Comprehensive bioinformatics analysis and experimental validation: An anoikis-related gene prognostic model for targeted drug development in head and neck squamous cell carcinoma.

We analyzed RNA-sequencing (RNA-seq) and clinical data from head and neck squamous cell carcinoma (HNSCC) patients in The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal to investigate the prognostic value of anoikis-related genes (ARGs) in HNSCC and develop new targeted drugs. Differentially expressed ARGs were screened using bioinformatics methods; subsequently, a prognostic model including three ARGs (CDKN2A, BIRC5, and PLAU) was constructed. Our results showed that the model-based risk score was a good prognostic indicator, and the potential of the three ARGs in HNSCC prognosis was validated by the TISCH database, the model's accuracy was validated in two independent cohorts of the Gene Expression Omnibus database. Immune correlation analysis and half-maximal inhibitory concentration were also performed to reveal the different landscapes of TIME between risk groups and to predict immuno- and chemo-therapeutic responses. Potential small-molecule drugs for HNSCC were subsequently predicted using the L1000FWD database. Finally, experiments were used to verify the database findings. The relative ARG mRNA expression levels in HNSCC and surrounding normal tissues remained consistent with the model results. BIRC5 knockdown inhibited anoikis resistance in WSU-HN6 and CAL-27 cells. Molecular docking, real-time PCR, cell counting kit-8 (CCK-8), plate clone, and flow cytometry analyses showed that small-molecule drugs predicted by the database may target the ARGs in the prognostic model, inhibit HNSCC cells survival rate, and promote anoikis . Therefore, we constructed a new ARG model for HNSCC patients that can predict prognosis and immune activity and identify a potential small-molecule drug for HNSCC, paving the way for clinically targeting anoikis in HNSCC.