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Purpose <p>Advanced lung squamous cell carcinoma (LUSC) has poor prognosis due to local invasion, metastasis, therapeutic resistance, and a dynamic tumor microenvironment. While anoikis resistance contributes to its malignancy, its influence on the tumor immune microenvironment (TIME) and clinical outcomes in LUSC remains unclear.</p> Methods <p>Anoikis resistance-associated genes (ARGs) in LUSC were identified using LASSO regression and univariate Cox proportional hazards analysis. A prognostic signature was constructed and validated in both internal (TCGA-LUSC) and external (GSE73403, GSE74777) cohorts. Functional characterization of signature genes was performed using single-cell transcriptomics and pathway enrichment analysis. Comprehensive immune profiling was conducted to explore the relationship between risk stratification and TIME features. Core genes were experimentally validated to confirm their biological relevance.</p> Results <p>The ARG-based model stratified patients into high- and low-risk subgroups with significant overall survival differences (log-rank <i>p</i> &lt; 0.001). The risk score was an independent prognostic factor (HR: 3.91; 95% CI: 2.08–7.34). The model demonstrated robust predictive performance across external datasets (AUC: &gt;0.7). High-risk patients displayed immunosuppressive TIME characteristics, including reduced CD8 + T cell infiltration (<i>p</i> = 0.004), increased stromal content (<i>p</i> &lt; 0.01), and lower predicted immunotherapy responsiveness (<i>p</i> = 0.007). Three key ARGs—<i>SDCBP</i>, <i>RPS6KA1</i>, and <i>ITGA3</i>—were identified as critical regulators of anoikis resistance, with functional assays confirming <i>SDCBP’s</i> oncogenic role in promoting malignancy.</p> Conclusion <p>This validated ARG-based prognostic model effectively predicts survival outcomes and revealed strong associations between anoikis resistance, immune landscape alterations, and immunotherapy response, offering a framework for risk stratification and precision immunotherapy in LUSC clinical management.</p>

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A novel anoikis resistance-associated gene model for prognostic prediction and immune microenvironment characterization in lung squamous cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Dong Ou,
  • HongPing Wang,
  • Yi Liu,
  • Jin Nie,
  • Daishun Liu

摘要

Purpose

Advanced lung squamous cell carcinoma (LUSC) has poor prognosis due to local invasion, metastasis, therapeutic resistance, and a dynamic tumor microenvironment. While anoikis resistance contributes to its malignancy, its influence on the tumor immune microenvironment (TIME) and clinical outcomes in LUSC remains unclear.

Methods

Anoikis resistance-associated genes (ARGs) in LUSC were identified using LASSO regression and univariate Cox proportional hazards analysis. A prognostic signature was constructed and validated in both internal (TCGA-LUSC) and external (GSE73403, GSE74777) cohorts. Functional characterization of signature genes was performed using single-cell transcriptomics and pathway enrichment analysis. Comprehensive immune profiling was conducted to explore the relationship between risk stratification and TIME features. Core genes were experimentally validated to confirm their biological relevance.

Results

The ARG-based model stratified patients into high- and low-risk subgroups with significant overall survival differences (log-rank p < 0.001). The risk score was an independent prognostic factor (HR: 3.91; 95% CI: 2.08–7.34). The model demonstrated robust predictive performance across external datasets (AUC: >0.7). High-risk patients displayed immunosuppressive TIME characteristics, including reduced CD8 + T cell infiltration (p = 0.004), increased stromal content (p < 0.01), and lower predicted immunotherapy responsiveness (p = 0.007). Three key ARGs—SDCBP, RPS6KA1, and ITGA3—were identified as critical regulators of anoikis resistance, with functional assays confirming SDCBP’s oncogenic role in promoting malignancy.

Conclusion

This validated ARG-based prognostic model effectively predicts survival outcomes and revealed strong associations between anoikis resistance, immune landscape alterations, and immunotherapy response, offering a framework for risk stratification and precision immunotherapy in LUSC clinical management.