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ORIGINAL RESEARCH article

Front. Genet.
Sec. Epigenomics and Epigenetics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1383852

Comparison of the classifiers based on mRNA, microRNA and lncRNA expression and DNA methylation profiles for the tumor origin detection

Provisionally accepted
Yun Feng Yun Feng 1,2Yilin Wang Yilin Wang 1,2*
  • 1 Shanghai Cancer Center, Fudan University, Shanghai, Shanghai Municipality, China
  • 2 Shanghai Medical College, Fudan University, Shanghai, Shanghai Municipality, China

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

    Background: Tumor tissue origin detection is of great importance in determining the appropriate course of treatment for cancer patients. Classifiers based on gene expression and DNA methylation profiles have been confirmed to be feasible and reliable to predict the tumor primary. However, few work has been performed to compare the performance of these classifiers based on different profiles.Genome Atlas (TCGA) project, eight machine learning methods were employed for the tumor tissue origin detection. We then evaluated the predictive performance using DNA methylation, mRNA, microRNA (miRNA) and long non-coding RNA (lncRNA) expression profiles in a comparative manner. A statistical method was introduced to select the most informative CpG sites.We found that LASSO is the most predictive models based on various profiles. Further analyses indicated that the results derived from DNA methylation (overall accuracy: 97.77%) are better than those derived from mRNA expression (overall accuracy: 88.01%), microRNA expression (overall accuracy: 91.03%) and lncRNA expression (overall accuracy: 95.7%). It has been suggested that we can achieve an overall accuracy > 90% using only 1000 methylated CpG sites for prediction.In this work, we comprehensively evaluated the performance of classifiers based on different profiles for the tumor origin detection. Our findings demonstrated the effectiveness of DNA methylation as biomarker for tracing tumor tissue origin using LASSO and neural network.

    Keywords: gene expression profile, DNA methylation profile, tumor tissue origin detection, machine learning, Cancer of unknown primary

    Received: 08 Feb 2024; Accepted: 16 May 2024.

    Copyright: © 2024 Feng and Wang. 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: Yilin Wang, Shanghai Cancer Center, Fudan University, Shanghai, 200032, Shanghai Municipality, 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.