scholarly journals Peer Review #1 of "From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma (v0.1)"

2017 ◽  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Big Data ◽  
Peer Review ◽  
Diagnosis And Prognosis ◽  
Gene Expression Signatures ◽  
Liver Hepatocellular Carcinoma

scholarly journals Identification of glycolysis related pathways in pancreatic adenocarcinoma and liver hepatocellular carcinoma based on TCGA and GEO datasets

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ji Li ◽  
Chen Zhu ◽  
Peipei Yue ◽  
Tianyu Zheng ◽  
Yan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Energy Metabolism ◽  
Pancreatic Adenocarcinoma ◽  
Digestive System ◽  
Prognostic Significance ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
System Structure ◽  
Liver Hepatocellular Carcinoma

Abstract Background Abnormal energy metabolism is one of the characteristics of tumor cells, and it is also a research hotspot in recent years. Due to the complexity of digestive system structure, the frequency of tumor is relatively high. We aim to clarify the prognostic significance of energy metabolism in digestive system tumors and the underlying mechanisms. Methods Gene set variance analysis (GSVA) R package was used to establish the metabolic score, and the score was used to represent the metabolic level. The relationship between the metabolism and prognosis of digestive system tumors was explored using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Volcano plots and gene ontology (GO) analyze were used to show different genes and different functions enriched between different glycolysis levels, and GSEA was used to analyze the pathway enrichment. Nomogram was constructed by R package based on gene characteristics and clinical parameters. qPCR and Western Blot were applied to analyze gene expression. All statistical analyses were conducted using SPSS, GraphPad Prism 7, and R software. All validated experiments were performed three times independently. Results High glycolysis metabolism score was significantly associated with poor prognosis in pancreatic adenocarcinoma (PAAD) and liver hepatocellular carcinoma (LIHC). The STAT3 (signal transducer and activator of transcription 3) and YAP1 (Yes1-associated transcriptional regulator) pathways were the most critical signaling pathways in glycolysis modulation in PAAD and LIHC, respectively. Interestingly, elevated glycolysis levels could also enhance STAT3 and YAP1 activity in PAAD and LIHC cells, respectively, forming a positive feedback loop. Conclusions Our results may provide new insights into the indispensable role of glycolysis metabolism in digestive system tumors and guide the direction of future metabolism–signaling target combined therapy.

scholarly journals Harnessing the biological complexity of Big Data from LINCS gene expression signatures

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0201937 ◽  
Author(s):  
Aliyu Musa ◽  
Shailesh Tripathi ◽  
Meenakshisundaram Kandhavelu ◽  
Matthias Dehmer ◽  
Frank Emmert-Streib
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Biological Complexity ◽  
Gene Expression Signatures

scholarly journals Distinct Chromosomal Bias of Gene Expression Signatures in the Progression of Hepatocellular Carcinoma

2004 ◽  
Vol 64 (20) ◽  
pp. 7263-7270 ◽  
Author(s):  
Yutaka Midorikawa ◽  
Shuichi Tsutsumi ◽  
Kunihiro Nishimura ◽  
Naoko Kamimura ◽  
Makoto Kano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Gene Expression Signatures

scholarly journals Systematic Analyses of the Role of the Reader Protein of N6-Methyladenosine RNA Methylation, YTH Domain Family 2, in Liver Hepatocellular Carcinoma

2020 ◽  
Vol 7 ◽  
Author(s):  
Xiang-yang Shao ◽  
Jin Dong ◽  
Han Zhang ◽  
Ying-song Wu ◽  
Lei Zheng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Domain Family ◽  
Rna Methylation ◽  
Yth Domain ◽  
Liver Hepatocellular Carcinoma

