Association of Elevated Yes-Associated Protein Expression with Gastric Cancer and Its Clinicopathological Features: A Meta-Analysis

Objectives: To evaluate the difference of YAP-positive expression between GC and adjacent tissues, as well as the association of elevated YAP expression with clinicopathological features of GC. Methods: PubMed, Embase, Web of Science databases and the Chinese National Knowledge Infrastructure (CNKI) were searched from inception up to December 2018. The pooled ORs and corresponding 95% CIs were used to assess the strength of association. The heterogeneity among eligible studies was evaluated by the Q-test and I values. The sensitivity analysis was performed by sequential omission of individual studies. Moreover, Begg’s test and Egger’s test were used to evaluate publication bias. Results: A total of 2229 patients from 16 studies were included in this meta-analysis. The results showed that positive YAP expression was closely correlated with GC but not adjacent non-tumor tissue (OR = 8.08, 95% CI = 4.41 14.80). Additionally, YAP overexpression was found to be associated with more advanced TNM stage (OR = 2.68, 95% CI = 1.61 4.48), deeper invasion depth (OR = 2.05, 95% CI = 1.32 3.19), and lymph node metastasis (OR = 1.95, 95% CI = 1.29 2.96). No significant correlation was observed between YAP overexpression and degree of differentiation (OR = 1.17, 95% CI = 0.63 2.16), as well as gender of patients (OR = 1.12, 95% CI = 0.91 1.37) or tumor size (OR = 1.11, 95% CI = 0.82 1.49) of gastric cancer. Conclusions: This meta-analysis demonstrated that YAP might be a promising diagnostic marker and even a therapeutic target for gastric cancer.


Introduction
Gastric cancer (GC) is one of the most common malignant tumors in the digestive system. It has become the third leading cause of cancer death worldwide.
Over 70% of GC cases occur in developing countries, and roughly half of the world's total occurs in eastern Asia (chiefly in China) [1]. Most GC patients present with advanced tumor due to the inconspicuous symptoms of early onset GC and the limited diagnostic conditions, making the sufferers lose the optimal opportunity for radical cure [2]. For these patients, systemic treatments, such as chemotherapy, are the main treatment option. Although great progress has been made for advanced GC chemotherapy in recent years, mortality is still unacceptably high. Therefore, searching for ideal diagnostic biomarkers and novel therapeutic targets remains critical for the treatment of GC.
Hippo signaling pathway is an evolutionarily conserved regulator for organ size control and tissue growth. Accumulating literature suggests that dysregulation of Hippo pathway leads to proliferation and anti-apoptosis associated with increased cancer risk [3] [4] [5]. As a pivotal downstream effector of Hippo signal cascade, Yes-associated protein (YAP) was considered as an oncoprotein, and its overexpression and accumulation in the nucleus were closely related to the poor clinical outcomes of various tumors including gastric cancer [6] [7]. Moreover, YAP was even reported as a potential target for GC therapy [8]. Nevertheless, the published clinical studies showed the data were still controversial, and the opposite role of YAP in GC was also reported [9]. Therefore, we conducted this meta-analysis to comprehensively assess the relationship between YAP overexpression and gastric cancer.

Search Strategy
We performed a systematic literature search in PubMed, Embase, Web of Science databases and the Chinese National Knowledge Infrastructure (CNKI) from inception up to December 2018. Relevant studies were identified using a combination of the following terms: "YAP" or "Yes-associated protein" or "Yes protein" or "Hippo" and "gastric cancer" or "gastric carcinoma" or "gastric neoplasm" or "stomach cancer" or "GC". To availably identify relevant studies, we also manually searched for references cited in the eligible articles. When two studies had partial overlaps, both studies should be considered.

Inclusion and Exclusion Criteria
The eligible literature in this study fulfill the following inclusion criteria: 1) patients were diagnosed as gastric cancer; 2) YAP expression was quantified by immunohistochemistry or other adequate methods; 3) sufferers were categorized into high YAP (or YAP-positive) and low YAP (or YAP-negative) groups; 4) the association between YAP expression and clinicopathological features was described, or YAP expression in human tumor and adjacent tissues was detected;

Data Extraction
Required data were extracted by two investigators independently based on the inclusion criteria listed above. Any discrepancies in data extraction were evaluated by discussion to reach a consensus. The extracted data included the first author's name, year of publication, country of origin, distribution of gender and age in patients, number of patients, staining location, method of detection, YAP expression in gastric cancer and adjacent normal tissue, and clinicopathological features.

Assessment of Study Quality
Two investigators independently evaluated the quality of each study according to Newcastle-Ottawa Scale (NOS) [10]. The NOS includes three parameters of quality for studies: selection of the study population, comparability of subjects, and exposure assessment, with scores ranging from 0 to 9 (Additional file 1: Table S1).
NOS scores > 5 was considered as high-quality studies.

