Integrated analysis of the RNA-Seq data of liver hepatocellular carcinoma

Xue, Huaru; Luo, Limei; Yao, Yang; Deng, Shibing; Huang, Xuelin

doi:10.4149/neo_2018_170212n98

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…These enriched processes are not shared by the four HCC groups funded. However, at least three groups shared translation, ribosome, metabolism of proteins, and cytoplasm ribosomal proteins[ 106 ]. The microarray analysis using 25 HCC samples identified thousands of differentially expressed genes, and the pathways of cell cycle response, DNA damage response, cell survival, and apoptosis were identified.…”

Section: Pathwaysmentioning

confidence: 99%

Systems biology and OMIC data integration to understand gastrointestinal cancers

Bispo¹,

Granger²,

Almeida³

et al. 2022

World J Clin Oncol

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Gastrointestinal (GI) cancers are a set of diverse diseases affecting many parts/ organs. The five most frequent GI cancer types are esophageal, gastric cancer (GC), liver cancer, pancreatic cancer, and colorectal cancer (CRC); together, they give rise to 5 million new cases and cause the death of 3.5 million people annually. We provide information about molecular changes crucial to tumorigenesis and the behavior and prognosis. During the formation of cancer cells, the genomic changes are microsatellite instability with multiple chromosomal arrangements in GC and CRC. The genomically stable subtype is observed in GC and pancreatic cancer. Besides these genomic subtypes, CRC has epigenetic modification (hypermethylation) associated with a poor prognosis. The pathway information highlights the functions shared by GI cancers such as apoptosis; focal adhesion; and the p21-activated kinase, phosphoinositide 3-kinase/Akt, transforming growth factor beta, and Toll-like receptor signaling pathways. These pathways show survival, cell proliferation, and cell motility. In addition, the immune response and inflammation are also essential elements in the shared functions. We also retrieved information on protein-protein interaction from the STRING database, and found that proteins Akt1, catenin beta 1 (CTNNB1), E1A binding protein P300, tumor protein p53 (TP53), and TP53 binding protein 1 (TP53BP1) are central nodes in the network. The protein expression of these genes is associated with overall survival in some GI cancers. The low TP53BP1 expression in CRC, high EP300 expression in esophageal cancer, and increased expression of Akt1/TP53 or low CTNNB1 expression in GC are associated with a poor prognosis. The Kaplan Meier plotter database also confirmed the association between expression of the five central genes and GC survival rates. In conclusion, GI cancers are very diverse at the molecular level. However, the shared mutations and protein pathways might be used to understand better and reveal diagnostic/prognostic or drug targets.

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Section: Pathwaysmentioning

confidence: 99%

Systems biology and OMIC data integration to understand gastrointestinal cancers

Bispo¹,

Granger²,

Almeida³

et al. 2022

World J Clin Oncol

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“…Coagulation factor II (F2) plays a major role in proteolysis to form thrombin in the first step of the coagulation cascade and eventually generates hemostasis. An enrichment analysis of genetic changes during the development of HCC identified several hub genes, including F2, which interacts in several groups of conditional specific PPI networks (42). Additionally, it was reported that F2 is associated with invasion in neuroendocrine prostate cancer (43).…”

Section: )mentioning

confidence: 99%

Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer

Liu

et al. 2020

Front. Oncol.

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Simultaneous identification of multiple single genes and multi-gene prognostic signatures with higher efficacy in liver cancer has rarely been reported. Here, 1,173 genes potentially related to the liver cancer prognosis were mined with Coremine, and the gene expression and survival data in 370 samples for overall survival (OS) and 319 samples for disease-free survival (DFS) were retrieved from The Cancer Genome Atlas. Numerous survival analyses results revealed that 39 genes and 28 genes significantly associated with DFS and OS in liver cancer, including 18 and 12 novel genes that have not been systematically reported in relation to the liver cancer prognosis, respectively. Next, totally 9,139 three-gene combinations (including 816 constructed by 18 novel genes) for predicting DFS and 3,276 three-gene combinations (including 220 constructed by 12 novel genes) for predicting OS were constructed based on the above genes, and the top 15 of these four parts three-gene combinations were selected and shown. Moreover, a huge difference between high and low expression group of these three-gene combination was detected, with median survival difference of DFS up to 65.01 months, and of OS up to 83.57 months. The high or low expression group of these three-gene combinations can predict the longest prognosis of DFS and OS is 71.91 months and 102.66 months, and the shortest is 6.24 months and 13.96 months. Quantitative real-time polymerase chain reaction and immunohistochemistry reconfirmed that three genes F2, GOT2, and TRPV1 contained in one of the above combinations, are significantly dysregulated in liver cancer tissues, low expression of F2, GOT2, and TRPV1 is associated with poor prognosis in liver cancer. Overall, we discovered a few novel single genes and multi-gene combinations biomarkers that are closely related to the long-term prognosis of liver cancer, and they can be potential therapeutic targets for liver cancer.

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“…Huang and co-workers performed the integrated analysis of RAN-Seq data of HCC. They identified the intrinsic gene expression pattern of HCC, which provides a novel perspective to understanding the heterogeneity of pathogenesis in the HCC tumorigenesis [ 18 ]. Thus, more studies may be performed on microarray datasets to reveal novel potential targets related to HCC pathophysiology.…”

Section: Introductionmentioning

confidence: 99%

The Novel Action of miR-193b-3p/CDK1 Signaling in HCC Proliferation and Migration: A Study Based on Bioinformatic Analysis and Experimental Investigation

Pang

Wan

et al. 2022

International Journal of Genomics

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Hepatocellular carcinoma (HCC) is a common human malignancy with high mortality and dismal prognosis. A growing number of novel targets underlying HCC pathophysiology have been detected using microarray high throughput screening platforms. This study carried out bioinformatics analysis to explore underlying biomarkers in HCC and assessed the potential action of the miR-193b-3p/CDK1 signaling pathway in HCC progression. A total of 241 common differentially expressed genes (DEGs) were screened from GSE33294, GSE104310, and GSE144269. Functional analysis results implicated that DEGs are significantly associated with “cell cycle,” “cell division,” and “proliferation.” The protein–protein interaction network analysis extracted ten hub genes from common DEGs. Ten hub genes were significantly overexpression in HCC tissues. Kaplan–Meier survival analysis revealed that 10 hub genes were linked with a poorer prognosis in HCC patients. Functional assays showed that CDK1 knockdown repressed HCC cell proliferation and migration. Luciferase reporter assay showed that miR-193b-3p could target CDK1 3′ untranslated region, and miR-193b-3p negatively modulated CDK1. Enforced CDK1 expression attenuated miR-193b-3p-modulated suppressive actions on HCC cell proliferation and migration. To summarize, we performed a comprehensive bioinformatics analysis and identified 10 hub genes linked to the prognosis in HCC patients. Functional analysis revealed that CDK1, negatively regulated by miR-193b-3p, may act as an oncogene to promote HCC cell proliferation and migration and may predict poor prognosis of HCC patients. However, the role of CDK1/miR-193b-3p may still require further investigation.

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Integrated analysis of the RNA-Seq data of liver hepatocellular carcinoma

Cited by 4 publications

References 37 publications

Systems biology and OMIC data integration to understand gastrointestinal cancers

Systems biology and OMIC data integration to understand gastrointestinal cancers

Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer

The Novel Action of miR-193b-3p/CDK1 Signaling in HCC Proliferation and Migration: A Study Based on Bioinformatic Analysis and Experimental Investigation

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