BackgroundYTH domain family (YTHDF) 2 acts as a “reader” protein for RNA methylation, which is important in tumor regulation. However, the effect of YTHDF2 in liver hepatocellular carcinoma (LIHC) has yet to be elucidated.MethodsWe explored the role of YTHDF2 in LIHC based on publicly available datasets [The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO)]. A bioinformatics approach was employed to analyze YTHDF2. Logistic regression analyses were applied to analyze the correlation between YTHDF2 expression and clinical characteristics. To evaluate the effect of YTHDF2 on the prognosis of LIHC patients, we used Kaplan–Meier (K–M) curves. Gene set enrichment analysis (GSEA) was undertaken using TCGA dataset. Univariate and multivariate Cox analyses were used to ascertain the correlations between YTHDF2 expression and clinicopathologic characteristics with survival. Genes co-expressed with YTHDF2 were identified and detected using publicly available datasets [LinkedOmics, University of California, Santa Cruz (UCSC), Gene Expression Profiling Interactive Analysis (GEPIA), and GEO]. Correlations between YTHDF2 and infiltration of immune cells were investigated by Tumor Immune Estimation Resource (TIMER) and GEPIA.ResultsmRNA and protein expression of YTHDF2 was significantly higher in LIHC tissues than in non-cancerous tissues. High YTHDF2 expression in LIHC was associated with poor prognostic clinical factors (high stage, grade, and T classification). K–M analyses indicated that high YTHDF2 expression was correlated with an unfavorable prognosis. Univariate and multivariate Cox analyses revealed that YTHDF2 was an independent factor for a poor prognosis in LIHC patients. GSEA revealed that the high-expression phenotype of YTHDF2 was consistent with the molecular pathways implicated in LIHC carcinogenesis. Analyses of receiver operating characteristic curves showed that YTHDF2 might have a diagnostic value in LIHC patients. YTHDF2 expression was associated positively with SF3A3 expression, which implied that they may cooperate in LIHC progression. YTHDF2 expression was associated with infiltration of immune cells and their marker genes. YTHDF2 had the potential to regulate polarization of tumor-associated macrophages, induce T-cell exhaustion, and activate T-regulatory cells.ConclusionYTHDF2 may be a promising biomarker for the diagnosis and prognosis of LIHC and may provide new directions and strategies for LIHC treatment.

scholarly journals Characterization and chromatin distribution of the H1 histones and high-mobility-group non-histone chromosomal proteins of trout liver and hepatocellular carcinoma

1991 ◽  
Vol 280 (2) ◽  
pp. 491-497 ◽  
Author(s):  
J R Davie ◽  
G P Delcuve
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
High Mobility ◽  
High Mobility Group ◽  
Hmg Proteins ◽  
Chromosomal Proteins ◽  
H1 Histone ◽  
Liver Chromatin ◽  
And Function ◽  
Liver Hepatocellular Carcinoma

The H1 histones serve as general repressors of gene expression by inducing the formation of a compact chromatin structure, whereas the high-mobility-group (HMG) non-histone chromosomal proteins have roles in maintaining the structure and function of transcriptionally active chromatin. The distribution of the H1 histone subtypes and HMG proteins among various trout tissues (liver, hepatocellular carcinoma, testis and erythrocyte) was determined. Histone H1b was present in the chromatin of liver, but not in the chromatin of hepatocellular carcinoma, testis or erythrocyte. Nuclease-resistant regions of liver chromatin had elevated levels of histone H1b. Histone H1b was isolated, and the N-terminal amino acid sequence of histone H1b was found to be highly similar to that of mammalian histone H1(0) and duck H5. HMG proteins T1, T2, T3, H6, C, D and F were associated with liver and hepatocellular-carcinoma chromatin, with hepatocellular carcinoma containing higher levels of HMG T1 and F. Testis and erythrocyte had HMG T2 and H6 as their predominant HMG proteins. Most of the HMG H6 of hepatocellular carcinoma, but not of liver, was located in a chromatin fraction that was soluble at physiological ionic strength and enriched in transcriptionally active DNA. These alterations in the chromatin distribution and content of hepatocyte HMG proteins and H1 histone subtypes may contribute to aberrant hepatocyte gene expression in the hepatocellular carcinoma.

scholarly journals From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3089 ◽  
Author(s):  
Hong Yang ◽  
Xin Zhang ◽  
Xiao-yong Cai ◽  
Dong-yue Wen ◽  
Zhi-hua Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Big Data ◽  
Natural Language Processing ◽  
Language Processing ◽  
Poor Prognosis ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Liver Hepatocellular Carcinoma ◽  
Genome Atlas

BackgroundLiver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.MethodsBig data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.ResultsA list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998],P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.DiscussionThe study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

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