Cervical cancer molecular subtype identification and prognosis classification by a metabolism-related gene expression

The high molecular phenotype heterogeneity of cervical cancer (CC) is the main focus of individualized therapy. Molecular classification may lead to personal treatment and new drug discovery. We summarized the molecular features by establishing a new classification of metabolism-related gene expression profiles.

Clinical information and messenger ribonucleic acid (mRNA) expression were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Twenty-two immune cells were detected by CIBERSORT method. K-means clustering algorithm based on 258 metabolism-related genes was used for CC classification. Univariate and multivariate Cox regression analyses were carried out to find out the optimal metabolism-related genes. A predictive model was established to evaluate the overall survival (OS) of patients. Then, a nomogram model was established to predict the OS of patients based on independent prognostic factors.

Based on the expression profiles of 258 survival-related metabolic genes, we identified two metabolism-related subtypes of CC. Cluster_A subtype was characterized with significant glucose metabolism, and had a poor prognosis; and cluster_B subtype exhibited high enrichment of lipid metabolism-related and immune-related signaling pathways. Then, seven metabolism-related genes (CYP4F12, NPL, CH25H, NOS2, SDR16C5, PGK1 and LYZ) were used to establish a metabolism-related risk signature. Patients in high risk groups had a worse prognosis than those in low risk group. Multivariate Cox regression analysis indicted that the metabolism-related risk signature could predict OS as an independent prognostic factor.

Our study provides new insight into the metabolic heterogeneity of CC and its relationship with immune landscape. The novel metabolism-related gene signature is an effective potential prognostic signature in the individualized prognosis prediction of CC.