首页|Southeast University Reports Findings in Liver Cancer (G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma)
Southeast University Reports Findings in Liver Cancer (G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma)
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Springer Nature
New research on Oncology - Liver Cancer is the subject of a report. According to news reporting originating in Jiangsu, People's Republic of China, by NewsRx journalists, research stated, “Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood.” Financial support for this research came from Jiangsu Provincial Key Medical Discipline. The news reporters obtained a quote from the research from Southeast University, “To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent validation of the effect of G6PD on the activity, migration, and invasive ability of hepatocellular carcinoma cells by using the low expression of LI-7 and SNU-449. Additionally, we employed machine learning to validate and compare the predictive capacity of four algorithms for LIHC patient prognosis. Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses showed that G6PD accounted for 46%, 31%, and 49% of the multiple factors leading to LIHC. Furthermore, we observed that G6PD knockdown in hepatocellular carcinoma cells led to reduced proliferation, migration, and invasion abilities. Remarkably, the Decision Tree C5.0 decision tree algorithm demonstrated superior discriminatory performance among the machine learning methods assessed.”
JiangsuPeople's Republic of ChinaAsiaAlgorithmsCancerCarcinomasCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineLiver CancerMachine LearningOncology
NETL
NSTL
Springer Nature
