the most common and severe complications for GC. Detection of ascitic fluids and blood samples is frequently used clinically for easy accessibility and enhanced modern technologies. Evidence supporting the association between FITC and GC OS was graded as highly suggestive while the associations between circulating tumor cells (CTCs) and several GC survival outcomes were deemed to be supported by weak evidence. These results demonstrate that the role of FITC as a specific prognostic indicator of GC is more certain than that of CTC. Previous studies also suggest that FITC is a convincing predictive and prognostic biomarker for GC (108, 109) while the prognostic role of CTCs still need further confirmation.

Angiogenesis, the formation of new vascular network, plays an essential role in tumorigenesis and metastasis. As a vital target for prognosis evaluation, indicators to assess disease severity qualitatively and quantitatively are urgently needed. The vascular endothelial growth factor (VEGF) and its receptors (VEGFRs), which may modulate angiogenesis, show promises in this regard. Numerous studies report increased VEGFs and VEGFRs in both resectable and advanced GC patients. Five relevant meta-analyses of more than 11,307 participants were included in our umbrella review. The association between tissue VEGF and GC OS was supported by highly suggestive evidence while the association between tissue VEGF and GC DFS and other associations concerning VEGF, circulating VEGF, VEGF-A, VEGF-C, VEGF-D, VEGFR-3, and GC survival outcomes were supported by weak evidence. These differences can be explained by inadequate data and data quality as almost all relevant meta-analyses included less than five studies, covered fewer than 1,000 cases, or had high heterogeneity. The results concerning VEGF-C, VEGF-D are basically consistent with those concerning VEGFR-3, as the former two are essential factors in combination with the latter.

Limitations

This umbrella review was the first to provide an overview of associations between prognostic biomarkers and GC, and several limitations exist in this work. First, the umbrella review included published meta-analyses, meaning that studies that had not been systematically evaluated were unintentionally excluded, leading to unreliable results. Second, we only focused on associations between prognostic biomarkers and GC survival outcomes, while predictive biomarkers, mostly genetic markers comprising essential component of biomarkers, were not taken into consideration. Third, the majority of cases included in these meta-analyses are from Eastern countries and in this regard, we should interpret the findings with caution when it comes to population of Western origin. Fourth, subgroup analysis was not performed due to insufficient data provided by the included meta-analyses. Future work is required to establish a more comprehensive review to assess the true associations between prognostic biomarkers and GC survival and translate these associations into clinical practice to the utmost extent.

In conclusion, the association between platelet count and GC OS was supported by strong evidence. Associations between FITC, CEA, NLR, foxp3+ Treg lymphocytes (both 1- and 3-year OS), CA 19-9, or VEGF and GC OS were supported by highly suggestive evidence, however, the results should be interpreted cautiously due to inadequate methodological quality as deemed by AMSTAR 2.0.

Author Contributions

ZWa and XZ conceived and designed the study. CZ and XZ performed the literature search, acquired, and collated the data, which were analyzed by YS, ZWu, JSh, ZG, and JSu. ZWa was guarantor. ZWa attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors drafted and critically revised the manuscript for important intellectual content, and gave final approval of the version to be published and contributed to the manuscript.

Funding

This work was funded by the National Key R&D Program of China (MOST-2017YFC0908300 and MOST-2017YFC0908305, ZWa), the guidance plan of Natural Science Foundation of Liaoning Province of China (20180550582, YS), the Program for Liaoning Innovative Research Team in University (LT2016005, ZWa). ZWa had full access to all the data and analyses.

Conflict of Interest

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2019.01321/full#supplementary-material

References

1. Karimi P, Islami F, Anandasabapathy S, Freedman ND, Kamangar F. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomark Prev. (2014) 23:700–13. doi: 10.1158/1055-9965.EPI-13-1057

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Bray F, Ren JS, Masuyer E, Ferlay J. Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer. (2013) 132:1133–45. doi: 10.1002/ijc.27711

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Zheng R, Zeng H, Zhang S, Chen W. Estimates of cancer incidence and mortality in China, 2013. Chin J Cancer. (2017) 36:66. doi: 10.1186/s40880-017-0234-3

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Hartgrink HH, Jansen EP, van Grieken NC, van de Velde CJ. Gastric cancer. Lancet. (2009) 374:477–90. doi: 10.1016/S0140-6736(09)60617-6

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Therapeut. (2001) 69:89–95. doi: 10.1067/mcp.2001.113989

