1. Exploring Selenoprotein P in Liver Cancer: Advanced Statistical Analysis and Machine Learning Approaches.
- Author
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Razaghi, Ali and Björnstedt, Mikael
- Subjects
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LIPID metabolism , *PROTEIN metabolism , *LIVER tumors , *STATISTICAL models , *DATA analysis , *RESEARCH funding , *TUMOR markers , *GENE expression , *LONGITUDINAL method , *STATISTICS , *MACHINE learning , *TRIGLYCERIDES , *HEPATOCELLULAR carcinoma , *HYPOXEMIA , *OVERALL survival , *REGRESSION analysis - Abstract
Simple Summary: This research explores the role of selenoprotein P, a protein crucial for transporting selenium in the body, in liver cancer. The study aims to understand how selenoprotein P levels relate to the severity of hepatocellular carcinoma and its impact on patient outcomes. Findings indicate that selenoprotein P expression varies significantly with cancer stage and patient demographics like race and gender. It also correlates strongly with hormone and lipid metabolism markers. Importantly, selenoprotein P shows potential as a predictor of patient survival and as a biomarker for hypoxia, a condition affecting cancer progression. These insights may lead to better diagnostic tools and personalized treatments for liver cancer, emphasizing the need for further studies to validate selenoprotein P's clinical utility in real-world settings. Selenoprotein P (SELENOP) acts as a crucial mediator, distributing selenium from the liver to other tissues within the body. Despite its established role in selenium metabolism, the specific functions of SELENOP in the development of liver cancer remain enigmatic. This study aims to unravel SELENOP's associations in hepatocellular carcinoma (HCC) by scrutinizing its expression in correlation with disease characteristics and investigating links to hormonal and lipid/triglyceride metabolism biomarkers as well as its potential as a prognosticator for overall survival and predictor of hypoxia. SELENOP mRNA expression was analyzed in 372 HCC patients sourced from The Cancer Genome Atlas (TCGA), utilizing statistical methodologies in R programming and machine learning techniques in Python. SELENOP expression significantly varied across HCC grades (p < 0.000001) and among racial groups (p = 0.0246), with lower levels in higher grades and Asian individuals, respectively. Gender significantly influenced SELENOP expression (p < 0.000001), with females showing lower altered expression compared to males. Notably, the Spearman correlation revealed strong positive connections of SELENOP with hormonal markers (AR, ESR1, THRB) and key lipid/triglyceride metabolism markers (PPARA, APOC3, APOA5). Regarding prognosis, SELENOP showed a significant association with overall survival (p = 0.0142) but explained only a limited proportion of variability (~10%). Machine learning suggested its potential as a predictive biomarker for hypoxia, explaining approximately 18.89% of the variance in hypoxia scores. Future directions include validating SELENOP's prognostic and diagnostic value in serum for personalized HCC treatment. Large-scale prospective studies correlating serum SELENOP levels with patient outcomes are essential, along with integrating them with clinical parameters for enhanced prognostic accuracy and tailored therapeutic strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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