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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1454977
This article is part of the Research Topic Advancements in Multi-Omics and Bioinformatics for the Management of Solid Malignancies View all 6 articles

Novel Prognostic Signature for Hepatocellular Carcinoma using a Comprehensive Machine Learning Framework to Predict Prognosis and Guide Treatment

Provisionally accepted
Shengzhou Zheng Shengzhou Zheng 1Zhixiong Su Zhixiong Su 1Yufang He Yufang He 1Lijie You Lijie You 1Guifeng Zhang Guifeng Zhang 2Jingbo Chen Jingbo Chen 2Lihu Lu Lihu Lu 3Zhenhua Liu Zhenhua Liu 2*
  • 1 Fujian Medical University, Fuzhou, China
  • 2 Fujian Provincial Hospital, Fuzhou, Fujian Province, China
  • 3 Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China

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

    Background: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene signature (HPRGS) and explore its clinical application value.Methods: TCGA-LIHC cohort was used for training, and the LIRI-JP cohort and HCC cDNA microarray were used for validation. Machine learning algorithms constructed a prognostic gene label for HCC. Kaplan-Meier (K-M), ROC curve, multiple analyses, algorithms, and online databases were used to analyze differences between high-and low-risk populations. A nomogram was constructed to facilitate clinical application.Results: We identified 119 differential genes based on transcriptome sequencing data from five independent HCC cohorts, and 53 of these genes were associated with overall survival (OS). Using 101 machine learning algorithms, the 10 most prognostic genes were selected. We constructed an HCC HPRGS with four genes (SOCS2, LCAT, ECT2, and TMEM106C). Good predictive performance of the HPRGS was confirmed by ROC, C-index, and K-M curves. Mutation analysis showed significant differences between the low-and high-risk patients. The low-risk group had a higher response to transcatheter arterial chemoembolization (TACE) and immunotherapy. Treatment response of high-and low-risk groups to small-molecule drugs was predicted. Linifanib was a potential drug for high-risk populations. Multivariate analysis confirmed that HPRGS were independent prognostic factors in TCGA-LIHC. A nomogram provided a clinical practice reference.We constructed an HPRGS for HCC, which can accurately predict OS and guide the treatment decisions for patients with HCC.

    Keywords: Hepatocellular Carcinoma, Prognosis signature, Treatment, Machine learning framework, TCGA

    Received: 26 Jun 2024; Accepted: 05 Sep 2024.

    Copyright: © 2024 Zheng, Su, He, You, Zhang, Chen, Lu and Liu. 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: Zhenhua Liu, Fujian Provincial Hospital, Fuzhou, 350001, Fujian Province, 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.