Based on Integrated Bioinformatics Analysis Identification of Biomarkers in Hepatocellular Carcinoma Patients from Different Regions

Abstract

Accumulating statistics have shown that liver cancer causes the second highest mortality rate of cancer-related deaths worldwide, of which 80% is hepatocellular carcinoma (HCC). Given the underlying molecular mechanism of HCC pathology is not fully understood yet, identification of reliable predictive biomarkers is more applicable to improve patients' outcomes. The results of principal component analysis (PCA) showed that the grouped data from 1557 samples in Gene Expression Omnibus (GEO) came from different populations, and the mean tumor purity of tumor tissues was 0.765 through the estimate package in R software. After integrating the differentially expressed genes (DEGs), we finally got 266 genes. Then, the protein-protein interaction (PPI) network was established based on these DEGs, which contained 240 nodes and 1747 edges. FOXM1 was the core gene in module 1 and highly associated with FOXM1 transcription factor network pathway, while FTCD was the core gene in module 2 and was enriched in the metabolism of amino acids and derivatives. The expression levels of hub genes were in line with The Cancer Genome Atlas (TCGA) database. Meanwhile, there were certain correlations among the top ten genes in the up- and downregulated DEGs. Finally, Kaplan–Meier curves and receiver operating characteristic (ROC) curves were plotted for the top five genes in PPI. Apart from CDKN3, the others were closely concerned with overall survival. In this study, we detected the potential biomarkers and their involved biological processes, which would provide a new train of thought for clinical diagnosis and treatment.

Advances in the early diagnosis of hepatocellular carcinoma

“… 164 A growing number of studies have screened critical genes in HCC as potential biomarkers through bioinformatics assessments. 165 , 166 , 167 A number of hub genes were targeted from protein–protein interaction networks (PPIs) of differentially express genes (DEGs), which were filtered using software from online databases. For instance, integrated bioinformatics analysis derived from 927 HCC tissues and 630 adjacent normal tissues in the Gene Expression Omnibus (GEO) database detected several core genes closely associated to functional pathways involved in the carcinogenesis and progression of HCC, including FOXM1, CCNA2, AURKA, CDKN3, CDC20 and FTCD.…”

“…For instance, integrated bioinformatics analysis derived from 927 HCC tissues and 630 adjacent normal tissues in the Gene Expression Omnibus (GEO) database detected several core genes closely associated to functional pathways involved in the carcinogenesis and progression of HCC, including FOXM1, CCNA2, AURKA, CDKN3, CDC20 and FTCD. 165 …”

Homer2 and Homer3 Act as Novel Biomarkers in Diagnosis of hepatitis B virus-induced Hepatocellular Carcinoma

“…International Publisher diagnosed at an advanced stage [3]. Currently, serum alpha-fetoprotein (AFP) levels combined with computed tomography (CT) and magnetic resonance imaging (MRI) are the most common non-invasive diagnostic methods, but all of them have sensitivity problems [9].…”

“…The mortality of Liver cancer ranks second among human cancers worldwide [1,2]. It is reported that hepatocellular carcinoma (HCC) occupy approximately 80% of liver cancers [3]. In 2018, about 840,000 HCC patients were newly diagnosed and 782,451 deaths were reported.…”

Circulating mRNA and microRNA profiling analysis in patients with ischemic stroke

“…Principal component analysis was subsequently conducted to reduce the dimension of multivariate data to two or three principal components, which can be visualized graphically with the least information loss. We used r software to calculate and visualize the principal component analysis (25). Probes without a corresponding gene symbol were then filtered and the average value of gene symbols with multiple probes was calculated using R software (26).…”