CrossRef Full Text | Google Scholar

6. Fuzery AK, Levin J, Chan MM, Chan DW. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin Proteom. (2013) 10:13. doi: 10.1186/1559-0275-10-13

PubMed Abstract | CrossRef Full Text | Google Scholar

7. La Thangue NB, Kerr DJ. Predictive biomarkers: a paradigm shift towards personalized cancer medicine. Nat Rev Clin Oncol. (2011) 8:587–96. doi: 10.1038/nrclinonc.2011.121

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Duffy MJ, Crown J. A personalized approach to cancer treatment: how biomarkers can help. Clin Chem. (2008) 54:1770–9. doi: 10.1373/clinchem.2008.110056

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Sawyers CL. The cancer biomarker problem. Nature. (2008) 452:548–52. doi: 10.1038/nature06913

CrossRef Full Text | Google Scholar

10. Simon R, Roychowdhury S. Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov. (2013) 12:358–69. doi: 10.1038/nrd3979

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Zhang BH, Yang BH, Tang ZY. Randomized controlled trial of screening for hepatocellular carcinoma. J Cancer Res Clin Oncol. (2004) 130:417–22. doi: 10.1007/s00432-004-0552-0

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Chen JG, Parkin DM, Chen QG, Lu JH, Shen QJ, Zhang BC, et al. Screening for liver cancer: results of a randomised controlled trial in Qidong, China. J Med Screen. (2003) 10:204–9. doi: 10.1258/096914103771773320

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. (2017) 358:j4008. doi: 10.1136/bmj.j4008

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Liebig C, Ayala G, Wilks J, Verstovsek G, Liu H, Agarwal N, et al. Perineural invasion is an independent predictor of outcome in colorectal cancer. J Clin Oncol. (2009) 27:5131–7. doi: 10.1200/JCO.2009.22.4949

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Ioannidis JP, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ. (2007) 335:914–6. doi: 10.1136/bmj.39343.408449.80

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. (2011) 342:d549. doi: 10.1136/bmj.d549

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Patsopoulos NA, Evangelou E, Ioannidis JP. Heterogeneous views on heterogeneity. Int J Epidemiol. (2009) 38:1740–2. doi: 10.1093/ije/dyn235

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Ioannidis JP, Trikalinos TA. An exploratory test for an excess of significant findings. Clin Trials. (2007) 4:245–53. doi: 10.1177/1740774507079441

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Salanti G, Ioannidis JP. Synthesis of observational studies should consider credibility ceilings. J Clin Epidemiol. (2009) 62:115–22. doi: 10.1016/j.jclinepi.2008.05.014

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Rezende LFM, Sa TH, Markozannes G, Rey-Lopez JP, Lee IM, Tsilidis KK, et al. Physical activity and cancer: an umbrella review of the literature including 22 major anatomical sites and 770 000 cancer cases. Br J Sports Med. (2018) 52:826–33. doi: 10.1136/bjsports-2017-098391

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Raglan O, Kalliala I, Markozannes G, Cividini S, Gunter MJ, Nautiyal J, et al. Risk factors for endometrial cancer: an umbrella review of the literature. Int J Cancer. (2018) 145:1719–30. doi: 10.1002/ijc.31961

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Luo G, Hu Y, Zhang Z, Wang P, Luo Z, Lin J, et al. Clinicopathologic significance and prognostic value of Ki-67 expression in patients with gastric cancer: a meta-analysis. Oncotarget. (2017) 8:50273–83. doi: 10.18632/oncotarget.17305

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Liu G, Xiong D, Zeng J, Chen B, Huang Z. Clinicopathological and prognostic significance of Ki-67 immunohistochemical expression in gastric cancer: a systematic review and meta-analysis. OncoTargets Ther. (2017) 10:4321–8. doi: 10.2147/OTT.S143089

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Liu G, Xiong D, Xiao R, Huang Z. Prognostic role of fibroblast growth factor receptor 2 in human solid tumors: a systematic review and meta-analysis. Tumour Biol. (2017) 39:1010428317707424. doi: 10.1177/1010428317707424

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Kim YS, Jeong H, Choi JW, Oh HE, Lee JH. Unique characteristics of ARID1A mutation and protein level in gastric and colorectal cancer: a meta-analysis. Saudi J Gastroenterol. (2017) 23:268–74. doi: 10.4103/sjg.SJG_184_17

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Jiang W, Liu K, Guo Q, Cheng J, Shen L, Cao Y, et al. Tumor-infiltrating immune cells and prognosis in gastric cancer: a systematic review and meta-analysis. Oncotarget. (2017) 8:62312–29. doi: 10.18632/oncotarget.17602

