Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer

Abstract

Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the development of more precise and specific methods for diagnosis. In this study, a deep learning model was applied to identify ten key genes from a pool of 68 common differentially expressed genes in the three transcriptomic datasets. The model demonstrated high performance, with the accuracy of 0.969, R² of 0.88, and PR-AUC of 0.98. Notably, selected genes have been previously reported as functionally important in various cancers. Among them, SCUBE2 stands out as a novel potential diagnostic biomarker in prostate cancer, showing a strong diagnostic performance in the TCGA dataset with AUC = 0.84, sensitivity = 0.76, and specificity = 0.84. SCUBE2 is a secreted glycoprotein known for its ability to suppress tumor growth, cell migration, and epithelial-mesenchymal transition (EMT) in several cancer types, including gliomas, breast, and colorectal cancers, mainly through its regulation of signaling pathways such as Hedgehog (Shh). Although its role in prostate cancer (PCa) has not been previously explored, its consistent downregulation across multiple PCa datasets in this study suggests it may act as a tumor suppressor, warranting further investigation. Another candidate, SLC16A5, showed moderate performance individually (AUC = 0.62, SP = 0.81, SE = 0.42 in GSE88808), but its combination with SCUBE2 significantly enhanced diagnostic accuracy (combined AUC = 0.76, SE = 0.75, SP = 0.71). SLC16A5 is a monocarboxylate transporter involved in metabolic reprogramming, and prior studies have linked its downregulation to immune infiltration and poor prognosis in PCa. Functional enrichment analysis of the ten identified genes revealed strong involvement of these genes in cancer-related processes, including gap junction assembly, tight junction formation, efflux transporter activity, and pathways such as Hedgehog signaling, leukocyte transendothelial migration, and cell-cell adhesion. Hub gene analysis further confirmed the central roles of identified genes such as CAV1, GJA1, AMACR, and CLDN8, which are well-documented in cancer progression, metastasis, or therapeutic resistance. In summary, this study identifies SCUBE2 as a novel potential diagnostic biomarker for prostate cancer and supports the use of AI-driven gene discovery in identifying key players in tumor biology. The combination of SCUBE2 with SLC16A5 not only enhances diagnostic precision but also opens new avenues for functional and clinical validation, ultimately contributing to the development of more accurate, multi-gene diagnostic panels for PCa.

Keywords: Artificial intelligence; Biomarker; Diagnostic biomarker; Prostate cancer; RNA-seq.