Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis

Abstract

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.

Machine Learning-Based Identification of B4GALNT1 as a Key Player in Hepatocellular Carcinoma: A Comprehensive Bioinformatics and Structural Analysis

“…There is a significant morbidity and death rates associated with liver hepatocellular carcinoma (LIHC), therefore improving clinical diagnosis and treatment of LIHC is important [45]. Several additional research have reported on the use of gene expression data to find LIHC biomarkers [46, 47].…”

GABRDpromotes the progression of breast cancer through CDK1-dependent cell cycle regulation

“…Most previous studies on GABRD have focused on neurological diseases, and several studies have recently revealed the possible functional role of GABRD in tumors. Studies based on genomics datasets including mRNA expression data from The Cancer Genome Atlas (TCGA) database showed that GABRD expression was associated with corticotrophinomas, hepatocellular carcinoma, kidney renal clear cell carcinoma, thyroid carcinoma and other tumors, which indicated that GABRD might 4 serve as a potential biomarker to predict patient prognoses (6)(7)(8). However, little is known about the involvement of GABRD in the tumorigenesis and progression of breast cancer.…”

Comprehensive analysis of N6‐methyladenosine‐related long non‐coding RNAs for prognosis prediction in liver hepatocellular carcinoma

“…Currently, in addition to traditional methods including liver transplantation, sorafenib, and other available treatments for LIHC, 3 many combination methods including combinations of different immune checkpoint inhibitors (ICIs) are under investigation 4,5 . For prognostic prediction of patients with LIHC, the patient's physical status, degree of liver dysfunction, tumor expansion, and alpha‐fetoprotein (AFP) concentration are the main factors 6,7 . Therefore, the identification and comprehensive analysis of novel molecular biomarkers will benefit the ability to predict patient prognosis in LIHC.…”

“… 4 , 5 For prognostic prediction of patients with LIHC, the patient's physical status, degree of liver dysfunction, tumor expansion, and alpha‐fetoprotein (AFP) concentration are the main factors. 6 , 7 Therefore, the identification and comprehensive analysis of novel molecular biomarkers will benefit the ability to predict patient prognosis in LIHC.…”