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Gu L, Chen M, Guo D, Zhu H, Zhang W, Pan J, et al. PD-L1 and gastric cancer prognosis: a systematic review and meta-analysis. PLoS ONE. (2017) 12:e0182692. doi: 10.1371/journal.pone.0182692

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Ge H, Yan Y, Guo L, He X, Yang X. Prognostic and clinical significance of VEGFR-3 in gastric cancer: a meta-analysis. Clin Chim Acta. (2017) 474:114–9. doi: 10.1016/j.cca.2017.09.013

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Fang M, Tao Y, Liu Z, Huang H, Lao M, Huang L, et al. Meta-analysis of the relationship between NM23 expression to gastric cancer risk and clinical features. BioMed Res Int. (2017) 2017:8047183. doi: 10.1155/2017/8047183

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Du P, Liu Y, Ren H, Zhao J, Zhang X, Patel R, et al. Expression of chemokine receptor CCR7 is a negative prognostic factor for patients with gastric cancer: a meta-analysis. Gastric Cancer. (2017) 20:235–45. doi: 10.1007/s10120-016-0602-8

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Chen H, Lu W, Huang C, Ding K, Xia D, Wu Y, et al. Prognostic significance of ZEB1 and ZEB2 in digestive cancers: a cohort-based analysis and secondary analysis. Oncotarget. (2017) 8:31435–48. doi: 10.18632/oncotarget.15634

CrossRef Full Text | Google Scholar

32. Cao F, Zhang C, Han W, Gao XJ, Ma J, Hu YW, et al. p-Akt as a potential poor prognostic factor for gastric cancer: a systematic review and meta-analysis. Oncotarget. (2017) 8:59878–88. doi: 10.18632/oncotarget.17001

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Brungs D, Chen J, Aghmesheh M, Vine KL, Becker TM, Carolan MG, et al. The urokinase plasminogen activation system in gastroesophageal cancer: a systematic review and meta-analysis. Oncotarget. (2017) 8:23099–109. doi: 10.18632/oncotarget.15485

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Zeng XQ, Li N, Ma LL, Tseng YJ, Zhao NQ, Chen SY. Prognostic value of focal adhesion kinase (FAK) in human solid carcinomas: a meta-analysis. PLoS ONE. (2016) 11:e0162666. doi: 10.1371/journal.pone.0162666

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Xu Z, Xu W, Cheng H, Shen W, Ying J, Cheng F, et al. The prognostic role of the platelet-lymphocytes ratio in gastric cancer: a meta-analysis. PLoS ONE. (2016) 11:e0163719. doi: 10.1371/journal.pone.0163719

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Wu P, Wu D, Zhao L, Huang L, Shen G, Huang J, et al. Prognostic role of STAT3 in solid tumors: a systematic review and meta-analysis. Oncotarget. (2016) 7:19863–83. doi: 10.18632/oncotarget.7887

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Wang XT, Kong FB, Mai W, Li L, Pang LM. MUC1 Immunohistochemical expression as a prognostic factor in gastric cancer: meta-analysis. Dis Markers. (2016) 2016:9421571. doi: 10.1155/2016/9421571

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Wang XL, Jiang JT, Wu CP. Prognostic significance of tumor-associated macrophage infiltration in gastric cancer: a meta-analysis. Genet Mol Res. (2016) 15:gmr15049040. doi: 10.4238/gmr15049040

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Tustumi F, Bernardo WM, Dias AR, Ramos MF, Cecconello I, Zilberstein B, et al. Detection value of free cancer cells in peritoneal washing in gastric cancer: a systematic review and meta-analysis. Clinics. (2016) 71:733–45. doi: 10.6061/clinics/2016(12)10

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Sun J, Chen X, Gao P, Song Y, Huang X, Yang Y, et al. Can the neutrophil to lymphocyte ratio be used to determine gastric cancer treatment outcomes? a systematic review and meta-analysis. Dis Markers. (2016) 2016:7862469. doi: 10.1155/2016/7862469

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Lu L, Wu M, Sun L, Li W, Fu W, Zhang X, et al. Clinicopathological and prognostic significance of cancer stem cell markers CD44 and CD133 in patients with gastric cancer: a comprehensive meta-analysis with 4729 patients involved. Medicine. (2016) 95:e5163. doi: 10.1097/MD.0000000000005163

