The Challenge of LUSC. Lung squamous cell carcinoma (LUSC) has a poor prognosis, with only a 27% five-year survival rate. Unlike lung adenocarcinoma, LUSC lacks effective targeted therapies and is more aggressive, with larger tumors, higher clinical stage, and a higher male-to-female ratio.
What Is Anoikis. Anoikis is a form of programmed cell death triggered when cells detach from the extracellular matrix. Under normal conditions, it prevents misplaced cells from surviving. Cancer cells frequently develop resistance to this process -- known as anoikis resistance -- enabling them to survive detachment, circulate in the bloodstream, and colonize distant sites.
The Immunosuppressive Barrier. Most LUSC patients fail to achieve durable responses to immune checkpoint inhibitors (ICIs) because the tumor microenvironment (TME) creates immune-excluded niches. Cancer-associated fibroblasts, hypoxia, and macrophage-driven extracellular matrix remodeling block immune infiltration and contribute to resistance to immunotherapy.
EMT and Stemness. Epithelial-to-mesenchymal transition (EMT) allows cancer cells to detach from the primary tumor, invade surrounding tissue, and spread. Cancer stem cells (CSCs), which self-renew and resist chemotherapy, further drive relapse after surgery. Anoikis resistance, EMT, and stemness are interconnected mechanisms that the current study investigates in LUSC.
Multi-Dataset Analysis. The study used gene expression and clinical data from three datasets: TCGA-LUSC (178 patients), GSE33479 (108 patients), and GSE12472 (35 patients), totaling 321 participants. Data were downloaded from UCSC Xena and Gene Expression Omnibus (GEO) and standardized by log2 transformation for cross-dataset comparability.
Identifying Differentially Expressed Genes. The limma R package was used to identify differentially expressed genes (DEGs) between tumor and normal samples in each dataset. A Venn diagram then identified genes that were consistently significant across all three datasets, yielding three common anoikis-related genes: S100A7, S100A8, and SPP1.
Immune Cell Deconvolution. Three independent computational algorithms -- CIBERSORT, quanTseq, and Support Vector Regression (SVR) -- were applied to estimate the relative proportions of 22 immune cell types in each tumor sample. This multi-method approach provides cross-validated immune infiltration estimates for B cells, T cells, macrophages, NK cells, dendritic cells, and regulatory T cells (Tregs).
Nomogram Construction. A prognostic nomogram was constructed integrating the three identified genes (S100A7, S100A8, SPP1) with clinical variables including age, smoking status, and tumor TNM stage. Model performance was evaluated with ROC curves, calibration plots, and decision curve analysis for 1-, 3-, and 5-year overall survival predictions.
Consistent Overexpression Across Datasets. All three genes -- S100A7, S100A8, and SPP1 -- showed significantly higher expression in LUSC tumor samples compared to normal lung tissue across all three datasets (p less than 0.05). Principal component analysis (PCA) confirmed substantial separation between tumor and normal gene expression profiles.
S100A7 and Anoikis Resistance. S100A7 is overexpressed in LUSC and has been implicated in promoting anoikis resistance, which enhances the metastatic potential of squamous cancer cells. In the Kaplan-Meier analysis, high S100A7 expression showed a hazard ratio of 0.96, though this did not reach statistical significance as a standalone predictor.
S100A8 and Cancer Stemness. S100A8 promotes stemness by interacting with key inflammatory pathways including NF-kB and MAPK, enhancing the survival and self-renewal capacity of cancer stem cells. Like S100A7, it was significantly upregulated in tumors versus normal tissue (p = 0.0049).
SPP1 and PI3K-Akt Signaling. SPP1 (osteopontin) is a multifunctional extracellular matrix protein that mediates anoikis resistance by activating PI3K-Akt-mTOR signaling. It was significantly upregulated in LUSC tumors (p less than 0.0001) and represents a potential therapeutic target. Together, S100A7, S100A8, and SPP1 collaborate to establish a microenvironment supporting tumor progression, immune evasion, and metastasis.
