ArticleDiscover oncology2025
Predictive value of MHC-related genes in cervical cancer: implications for immunotherapy and prognostic nomogram development.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundWhile cervical cancer (CC) is increasingly detected at earlier stages due to improved screening, clinical dilemmas persist in stratifying precancerous lesions and managing advanced disease. Major histocompatibility complex-related genes (MHCRGs), which regulate antigen presentation and immune surveillance, may serve as promising biomarkers for prognosis and therapeutic guidance in CC.
methodsWe identified MHCRGs via literature review and constructed a prognostic signature using LASSO and multivariate Cox regression based on TCGA-CESC data. Bioinformatic analyses assessed associations between the MHCRG signature, immune cell infiltration, immunotherapy response, and drug sensitivity. Validation was conducted using external GEO datasets (GSE29570, GSE63514), and further confirmed through qRT-PCR and Western blot in clinical CC samples. Functional assays, including gene knockdown in CC cell lines, were performed to explore the biological roles of key MHCRGs in tumor progression.
resultsA five-gene signature (CANX, HLA-DMB, HLA-DPB1, PSMB6, PSMB7) was identified as predictive of prognosis, immune infiltration, and therapeutic responsiveness. CANX was significantly upregulated, while HLA-DMB and HLA-DPB1 were downregulated in high-risk groups across all datasets and clinical tissues. qRT-PCR and WB results aligned with bioinformatic predictions. Functional experiments demonstrated that silencing CANX suppressed CC cell proliferation and invasion. These findings support the oncogenic role of CANX and the immune-regulatory potential of the other MHCRGs. The MHCscore remained an independent prognostic factor in multivariate analysis.
conclusionsWe propose a robust MHCRG-based gene signature, validated through both bioinformatic analyses and in vitro functional experiments. These findings offer translational potential for improving risk stratification and guiding immunotherapy in CC patients.
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