A single-cell transcriptomics-based prognostic model identifies TM4SF1 as a key promoter of malignant progression in lung adenocarcinoma
Abstract
BackgroundIntratumor heterogeneity in lung adenocarcinoma (LUAD) profoundly influences tumor progression and immune evasion. However, current prognostic models largely fail to capture the complex cellular dynamics of the tumor microenvironment (TME) at single-cell resolution.MethodsWe integrated single-cell RNA sequencing with bulk transcriptomics to derive cell-type-specific signatures. A systematic evaluation of 101 machine learning algorithm combinations was performed across multiple independent cohorts to construct a robust prognostic model. Immune landscape, mutational features, intercellular communication, and immunotherapy responsiveness were comprehensively evaluated. The biological functions of the identified hub gene, transmembrane 4 L six family member 1 (TM4SF1), were rigorously validated in vitro.ResultsThe optimal StepCox[forward]-Ridge model showed strong prognostic stratification in five cohorts. Low-risk tumors exhibited an immune-inflamed phenotype, suggesting greater predicted good responses to immune checkpoint blockade. In contrast, high-risk tumors had an immunosuppressive TME with overactive cell communication driven by fibroblasts and myeloid cells. TM4SF1 was a key driver in this high-risk signature. TM4SF1 silencing significantly inhibited LUAD cell proliferation, migration, and invasion, and strongly induced apoptosis. TM4SF1 depletion reversed tumor immune evasion by reducing PD-L1, increasing MHC-I, and lowering the secretion of CCL5.ConclusionsThis single-cell-guided machine-learning framework provides strong prognostic stratification in LUAD and captures immune phenotypes associated with potential immunotherapy responsiveness. TM4SF1 emerged as a candidate mediator associated with malignant progression and immune-related phenotypes in LUAD, warranting further mechanistic and in vivo investigation. This suggests it could be a useful therapeutic target.
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BibTeX
@article{li2026singlecell,
title = {{A single-cell transcriptomics-based prognostic model identifies TM4SF1 as a key promoter of malignant progression in lung adenocarcinoma}},
author = {Jiahua Li and Tianci Liu and Ling Li and Weiguo Lu and Junyuan Huang and Xi Li and Yingchun Zhou},
journal = {Frontiers in Oncology},
year = {2026},
month = nov,
doi = {10.3389/fonc.2026.1948068},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1948068},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A single-cell transcriptomics-based prognostic model identifies TM4SF1 as a key promoter of malignant progression in lung adenocarcinoma
AU - Jiahua Li
AU - Tianci Liu
AU - Ling Li
AU - Weiguo Lu
AU - Junyuan Huang
AU - Xi Li
AU - Yingchun Zhou
PY - 2026
DA - 2026/11/06
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1948068
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1948068
AB - BackgroundIntratumor heterogeneity in lung adenocarcinoma (LUAD) profoundly influences tumor progression and immune evasion. However, current prognostic models largely fail to capture the complex cellular dynamics of the tumor microenvironment (TME) at single-cell resolution.MethodsWe integrated single-cell RNA sequencing with bulk transcriptomics to derive cell-type-specific signatures. A systematic evaluation of 101 machine learning algorithm combinations was performed across multiple independent cohorts to construct a robust prognostic model. Immune landscape, mutational features, intercellular communication, and immunotherapy responsiveness were comprehensively evaluated. The biological functions of the identified hub gene, transmembrane 4 L six family member 1 (TM4SF1), were rigorously validated in vitro.ResultsThe optimal StepCox[forward]-Ridge model showed strong prognostic stratification in five cohorts. Low-risk tumors exhibited an immune-inflamed phenotype, suggesting greater predicted good responses to immune checkpoint blockade. In contrast, high-risk tumors had an immunosuppressive TME with overactive cell communication driven by fibroblasts and myeloid cells. TM4SF1 was a key driver in this high-risk signature. TM4SF1 silencing significantly inhibited LUAD cell proliferation, migration, and invasion, and strongly induced apoptosis. TM4SF1 depletion reversed tumor immune evasion by reducing PD-L1, increasing MHC-I, and lowering the secretion of CCL5.ConclusionsThis single-cell-guided machine-learning framework provides strong prognostic stratification in LUAD and captures immune phenotypes associated with potential immunotherapy responsiveness. TM4SF1 emerged as a candidate mediator associated with malignant progression and immune-related phenotypes in LUAD, warranting further mechanistic and in vivo investigation. This suggests it could be a useful therapeutic target.
SN - 2234-943X
ER -