BMC cancer · 2026
PDCD1 gene polymorphisms linked with altered risk of female-specific hormone-dependent cancers: a systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Zidi Sabrina, BenAbdelmoumenMardassi Boutheina, Sahraoui Ghada, Almawi Wassim Y
Аннотация
BACKGROUND: The programmed cell death-1 (PDCD1) gene plays a critical role in immune regulation and tumor immune evasion, making it a key target in cancer immunotherapy. Genetic variants in PDCD1, including rs2227981, rs2227982, rs7421861, and rs11568821, may influence susceptibility to female-specific cancers. This systematic review and meta-analysis aimed to evaluate the association of these variants with the risk of breast, cervical, ovarian, and endometrial cancers across ethnic populations.
METHODS: A systematic literature search was conducted in PubMed, Scopus, Web of Science, and Google Scholar for studies published up to January 2025. Eligible studies were case-control in design and reported genotypic or allelic frequencies. Risk of bias was assessed using the Newcastle-Ottawa Scale. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated under dominant, recessive, and allelic genetic models. Heterogeneity was evaluated using the I² statistic. Publication bias was assessed using funnel plots, Egger's test, and Begg's test. Sensitivity analyses were performed to evaluate the robustness of the results.
RESULTS: Eleven case-control studies were included, comprising 3534 breast cancer, 1468 cervical cancer, 1160 ovarian cancer, and 873 endometrial cancer cases, each with corresponding controls. The rs2227981 C allele and rs2227982 T allele were significantly associated with increased risk of female-specific cancers (p < 0.0001). Similarly, rs7421861 and rs11568821 showed significant associations across multiple genetic models. Heterogeneity ranged from low to high (I² = 19.5%-84%). No significant publication bias was detected (Egger's test p = 0.586; Begg's test p = 0.548). However, variability between studies and limited sample sizes should be considered when interpreting the findings.
CONCLUSIONS: PDCD1 gene polymorphisms are associated with susceptibility to female-specific cancers, underscoring the importance of genetic variability in immune regulation and oncogenesis. Further large-scale and functional studies are needed to validate these findings and to explore gene-environment interactions.
METHODS: A systematic literature search was conducted in PubMed, Scopus, Web of Science, and Google Scholar for studies published up to January 2025. Eligible studies were case-control in design and reported genotypic or allelic frequencies. Risk of bias was assessed using the Newcastle-Ottawa Scale. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated under dominant, recessive, and allelic genetic models. Heterogeneity was evaluated using the I² statistic. Publication bias was assessed using funnel plots, Egger's test, and Begg's test. Sensitivity analyses were performed to evaluate the robustness of the results.
RESULTS: Eleven case-control studies were included, comprising 3534 breast cancer, 1468 cervical cancer, 1160 ovarian cancer, and 873 endometrial cancer cases, each with corresponding controls. The rs2227981 C allele and rs2227982 T allele were significantly associated with increased risk of female-specific cancers (p < 0.0001). Similarly, rs7421861 and rs11568821 showed significant associations across multiple genetic models. Heterogeneity ranged from low to high (I² = 19.5%-84%). No significant publication bias was detected (Egger's test p = 0.586; Begg's test p = 0.548). However, variability between studies and limited sample sizes should be considered when interpreting the findings.
CONCLUSIONS: PDCD1 gene polymorphisms are associated with susceptibility to female-specific cancers, underscoring the importance of genetic variability in immune regulation and oncogenesis. Further large-scale and functional studies are needed to validate these findings and to explore gene-environment interactions.