Identification of subtype-specific genes signature by WGCNA for prognostic prediction in diffuse type gastric cancer

Zhou, Qi; Zhou, Liqiang; Li, Shihao; Yuan, Yi‐Wu; Liu, Li; Wang, Jinliang; Wu, Deng-Zhong; Wu, You; Xin, Lin

doi:10.18632/aging.103743

Cited by 8 publications

(7 citation statements)

References 24 publications

(23 reference statements)

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“…To evaluate the biological functions of each m 5 C modification-related gene, we transformed the RNA-seq data of all samples into transcripts per million (TPM) values. The methods have been described in a previous study [31]. The insufficient, duplicated, and zero expression genes will be eliminated.…”

Section: Functional Annotations and Pathway Enrichment Analysismentioning

confidence: 99%

Pan-cancer analysis of m5C regulator genes reveals consistent epigenetic landscape changes in multiple cancers

Zhang

et al. 2021

World J Surg Onc

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Background 5-Methylcytosine (m5C) is a reversible modification to both DNA and various cellular RNAs. However, its roles in developing human cancers are poorly understood, including the effects of mutant m5C regulators and the outcomes of modified nucleobases in RNAs. Methods Based on The Cancer Genome Atlas (TCGA) database, we uncovered that mutations and copy number variations (CNVs) of m5C regulatory genes were significantly correlated across many cancer types. We then assessed the correlation between the expression of individual m5C regulators and the activity of related hallmark pathways of cancers. Results After validating m5C regulators’ expression based on their contributions to cancer development and progression, we observed their upregulation within tumor-specific processes. Notably, our research connected aberrant alterations to m5C regulatory genes with poor clinical outcomes among various tumors that may drive cancer pathogenesis and/or survival. Conclusion Our results offered strong evidence and clinical implications for the involvement of m5C regulators.

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Section: Functional Annotations and Pathway Enrichment Analysismentioning

confidence: 99%

Pan-cancer analysis of m5C regulator genes reveals consistent epigenetic landscape changes in multiple cancers

Zhang

et al. 2021

World J Surg Onc

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“…The methods have been described in a previous study. [26] The insu cient, duplicated, and zero expression genes will be eliminated. Furthermore, the gene set variation analysis (GSVA) was applied to determine transcriptomic activities and explore the biological processes of m 5 C regulators.…”

Section: Functional Annotations and Pathways Enrichment Analysismentioning

confidence: 99%

Pan-Cancer Analysis of m5C Regulator Genes Reveals Consistent Epigenetic Landscape Changes in Multiple Cancers

Xue,

Shi,

Zhang

et al. 2020

SSRN Journal

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Backgroud 5-methylcytosine (m 5 C) is a reversible modi cation to both DNA and various cellular RNAs. However, its roles in developing human cancers are poorly understood, including the effects of mutant m 5 C regulators and the outcomes of modi ed nucleobases in RNAs. MethodsBased on The Cancer Genome Atlas (TCGA) database, we uncovered that mutations and copy number variations (CNVs) of m 5 C regulatory genes were signi cantly correlated across 33 cancer types. We then assessed the correlation between the expression of individual m 5 C regulators and the activity of related hallmark pathways of cancers. After validating m 5 C regulators' expression based on their contributions to cancer development and progression, we observed their up-regulation within tumor-speci c processes. ResultsNotably, our research connected aberrant alterations to m 5 C regulatory genes with poor clinical outcomes among various tumors that may drive cancer pathogenesis and/or survival. ConclusionsOur results offered strong evidence and clinical implications for the involvement of m 5 C regulators.

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“…Weighed gene coexpression network analysis (WGCNA) is one of these significant algorithms that provides a better understanding of gene coexpression networks and gene functions [ 6 ]. WGCNA can detect modules of highly correlated genes among samples to relate modules to external sample traits, providing valuable insights into predicting possible functions of coexpressed genes [ 7 ]. Moreover, differential gene expression analysis is usually used in transcriptomics datasets to study underlying biological and molecular mechanisms and to identify quantitative differences in the expression level of the gene between different groups [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

et al. 2021

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Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

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Identification of subtype-specific genes signature by WGCNA for prognostic prediction in diffuse type gastric cancer

Cited by 8 publications

References 24 publications

Pan-cancer analysis of m5C regulator genes reveals consistent epigenetic landscape changes in multiple cancers

Pan-cancer analysis of m5C regulator genes reveals consistent epigenetic landscape changes in multiple cancers

Pan-Cancer Analysis of m5C Regulator Genes Reveals Consistent Epigenetic Landscape Changes in Multiple Cancers

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

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