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• W4380031640 abstract "<p>Hepatitis B virus (HBV) is a major risk factor for hepatocellular carcinoma (HCC), characterized by genomic instability and chronic DNA replication stress. This study presents a robust machine-learning framework using random survival forest to develop a DNA replication stress-related prognostic index (<i>PI_RS</i>) for HBV-associated HCC. Transcriptomic expression profiles from 606 HCC cases were used to construct <i>PI_RS</i>, which outperformed population-based predictors, demonstrating superior prognostic prediction in HBV-associated HCC. Lower <i>PI_RS</i> scores were associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways, and increased responsiveness to immunotherapy. Conversely, higher <i>PI_RS</i> scores correlated with elevated Ki-67 marker, cancer stemness, and enrichment in DNA replication stress, cell cycle pathways, and chromatin remodelers, resulting in an 'immune-cold' phenotype and unfavorable clinical outcomes. Through large-scale <i>in-silico</i> drug screening, potential therapeutic targets (<i>TOP2A</i>, <i>PRMT1</i>, <i>CSNK1D</i>, and <i>PPIH</i>) and five agents, including topoisomerase and CDK inhibitors, were identified for patients with high <i>PI_RS</i> scores. These findings hold promise for optimizing therapeutic strategies in HCC and providing insights into the management of HBV carriers. In summary, our machine-learning approach yielded <i>PI_RS</i> as a powerful predictor for assessing prognosis in HBV-associated HCC. This analytic framework improves population-based therapeutic strategies, facilitates personalized treatment, and ushers in a new era of precision medicine in HCC.</p>" @default.
• W4380031640 created "2023-06-10" @default.
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• W4380031640 date "2023-01-01" @default.
• W4380031640 modified "2023-10-06" @default.
• W4380031640 title "DNA replication stress stratifies prognosis and enables exploitable therapeutic vulnerabilities of HBV-associated hepatocellular carcinoma: An in-silico precision oncology strategy" @default.
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• W4380031640 doi "https://doi.org/10.59717/j.xinn-med.2023.100014" @default.
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