A Novel Signature Based on Anoikis Associated with BCR-Free Survival for Prostate Cancer

This study aimed to elucidate the role of anoikis in the progression of prostate cancer (PCa) and to develop a prognostic signature based on anoikis-related genes (ARGs). To achieve this, PCa cases were subjected to nonnegative matrix factorization (NMF) analysis, which allowed for the identification of distinct patterns of anoikis modification. Additionally, immune infiltration was evaluated using single-sample gene-set enrichment analysis (ssGSEA). Survival analysis was performed using the Kaplan-Meier method, and a risk score was generated based on the expression levels of ARGs to quantitatively assess the modification of anoikis in PCa. Using the Least Absolute Shrinkage and Selection Operator (LASSO) method, four hub-genes were identified, and patients were classified into different risk groups based on their individual scores. Importantly, the low-risk subtype was characterized by a significantly improved biochemical recurrence-free survival, underscoring the clinical relevance of the ARG-based prognostic signature. To further improve the prognostic accuracy of the signature, patient age, pathological T stage, Gleason score, and prostate-specific antigen level were incorporated into the analysis, yielding a comprehensive prognostic signature. The clinical relevance of this signature was illustrated through a nomogram, providing a visual representation of the prognostic implications of the ARG-based signature. Taken together, these findings highlight the potential of ARGs in predicting the clinical outcomes of PCa patients and provide a novel and clinically relevant prognostic signature based on the modification of anoikis in PCa.

Automated summary

This study aimed to investigate the role of anoikis in prostate cancer progression and develop a prognostic signature based on anoikis-related genes (ARGs). Nonnegative matrix factorization (NMF) analysis identified distinct patterns of anoikis modification, while single-sample gene-set enrichment analysis (ssGSEA) evaluated immune infiltration. Survival analysis and risk score generation using ARG expression levels quantitatively assessed the modification of anoikis. Four hub-genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method, and patients were classified into different risk groups based on their scores. The low-risk subtype had significantly improved biochemical recurrence-free survival, highlighting the clinical relevance of the ARG-based prognostic signature.