Combinatorial chemistry & high throughput screening · 2026
Identification of a Cuproptosis-Related Molecular Signature for Predicting Biochemical Recurrence in Prostate Cancer.
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Chen Dong-Ning, Wu Xiao-Hui, You Qi, Zhuang Rui-Bin, Ruan Zhong-Tian, Zhu Jun-Ming, Zheng Qing-Shui, Chen Ye-Hui, Wei Yong, Lin Xiao-Dan, Xue Xue-Yi
Аннотация
BACKGROUND: This study aimed to develop and validate a Cuproptosis-Related Gene (CRG) signature for predicting Biochemical Recurrence-Free Survival (BCRFS) and characterizing the Tumor Immune Microenvironment (TIME) in Prostate Cancer (PCa).
METHODS: Transcriptomic and clinical data were collected from TCGA (n=405) and GEO (GSE70770, n=203). Consensus clustering based on 10 CRGs defined molecular subtypes. Differentially expressed genes between clusters were subjected to LASSO Cox regression to construct a prognostic signature in the TCGA cohort, followed by validation in GEO and combined cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) and Immunohistochemistry (IHC) were conducted for experimental validation.
RESULTS: Two CRG-based subtypes were identified, characterized by distinct clinicopathological features, immune checkpoint expression, and BCRFS. A six-gene signature (CALML5, MMP11, UBE2C, ANPEP, TMEM59L, COMP) stratified patients into high- and low-risk groups with significantly different BCRFS (log-rank P<0.001). The model showed good predictive accuracy (AUCs 0.717-0.837 at 1 year, 0.728-0.771 at 3 years, 0.683-0.695 at 5 years) and remained independent of clinicopathological factors. High-risk patients exhibited elevated immune/stromal scores, altered immune infiltration, and higher immune checkpoint expression. qRT-PCR confirmed upregulation of CALML5, MMP11, UBE2C, and COMP in PCa cell lines, while IHC validated differential protein expression of all six genes between PCa and BPH tissues (all P<0.05).
DISCUSSION: This six-gene CRG signature predicts BCRFS and reflects immune heterogeneity in PCa. Its integration into prognostic models may guide personalized management and inform immunotherapy strategies, warranting further validation in prospective clinical studies.
CONCLUSION: This study initially identified two cuproptosis-related molecules based on the expression patterns of cuproptosis-related genes. In addition, we developed a new cuproptosisrelated molecular signature with great predictive performance for BCRFS and tumor immune environment using six DERRGs (including CALML5, MMP11, UBE2C, ANPEP, TMEM59L, COMP). These findings would be conducive to a deeper cognition of the potential mechanism of cuproptosis of PCa.
METHODS: Transcriptomic and clinical data were collected from TCGA (n=405) and GEO (GSE70770, n=203). Consensus clustering based on 10 CRGs defined molecular subtypes. Differentially expressed genes between clusters were subjected to LASSO Cox regression to construct a prognostic signature in the TCGA cohort, followed by validation in GEO and combined cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) and Immunohistochemistry (IHC) were conducted for experimental validation.
RESULTS: Two CRG-based subtypes were identified, characterized by distinct clinicopathological features, immune checkpoint expression, and BCRFS. A six-gene signature (CALML5, MMP11, UBE2C, ANPEP, TMEM59L, COMP) stratified patients into high- and low-risk groups with significantly different BCRFS (log-rank P<0.001). The model showed good predictive accuracy (AUCs 0.717-0.837 at 1 year, 0.728-0.771 at 3 years, 0.683-0.695 at 5 years) and remained independent of clinicopathological factors. High-risk patients exhibited elevated immune/stromal scores, altered immune infiltration, and higher immune checkpoint expression. qRT-PCR confirmed upregulation of CALML5, MMP11, UBE2C, and COMP in PCa cell lines, while IHC validated differential protein expression of all six genes between PCa and BPH tissues (all P<0.05).
DISCUSSION: This six-gene CRG signature predicts BCRFS and reflects immune heterogeneity in PCa. Its integration into prognostic models may guide personalized management and inform immunotherapy strategies, warranting further validation in prospective clinical studies.
CONCLUSION: This study initially identified two cuproptosis-related molecules based on the expression patterns of cuproptosis-related genes. In addition, we developed a new cuproptosisrelated molecular signature with great predictive performance for BCRFS and tumor immune environment using six DERRGs (including CALML5, MMP11, UBE2C, ANPEP, TMEM59L, COMP). These findings would be conducive to a deeper cognition of the potential mechanism of cuproptosis of PCa.