Construction of a prognostic model based on eight ubiquitination-related genes via machine learning and potential therapeutics analysis for cervical cancer

Aug 5, 2026

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Introduction and objective: Ubiquitination participates in multiple biological processes, yet its ability to predict cervical cancer prognosis remains unclear. Methods: Ubiquitination-related genes were collected from established resources, differential expression between normal and tumor tissues was identified, and prognostic associations were screened with univariate and multivariate Cox analyses. Machine learning selected a robust gene set, followed by substrate prediction and functional pathway analyses, plus therapeutic compound identification. Results and conclusion: An eight-gene ubiquitination prognostic signature stratified patients into high- and low-risk groups with distinct overall survival, suggesting new biomarker guidance and drug development directions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Methods

Differential expression and survival screening

Machine-learning gene signature construction and validation

Substrate prediction and functional analysis

Results

Differentially expressed ubiquitination-related genes

Eight-gene prognostic signature and risk stratification

Functional pathway and compound prediction

Conclusion

TYPE Original Research PUBLISHED 14 March 2023 DOI 10.3389/fgene.2023.1142938 OPEN ACCESS EDITED BY Ming Jun Zheng, Ludwig Maximilian University of Munich, Germany REVIEWED BY Guohua Zhang, University of Texas Health Science Center at Houston, United States Xiao Li, Women ’s Hospital School of Medicine Zhejiang University, China S L, Huazhong University of Science and Technology, China *CORRESPONDENCE Wenjing Zhang, [zhangwenjing2987@sina.com](zhangwenjing2987@sina.com) SPECIALTY SECTION This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics RECEIVED 12 January 2023 ACCEPTED 01 March 2023 PUBLISHED 14 March 2023 CITATION Hao Y, Guy MM, Liu Q, Li R, Mao Z, Jiang N, Wang B, Cui B and Zhang W (2023), Construction of a prognostic model based on eight ubiquitinationrelated genes via machine learning and potential therapeutics analysis for cervical cancer. Front. Genet. 14:1142938 . doi: 10.3389/fgene.2023.1142938 COPYRIGHT © 2023 Hao, Guy, Liu, Li, Mao, Jiang, Wang, Cui and Zhang. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Construction of a prognostic model based on eight ubiquitination-related genes via machine learning and potential therapeutics analysis for cervical cancer Yiping Hao, Mutangala Muloye Guy, Qingqing Liu, Ruowen Li, Zhonghao Mao, Nan Jiang, Bingyu Wang, Baoxia Cui and Wenjing Zhang* Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, China Introduction: Ubiquitination is involved in many biological processes and its predictive value for prognosis in cervical cancer is still unclear. Methods: To further explore the predictive value of the ubiquitination-related genes we obtained URGs from the Ubiquitin and Ubiquitin-like Conjugation Database, analyzed datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases, and then selected differentially expressed ubiquitination-related genes between normal and cancer tissues. Then, DURGs signiﬁcantly associated with overall survival were selected through univariate Cox regression. Machine learning was further used to select the DURGs. Then, we constructed and validated a reliable prognostic gene signature by multivariate analysis. In addition, we predicted the substrate proteins of the signature genes and did a functional analysis to further understand the molecular biology mechanisms. The study provided new guidelines for evaluating cervical cancer prognosis and also suggested new directions for drug development. Results: By analyzing 1,390 URGs in GEO and TCGA databases, we obtained 175 DURGs. Our results showed 19 DURGs were related to prognosis. Finally, eight DURGs were identiﬁed via machine learning to construct the ﬁrst ubiquitination prognostic gene signature. Patients were stratiﬁed into highrisk and low-risk groups and the prognosis was worse in the high-risk group. In addition, these gene protein levels were mostly consistent with their transcript level. According to the functional analysis of substrate proteins, the signature genes may be involved in cancer development through the transcription factor activity and the classical P53 pathway ubiquitination-related signaling pathways. Additionally, 71 small molecular compounds were identiﬁed as potential drugs. Conclusion: We systematically studied the inﬂuence of ubiquitination-related genes on prognosis in cervical cancer, established a prognostic model through a machine learning algorithm, and veriﬁed it. Also, our study provides a new treatment strategy

FAQs

What is the main goal of the study on cervical cancer?

To build and validate a prognostic model based on ubiquitination-related genes and evaluate potential therapeutic directions.

How were ubiquitination-related genes selected for the prognostic signature?

Genes were gathered from a ubiquitin-related database, differentially expressed between normal and cancer tissues were identified, prognostic relevance was assessed using Cox regression, and machine learning was used to select the final gene set.

How does the model evaluate patient prognosis?

Patients are stratified into high-risk and low-risk groups according to the eight-gene signature, and the study reports worse overall survival in the high-risk group.

Aug 5, 2026

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