OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Wang, Tao; Ruan, Su; Zhao, Xiaolu; Shi, Xiaohui; Teng, Huajing; Zhong, Jianing; You, Mingcong; Xia, Kun; Sun, Zhen; Mao, Fengbiao

doi:10.1093/nar/gkaa1033

Cited by 76 publications

(62 citation statements)

References 85 publications

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“…OncoVar is an integrated database and analysis platform (https://oncovar.org/), by reanalyzing 10,769 exomes from 33 cancer types in TCGA and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC), it could be used to identify driver mutations and driver genes. 23 Genes were classified into four grades from nonpathogenic to pathogenic: level 0 (non-pathogenic, score = 0), level 1 (possible pathogenic, score = 1), level 2 (likely pathogenic, 1 < score ≤ 10), level 3 (probable pathogenic, 10 < score < 20) and level 4 (pathogenic, score ≥ 20).…”

Section: Oncovar Analysismentioning

confidence: 99%

Integrated Bioinformatic Analysis of SARS-CoV-2 Infection Related Genes ACE2, BSG and TMPRSS2 in Aerodigestive Cancers

He¹,

Hua²,

Sun³

et al. 2021

JIR

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Background Cancer patients are more vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than the general population, with lung epithelial cells or enterocytes being the main targets. However, the expressions of SARS-CoV-2 entry-related genes in aerodigestive cancers have not been fully elucidated. Methods In this study, the expressions of SARS-CoV-2 receptors and cofactors, including angiotensin I-converting enzyme 2 (ACE2), basigin (BSG) and transmembrane serine protease 2 (TMPRSS2), were comprehensively assessed. We compared BSG and TMPRSS2 expressions between aerodigestive cancers and matched normal tissues through Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Furthermore, expressions in healthy colon tissues at different anatomical locations were explored using the Genotype-Tissue Expression (GTEx) dataset. In addition, expressions among different tumor stages and the prognostic values were detected through GEPIA2. Moreover, the correlation between gene expression and immune infiltration was explored via Tumor Immune Estimation Resource (TIMER). Finally, expressions in primary colorectal cancer (CRC), lung metastasis and liver metastasis were investigated using the Gene Expression Omnibus (GEO) dataset GSE41258. Results Similar to ACE2, TMPRSS2 and BSG were also highly expressed in the digestive tracts. Intriguingly, BSG/TMPRSS2 expression in adjacent normal colon tissue or lung tissue was higher than that in corresponding healthy tissue, whereas they varied not among different tumor stages and correlated not with prognosis in aerodigestive cancers. Moreover, ACE2 was expressed at higher levels in lung metastases from CRC than in normal lung tissues. Conclusion SARS-CoV-2 entry genes were highly expressed in CRC, and we reported for the first time higher expression of ACE2 in lung metastases from CRC than in normal lung, indicating that these patients may be more susceptible to extrapulmonary or pulmonary SARS-CoV-2 infection. Since our study is a bioinformatic analysis, further experimental evidences and clinical data are urgently needed.

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Section: Oncovar Analysismentioning

confidence: 99%

Integrated Bioinformatic Analysis of SARS-CoV-2 Infection Related Genes ACE2, BSG and TMPRSS2 in Aerodigestive Cancers

He¹,

Hua²,

Sun³

et al. 2021

JIR

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“…RNA-Seq data (GSE147507) for two uninfected human lung biopsies and two lung samples from COVID-19 deceased patient, and RNA-Seq data (CRA002390) for three PBMC samples from healthy donors and COVID-19 patients were analyzed by DESeq2 [16] to identify differentially expressed genes, respectively. Similar to our previous study [17] , false discovery rate (FDR q-value) was calculated by adjusting P -values with the Benjamini-Hochberg method. Genes with FDR q-value < 0.05 and |Log2 (Fold change) | > 1 were considered as DEGs.…”

Section: Identification Of Differential Expressed Genes (Degs)mentioning

confidence: 99%

Transcriptomic signatures and repurposing drugs for COVID-19 patients: findings of bioinformatics analyses

Li¹,

Ruan²,

Zhao³

et al. 2021

Computational and Structural Biotechnology Journal

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“…In addition, Wang et al (2021) study constructed a platform, OncoVar, which identified several important drive genes in each cancer type. Immune-related genes were selected and survival analysis was conducted (Supplementary Table 9).…”

Section: External Validationmentioning

confidence: 99%

Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma

et al. 2021

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Lung adenocarcinoma has entered into an era of immunotherapy with the development of immune checkpoint inhibitors (ICIs). The identification of immune subtype is crucial to prolonging survival in patients. The tumor microenvironment (TME) and metabolism have a profound impact on prognosis and therapy. The majority of previous studies focused on only one aspect, while both of them are essential to the understanding of tumorigenesis and development. We hypothesized that lung adenocarcinoma can be stratified into immune subgroups with alterations in the TME infiltration. We aimed to explore the “TME-Metabolism-Risk” patterns in each subtypes and the mechanism behind. Glycolysis and cholesterol were selected for the analysis of metabolic states based on the first half of the study. Bioinformatic analysis was performed to investigate the transcriptomic and clinical data integrated by three lung adenocarcinoma cohorts (GSE30219, GSE31210, GSE37745, N = 415). The results were validated in an independent cohort (GSE50081, N = 127). In total, 415 lung adenocarcinoma samples were integrated and analyzed. Four major immune subtypes were indentified using bioinformatic analysis. Subtype NC1, characterized by a high level of glycolysis, with extremely low microenvironment cell infiltration. Subtype NC2, characterized by the “Silence” and “Cholesterol biosynthesis Predominant” metabolic states, with a middle degree infiltration of microenvironment cell. Subtype NC3, characterized by the lack of “Cholesterol biosynthesis Predominant” metabolic state, with abundant microenvironment cell infiltration. Subtype NC4, characterized by “Mixed” metabolic state, with a relatively low microenvironment cell infiltration. Least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were performed to calculate the risk of each sample, and we attempted to find out the potential immune escape mechanism in different subtypes. The result revealed that the lack of immune cells infiltration might contribute to the immune escape in subtypes NC1 and NC4. NC3 was characterized by the high expression of immune checkpoint molecules and fibroblasts. NC2 had defects in activation of innate immune cells. There existed an obviously survival advantage in subtype NC2. Gene set enrichment analysis (GSEA) and Gene Ontology analysis indicated that the PI3K-AKT-mTOR, TGF-β, MYC-related pathways might be correlated with this phenomenon. In addition, some differentially expressed genes (DEGs) were indentified in subtype NC3, which might be potential targets for survival phenotype transformation.

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OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Cited by 76 publications

References 85 publications

Integrated Bioinformatic Analysis of SARS-CoV-2 Infection Related Genes ACE2, BSG and TMPRSS2 in Aerodigestive Cancers

Integrated Bioinformatic Analysis of SARS-CoV-2 Infection Related Genes ACE2, BSG and TMPRSS2 in Aerodigestive Cancers

Transcriptomic signatures and repurposing drugs for COVID-19 patients: findings of bioinformatics analyses

Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma

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