Global analysis of gene expression signature and diagnostic/prognostic biomarker identification of hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world. The landscape of HCC’s molecular alteration signature has been explored over the last few decades. Even so, more comprehensive research is still needed to improve understanding of tumorigenesis and progression of HCC, as well as to identify potential biomarkers for the malignancy. In this research, a comprehensive bioinformatics analysis was conducted based on the publicly available databases from both the Cancer Genome Atlas (TCGA) program and the gene expression omnibus (GEO) database. R/Bioconductor was used to analyze differentially expressed genes (DEGs) between HCC tumor and normal control (NC) samples, and then a protein-protein interaction (PPI) network of DEGs was established through the STRING platform. Finally, the application of specific candidate genes as diagnostic or prognostic biomarkers of HCC was explored and evaluated by ROC and survival analysis. A total of 310 DEGs were detected in the HCC tumor samples. Thirty-six hub DEGs in the PPI network and 10 candidates of the 36 genes showed significant alterations in tumor expression, including CDKN3, TOP2A, UBE2C, CDC20, PBK, ASPM, KIF20A, NCAPG, CCNB2, CYP3A4. The 10-gene signature had relatively significant effects when distinguishing tumors from normal samples (sensitivity >70%, specificity >70%, AUC >0.8, p < 0.001). Eight candidate genes were negatively correlated with the overall survival rate of the patients ( p < 0.05) and were all up-regulated in HCC tumor samples. The age and gender factors had no significant impact on the overall survival rate of HCC patients ( p > 0.05), and the TNM stage status factor had a significant negative prognosis correlation ( p < 0.05). This research provides evidence for a better understanding of tumorigenesis and progression of HCC and helps to explore candidate targets for disease diagnosis and treatment.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

“…In the case of our study, TOP2A was also considered as a key or core gene for the progression and development of HCC. This finding was coincided with previous studies 12,14,15,18,[20][21][22][23][24][25]27,[32][33][34][35][36]39,[41][42][43]45,46,48,50,51,[54][55][56][57][58]61 .…”

The expression of cuproptosis-related genes in hepatocellular carcinoma and their relationships with prognosis

“…Twenty-five candidate cuproptosis-related genes expression were significantly unequal between tumor and normal liver tissues, and they were also significantly correlated with poor survival in HCCs. Previous studies have found that ABCB6 (34), CDKN2A (35), CDKN3 (36), and TPI1 (37) (catalyzing the conversion of dihydroxyacetone phosphate (DHAP) and D- ACO1, aconitase 1; ACP1, acid phosphatase 1; AOC1, amine oxidase copper containing 1; ATP6AP1, ATPase H+ transporting accessory protein 1; ATP7A, copper-transporting p-type adenosine triphosphatase 1; CI, confidence interval; CIAPIN1, cytokine induced apoptosis inhibitor 1; COA6, cytochrome c oxidase assembly factor 6; DLAT, dihydrolipoamide Sacetyltransferase; FDX1, ferredoxin 1; FDX2, ferredoxin 2; HR, hazard ratio; ISCA2, iron-sulfur cluster assembly 2; LIPT1, lipoyltransferase 1; MT-CO1, mitochondrially encoded cytochrome c oxidase I; MTF1, metal regulatory transcription factor 1; NDOR1, NADPH dependent diflavin oxidoreductase 1; NUBP2, nucleotide binding protein 2; PDHA1, pyruvate dehydrogenase E1 subunit alpha 1; SCO2, synthesis of cytochrome c oxidase 2; SLC25A3, solute carrier family 25 member 3; TMEM199, transmembrane protein 199; *P < 0.05; **P < 0.01; ***P < 0.001.…”

Identification of Ferroptosis-Related Genes Signature Predicting the Efficiency of Invasion and Metastasis Ability in Colon Adenocarcinoma

“…Specifically, these datasets included 114 COAD samples processed on the GPL16694 Agilent-022522 SurePrint G3 CGH Array 4x180K (Probe Name Version) of GSE75500, which were applied as validation sets. Quality controls included relative expression (RLE) and standardized scale-free standard error (NUSE) implemented in the affyPLM package provided by Bioconductor ( Wang J et al, 2021 ) ( www.bioconductor.org ). Raw gene expression data were background corrected using the Robust Multi-Array Average (RMA) method, standardized using the Quantiles method, and summarized by the median polish method.…”