Abstract

Background:Human endogenous retrovirus-H long terminal repeat-associating protein 2 (HHLA2), a newly discovered member of the B7 family, is overexpressed in numerous tumors. However, the prognostic impact of HHLA2 in human cancers remains controversial. Thus, we performed this meta-analysis to explore the prognostic value of HHLA2 in Chinese patients with solid tumors.Methods:PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure, and WanFang databases were systematically searched for eligible studies that evaluated the impact of HHLA2 on overall survival (OS) in patients with cancer. Hazard ratios (HRs) and 95% confidence intervals (CIs) were combined to evaluate the association between HHLA2 expression and OS in solid tumors. Odds ratios (ORs) and 95% CIs were pooled to assess the correlation between HHLA2 expression and clinicopathological characteristics in solid tumors.Results:A total of 12 studies, including 15 cohorts and 1747 patients, were included in this meta-analysis. We found that high HHLA2 expression was significantly associated with shorter OS (HR = 1.65, 95% CI: 1.12–2.43). Subgroup analysis by cancer type demonstrated that high HHLA2 expression was associated with poor OS in patients with clear cell renal cell carcinoma (HR = 3.42, 95% CI: 2.39–4.91), gastric cancer (HR = 2.03, 95% CI: 1.31–3.16), intrahepatic cholangiocarcinoma (HR = 1.77, 95% CI: 1.24–2.53), lung cancer (HR = 2.14, 95% CI: 1.33–3.44) and other cancer types (HR = 2.08, 95% CI: 1.34–3.24), but not in patients with epithelial ovarian cancer (HR = 0.52, 95% CI: 0.08–3.56). Nevertheless, high HHLA2 expression was associated with better OS in patients with pancreatic ductal adenocarcinoma (HR = 0.45, 95% CI: 0.32–0.64). Furthermore, high HHLA2 expression was associated with old age (OR = 1.30, 95% CI: 1.03–1.63), lymph node metastasis (OR = 1.99, 95% CI: 1.41–2.81), and vascular invasion (OR = 1.69, 95% CI: 1.18–2.42).Conclusions:HHLA2 may serve as a potential prognostic biomarker for solid tumors in Chinese population, by predict the prognosis of cancer patients based on their tumor types.

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