ArticleJournal of cancer research and clinical oncology2023
A novel anoikis-related risk model predicts prognosis in patients with colorectal cancer and responses to different immunotherapy strategies.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A novel anoikis related gene prognostic model for colorectal cancer based on single cell sequencing and bulk transcriptome analyses.Scientific reports · 2025Article
- Multi-cohort validation based on a novel prognostic signature of anoikis for predicting prognosis and immunotherapy response of esophageal squamous cell carcinoma.Frontiers in oncology · 2025Article
- Identification of the molecular subtypes and signatures to predict the prognosis, biological functions, and therapeutic response based on the anoikis-related genes in colorectal cancer.Cancer medicine · 2024Article
- Multi-cohort validation of Ascore: an anoikis-based prognostic signature for predicting disease progression and immunotherapy response in bladder cancer.Molecular cancer · 2024Article
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- Identification and validation of a novel anoikis-related long non-coding RNA signature for pancreatic adenocarcinoma to predict the prognosis and immune response.Journal of cancer research and clinical oncology · 2023Article
- Machine learning-driven transcriptomic and single-cell profiling of programed cell death patterns in colon cancer.Science progressArticle
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2 authors.
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Abstract
purposeWe aimed to study the role of anoikis-related genes (ARGs) in colorectal cancer (CRC) using bioinformatics.
methodsGSE39582 and GSE39084, which collectively contain 363 CRC samples, were downloaded from the NCBI Gene Expression Omnibus (GEO) database as a test set. TCGA-COADREAD, with 376 CRC samples, was downloaded from the UCSC database as a validation set. Univariate Cox regression analysis was used to screen for ARGs that were significantly associated with prognosis. The top 10 ARGs were used to classify the samples into different subtypes based on unsupervised cluster analysis. The immune environments of the different subtypes were analyzed. ARGs that were significantly associated with CRC prognosis were used to construct a risk model. Univariate and multivariate Cox regression analyses were used to screen independent prognostic factors and construct a nomogram.
resultsFour anoikis-related subtypes (ARSs) with differential prognoses and immune microenvironments were identified. KRAS and epithelial-mesenchymal transition pathways were enriched in subtype B, which had the worst prognosis. Three ARGs (DLG1, AKT3, and LPAR1) were used to construct the risk model. Both the test and validation sets showed worse outcomes for patients in the high-risk group than those in the low-risk group. Risk score was found to be an independent prognostic factor for CRC. Moreover, there was a difference in drug sensitivity between the high- and low-risk groups.
conclusionThe identified ARGs and risk scores were associated with CRC prognosis and could predict the responses of patients with CRC to immunotherapy strategies.
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