ArticleScientific reports2024
A prognostic model for anoikis-related genes in pancreatic cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Oligomeric Proanthocyanidins Reverse Lenvatinib Resistance in Hepatocellular Carcinoma Through ITGA3-Mediated Pathway.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Machine learning-based construction of a programmed cell death-related model reveals prognosis and immune infiltration in pancreatic adenocarcinoma patients.Scientific reports · 2025Article
- Non-canonical ALK7 pathways promote pancreatic cancer metastasis through β-catenin/MMP-mediated basement membrane breakdown and intravasation.Molecular cancer · 2025Article
- The Oncoprotein Mucin 1 in Pancreatic Cancer Onset and Progression: Potential Clinical Implications.Biomolecules · 2025Review
- Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer.Discover oncology · 2025Article
- Development and Validation of an Anoikis-Related Gene Signature for Prognostic Prediction in Cervical Cancer.International journal of general medicine · 2025Article
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4 authors.
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Abstract
Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.
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