Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

Shiyan Li,Cong Feng,Na Qi,Fengjuan Han,Qingling Wang,Liyang Wen,Jia Li

doi:10.1186/s12957-021-02384-2

Abstract

AimThis study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer.MethodsBased on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve.ResultsWe screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable.ConclusionsThis study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

Highlights

Cervical cancer is one of the most common tumors in gynecology with high incidence and mortality rate, ranking fourth in the world for common female malignancies [1, 2]
Screening DEGs Based on the The Cancer Genome Atlas (TCGA) and GTEx databases, 1265 differentially expressed genes were obtained using the limma package in R with FDR < 0.05 and |logFC| > 1, of which 620 were downregulated and 645 were upregulated in cervical cancer (Fig. 1A, B)
For GO-biological process (BP) analysis, downregulated DEGs were markedly enriched in mitosis chromosomes segregation, sister chromatid segregation, and organelle fission

Summary

Introduction

Cervical cancer is one of the most common tumors in gynecology with high incidence and mortality rate, ranking fourth in the world for common female malignancies [1, 2]. Surgical treatment is only suitable for early-stage patients, and postoperative radiotherapy will cause irreversible damage to the ovaries and uterus [5, 6]. Emerging therapies such as targeted therapy and immunotherapy have become a hot spot in cervical cancer research [7], and new diagnostic molecular markers and therapeutic targets are still to be discovered. During the last two decades, the advances of markers screened by high throughput genome sequencing and bioinformatics analysis received more and more attention [10]. The analysis of gene function, pathway and interaction is the basis of molecular targeted therapy

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