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Identification and validation of a novel anoikis‐related prognostic model for prostate cancer
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Identification and validation of a novel anoikis‐related prognostic model for prostate cancer
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Identification and validation of a novel anoikis‐related prognostic model for prostate cancer
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Journal Article
Identification and validation of a novel anoikis‐related prognostic model for prostate cancer
2024
Overview
Background Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis‐related prognostic model for prostate cancer (PCa). Methods We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis‐related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high‐risk group patients. Results Two subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single‐nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP‐derived and four PRISM‐derived compounds were identified for high‐risk patients. Conclusions The anoikis‐related prognostic model developed in this study could be a useful tool for clinical decision‐making. This study may provide a new perspective for the treatment of anoikis‐related PCa. Our study has identified a novel anoikis‐related signature consisting of four genes, which demonstrated significant prognostic value for PCa patients. Our findings suggest that this signature could serve as a valuable tool for predicting patient outcomes and guiding personalized treatment strategies for PCa. The identification of immune cells and drug sensitivity information could also provide potential targets for developing novel immunotherapies and personalized treatments for PCa patients.