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Lu L, Huang F, Zhao Z, Li C, Liu T, Li W, et al. CD44v6: a metastasis-associated biomarker in patients with gastric cancer?: a comprehensive meta-analysis with heterogeneity analysis. Medicine. (2016) 95:e5603. doi: 10.1097/MD.0000000000005603

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Liu QR, Li YF, Deng ZQ, Cao JQ. Prognostic significance of dickkopf-1 in gastric cancer survival: a meta-analysis. Genet Test Mol Biomark. (2016) 20:170–5. doi: 10.1089/gtmb.2015.0154

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Jiang B, Chen JH, Yuan WZ, Ji JT, Liu ZY, Wu L, et al. Prognostic and clinical value of Sirt1 expression in gastric cancer: a systematic meta-analysis. J Huazhong Univ Sci Technol Med Sci. (2016) 36:278–84. doi: 10.1007/s11596-016-1580-0

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Ji K, Zhang L, Zhang M, Chu Q, Li X, Wang W. Prognostic value and clinicopathological significance of p-stat3 among gastric carcinoma patients: a systematic review and meta-analysis. Medicine. (2016) 95:e2641. doi: 10.1097/MD.0000000000002641

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Huang T, Qiu X, Xiao J, Wang Q, Wang Y, Zhang Y, et al. The prognostic role of Leucine-rich repeat-containing G-protein-coupled receptor 5 in gastric cancer: a systematic review with meta-analysis. Clin Res Hepatol Gastroenterol. (2016) 40:246–53. doi: 10.1016/j.clinre.2015.07.009

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Han W, Shi CT, Cao FY, Cao F, Chen MB, Lu RZ, et al. Prognostic value of NME1 (NM23-H1) in patients with digestive system neoplasms: a systematic review and meta-analysis. PLoS ONE. (2016) 11:e0160547. doi: 10.1371/journal.pone.0160547

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Gu X, Gao XS, Ma M, Qin S, Qi X, Li X, et al. Prognostic significance of osteopontin expression in gastric cancer: a meta-analysis. Oncotarget. (2016) 7:69666–73. doi: 10.18632/oncotarget.11936

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Gao Y, Cui J, Xi H, Cai A, Shen W, Li J, et al. Association of thymidylate synthase expression and clinical outcomes of gastric cancer patients treated with fluoropyrimidine-based chemotherapy: a meta-analysis. OncoTargets Ther. (2016) 9:1339–50. doi: 10.2147/OTT.S98540

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Deng K, Zhu H, Chen M, Wu J, Hu R, Tang C. Prognostic significance of molecular analysis of peritoneal fluid for patients with gastric cancer: a meta-analysis. PLoS ONE. (2016) 11:e0151608. doi: 10.1371/journal.pone.0151608

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Cao GD, Chen K, Xiong MM, Chen B. HER3, but not HER4, plays an essential role in the clinicopathology and prognosis of gastric cancer: a meta-analysis. PLoS ONE. (2016) 11:e0161219. doi: 10.1371/journal.pone.0161219

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Zhang CT, He KC, Pan F, Li Y, Wu J. Prognostic value of Muc5AC in gastric cancer: a meta-analysis. World J Gastroenterol. (2015) 21:10453–60. doi: 10.3748/wjg.v21.i36.10453

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Xin-Ji Z, Yong-Gang L, Xiao-Jun S, Xiao-Wu C, Dong Z, Da-Jian Z. The prognostic role of neutrophils to lymphocytes ratio and platelet count in gastric cancer: a meta-analysis. Int J Surg. (2015) 21:84–91. doi: 10.1016/j.ijsu.2015.07.681

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Wu Y, Li Z, Zhang C, Yu K, Teng Z, Zheng G, et al. CD44 family proteins in gastric cancer: a meta-analysis and narrative review. Int J Clin Exp Med. (2015), 8:3595–3606.

PubMed Abstract | Google Scholar

55. Wu P, Wu D, Li L, Chai Y, Huang J PD-L1 and survival in solid tumors: a meta-analysis. PLoS ONE. (2015) 10:e0131403. doi: 10.1371/journal.pone.0131403

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Wei K, Jiang L, Wei Y, Wang Y, Qian X, Dai Q, et al. The prognostic significance of p53 expression in gastric cancer: a meta-analysis. J Cancer Res Clin Oncol. (2015) 141:735–748. doi: 10.1007/s00432-014-1844-7

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Song YX, Huang XZ, Gao P, Sun JX, Chen XW, Yang YC, et al. Clinicopathologic and prognostic value of serum carbohydrate antigen 19-9 in gastric cancer: a meta-analysis. Dis Markers. (2015) 2015:549843. doi: 10.1155/2015/549843

