Integrative analysis of highly mutated genes in hepatitis B virus‐related hepatic carcinoma

Kong, Fanyun; Kong, Delong; Yang, Xiaoying; Yuan, Dongchen; Zhang, Ning; Huang, Xuan; You, Hua; Zheng, Kuiyang; Tang, Renxian

doi:10.1002/cam4.2903

Cited by 15 publications

(16 citation statements)

References 63 publications

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“…For our work, Fig. 1 describes an improved pipeline to perform the four most widely used analyses developed collectively based on prior works [7][8][9][10][11][12][13][14][15] , each of which could be highly adjustable. We envision this pipeline as a general but unlimited solution for cancer researchers wishing to deal with driver genes integrating multi-omics data for which parts of the proposed protocol can permit users to explore any other implementation suitable for Table 5.…”

Section: Discussionmentioning

confidence: 99%

“…At Steps 3 and 4, the work issues the main focus on the associations between clinical features and changed expression levels of identified driver genes. Based on the belief that gene expression is considered the first level of phenotype affected by the mutation/change on the gene, it is reasonable to investigate how the mutation affects its phenotype 7,8,10,12,15,49 . For example, a large number of non-coding drivers can regulate the expression of genes and driver genes 86,87 .…”

Section: Discussionmentioning

confidence: 99%

“…For the identification stage, somatic mutation data is inputted to identify driver genes using the OncodriveCLUSTL 36 and OncodriveFML 37 tools (Step 1). For the analysis stage, we provide the four most widely focused analyses in the cancer driver exploring studies [7][8][9][10][11][12][13][14][15] . Firstly, the identified driver genes will be annotated by the tool g:Profiler 38 (Step 2).…”

Section: Methodsmentioning

confidence: 99%

“…In this section, we analyzed associations between clinical features of interest and the identified driver genes. More specifically, gene expression data were used to examine the associations between individual drivers and several familiar clinical features, including survival rates 11,13,15,49 , numbers of positive lymph nodes 11,13 , Nottingham prognostic index, and pathologic stages 11,15 of 1,904 patients.…”

Section: Selection Of Tools For Unsupervised Identification Of Co-expmentioning

confidence: 99%

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Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

Nguyen¹,

2020

Sci Rep

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The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Selection Of Tools For Unsupervised Identification Of Co-expmentioning

confidence: 99%

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Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

Nguyen¹,

2020

Sci Rep

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“…To select features, we used a list of significantly mutated genes in the analyzed cohort identified by Ng et al [26]. In addition, we included possible drivers of HCC identified in other studies [37][38][39]. To select continuous features, we applied MOFA and performed latent factor analysis.…”

Section: Hcc Patient Subtypingmentioning

confidence: 99%

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Suter

Dazert

Kuipers

et al. 2021

Preprint

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Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.Author summaryMulti-omics approaches to cancer subtyping can provide more insights into molecular changes in tumors compared to single-omics approaches. However, most multi-omics clustering methods do not take into account that gene products interact, for example, as parts of protein complexes or signaling networks. Here we present bnClustOmics, a Bayesian network mixture model for unsupervised clustering of multi-omics data, which can represent dependencies among molecular changes of various omics types explicitly. Unlike other approaches that use data from public interaction databases as ground truth, bnClustOmics learns the dependencies between genes from the analyzed multi-omics dataset. At the same time, our approach can also account for prior knowledge from public interaction databases and use it to guide network learning without losing the ability to learn new dependencies. We applied bnClustOmics to a multi-omics HCC dataset and identified three subtypes similar to those identified in other HCC studies. The cluster-specific networks learned by bnClustOmics revealed additional insights into the molecular characterization of the discovered subgroups and highlighted the changes in signaling networks leading to distinct HCC phenotypes.

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Identification of molecular heterogeneity of hepatocellular carcinoma based on immune gene expression signatures

et al. 2021

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Integrative analysis of highly mutated genes in hepatitis B virus‐related hepatic carcinoma

Cited by 15 publications

References 63 publications

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Identification of molecular heterogeneity of hepatocellular carcinoma based on immune gene expression signatures

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