G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma
Abstract Background 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. Materials & methods To address this gap, we conducted a comprehensiv...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2024-01-01
|
Series: | BMC Cancer |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12885-024-11887-6 |
_version_ | 1797274294227566592 |
---|---|
author | Fei Li Boshen Wang Hao Li Lu Kong Baoli Zhu |
author_facet | Fei Li Boshen Wang Hao Li Lu Kong Baoli Zhu |
author_sort | Fei Li |
collection | DOAJ |
description | Abstract Background 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. Materials & methods 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. Results 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. Conclusion The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma. |
first_indexed | 2024-03-07T14:56:20Z |
format | Article |
id | doaj.art-4c7cbab1930d4e929f5c5810267f9120 |
institution | Directory Open Access Journal |
issn | 1471-2407 |
language | English |
last_indexed | 2024-03-07T14:56:20Z |
publishDate | 2024-01-01 |
publisher | BMC |
record_format | Article |
series | BMC Cancer |
spelling | doaj.art-4c7cbab1930d4e929f5c5810267f91202024-03-05T19:23:42ZengBMCBMC Cancer1471-24072024-01-0124111910.1186/s12885-024-11887-6G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinomaFei Li0Boshen Wang1Hao Li2Lu Kong3Baoli Zhu4Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast UniversityKey Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast UniversityKey Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast UniversityKey Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast UniversityInstitute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Prevention and ControlAbstract Background 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. Materials & methods 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. Results 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. Conclusion The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma.https://doi.org/10.1186/s12885-024-11887-6G6PDLiver hepatocellular carcinomaPrognosticMachine learningImmunologyDrug Sensitivity |
spellingShingle | Fei Li Boshen Wang Hao Li Lu Kong Baoli Zhu G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma BMC Cancer G6PD Liver hepatocellular carcinoma Prognostic Machine learning Immunology Drug Sensitivity |
title | G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma |
title_full | G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma |
title_fullStr | G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma |
title_full_unstemmed | G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma |
title_short | G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma |
title_sort | g6pd and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma |
topic | G6PD Liver hepatocellular carcinoma Prognostic Machine learning Immunology Drug Sensitivity |
url | https://doi.org/10.1186/s12885-024-11887-6 |
work_keys_str_mv | AT feili g6pdandmachinelearningalgorithmsasprognosticanddiagnosticindicatorsofliverhepatocellularcarcinoma AT boshenwang g6pdandmachinelearningalgorithmsasprognosticanddiagnosticindicatorsofliverhepatocellularcarcinoma AT haoli g6pdandmachinelearningalgorithmsasprognosticanddiagnosticindicatorsofliverhepatocellularcarcinoma AT lukong g6pdandmachinelearningalgorithmsasprognosticanddiagnosticindicatorsofliverhepatocellularcarcinoma AT baolizhu g6pdandmachinelearningalgorithmsasprognosticanddiagnosticindicatorsofliverhepatocellularcarcinoma |