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Updated: Jul 2, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
A novel anoikis resistance-associated gene model for prognostic prediction and immune microenvironment
Dong Ou1, HongPing Wang2, Yi Liu3
1Department of Oncology, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zunyi, 563000, China.
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.
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