Statistical Analysis
We implemented the meta-analysis based on the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Checklist (Additional file 2: Table S2). The odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were used to assess the association between YAP expression and gastric cancer or the clinicopathological features of gastric cancer. The heterogeneity among eligible studies was evaluated by the Q-test. P-value < 0.1 indicated that the heterogeneity was significant. I 2 values were also calculated to quantify the heterogeneity: I 2 < 25%, 25% < I 2 < 50%, 50% < I 2 < 75%, and I 2 > 75%, indicated no heterogeneity, moderate heterogeneity, large heterogeneity, and extreme heterogeneity, respectively. When P-value > 0.1 and I 2 < 25%, the heterogeneity was considered not significant, and then the pooled OR and 95% CI were assessed by the fixed-effects model; otherwise, the random-effects model was performed [11].
The sensitivity analysis was carried out by sequential omission of individual studies to test the stability of meta-analysis results. Moreover, Begg's test and Egger's test were used to evaluate the publication bias; P-value < 0.05 indicated the presence of publication bias. All statistical analyses were performed using the software STATA version 12.0 (Stata Corporation, College Station, TX, USA).

Study Characteristics
The flow chart of the study selection process was presented in Figure 1  According to the inclusion and exclusion criteria, data from 16 studies including 2229 patients were included in this meta-analysis [9] [12]- [26]. As shown in Table   1 (Figure 2(a)).

Association of YAP Overexpression with GC Clinicopathological Features
The main results of meta-analysis and heterogeneity test for the association study of YAP overexpression with gastric cancer clinicopathological features were summarised in Table 3. The elevated YAP expression was correlated with more advanced TNM stage (OR = 2.68, 95% CI = 1.61 -4.48) (Figure 3(a)), deeper invasion depth (OR = 2.05, 95% CI = 1.32 -3.19) (Figure 3

Sensitivity Analysis
We conducted sensitivity analysis by sequential omission of individual studies to probe the change in the odds ratio and 95% confidence interval of meta-analysis.
As shown in Figure 2

Publication Bias
Begg-Mazumdar adjusted rank correlation test and Egger's regression test were performed to assess the publication bias. The results showed that the shape of Begg's funnel plots appeared to be symmetrical (data not shown). Meanwhile, the P-values were all greater than 0.05 in both Begg's test and Egger's test (Table 3).
These results suggested the absence of significant publication bias in the overall meta-analysis.

Discussion
Gastric cancer is characteristic of poor prognosis and high death rate, and there is still a great need to identify diagnostic markers as well as develop novel therapeutic strategies for GC therapy. Hippo pathway regulates tissue growth and organ size via YAP-TEAD complex. Inactivation of Hippo cascade leads to the elevated expression and nucleus accumulation of YAP, which is significantly associated with poor clinical outcomes of most cancers. Therefore, Hippo cascade acts as a tumor suppressor pathway, and YAP is considered to be an oncoprotein in multiple cancers including GC. There are many published clinical data supporting this conclusion. Zhang reported that YAP was strongly expressed in GC, and knockdown of YAP could inhibit the proliferation and metastasis of GC cells [27]. The similar results could also be observed in the study of Zhou and his colleagues [28]. Moreover, Yan showed that YAP acted as a tumor promoter in gastric cancer, and involved in the survival and migration of GC cells through the activation of the SIRT1/Mfn2/mitochondrial autophagy axis [29]. However, there are still inconsistent results published. Suh and colleagues supported that YAP functioned as a tumor suppressor in GC [9]. Zhang also indicated that there Q. Peng et al. was no significant correlation between YAP expression and clinicopathological characteristics in GC, and YAP was not a potential marker for diagnosis or prognosis of GC [14]. In this account, a meta-analysis including 16 studies was performed to comprehensively evaluate the relevance of elevated YAP expression with GC and its pathological parameters. The results of pooled data showed a significant correlation between positive YAP expression and GC, but not adjacent non-tumor tissue. Furthermore, the elevated YAP expression in GC was closely related to more advanced TNM stage, deeper invasion depth and lymph node metastasis. The heterogeneity and several limitations of this meta-analysis should be acknowledged. First, all of the eligible studies in this meta-analysis were carried out in Asian population, therefore, it might be insufficient to provide support for other ethnic groups. Second, the evaluation criteria of positive YAP expression differed among included studies, which might influence the results of pooled data and contribute to the heterogeneity. Third, the data we extracted contain both YAP nuclear staining and overall YAP expression, the difference in staining location is also a potential heterogeneous source due to the expression characteristics of YAP in different cell states.
In conclusion, this meta-analysis demonstrated that YAP-positive expression in gastric cancer was significantly higher than that in adjacent non-tumor tissues. Additionally, the overexpression of YAP was closely correlated with more advanced TNM stage, deeper invasion depth and lymph node metastasis. Therefore, YAP may be a promising diagnostic marker and even a therapeutic target for GC. However, the results of this study should be interpreted cautiously due to the existence of heterogeneity and limitations. Hence, well-designed prospective studies based on larger sample sizes, as well as the corresponding basic research are still warranted to validate the present findings.  Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

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Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.
2 Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
2 Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

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Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

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Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

Risk of bias in individual studies
12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

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Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means).

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Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

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Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

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Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

Study selection
17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. Table 1 Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12).
4 Table 3 Journal of Biosciences and Medicines

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Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot. Table 3  Figure 3 & 4  3-4 Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. Table 2  Figure 2  4 Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 4 Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

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Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

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Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).