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Song P, Yin Q, Lu M, Fu BO, Wang B, Zhao Q. Prognostic value of excision repair cross-complementation group 1 expression in gastric cancer: a meta-analysis. Exp Therapeut Med. (2015) 9:1393–400. doi: 10.3892/etm.2015.2284

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Pecqueux M, Fritzmann J, Adamu M, Thorlund K, Kahlert C, Reissfelder C, et al. Free intraperitoneal tumor cells and outcome in gastric cancer patients: a systematic review and meta-analysis. Oncotarget. (2015) 6:35564–78. doi: 10.18632/oncotarget.5595

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Meng W, Gu T, Gao LM, Zong ZG, Meng L, Fu ZZ, et al. Correlation of cadherin-17 protein expression with clinicopathological features and prognosis of patients with sporadic gastric cancer. Brazil J Med Biol Res. (2015) 48:1077–86. doi: 10.1590/1414-431x20154645

PubMed Abstract | CrossRef Full Text | Google Scholar

61. Ma G, Wang Q, Lv C, Qiang F, Hua Q, Chu H, et al. The prognostic significance of HOTAIR for predicting clinical outcome in patients with digestive system tumors. J Cancer Res Clin Oncol. (2015) 141:2139–45. doi: 10.1007/s00432-015-1980-8

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Liu JX, Wei ZY, Chen JS, Lu HC, Hao L, Li WJ. Prognostic and clinical significance of claudin-4 in gastric cancer: a meta-analysis. World J Surg Oncol. (2015) 13:207. doi: 10.1186/s12957-015-0626-2

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Gao Y, Cui J, Xi H, Shen W, Zhang K, Li J, et al. Association of prognosis with insulin-like growth factor receptor type I expression in gastric cancer patients: a meta-analysis. Zhonghua Wei Chang Wai Ke Za Zhi. (2015) 18:1051–5. doi: 10.3760/cma.j.issn.1671-0274.2015.10.019

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Dong Y, Chen G, Gao M, Tian X. Increased expression of MMP14 correlates with the poor prognosis of Chinese patients with gastric cancer. Gene. (2015) 563:29–34. doi: 10.1016/j.gene.2015.03.003

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Deng K, Yang L, Hu B, Wu H, Zhu H, Tang C. The prognostic significance of pretreatment serum CEA levels in gastric cancer: a meta-analysis including 14651 patients. PLoS ONE. (2015) 10:e0124151. doi: 10.1371/journal.pone.0124151

PubMed Abstract | CrossRef Full Text | Google Scholar

66. Dai J, Yang L, Wang J, Xiao Y, Ruan Q. Prognostic value of FOXM1 in patients with malignant solid tumor: a meta-analysis and system review. Dis Markers. (2015) 2015:352478. doi: 10.1155/2015/352478

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Chen J, Liu X, Jiao H, Peng L, Huo Z, Yang W, et al. Prognostic and clinical significance of STAT3 and MMP9 in patients with gastric cancer: a meta-analysis of a Chinese cohort. Int J Clin Exp Med. (2015) 8:546–57.

PubMed Abstract | Google Scholar

68. Zhang ZY, Dai ZL, Yin XW, Li SH, Li SP, Ge HY. Meta-analysis shows that circulating tumor cells including circulating microRNAs are useful to predict the survival of patients with gastric cancer. BMC Cancer. (2014) 14:773. doi: 10.1186/1471-2407-14-773

PubMed Abstract | CrossRef Full Text | Google Scholar

69. Zhang Y, Wang Y, Wan Z, Liu S, Cao Y, Zeng Z. Sphingosine kinase 1 and cancer: a systematic review and meta-analysis. PLoS ONE. (2014) 9:e90362. doi: 10.1371/journal.pone.0090362

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Wang Z, Hao B, Yang Y, Wang R, Li Y, Wu Q. Prognostic role of SPARC expression in gastric cancer: a meta-analysis. Archiv Med Sci. (2014) 10:863–9. doi: 10.5114/aoms.2014.46207

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Wang Y, Zhou LB, Li XH. S100A4 expression and prognosis of gastric cancer: a meta-analysis. Genet Mol Res. (2014) 13:10398–403. doi: 10.4238/2014.December.12.1

