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

ORIGINAL RESEARCH article

Front. Oncol.

Sec. Thoracic Oncology

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1630663

This article is part of the Research TopicInnovations in Biomarker-Based Lung Cancer ScreeningView all 13 articles

Construction and Validation of Immune prognosis model for lung adenocarcinoma based on machine learning

Provisionally accepted
Jinyu  ZhengJinyu ZhengXiaoyi  XuXiaoyi XuMiao  FuMiao FuJie  YangJie Yang*
  • Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

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

Lung adenocarcinoma is a common malignant tumor characterized by high rates of recurrence and metastasis, underscoring the urgent need for novel prognostic biomarkers. This study integrated transcriptomic and clinicopathological data to construct an effective immune-related prognostic model. A total of 55 tumor and 38 adjacent normal tissue samples from the TCGA database were used as the training set, with 30 additional samples for model validation. An independent external validation cohort consisting of 10 LUAD samples and their paired normal tissues was obtained from the First Affiliated Hospital of Wenzhou Medical University. Differential expression analysis and weighted gene co-expression network analysis identified key gene modules associated with LUAD progression. Functional enrichment analysis revealed that these genes are primarily involved in cell proliferation and immune-inflammatory responses. By intersecting immune-related genes from the IMMPORT database, 68 prognosis-associated genes were identified. Using Random Forest, LASSO regression, and SVM-RFE algorithms, four hub genes—CBLC, GDF10, LTBP4, and FABP4—were selected to construct the prognostic model. The model demonstrated strong predictive performance through multivariate Cox regression and ROC analysis, and its effectiveness was further validated using an artificial neural network. Immune infiltration analysis showed increased levels of CD4+ T cells, macrophages, and dendritic cells in LUAD, with significant differences in immune subtypes and survival outcomes. This study provides new insights into the immunobiology of LUAD and lays the foundation for the development of personalized therapeutic strategies.

Keywords: Lung Adenocarcinoma, Immune-related markers, weighted gene co-expression network analysis, machine learning, Prognostic ModelAbstract

Received: 18 May 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Zheng, Xu, Fu and Yang. 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: Jie Yang, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, 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.