A Model Comprising Four Cuproptosis-associated lncRNAs for Predicting the Immune Landscape and Prognosis in Cervical Cancer Patients

Abstract

Cuproptosis, a newly discovered process of copper-dependent cellular demise, is initiated by the direct interaction of Cu2+ with lipoylated components within the mitochondrial tricarboxylic acid (TCA) cycle. This mechanism hinders cellular respiration and influences carcinogenesis, angiogenesis, and metastasis. The specific role of cuproptosis-related long non-coding RNAs (CRLs) in cervical cancer remains poorly understood. This research developed a predictive model using CRLs and investigated its potential molecular roles in the tumor microenvironment, as well as its influence on clinical outcomes in cervical cancer. We initially assessed putative CRLs from TCGA cervical cancer transcriptome data by linking cuproptosis regulators with lncRNA expression using Pearson correlation analysis. From 188 differentially expressed lncRNAs, univariate Cox and LASSO regression analysis developed a four-CRL prognostic model consisting of AC096992.2, MKLN1-AS, BAIAP2-DT, and LINC02356. Patients were categorized into two groups, high-risk and low-risk, based on a computed risk score. Multivariate Cox analysis, which included clinicopathological factors, confirmed substantial survival differences among these groups. Additionally, distinct profiles of immune checkpoint markers and tumor-infiltrating immune cells were discerned between the two cohorts. Our CRL model serves as an independent predictive tool for cervical cancer, deepens our understanding of CRL-mediated carcinogenesis, and provides valuable insights for the development of novel therapeutic options.