The potential prognostic value of Cartilage acidic protein 1 and secretoglobin family 1D member 1 in prostate cancer

Abstract

Background Prostate cancer is a prevalent malignancy with a broad range of clinical outcomes, necessitating improved prognostic biomarkers for precise patient stratification and personalized treatment. This study leverages machine learning techniques to identify and validate novel prognostic biomarkers using DNAseq and RNAseq data for prostate cancer. Methods Whole genome sequencing and gene expression profiling in patients were used from TCGA to identify DEGs and genetic alterations. Next, deep learning was utilized to determine key genes. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the involved biological process and pathways. Survival analysis of patients with prostate cancer with reference on dysregulated genes was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to build a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to machine learning -based analysis and the Receiver operating characteristic (ROC) curve. Results We identified a total of 609 DEGs in patients, of which 358 were downregulated and 251 were upregulated. Deep learning results identified 20 genes, and these were combined with the analysis of DNA-seq. Survival analysis of patients with prostatic cancer showed that dysregulated expression of ASB12, BLOC1S1, CRTAC1, KCNQ1, KISS1, M2T2A, RNF207, SCGB1D1, SLC13A2, SORBS1, TGFBR3, WSCD2, ANFKFY1, CRYBA4, MIR204, QRFP, SNX15, and YWHAH genes were related with a poor clinical prognosis. The combio-ROC curve analysis reveals ed that TGFRB3, SCGB1D1 and CRTAC1 were potential diagnostic biomarkers with a great higher sensitivity and AUC than currently available biomarkers. Also, the combination of SCGB1D1 and CRTAC1 demonstrated the greatest accuracy, sensitivity, and specificity towards diagnostic applications. The potential value of these markers was validated in six other datasets. Conclusion Our findings demonstrated the potential value of SCGB1D1 and CRTAC1 as novel biomarkers and therapeutic targets in prostate cancer which had a higher AUC, sensitivity, and specificity compared to PSA, indicating further functional investigations on the potential value of emerging markers in prostate cancer.