ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Metabolism
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1690077
This article is part of the Research TopicAdvances in Biomarkers and Drug Targets: Harnessing Traditional and AI Approaches for Novel Therapeutic MechanismsView all 8 articles
Mitochondrial Non-coding RNAs as Novel Biomarkers and Therapeutic Targets in Lung Cancer Integration of Traditional Bioinformatics and Machine Learning Approaches
Provisionally accepted
- 1The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- 2Zhejiang Cancer Hospital, Hangzhou, China
- 3Qinghai Fifth People's Hospital, Xining, China
- 4Qinghai Fifth People's Hospital., Xining, China
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Background: Lung cancer diagnosis requires cost-effective biomarkers. Mitochondrial non-coding RNAs (mtRNAs) represent unexplored diagnostic targets. Methods: We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). Top-ranked t00043332 was functionally validated in A549/PC9 cells. Results: Ten mtRNAs distinguished cancer from normal tissues. Random Forest and Logistic Regression achieved superior classification (AUC > 0.92) versus SVM. Nine mtRNAs were upregulated, one downregulated in cancer. No survival associations were observed. t00043332 overexpression promoted proliferation, migration, invasion, and apoptosis resistance. Conclusion: mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.
Keywords: lung cancer, machine learning, Biomarker Discovery, Mitochondrial non-coding RNAs, Potential drug targets
Received: 22 Aug 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Liu, Wang, Hua, Jiang, Zhang, Sun and Ren. 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:
Mao Hua, 417335881@qq.com
Xin Sun, endeavour.sun@gmail.com
Hong Ren, renhongs2000@126.com
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