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Wang S, Zheng G, Cheng B, Chen F, Wang Z, Chen Y, et al. Circulating tumor cells (CTCs) detected by RT-PCR and its prognostic role in gastric cancer: a meta-analysis of published literature. PLoS ONE. (2014) 9:e99259. doi: 10.1371/journal.pone.0099259

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Wang HL, Zhou PY, Liu P, Zhang Y. Abnormal FHIT protein expression may be correlated with poor prognosis in gastric cancer: a meta-analysis. Tumour Biol. (2014) 35:6815–21. doi: 10.1007/s13277-014-1936-7

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Shen W, Xi H, Wei B, Chen L. The prognostic role of matrix metalloproteinase 2 in gastric cancer: a systematic review with meta-analysis. J Cancer Res Clin Oncol. (2014) 140:1003–9. doi: 10.1007/s00432-014-1630-6

PubMed Abstract | CrossRef Full Text | Google Scholar

75. Shao Y, Geng Y, Gu W, Ning Z, Jiang J, Pei H. Prognostic role of high Bmi-1 expression in Asian and Caucasian patients with solid tumors: a meta-analysis. Biomed Pharmacother. (2014) 68:969–77. doi: 10.1016/j.biopha.2014.10.017

PubMed Abstract | CrossRef Full Text | Google Scholar

76. Peng Z, Zhu Y, Wang Q, Gao J, Li Y, Li Y, et al. Prognostic significance of MET amplification and expression in gastric cancer: a systematic review with meta-analysis. PLoS ONE. (2014) 9:e84502. doi: 10.1371/journal.pone.0084502

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Lin S, Qi W, Han K, Gan Z, Yao Y, Miu D. Prognostic value of SOX2 in digestive tumors: a meta-analysis. Hepatogastroenterology. (2014) 61:1274–78. doi: 10.5754/hge14079

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Li T, Chen J, Liu QL, Huo ZH, Wang ZW. Meta-analysis: E-cadherin immunoexpression as a potential prognosis biomarker related to gastric cancer metastasis in Asian patients. Eur Rev Med Pharmacol Sci. (2014) 18:2693–703.

PubMed Abstract | Google Scholar

79. Li LF, Wei ZJ, Sun H, Jiang B. Abnormal beta-catenin immunohistochemical expression as a prognostic factor in gastric cancer: a meta-analysis. World J Gastroenterol. (2014) 20:12313–321. doi: 10.3748/wjg.v20.i34.12313

PubMed Abstract | CrossRef Full Text | Google Scholar

80. Guo L, Yang TF, Liang SC, Guo JX, Wang Q. Role of EZH2 protein expression in gastric carcinogenesis among Asians: a meta-analysis. Tumour Biology. (2014) 35:6649–56. doi: 10.1007/s13277-014-1888-y

PubMed Abstract | CrossRef Full Text | Google Scholar

81. Gu J, Zheng L, Wang Y, Zhu M, Wang Q, Li X. Prognostic significance of HER2 expression based on trastuzumab for gastric cancer (ToGA) criteria in gastric cancer: an updated meta-analysis. Tumour Biol. (2014) 35:5315–21. doi: 10.1007/s13277-014-1693-7

PubMed Abstract | CrossRef Full Text | Google Scholar

82. Chen J, Li T, Liu Q, Jiao H, Yang W, Liu X, et al. Clinical and prognostic significance of HIF-1alpha, PTEN, CD44v6, and survivin for gastric cancer: a meta-analysis. PLoS ONE. (2014) 9:e91842. doi: 10.1371/journal.pone.0091842

PubMed Abstract | CrossRef Full Text | Google Scholar

83. Cao W, Fan R, Yang W, Wu Y. VEGF-C expression is associated with the poor survival in gastric cancer tissue. Tumour Biol. (2014) 35:3377–83. doi: 10.1007/s13277-013-1445-0

PubMed Abstract | CrossRef Full Text | Google Scholar

84. Zhang ZG, Zhang QN, Wang XH, Tian JH. Hypoxia-inducible factor 1 alpha (HIF-1alpha) as a prognostic indicator in patients with gastric tumors: a meta-analysis. Asian Pacific J Cancer Prev. (2013) 14:4195–98. doi: 10.7314/APJCP.2013.14.7.4195

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Yu S, Yu Y, Zhao N, Cui J, Li W, Liu T. C-Met as a prognostic marker in gastric cancer: a systematic review and meta-analysis. PLoS ONE. (2013) 8:e79137. doi: 10.1371/journal.pone.0079137

