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ORIGINAL RESEARCH article

Front. Genet.
Sec. Cancer Genetics and Oncogenomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1419099

A Comprehensive in Silico Analysis and Experimental Validation of miRNAs Capable of Discriminating between Lung Adenocarcinoma and Squamous Cell Carcinoma

Provisionally accepted
Zahra Javanmardifard Zahra Javanmardifard 1Saeid Rahmani Saeid Rahmani 2Hadi Bayat Hadi Bayat 3Hanifeh Mirtavoos Mahyari Hanifeh Mirtavoos Mahyari 4Mostafa Ghanei Mostafa Ghanei 5Seyed Javad Mowla Seyed Javad Mowla 1*
  • 1 Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Alborz, Iran
  • 2 Institute for Research in Fundamental Sciences (IPM), Tehran, Tehran, Iran
  • 3 Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
  • 4 Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Tehran, Alborz, Iran
  • 5 Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran, Alborz, Iran

The final, formatted version of the article will be published soon.

    Background: Accurate differentiation between lung adenocarcinoma (AC) and lung squamous cell carcinoma (SCC) is crucial owing to their distinct therapeutic approaches.MicroRNAs (miRNAs) exhibit variable expression across subtypes, making them promising biomarkers for discrimination. This study aimed to identify miRNAs with robust discriminatory potential between AC and SCC and elucidate their clinical significance.Methods: MiRNA expression profiles for AC and SCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and supervised machine learning methods (Support Vector Machine, Decision trees and Naïve Bayes) were employed. Clinical significance was assessed through receiver operating characteristic (ROC) curve analysis, survival analysis, and correlation with clinicopathological features. Validation was conducted using reverse transcription quantitative polymerase chain reaction (RT-qPCR).Furthermore, signaling pathway and gene ontology enrichment analyses were conducted to unveil biological functions.Results: Five miRNAs (miR-205-3p, miR-205-5p, miR-944, miR-375 and miR-326) emerged as potential discriminative markers. The combination of miR-944 and miR-326 yielded an impressive area under the curve of 0.985. RT-qPCR validation confirmed their biomarker potential. miR-326 and miR-375 were identified as prognostic factors in AC, while miR-326 and miR-944 correlated significantly with survival outcomes in SCC. Additionally, exploration of signaling pathways implicated their involvement in key pathways including PI3K-Akt, MAPK, FoxO, and Ras.Conclusions: This study enhances our understanding of miRNAs as discriminative markers between AC and SCC, shedding light on their role as prognostic indicators and their association with clinicopathological characteristics. Moreover, it highlights their potential involvement in signaling pathways crucial in non-small cell lung cancer pathogenesis.

    Keywords: microRNA, NSCLC, machine learning, Feature Selection, prognosis, qPCR

    Received: 17 Apr 2024; Accepted: 10 Sep 2024.

    Copyright: © 2024 Javanmardifard, Rahmani, Bayat, Mirtavoos Mahyari, Ghanei and Mowla. 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: Seyed Javad Mowla, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Alborz, Iran

    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.