Construction of a Prognostic Model for Cervical Cancer Related to lncRNA Based on Differential Co-expression Network and Functional Study of Key Gene EGFR-AS1.

ABSTRACT: Cervical cancer is a common gynecological malignancy, and the average age of onset is decreasing gradually. Therefore, an effective predictive model is urgently needed to improve the personalized treatment of cervical cancer patients. Long non-coding RNAs (lncRNAs) play crucial roles in the occurrence, development, and prognosis of malignant tumors. In this study, we used cervical cancer multi-omics data and single-cell sequencing data for analysis, and established a 33-lncRNA-CESC model by using the random forest algorithm in ensemble learning and mRNA and lncRNA co-expression network technology. The results demonstrated that the model exhibited strong discriminative ability, accuracy, and clinical utility. Furthermore, we investigated the relationship between the model and immune cell in

SUBMITTER: Du K 

PROVIDER: S-EPMC12036091 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature