Integrative network analysis identifies key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma

Zheng, Siyuan; Tansey, William P.; Hiebert, Scott W.; Zhao, Zhongming

doi:10.1186/1755-8794-4-62

Cited by 40 publications

(43 citation statements)

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“…Through this systematic analysis, we proposed changes of global regulatory patterns in HCC progression, and identified some key miRNA regulators contributed to HCC metastasis whose regulatory patterns and biological implication were also deduced. Before this, although multi-perspective data have been integrated into HCC-related analyses [6-8], no peer works providing global landscape of combinatorial gene regulatory network or identifying module classifiers for risk prediction has ever been reported in the specific context of venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC.…”

Section: Discussionmentioning

confidence: 99%

“…A priori knowledge such as protein interactions, pathways, clinical factors, or other disease-related information from databases, integrated with gene signature analysis have helped marker gene prioritization [4-8]. In addition, gene relationships among different disease statuses were investigated through systematic network analyses [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

Zeng

Huang

et al. 2012

BMC Genomics

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BackgroundHepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling.MethodsBy utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks.ResultsGlobally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis.ConclusionIn this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

Zeng

Huang

et al. 2012

BMC Genomics

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“…Third, in this study, the direction of biological regulation was based on the available tumor and normal samples from Discovery_data. However, complex diseases incorporate many changes at the genomic and transcriptomic levels through a gradual process (Zheng et al 2011). Matched miRNA and gene expression profiles from time point data could be used to address meaningful node association changes over different time points.…”

Section: Discussionmentioning

confidence: 99%

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer

Mitra

Edmonds

Sun

et al. 2014

RNA

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While previous studies reported aberrant expression of microRNAs (miRNAs) in non-small cell lung cancer (NSCLC), little is known about which miRNAs play central roles in NSCLC's pathogenesis and its regulatory mechanisms. To address this issue, we presented a robust computational framework that integrated matched miRNA and mRNA expression profiles in NSCLC using feed-forward loops. The network consists of miRNAs, transcription factors (TFs), and their common predicted target genes. To discern the biological meaning of their associations, we introduced the direction of regulation. A network edge validation strategy using three independent NSCLC expression profiling data sets pinpointed reproducible biological regulations. Reproducible regulation, which may reflect the true molecular interaction, has not been applied to miRNA-TF coregulatory network analyses in cancer or other diseases yet. We revealed eight hub miRNAs that connected to a higher proportion of targets validated by independent data sets. Network analyses showed that these miRNAs might have strong oncogenic characteristics. Furthermore, we identified a novel miRNA-TF co-regulatory module that potentially suppresses the tumor suppressor activity of the TGF-β pathway by targeting a core pathway molecule (TGFBR2). Follow-up experiments showed two miRNAs (miR-9-5p and miR-130b-3p) in this module had increased expression while their target gene TGFBR2 had decreased expression in a cohort of human NSCLC. Moreover, we demonstrated these two miRNAs directly bind to the 3 ′ untranslated region of TGFBR2. This study enhanced our understanding of miRNA-TF co-regulatory mechanisms in NSCLC. The combined bioinformatics and validation approach we described can be applied to study other types of diseases.

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“…Zheng et al . [42] proposed a methodology by integrating gene expression profiles and high-throughput protein-protein interaction data in order to find the dysregulated biological networks in the progression of hepatitis C virus induced hepatocellular carcinoma (denoted as HCV-induced HCC) disease. Furthermore, Mallik et al .…”

Section: Literature Reviewmentioning

confidence: 99%

Towards integrated oncogenic marker recognition through mutual information‐based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles

Mallik

Zhao

2017

Quant. Biol.

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Background Marker detection is an important task in complex disease studies. Here we provide an association rule mining (ARM) based approach for identifying integrated markers through mutual information (MI) based statistically significant feature extraction, and apply it to acute myeloid leukemia (AML) and prostate carcinoma (PC) gene expression and methylation profiles. Methods We first collect the genes having both expression and methylation values in AML as well as PC. Next, we run Jarque-Bera normality test on the expression/methylation data to divide the whole dataset into two parts: one that ollows normal distribution and the other that does not follow normal distribution. Thus, we have now four parts of the dataset: normally distributed expression data, normally distributed methylation data, non-normally distributed expression data, and non-normally distributed methylated data. A feature-extraction technique, “mRMR” is then utilized on each part. This results in a list of top-ranked genes. Next, we apply Welch t-test (parametric test) and Shrink t-test (non-parametric test) on the expression/methylation data for the top selected normally distributed genes and non-normally distributed genes, respectively. We then use a recent weighted ARM method, “RANWAR” to combine all/specific resultant genes to generate top oncogenic rules along with respective integrated markers. Finally, we perform literature search as well as KEGG pathway and Gene-Ontology (GO) analyses using Enrichr database for in silico validation of the prioritized oncogenes as the markers and labeling the markers as existing or novel. Results The novel markers of AML are {ABCB11↑∪KRT17↓} (i.e., ABCB11 as up-regulated, & KRT17 as down-regulated), and {AP1S1-∪KRT17↓∪NEIL2-∪DYDC1↓}) (i.e., AP1S1 and NEIL2 both as hypo-methylated, & KRT17 and DYDC1 both as down-regulated). The novel marker of PC is {UBIAD1¶∪APBA2‡∪C4orf31‡} (i.e., UBIAD1 as up-regulated and hypo-methylated, & APBA2 and C4orf31 both as down-regulated and hyper-methylated). Conclusion The identified novel markers might have critical roles in AML as well as PC. The approach can be applied to other complex disease.

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Integrative network analysis identifies key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma

Cited by 40 publications

References 50 publications

Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer

Towards integrated oncogenic marker recognition through mutual information‐based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles

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