Zhang Jun, Bao Yingna, Yu Zhilong, Lin Yu
State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot, Inner Mongolia Autonomous Region 010050, P.R. China.
Department of Biochemistry and Molecular Biology, School of Life Sciences, Inner Mongolia University, Hohhot, Inner Mongolia Autonomous Region 010050, P.R. China.
Oncol Lett. 2025 May 14;30(1):341. doi: 10.3892/ol.2025.15087. eCollection 2025 Jul.
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.
宫颈鳞状细胞癌（CSCC）是全球影响女性的最常见妇科恶性肿瘤之一。本研究旨在开发一种基于N-甲基鸟苷相关长链非编码RNA（lncRNA）的预测模型，以评估风险分层、分析免疫浸润并指导CSCC中敏感药物的选择。对来自癌症基因组图谱和基因型-组织表达数据库的转录组数据进行Pearson相关性分析、单变量Cox分析以及最小绝对收缩和选择算子回归分析，以构建CSCC的预后风险预测模型。在评估该模型对CSCC的预后价值之前，先对其稳定性进行了测试。进一步的富集分析、免疫浸润分析和耐药性分析为临床转化提供了方向。使用逆转录定量PCR对用于构建模型的lncRNA进行了验证。所开发的预测模型具有稳定性，未来可能对CSCC的免疫治疗和药物选择具有显著的临床转化价值。
