ArticleOncology research2023
Comprehensive bioinformatics analysis and experimental validation: An anoikis-related gene prognostic model for targeted drug development in head and neck squamous cell carcinoma.
Article in Oncology research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Identification of an Anoikis-Related Gene Signature to Predict the Prognosis in Patients With Oral Squamous Cell Carcinoma.International dental journal · 2026Article
- An anoikis-related four-gene prognostic signature and functional characterization of ANXA5 in laryngeal squamous cell carcinoma.Frontiers in immunology · 2026Article
- Development of a cancer metastasis-associated risk model via multi-machine-learning algorithms for prognostic risk evaluation and clinical application in oral squamous cell carcinoma.Journal of translational medicine · 2025Article
- An anoikis-related lncRNA signature may predict the prognosis, immune infiltration, and drug sensitivity in esophageal cancer.Heliyon · 2024Article
- Predicting the prognosis, immune response, and immunotherapy in head and neck squamous cell carcinoma using a novel risk model based on anoikis-related lncRNAs.European journal of medical research · 2023Article
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6 authors.
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Abstract
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,
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