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Development and validation of a 5mC-based prognostic model for lung adenocarcinoma survival: a multicentre, retrospective study

ABSTRACT: Lung adenocarcinoma (LUAD) exhibits marked prognostic heterogeneity. Existing methylation-based prognostic models lack robustness, accuracy, or external validation, limiting their clinical utility. We develop MethPro-LUAD, a prognostic model based on eight 5-methylcytosine features, using TCGA-LUAD data (training, N=269; testing, N=180), and validate the model across two independent hospital cohorts (Beijing, N=195; Nanjing, N=88) and two external GEO datasets (N=82 and N=155) to ensure its generalizability and effectiveness. MethPro-LUAD stratifies patients into high- and low-risk groups, with high-risk individuals consistently showing significantly shorter overall survival across all cohorts. In addition, the model’s predictive performance remains robust in subgroups defined by age, sex, stage, and EGFR mutation, and in disease-free survival analyses. Compared with other reported LUAD prognostic models, MethPro-LUAD demonstrates better performance across cohorts, which offers valuable support for personalized postoperative treatment and follow-up, and warrants further prospective evaluation, highlighting its strong potential for clinical translation through feasible targeted assays.

ORGANISM(S): Homo sapiens

PROVIDER: GSE295570 | GEO | 2026/03/06

REPOSITORIES: GEO

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Project description:For developing a accurate prognostic signature for “pan-driver-gene-negative” LUAD, we employed whole genome microarray expression profiling as a discovery platform to identify candidate genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on whole-genome microarrays indicated that the Wnt/β-catenin pathway was activated in “pan-driver-gene-negative” LUAD. Furthermore, the Wnt/β-catenin-pathway-based gene expression profiles revealed 39 transcripts differentially expressed by diagnostic status, with 30 genes being upregulated and 9 downregulated. Finally, a Wnt/β-catenin-pathway-based signature (CSDW) comprising 4 genes (β-catenin, Wnt2b, DVL3 and SOX9) was developed to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter overall survival (hazard ratio [HR] 10·42, 6·46–16·79; p<0·001) than patients with low-risk scores. The CSDW performance was further validated in an internal cohort and two external cohorts. The protein expression levels of several hub genes, including β-catenin, SOX9, DVL3 and Wnt2b, were strongly correlated with lymphatic metastasis and distant organ metastasis. Furthermore, a nomogram comprising CSDW and other variables was generated to predict progression-free survival and overall survival in the training cohort and performed well in the three independent validation cohorts (C-index: 0·725, 0·697 and 0·693, respectively). For developing a accurate prognostic signature for “pan-driver-gene-negative” LUAD, we employed whole genome microarray expression profiling as a discovery platform to identify candidate genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on whole-genome microarrays indicated that the Wnt/β-catenin pathway was activated in “pan-driver-gene-negative” LUAD. Furthermore, the Wnt/β-catenin-pathway-based gene expression profiles revealed 39 transcripts differentially expressed by diagnostic status, with 30 genes being upregulated and 9 downregulated. Finally, a Wnt/β-catenin-pathway-based signature (CSDW) comprising 4 genes (β-catenin, Wnt2b, DVL3 and SOX9) was developed to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter overall survival (hazard ratio [HR] 10·42, 6·46–16·79; p<0·001) than patients with low-risk scores. The CSDW performance was further validated in an internal cohort and two external cohorts. The protein expression levels of several hub genes, including β-catenin, SOX9, DVL3 and Wnt2b, were strongly correlated with lymphatic metastasis and distant organ metastasis. Furthermore, a nomogram comprising CSDW and other variables was generated to predict progression-free survival and overall survival in the training cohort and performed well in the three independent validation cohorts (C-index: 0·725, 0·697 and 0·693, respectively).

Project description:Glutamine (Gln) metabolism has been reported to play an essential role in cancer. However, a comprehensive analysis of its role in lung adenocarcinoma is still unavailable. This study established a novel system of quantification of Gln metabolism to predict the prognosis and immunotherapy efficacy in lung cancer. Further, the Gln metabolism in tumor microenvironment (TME) was characterized and the Gln metabolism-related genes were identified for targeted therapy. We comprehensively evaluated the patterns of Gln metabolism in 513 patients diagnosed with lung adenocarcinoma (LUAD) based on 73 Gln metabolism-related genes. Based on differentially expressed genes (DEGs), a risk model was constructed using Cox regression and Lasso regression analysis. The prognostic efficacy of the model was validated using an individual LUAD cohort form Shandong Provincial Hospital, an integrated LUAD cohort from GEO and pan-cancer cohorts from TCGA databases. Five independent immunotherapy cohorts were used to validate the model performance in predicting immunotherapy efficacy. Next, a series of single-cell sequencing analyses were used to characterize Gln metabolism in TME. Finally, single-cell sequencing analysis, transcriptome sequencing, and a series of in vitro experiments were used to explore the role of EPHB2 in LUAD. Patients with LUAD were eventually divided into low- and high-risk groups. Patients in low-risk group were characterized by low levels of Gln metabolism, survival advantage, “hot” immune phenotype and benefit from immunotherapy. Compared with other cells, tumor cells in TME exhibited the most active Gln metabolism. Among immune cells, tumor-infiltrating T cells exhibited the most active levels of Gln metabolism, especially CD8 T cell exhaustion and Treg suppression. EPHB2, a key gene in the model, was shown to promote LUAD cell proliferation, invasion and migration, and regulated the Gln metabolic pathway. Finally, we found that EPHB2 was highly expressed in macrophages, especially M2 macrophages. It may be involved in the M2 polarization of macrophages and mediate the negative regulation of M2 macrophages in NK cells. This study revealed that the Gln metabolism-based model played a significant role in predicting prognosis and immunotherapy efficacy in lung cancer. We further characterized the Gln metabolism of TME and investigated the Gln metabolism-related gene EPHB2 to provide a theoretical framework for anti-tumor strategy targeting Gln metabolism.

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Development and validation of a 5mC-based prognostic model for lung adenocarcinoma survival: a multicentre, retrospective study

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