Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients

Xiao Huo; Xiaoshuang Zhou; Peng Peng; Mei Yu; Ying Zhang; Jiaxin Yang; Dongyan Cao; Hengzi Sun; Keng Shen

doi:10.2147/OTT.S276553

Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients

Onco Targets Ther. 2021 Feb 5:14:809-822. doi: 10.2147/OTT.S276553. eCollection 2021.

Authors

Xiao Huo¹, Xiaoshuang Zhou^{2

3}, Peng Peng², Mei Yu², Ying Zhang², Jiaxin Yang², Dongyan Cao², Hengzi Sun⁴, Keng Shen²

Affiliations

¹ Medical Research Center, Peking University Third Hospital, Beijing,, People's Republic of China.
² Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
³ Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Beijing, People's Republic of China.
⁴ Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China.

Abstract

Background: Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary.

Methods: Samples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed.

Results: We obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, and DPAGT1 and low expression of CCL17 and DES were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: p ˂ 0.001, AUC = 0.82; testing set: p ˂ 0.01, AUC = 0.59).

Conclusion: Our study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment.

Keywords: Gene Expression Omnibus; bioinformatics; cervical cancer; overall survival; prognostic signature.

Grants and funding

This study was funded by the National Natural Science Foundation of China [81402140] and [CAMS Innovation Fund for Medical Sciences (CIFMS) CAMS-2017-I2M-1-002] (Shen Keng).