Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma

To elucidate the diagnostic value and clinical relevance of protein kinase D3 (PRKD3) in hepatocellular carcinoma (HCC), we analyzed data retrieved from The Cancer Genome Atlas (TCGA) database, which revealed high expression of PRKD3 in HCC tissues. Subsequently, we collected a total of 392 clinical plasma samples from healthy individuals, patients with cirrhosis or decompensated cirrhosis, and patients with HCC. Plasma PRKD3 levels were then determined across HCC patients and individuals at high risk of developing the disease. The results revealed significantly elevated PRKD3 concentrations in patients with cirrhosis, decompensated cirrhosis, and HCC compared to healthy controls (P<0.01). The areas under the receiver operating characteristic (ROC) curve for these three groups were 0.8107, 0.7899, and 0.7177, respectively. To further evaluate the efficacy of PRKD3 as an adjunctive diagnostic biomarker for HCC, we employed a panel of machine learning algorithms as primary classifiers, including extra trees (ET), gradient boosting (GB), random forest (RF), and support vector machine (SVM). A multi-parameter joint diagnostic model was constructed by combining PRKD3 expression data with a set of clinical parameters, including gender, age, total bilirubin (TBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), albumin (ALB), alpha-fetoprotein (AFP), and prothrombin induced by vitamin K absence-II (PIVKA-II). This integrated approach exhibited substantially improved diagnostic performance, achieving an accuracy of 0.861, sensitivity of 0.863, specificity of 0.925, and precision of 0.862. Collectively, these findings highlight the potential of PRKD3 as an integral component of a comprehensive diagnostic tool for the early identification of HCC.