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

Nguyen, Quang-Huy; Le, Duc-Hau

doi:10.1038/s41598-020-77318-1

Cited by 13 publications

(24 citation statements)

References 87 publications

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“…In our previous study [31], the breast cancer data were used to detect validated 31 breast-cancer-associated genes, and we then did a cluster of those genes to functional modules using iWGCNA. Here, we revisited the results to be convenient for the comparison.…”

Section: Resultsmentioning

confidence: 99%

“…We used the two expression data above to validate the performance of overlappingCGM with WGCNA and an improved version of WGCNA proposed by us [31], temporarily called improved WGCNA (iWGCNA) in this study. For WGCNA, we applied it to the gene expression data using the blockwiseModules function (v1.69).…”

Section: Comparison Of Overlappingcgm With Wgcna and Its Improved Vermentioning

confidence: 99%

“…Biological processes and KEGG pathways with adjusted P-values  0.05 (G:SCS multiple testing correction method [39], two-tailed) were considered to be statistically significant. In our previous study [38], the breast cancer data were used to detect 31 validated breast-cancer-associated genes, and we then clustered those genes to functional modules using iWGCNA. Here, we revisited the results to be convenient for the comparison.…”

Section: Comparison Of Ocem With Wgcna and Its Improved Version Iwgcnamentioning

confidence: 99%

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oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

Nguyen¹,

Le²

2021

Preprint

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When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. A recent study have reported that the decomposition methods are the most appropriate ones for solving these challenges. In this study, we represent a R tool, termed overlapping co-expressed gene module (overlappingCGM), which possesses those methods with a wholly automatic analysis framework to help non-technical users to easily perform complicated statistical analyses and then gain robust results. We also develop a novel auxiliary statistical approach to select the optimal number of principle components using a permutation procedure. Two example datasets are used, related to human breast cancer and mouse metabolic syndrome, to enable the illustration of the straightforward use of the tool. Computational experiment results show that overlappingCGM outperforms state-of-the-art techniques. The R scripts used in the study, including all information on the tool and its usage are made publicly available at https://github.com/huynguyen250896/overlappingCGM.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison Of Overlappingcgm With Wgcna and Its Improved Vermentioning

confidence: 99%

Section: Comparison Of Ocem With Wgcna and Its Improved Version Iwgcnamentioning

confidence: 99%

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oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

Nguyen¹,

Le²

2021

Preprint

Self Cite

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“…We used the two expression data above to validate the performance of oCEM with WGCNA and an improved version of WGCNA proposed by us [ 37 ], temporarily called improved WGCNA (iWGCNA) in this study. For WGCNA, we applied it to the gene expression data using the blockwiseModules function (v1.69).…”

Section: Methodsmentioning

confidence: 99%

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

Nguyen¹,

2022

BMC Genomics

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Background When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. Results This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. Conclusions oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM.

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“…Expression levels higher than the median were classified into the high expression group; otherwise, they were classified into the low expression group. ( Jin et al, 2019 ; Nguyen and Le, 2020 ; Pak et al, 2020 ; Wei et al, 2020 ). We performed univariate Cox regression analysis and multivariate Cox regression analysis in each cohort using the “survival” R package and “geneSA” package.…”

Section: Methodsmentioning

confidence: 99%

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments

Sung

Lee

et al. 2021

Front. Genet.

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Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.

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

Cited by 13 publications

References 87 publications

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments

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