Identification of a 26-lncRNAs Risk Model for Predicting Overall Survival of Cervical Squamous Cell Carcinoma Based on Integrated Bioinformatics Analysis

Abstract

As a common malignancy in women, cervical squamous cell carcinoma is a major cause of cancer-related mortality globally. Recent studies have demonstrated that long non-coding RNA (lncRNA) can function as potential biomarkers in cancer prognosis; however, little is known about its role in cervical cancer. In this study, we downloaded the gene expression profiles along with the clinical data of patients with cervical squamous cell carcinoma from The Cancer Genome Atlas. By applying bioinformatics analysis including random forest selection and Least Absolute Shrinkage and Selection Operator (LASSO) cox regression model along with 10-fold cross-validation, we constructed a 26-lncRNAs risk model that can be used to predict the overall survival of cervical squamous cell carcinoma. After that, Kaplan–Meier analysis combined with log-rank p test was applied to assess the predictive accuracy of the 26-lncRNAs risk model. Further analysis showed that the prognostic value of 26-lncRNAs risk model was independent of other clinicopathological factors. At last, lncRNAs in the model were put into gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways analysis, which suggested that these lncRNAs might contribute to cancer-associated processes such as cell cycle and apoptosis. This study indicated that lncRNAs signature could be a useful marker to predict the prognosis of cervical squamous cell carcinoma.