A single-cell transcriptomics-based prognostic model identifies TM4SF1 as a key promoter of malignant progression in lung adenocarcinoma

Background Intratumor 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. Methods We 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 . Results The 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. Conclusions This 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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Publication Details

Journal
Frontiers in Oncology
Published
2026-11-06
DOI
https://doi.org/10.3389/fonc.2026.1948068
Primary Topic
Lung Cancer Research Studies
Type
article
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article

A single-cell transcriptomics-based prognostic model identifies TM4SF1 as a key promoter of malignant progression in lung adenocarcinoma

Tianci Liu, Jiahua Li, Weiguo Lu, Junyuan Huang et al.
4 links
Frontiers in Oncology
Lung Cancer Research Studies
article

A single-cell transcriptomics-based prognostic model identifies TM4SF1 as a key promoter of malignant progression in lung adenocarcinoma

Tianci Liu, Jiahua Li, Weiguo Lu, Junyuan Huang, Yingchun Zhou, Ling Li, Xi Li
article en

Abstract

Background Intratumor 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. Methods We 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 . Results The 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. Conclusions This 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.

Frontiers in OncologyVol. 16
Guangzhou University of Chinese Medicine (CN), First Affiliated Hospital of Guangzhou University of Chinese Medicine (CN)
Good health and well-being
Openalex Percentile: Top 16%
Lung Cancer Research Studies
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