Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1653794

This article is part of the Research TopicColorectal Cancer Immunotherapy and Immune MechanismsView all 13 articles

A Machine Learning-Derived Immune-Related Prognostic Model Identifies PLXNA3 as a Functional Risk Gene in Colorectal Cancer

Provisionally accepted
Hanzhang LyuHanzhang Lyu1,2Song WangSong Wang3Guodong GuoGuodong Guo1,2Weiteng LinWeiteng Lin1,2Chenshen HuangChenshen Huang2*Hong ChenHong Chen2Chao XuChao Xu2Liming LiuLiming Liu4*Qi HuangQi Huang3*Fangqin XueFangqin Xue2*
  • 1Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
  • 2Fuzhou University Affiliated Provincial Hospital, Fuzhou University School of Medicine, Fuzhou, China
  • 3Tongji Hospital Affiliated to Tongji University, Shanghai, China
  • 4Shanghai Jing'an District Shibei Hospital, Shanghai, China

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

Select one of your emails

You have multiple emails registered with Frontiers:

Notify me on publication

Please enter your email address:

If you already have an account, please login

You don't have a Frontiers account ? You can register here

Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers. To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms-such as LASSO, CoxBoost, and StepCox-based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Plexin-A3(PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. Multi-omics validation confirmed PLXNA3's prognostic relevance. Spatial and single-cell transcriptomics demonstrated their enrichment in malignant epithelial regions and negative association with immune cell infiltration, particularly CD8⁺ T cells and plasma cells. Transcription factor (TF) and microRNA (miRNA) correlation analyses revealed potential upstream regulators of PLXNA3 linked to tumor stemness and immune suppression. Functional enrichment indicated its association with cell cycle, DNA damage repair, and interferon signaling pathways. Immunohistochemistry (IHC) confirmed PLXNA3 overexpression in tumor tissues and its correlation with nodal metastasis. Moreover, drug sensitivity profiling and Connectivity Map (CMap) analysis identified potential compounds, including imatinib, MS-275 and fasudil, capable of reversing PLXNA3-driven transcriptional programs. In summary, this study identifies PLXNA3 as a novel immune-related biomarker in colorectal cancer and elucidates its multifaceted role in tumor progression, immune evasion, and therapeutic resistance. These findings provide a foundation for incorporating PLXNA3 into precision oncology frameworks for gastrointestinal malignancies.

Keywords: colorectal cancer, PLXNA3, Prognostic model, immune microenvironment, machine learning, single-cell RNA sequencing, Spatial transcriptomics, drug sensitivity

Received: 25 Jun 2025; Accepted: 13 Aug 2025.

Copyright: © 2025 Lyu, Wang, Guo, Lin, Huang, Chen, Xu, Liu, Huang and Xue. 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:
Chenshen Huang, Fuzhou University Affiliated Provincial Hospital, Fuzhou University School of Medicine, Fuzhou, China
Liming Liu, Shanghai Jing'an District Shibei Hospital, Shanghai, China
Qi Huang, Tongji Hospital Affiliated to Tongji University, Shanghai, China
Fangqin Xue, Fuzhou University Affiliated Provincial Hospital, Fuzhou University School of Medicine, Fuzhou, 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.

Supplementary Material