Gutierrez-Chakraborty Elizabeth, Chakraborty Debaditya, Das Debodipta, Bai Yidong
Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, TX, United States.
College of Engineering and Integrated Design, University of Texas at San Antonio, TX, United States.
Expert Syst Appl. 2024 Oct 15;252(Pt B). doi: 10.1016/j.eswa.2024.124239. Epub 2024 May 15.
Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies.
肝细胞癌（HCC）仍然是一项全球性的健康挑战，死亡率很高，这在很大程度上是由于诊断延迟和当前治疗方法的疗效欠佳。鉴于迫切需要更可靠的非侵入性诊断工具和新颖的治疗策略，本研究聚焦于使用可解释人工智能（XAI）来发现和应用HCC的新型遗传生物标志物。尽管HCC研究取得了进展，但目前的生物标志物，如甲胎蛋白（AFP），在敏感性和特异性方面存在局限性，因此需要转向更精确、更可靠的标志物。本文提出了一种创新的多模型XAI和概率因果推理框架，以识别和验证用于HCC预后的关键遗传生物标志物。我们的方法包括分析临床和基因表达数据，以识别具有预后意义的潜在生物标志物。该研究利用了针对大量基因表达数据集进行验证的强大人工智能模型，不仅证明了预测准确性，还通过可解释的指标证明了所识别生物标志物的临床相关性。研究结果突出了TOP3B、SSBP3和COX7A2L等生物标志物的重要性，这些标志物在多个模型中始终具有影响力，表明它们在提高HCC预后预测准确性方面的作用超越了AFP。值得注意的是，该研究还强调了这些生物标志物与西班牙裔人群的相关性，这与针对特定人群的研究这一更大目标相一致。XAI在生物标志物发现中的应用代表了HCC研究的一项重大进展，为该疾病提供了更细致入微的理解，并为改进诊断和治疗策略奠定了基础。
