Frontiers in bioscience (Landmark edition) · 2025
An Anoikis-Based Prognostic Signature Identifies MYC as a Key Regulator of Malignancy and Immunotherapy Resistance in Bladder Cancer.
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Tang Zhen, Peng Jiao, Pan Xiaoyu, Kong Yuefeng, Lin Haisong, Wei Suchun, Zhan Yi, Zeng Siping
Аннотация
BACKGROUND: Resistance to anoikis is a critical mechanism that enables metastatic dissemination. Abrogation of this cellular safeguard is therefore a hallmark of aggressive cancer progression. Despite the importance of anoikis, there are still few biomarkers among anoikis-related genes (ARGs) that could aid in the prognostication of bladder cancer (BC) patients and potentially serve as drug targets.
METHODS: This study leveraged bioinformatics analyses of publicly available BC datasets to evaluate the association between differentially expressed ARGs and patient prognosis. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was employed to build a novel prognostic signature model for BC based on ARGs. This model was also used to predict the response of BC to anticancer drugs. Additionally, immunohistochemistry was used to assess expression of the key gene, MYC, in BC samples obtained from patients undergoing surgery and from those receiving immune checkpoint inhibitor (ICI) therapy.
RESULTS: The ARG-based signature, developed and validated through the analysis of public databases, was an independent predictor of patient outcomes. Furthermore, it effectively stratified patients into two cohorts (high- and low-risk), allowing investigation of differential drug sensitivities. The risk stratification model identified 10 ARGs (IGF1, CALR, E2F1, MYC, PLK1, SATB1, FASN, ID2, RAC3, and GKN1) as potential therapeutic vulnerabilities. Notably, MYC was identified as a central hub gene within the ARG signature. Elevated MYC expression was strongly associated with worse prognosis in muscle-invasive bladder cancer (MIBC), and with a diminished response to immunotherapy.
CONCLUSION: This work demonstrated significant prognostic value for the ARG-based model. Specific ARGs could function as crucial biomarkers for patient outcome, while simultaneously offering new avenues for therapeutic intervention.
METHODS: This study leveraged bioinformatics analyses of publicly available BC datasets to evaluate the association between differentially expressed ARGs and patient prognosis. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was employed to build a novel prognostic signature model for BC based on ARGs. This model was also used to predict the response of BC to anticancer drugs. Additionally, immunohistochemistry was used to assess expression of the key gene, MYC, in BC samples obtained from patients undergoing surgery and from those receiving immune checkpoint inhibitor (ICI) therapy.
RESULTS: The ARG-based signature, developed and validated through the analysis of public databases, was an independent predictor of patient outcomes. Furthermore, it effectively stratified patients into two cohorts (high- and low-risk), allowing investigation of differential drug sensitivities. The risk stratification model identified 10 ARGs (IGF1, CALR, E2F1, MYC, PLK1, SATB1, FASN, ID2, RAC3, and GKN1) as potential therapeutic vulnerabilities. Notably, MYC was identified as a central hub gene within the ARG signature. Elevated MYC expression was strongly associated with worse prognosis in muscle-invasive bladder cancer (MIBC), and with a diminished response to immunotherapy.
CONCLUSION: This work demonstrated significant prognostic value for the ARG-based model. Specific ARGs could function as crucial biomarkers for patient outcome, while simultaneously offering new avenues for therapeutic intervention.