PubMed Abstract | CrossRef Full Text | Google Scholar

86. Yu Q, Yu XF, Zhang SD, Wang HH, Wang HY, Teng LS. Prognostic role of C-reactive protein in gastric cancer: a meta-analysis. Asian Pacific J Cancer Prev. (2013) 14:5735–40. doi: 10.7314/APJCP.2013.14.10.5735

PubMed Abstract | CrossRef Full Text | Google Scholar

87. Tan VY, Lewis SJ, Adams JC, Martin RM. Association of fascin-1 with mortality, disease progression and metastasis in carcinomas: a systematic review and meta-analysis. BMC Med. (2013) 11:52. doi: 10.1186/1741-7015-11-52

PubMed Abstract | CrossRef Full Text | Google Scholar

88. Liu JL, Gao W, Kang QM, Zhang XJ, Yang SG. Prognostic value of survivin in patients with gastric cancer: a systematic review with meta-analysis. PLoS ONE. (2013) 8:e71930. doi: 10.1371/journal.pone.0071930

PubMed Abstract | CrossRef Full Text | Google Scholar

89. Hu L, Luo H, Wang W, Li H, He T. Poor prognosis of phosphatase of regenerating liver 3 expression in gastric cancer: a meta-analysis. PLoS ONE. (2013) 8:e76927. doi: 10.1371/journal.pone.0076927

PubMed Abstract | CrossRef Full Text | Google Scholar

90. Chen C, Yang JM, Hu TT, Xu TJ, Yan G, Hu SL, et al. Prognostic role of human epidermal growth factor receptor in gastric cancer: a systematic review and meta-analysis. Archiv Med Res. (2013) 44:380–9. doi: 10.1016/j.arcmed.2013.07.001

PubMed Abstract | CrossRef Full Text | Google Scholar

91. Zhang QW, Liu L, Chen R, Wei YQ, Li P, Shi HS, et al. Matrix metalloproteinase-9 as a prognostic factor in gastric cancer: a meta-analysis. Asian Pacific J Cancer Prev. (2012) 13:2903–8. doi: 10.7314/APJCP.2012.13.6.2903

PubMed Abstract | CrossRef Full Text | Google Scholar

92. Wang XT, Wei WY, Kong FB, Lian C, Luo W, Xiao Q, et al. Prognostic significance of Cdx2 immunohistochemical expression in gastric cancer: a meta-analysis of published literatures. J Exp Clin Cancer Res. (2012) 31:98. doi: 10.1186/1756-9966-31-98

PubMed Abstract | CrossRef Full Text | Google Scholar

93. Peng L, Zhan P, Zhou Y, Fang W, Zhao P, Zheng Y, et al. Prognostic significance of vascular endothelial growth factor immunohistochemical expression in gastric cancer: a meta-analysis. Mol Biol Rep. (2012) 39:9473–84. doi: 10.1007/s11033-012-1812-8

PubMed Abstract | CrossRef Full Text | Google Scholar

94. Liu L, Ma XL, Xiao ZL, Li M, Cheng SH, Wei YQ. Prognostic value of vascular endothelial growth factor expression in resected gastric cancer. Asian Pacific J Cancer Prev. (2012) 13:3089–97. doi: 10.7314/APJCP.2012.13.7.3089

PubMed Abstract | CrossRef Full Text | Google Scholar

95. Chen J, Li T, Wu Y, He L, Zhang L, Shi T, et al. Prognostic significance of vascular endothelial growth factor expression in gastric carcinoma: a meta-analysis. J Cancer Res Clin Oncol. (2011) 137:1799–812. doi: 10.1007/s00432-011-1057-2

PubMed Abstract | CrossRef Full Text | Google Scholar

96. Lei YY, Huang JY, Zhao QR, Jiang N, Xu HM, Wang ZN, et al. The clinicopathological parameters and prognostic significance of HER2 expression in gastric cancer patients: a meta-analysis of literature. World J Surg Oncol. (2017) 15:68. doi: 10.1186/s12957-017-1132-5

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Huang Y, Liao H, Zhang Y, Yuan R, Wang F, Gao Y, et al. Prognostic value of tumor-infiltrating FoxP3+ T cells in gastrointestinal cancers: a meta analysis. PloS One. (2014) 9:e94376. doi: 10.1371/journal.pone.0094376

PubMed Abstract | CrossRef Full Text | Google Scholar

98. Wang ZQ, Sun BJ. C-erbB-2 expression and prognosis of gastric cancer: a meta-analysis. Genet Mol Res. (2015) 14:1782–7. doi: 10.4238/2015.March.13.5

