Identification of cervical squamous cell carcinoma feature genes and construction of a prognostic model based on immune-related features

As heterogeneity of cervical squamous cell carcinoma (CSCC), prognosis assessment for CSCC patients remain challenging. To develop novel prognostic strategies for CSCC patients, associated biomarkers are urgently needed. This study aimed to cluster CSCC samples from a molecular perspective. CSCC expression data sets were obtained from The Cancer Genome Atlas and based on the accessed expression profile, a co-expression network was constructed with weighted gene co-expression network analysis to form different gene modules. Tumor microenvironment was evaluated using ESTIMATE algorithm, observing that the brown module was highly associated with tumor immunity. CSCC samples were clustered into three subtypes by consensus clustering based on gene expression profiles in the module. Gene set variation analysis showed differences in immune-related pathways among the three subtypes. CIBERSORT and single-sample gene set enrichment analysis analyses showed the difference in immune cell infiltration among subtype groups. Also, Human leukocyte antigen protein expression varied considerably among subtypes. Subsequently, univariate, Lasso and multivariate Cox regression analyses were performed on the genes in the brown module and an 8-gene prognostic model was constructed. Kaplan-Meier analysis illuminated that the low-risk group manifested a favorable prognosis, and receiver operating characteristic curve showed that the model has good predictive performance. qRT-PCR was used to examine the expression status of the prognosis-associated genes. In conclusion, this study identified three types of CSCC from a molecular perspective and established an effective prognostic model for CSCC, which will provide guidance for clinical subtype identification of CSCC and treatment of patients.