Immunosuppressive Microenvironment. CIBERSORT analysis revealed a predominantly immunosuppressive tumor microenvironment in LUSC, characterized by high M2 macrophage infiltration and elevated regulatory T cells (Tregs), combined with low CD8+ cytotoxic T cell infiltration. This immune composition is typically associated with poor prognosis and resistance to immunotherapy.
Consistent Multi-Method Immune Profiling. All three immune deconvolution methods -- CIBERSORT, quanTseq, and SVR -- showed consistent patterns of immune cell composition. Significant differences in infiltration levels were observed between high and low gene expression groups specifically for T cells, macrophages, Tregs, and dendritic cells.
Nomogram Performance. The prognostic nomogram integrating S100A7, S100A8, SPP1, age, smoking, and TNM stage achieved AUC values of 0.650 at 1 year, 0.632 at 3 years, and 0.657 at 5 years. These represent moderate discriminative ability, with calibration curves confirming reasonable agreement between predicted and observed survival probabilities.
Paradox of High Immune Scores. Paradoxically, higher overall immune scores in LUSC correlated with poorer survival outcomes. This reflects the dominance of immunosuppressive cell types (M2 macrophages, Tregs) over cytotoxic immune cells, highlighting the complexity of the tumor immune microenvironment in determining treatment response.
Potential Immunotherapy Strategies. Patients with higher CD8+ T cell infiltration may respond better to immune checkpoint inhibitors, while those with high M2 macrophages or Treg populations might benefit more from therapies targeting these suppressive cells. Immune profiling could thus guide personalized treatment selection in LUSC.
Macrophage and Treg Targeting. Therapies aimed at modulating macrophage polarization from M2 toward M1 (pro-inflammatory) phenotypes, or depleting Tregs, could restore immune function and enhance anti-tumor responses. These approaches could complement existing immune checkpoint blockade strategies.
Anoikis Resistance Pathways. Multiple molecular pathways converge on anoikis resistance in LUSC: ITGBL1 promotes resistance via the AKT/FBLN2 axis; GDH1 via CamKK2-AMPK signaling; and mTORC1 activation via the integrin-GSK3b-FTO axis governing m6A methylation. The S100A7/S100A8/SPP1 axis represents another convergence point, collectively sustaining stem-like properties and metastatic behavior.
ECM Remodeling and Radioresistance. Enriched KEGG pathways included ECM-receptor interaction, complement and coagulation cascades, and transcriptional misregulation in cancer. Radioresistant LUSC cells show enhanced DNA repair through ATM/CHK2 and DNA-PKcs/Ku70 pathways, which also contribute to increased invasiveness and ECM remodeling, suggesting a link between treatment resistance and anoikis resistance mechanisms.
Main Finding. This study demonstrates a significant impact of anoikis resistance and immune cell infiltration on prognosis and therapeutic response in LUSC. The three identified genes -- S100A7, S100A8, and SPP1 -- integrate molecular biology with the immune microenvironment to create a comprehensive prognostic framework.
Personalized Immunotherapy. The nomogram integrating anoikis-related gene expression with clinical variables provides a tool for predicting individual patient outcomes and could guide personalized immunotherapy strategies. Patients classified as high-risk based on the model may warrant more aggressive treatment or enrollment in clinical trials.
Overcoming Immune Evasion. Targeting the anoikis resistance-immune evasion axis -- particularly by modulating S100A7, S100A8, and SPP1 signaling -- could disrupt the mechanisms that allow LUSC cells to maintain stem-like properties and evade immune destruction, potentially enhancing the efficacy of immunotherapy.
Limitations and Future Work. The study was limited by its database analysis design and relatively small sample sizes. Future prospective clinical trials are needed to validate the prognostic nomogram in real-world settings, and functional experiments are required to establish causal relationships between the identified genes and anoikis resistance mechanisms in LUSC.