PubMed Abstract | CrossRef Full Text | Google Scholar

99. Tatsuta M, Itoh T, Okuda S, Yamamura H, Baba M, Tamura H. Carcinoembryonic antigen in gastric juice as an aid in diagnosis of early gastric cancer. Cancer. (1980) 46:2686–92. doi: 10.1002/1097-0142(19801215)46:12<2686::AID-CNCR2820461225>3.0.CO;2-E

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Kim DY, Kim HR, Shim JH, Park CS, Kim SK, Kim YJ. Significance of serum and tissue carcinoembryonic antigen for the prognosis of gastric carcinoma patients. J Surg Oncol. (2000) 74:185–92. doi: 10.1002/1096-9098(200007)74:3<185::AID-JSO4>3.0.CO;2-0

CrossRef Full Text | Google Scholar

101. Best MG, Wesseling P, Wurdinger T. Tumor-educated platelets as a noninvasive biomarker source for cancer detection and progression monitoring. Cancer Res. (2018) 78: 3407–12. doi: 10.1158/0008-5472.CAN-18-0887

PubMed Abstract | CrossRef Full Text | Google Scholar

102. Pinedo HM, Verheul HM, D'Amato RJ, Folkman J. Involvement of platelets in tumour angiogenesis? Lancet. (1998) 352:1775–7. doi: 10.1016/S0140-6736(98)05095-8

PubMed Abstract | CrossRef Full Text | Google Scholar

103. Stone RL, Nick AM, McNeish IA, Balkwill F, Han HD, Bottsford-Miller J, et al. Paraneoplastic thrombocytosis in ovarian cancer. N Engl J Med. (2012) 366: 610–8. doi: 10.1056/NEJMoa1110352

PubMed Abstract | CrossRef Full Text | Google Scholar

104. Graziano V, Grassadonia A, Iezzi L, Vici P, Pizzuti L, Barba M, et al. Combination of peripheral neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio is predictive of pathological complete response after neoadjuvant chemotherapy in breast cancer patients. Breast. (2019) 44:33–8. doi: 10.1016/j.breast.2018.12.014

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. (2017) 111:176–81. doi: 10.1016/j.lungcan.2017.07.024

PubMed Abstract | CrossRef Full Text | Google Scholar

106. Asaoka T, Miyamoto A, Maeda S, Tsujie M, Hama N, Yamamoto K, et al. Prognostic impact of preoperative NLR and CA19-9 in pancreatic cancer. Pancreatology. (2016) 16: 434–40. doi: 10.1016/j.pan.2015.10.006

PubMed Abstract | CrossRef Full Text | Google Scholar

107. Dolcetti R, Viel A, Doglioni C, Russo A, Guidoboni M, Capozzi E, et al. High prevalence of activated intraepithelial cytotoxic T lymphocytes and increased neoplastic cell apoptosis in colorectal carcinomas with microsatellite instability. Am J Pathol. (1999) 154:1805–13. doi: 10.1016/S0002-9440(10)65436-3

PubMed Abstract | CrossRef Full Text | Google Scholar

108. De Andrade JP, Mezhir JJ. The critical role of peritoneal cytology in the staging of gastric cancer: an evidence-based review. J Surg Oncol. (2014) 110:291–7. doi: 10.1002/jso.23632

PubMed Abstract | CrossRef Full Text | Google Scholar

109. Nieveen van Dijkum EJ, Sturm PD, de Wit LT, Offerhaus J, Obertop H, Gouma DJ. Cytology of peritoneal lavage performed during staging laparoscopy for gastrointestinal malignancies: is it useful? Ann Surg. (1998) 228:728–33. doi: 10.1097/00000658-199812000-00002

CrossRef Full Text | Google Scholar

Keywords: biomarkers, gastric cancer, umbrella review, prognostic, survival

Citation: Zhou C, Zhong X, Song Y, Shi J, Wu Z, Guo Z, Sun J and Wang Z (2019) Prognostic Biomarkers for Gastric Cancer: An Umbrella Review of the Evidence. Front. Oncol. 9:1321. doi: 10.3389/fonc.2019.01321

Received: 16 August 2019; Accepted: 12 November 2019;
Published: 29 November 2019.

Edited by:

Sungsoo Park, Korea University, South Korea

Reviewed by:

Mehmet Akce, Emory University, United States
Bangshun He, Nanjing Medical University, China

Copyright © 2019 Zhou, Zhong, Song, Shi, Wu, Guo, Sun and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhenning Wang, josieon826@sina.cn

These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.