Application and clinical translational value of a predictive model based on N-methylguanosine-related long non-coding RNAs in cervical squamous cell carcinoma.

Cervical squamous cell carcinoma (CSCC) is one of the most common gynecological malignancies affecting women globally. The present study aimed to develop a predictive model based on N-methylguanosine-related long non-coding RNAs (lncRNAs) to evaluate risk stratification, analyze immune infiltration and guide the selection of sensitive drugs in CSCC. Pearson's correlation, univariate Cox and Least Absolute Shrinkage and Selection Operator regression analyses of transcriptome data from The Cancer Genome Atlas and the Genotype-Tissue Expression database were conducted to construct a prognostic risk prediction model for CSCC. The stability of the model was tested before evaluating its prognostic value in CSCC. Further analysis of enrichment, immune infiltration and drug resistance provided directions for clinical translation. The lncRNAs used to construct the model were validated using reverse transcription-quantitative PCR. The developed predictive model was stable and may hold notable clinical translational value for immunotherapy and drug selection in CSCC in the future.