Cuproptosis illustrates tumor micro-environment features and predicts prostate cancer therapeutic sensitivity and...

Prostate cancer (PCA) is a common malignant genitourinary tumor that significantly impacts patient survival. Cuproptosis, a copper-dependent programmed cell death mechanism, plays a vital role in tumor development, therapy resistance, and immune microenvironment regulation in PCA. However, research on cuproptosis in prostate cancer is still in its early stages.

Using the publicly available datasets TCGA and GEO, we first acquired the transcriptome and clinical information of PCA patients. The expression of cuprotosis-related genes (CRG) was identified and a prediction model was established based on LASSO-COX method. The predictive performance of this model was evaluated based on Kaplan-Meier method. Using GEO datasets, we further confirmed the critical genes level in the model. Tumor responses to immune checkpoint (ICP) inhibitors were predicted based on Tumor Immune Dysfunction and Exclusion (TIDE) score. The Genomics of Drug Sensitivity in Cancer (GDSC) was utilized to forecast drug sensitivity in cancer cells, whereas the GSVA was employed to analyze enriched pathways related to the cuproptosis signature. Subsequently, the function of PDHA1 gene in PCA was verified.

A predictive risk model on basis of five cuproptosis-related genes (ATP7B, DBT, LIPT1, GCSH, PDHA1) were established. The progression free survival of low-risk group was obviously longer than the high-risk group, and exhibit better response to ICB therapy.Furthermore,PDHA1 is very important in the pathological process of PCA according to regressions analysis result, and the validation of external data sets were conducted. High PDHA1 expression patients with PCA not only had a shorter PFS and were less likely to benefit from ICB treatment, but they were also less responsive to multiple targeted therapeutic drugs. In preliminary research, PDHA1 knockdown significantly decreased the proliferation and invasion of PCA cells.

This study established a novel cuproptosis-related gene-based prostate cancer prediction model that accurately predicts the prognosis of PCA patients. The model benefits individualized therapy and can assist clinicians in making clinical decisions for PCA patients. Furthermore, our data show that PDHA1 promotes PCA cell proliferation and invasion while modulating the susceptibility to immunotherapy and other targeted therapies. PDHA1 can be regarded as an important target for PCA therapy.