Development of an Anoikis-Related Gene Signature for Lung Squamous Cell Carcinomato Predict Prognosis, Immune Landscape, and Immunotherapy Response

Abstract

Background. Anoikis, a form of programed cell death, plays a pivotal role in the invasion and metastasis of various tumors, including lung squamous cell carcinoma (LUSC). This study aims to construct a prognostic model for LUSC, leveraging anoikis-related genes (ARGs). Methods. A total of 357 ARGs were extracted from the GeneCards database and Harmonizome portals. Subsequently, ARGs influencing LUSC patients’ prognosis were identified using univariate Cox regression analysis. Unsupervised clustering analysis was carried out utilizing the “consensusplus” R package, and LASSO regression was deployed to craft a risk regression model. The ‘IBOR’ R package quantified the immune cell infiltration abundance. Moreover, the “maftools” R package, paired with the GISTIC online tool, facilitated the assessment of gene copy number variations. Experimental validation was conducted through RT-PCR, evaluating the differential expression of eight pivotal genes, and cellular functional assays discerned the influences of the CHEK2 and SDCBP genes on LUSC cells’ migratory and invasive capabilities. Results. Fifteen survival-associated ARGs delineated three molecular subtypes within the TCGA-LUSC cohort. An eight ARG-based risk prognostic model was constructed, delineating significant survival disparities between high and low-risk groups. Notably, the low-risk group manifested a diminished propensity for immune therapy evasion and gene mutations. A comprehensive nomogram, incorporating risk scores and clinical attributes, was fashioned, exemplifying remarkable predictive acumen. Cellular functional assays substantiated that the modulation of CHEK2 and SDCBP expressions conspicuously influenced the migratory and invasive propensities of LUSC cells. Conclusions. This rigorous study unveils novel anoikis-related biomarkers integral to LUSC prognostication. The meticulously constructed risk prognostic model, underscored by these biomarkers, augurs a potent predictive tool for enhancing the LUSC patient prognosis and therapeutic strategies.