Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis

Maryam Gholizadeh; Seyed Reza Mazlooman; Morteza Hadizadeh; Marek Drozdzik; Saeid Eslami

doi:10.1016/j.mex.2023.102021

Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis

MethodsX. 2023 Jan 18:10:102021. doi: 10.1016/j.mex.2023.102021. eCollection 2023.

Authors

Maryam Gholizadeh¹, Seyed Reza Mazlooman², Morteza Hadizadeh³, Marek Drozdzik⁴, Saeid Eslami¹

Affiliations

¹ Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91388-13944, Iran.
² Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1477893780, Iran.
³ Physiology Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman 7616913555, Iran.
⁴ Department of Experimental and Clinical Pharmacology, Pomeranian Medical University, Szczecin 70-111, Poland.

Abstract

One methodology extensively used to develop biomarkers is the precise detection of highly responsive genes that can distinguish cancer samples from healthy samples. The purpose of this study was to screen for potential hepatocellular carcinoma (HCC) biomarkers based on non-fusion integrative multi-platform meta-analysis method. The gene expression profiles of liver tissue samples from two microarray platforms were initially analyzed using a meta-analysis based on an empirical Bayesian method to robust discover differentially expressed genes in HCC and non-tumor tissues. Then, using the bioinformatics technique of weighted correlation network analysis, the highly associated prioritized Differentially Expressed Genes (DEGs) were clustered. Co-expression network and topological analysis were utilized to identify sub-clusters and confirm candidate genes. Next, a diagnostic model was developed and validated using a machine learning algorithm. To construct a prognostic model, the Cox proportional hazard regression analysis was applied and validated. We identified three genes as specific biomarkers for the diagnosis of HCC based on accuracy and feasibility. The diagnostic model's area under the curve was 0.931 with confidence interval of 0.923-0.952.•Non-fusion integrative multi-platform meta-analysis method.•Classification methods and biomarkers recognition via machine learning method.•Biomarker validation models.

Keywords: Machine learning; Meta-analysis; Non-fusion integrative Meta-analysis; Non-fusion integrative method; Survival analysis.