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A three-gene radioresistance signature predicts tumor progression in cervical cancer
Hong Yu1, Yuting Xiu2, Fanxu Meng2
1Jilin Province Institute of Cancer Prevention and Treatment, Jilin Cancer Hospital, Changchun, China.
Background:
As a primary curative treatment for locally advanced cervical cancer, radiotherapy is frequently undermined by radioresistant tumor cells that evade cell death and subsequently drive post-treatment tumor progression. This study aimed to identify candidate genes associated with radioresistance in cervical cancer and to explore their potential in predicting unfavorable outcomes among radioresistant patients, thereby providing a reference for future research.
Methods:
We screened for co-expressed genes using transcriptomic data from radiation non-complete response (NCR) cervical cancer patients in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analyses were conducted to identify the most significant radioresistance-associated genes for constructing a prognostic model. The predictive performance of this model was further validated through logistic regression, weighted gene co-expression network analysis (WGCNA), and pan-cancer analyses. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) was performed to quantify the expression levels of key genes in cervical cancer tissue samples from radiosensitive and radioresistant patients.
Results:
The resulting prognostic model comprised three genes: MTMR11, VANGL1, and CD46. This gene panel was significantly associated with the prognosis of cervical cancer patients receiving radiotherapy and showed acceptable predictive performance across multiple cancer types. qRT-PCR analysis revealed that the expression patterns of MTMR11 and VANGL1 were generally consistent with radioresistance of cervical cancer, whereas CD46 exhibited an unexpected expression trend.
Conclusions:
Our findings indicate that MTMR11, VANGL1, and CD46 are associated with radioresistance and prognosis in cervical cancer. Their potential clinical utility, especially in predicting radiotherapy response at the individual patient level, requires further validation in larger, independent, and prospective cohorts.
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