Translational oncology · 2026
Multi-omics integration identifies prognostic genes linked to adaptive immunity response and radiosensitivity in cervical cancer.
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Quan Rui, Wang Jinbao, Cheng Guanghui
Аннотация
BACKGROUND: Radiotherapy is central to cervical cancer (CC) treatment, yet radiosensitivity remains a key determinant of prognosis. Although adaptive immune response (AIR) influences tumor progression, its role in modulating CC radiosensitivity and the underlying molecular mechanisms remain largely unexplored. This study aims to elucidate the link between AIR and radiosensitivity, and to identify novel therapeutic targets.
METHODS: Multi-omics data were integrated to identify AIR-related prognostic genes via differential expression, weighted gene co-expression network analysis (WGCNA), univariate Cox, and least absolute shrinkage and selection operator (LASSO) analyses. A risk model was constructed and validated. Functional enrichment, immune infiltration, and drug sensitivity were assessed. Clinical validation used immunohistochemistry, and a radioresistant cell line was established. The expression levels of the key genes were measured by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blotting; colony formation assays evaluated radiosensitivity following their knockdown.
RESULTS: Two genes (ITM2A and TCL1A) were identified to construct a prognostic risk model. High-risk groups exhibited distinct immune pathways, reduced immune infiltration, and altered drug sensitivity. Both genes correlated with mast cell differentiation. Notably, ITM2A and TCL1A were downregulated in radioresistant phenotypes, and their knockdown significantly enhanced post-irradiation colony formation versus controls, indicating a radiosensitizing role.
CONCLUSIONS: ITM2A and TCL1A are identified as novel AIR-related radiosensitivity genes. Our risk model may serve as a potential prognostic stratification tool for CC, offering insights into combinatorial immunotherapy and radiosensitization strategies.
METHODS: Multi-omics data were integrated to identify AIR-related prognostic genes via differential expression, weighted gene co-expression network analysis (WGCNA), univariate Cox, and least absolute shrinkage and selection operator (LASSO) analyses. A risk model was constructed and validated. Functional enrichment, immune infiltration, and drug sensitivity were assessed. Clinical validation used immunohistochemistry, and a radioresistant cell line was established. The expression levels of the key genes were measured by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blotting; colony formation assays evaluated radiosensitivity following their knockdown.
RESULTS: Two genes (ITM2A and TCL1A) were identified to construct a prognostic risk model. High-risk groups exhibited distinct immune pathways, reduced immune infiltration, and altered drug sensitivity. Both genes correlated with mast cell differentiation. Notably, ITM2A and TCL1A were downregulated in radioresistant phenotypes, and their knockdown significantly enhanced post-irradiation colony formation versus controls, indicating a radiosensitizing role.
CONCLUSIONS: ITM2A and TCL1A are identified as novel AIR-related radiosensitivity genes. Our risk model may serve as a potential prognostic stratification tool for CC, offering insights into combinatorial immunotherapy and radiosensitization strategies.