Correlation between p-STAT3 overexpression and prognosis in lung cancer: A systematic review and meta-analysis

OBJECTIVE: Previous studies have shown the correlation between p-STAT3 overexpression and prognosis in a variety of human tumors. However, their correlation in lung cancer remains controversial. We performed a systematic review and meta-analysis to explore the correlation between p-STAT3 overexpression and prognosis in lung cancer patients. METHODS: We searched PubMed, Embase, Web of Science, CNKI, VIP, and WanFang Data to identify relevant studies. Two reviewers independently screened the literature search results, extracted data, and assessed the methodological quality of the included studies. Then, meta-analysis was performed by using Review Manager 5.3 and STATA 14 software. A random-effect model was employed to evaluate all related pooled results. Statistical heterogeneity of each study was assessed by I(2). Publication bias was determined by funnel plot and the Begg’s or Egger’s tests. RESULTS: Eventually, 13 studies were included in present meta-analysis. Among these 13 studies, 8 studies were associated with the overall survival of lung cancer and 10 studies with other clinicopathological characteristics. The results of this meta-analysis suggested that p-STAT3 overexpression may be a poor prognosis biomarker in lung cancer (HR: 1.23; 95% CI: 1.04–1.46; P = 0.02). In terms of other clinicopathological characteristics, p-STAT3 overexpression was more frequent to advanced TNM stages ranging from III to IV (OR: 1.92; 95% CI: 1.13–3.27; P = 0.02) and lymphatic node metastasis (OR: 1.81; 95% CI: 1.20–2.72; P = 0.004). But, it was not associated with tumor differentiation (OR: 0.82; 95% CI: 0.44–1.53; P = 0.54). CONCLUSION: p-STAT3 overexpression has significant correlation with poorer overall survival of lung cancer patients, as well as with more advanced TNM stages and lymph node metastasis. Thus, it may serve a biomarker for poor prognosis in lung cancer. Nevertheless, our findings should be confirmed by large prospective studies.