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ORIGINAL RESEARCH article
Front. Genet.
Sec. Epigenomics and Epigenetics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1524821
Comprehensive analysis of mRNA and lncRNA expression for predicting lymph node metastasis in cervical cancer: A novel 7-gene signature approach
Provisionally accepted- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital., Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
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Objective Lymph node metastasis (LNM) critically determines recurrence and survival in cervical cancer (CC), yet current imaging-based methods lack accuracy. Retroperitoneal lymph node dissection brings many adverse events. This study aimed to develop a clinically actionable molecular signature to predict LNM, enabling personalized surgical planning and improved patient outcomes. Methods Transcriptome profiles and clinical data from 193 CC patients, encompassing information on LNM from The Cancer Genome Atlas (TCGA) and an external cohort (GSE26511), were analyzed. The differential expression of mRNAs and lncRNAs was identified using DESeq2. Subsequently, dual machine learning strategies—LASSO regression and the Boruta algorithm—were applied to select robust biomarkers. Finally, the 7-mRNA-lncRNA gene cluster was verified in tumor tissues of CC patients with and without LNM using qRT-PCR.Results The 7-mRNA-lncRNA gene cluster included 4 mRNAs (ART3, HRG, MAPT, and SYTL5) and 3 lncRNAs (AC011239.1, AC125616.1, and RUVBL1.AS1). The expression patterns of the seven DEGs align with their levels in cervical cancer (CC) tissues. The signature demonstrated high predictive accuracy (AUC: 0.855 in training and 0.807 in testing cohorts). External validation using the GSE26511 dataset confirmed its clinical applicability (AUC: 0.611). Patients with high LNM scores exhibited poorer survival outcomes compared to those with low LNM scores (p=0.0034). Conclusions We constructed a reliable prediction model of LNM in CC patients with a 7 mRNA-lncRNA gene cluster. This model guides lymphadenectomy decisions, reduces overtreatment, and enhances patient survival. Our work bridges molecular insights with clinical practice and provides a foundation for further research into the management of CC.
Keywords: cervical cancer, lymph node metastasis, mRNA, lncRNA, Prediction model
Received: 08 Nov 2024; Accepted: 07 Apr 2025.
Copyright: © 2025 Wei, Wang and Wu. This is an open-access 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) or licensor 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.
* Correspondence: Yumei Wu, Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital., Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.