ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies

Calinawan, Anna; Song, Xiaoyu; Ji, Jiayi; Dhanasekaran, Saravana M.; Petralia, Francesca; Wang, Pei; Reva, Boris

doi:10.1101/2020.02.05.935650

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…mRNA expression profiles in the GSE40435 [21], GSE66272 [22], GSE67501 [23], GSE105261 [24], GSE126964 [25], and GSE150404 (Miekus et al, not yet published) file were downloaded from the Gene Expression Omnibus (GEO) data repository. CPTAC (Clinical Proteomic Tumor Analysis Consortium) data was downloaded from the ProTrack web tool [26]. The association between BUB1B and TP53 mutation was analyzed by the TIMER2 web tool [27].…”

Section: Methodsmentioning

confidence: 99%

BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

et al. 2021

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Introduction: BUB1 mitotic checkpoint serine/threonine kinase B encoded by BUB1B gene is a member of the spindle assembly checkpoint family. Several reports have demonstrated that overexpression of BUB1B is associated with cancer progression and prognosis. Objective: This study aims to clarify the expression and function of BUB1B in renal cell carcinoma (RCC). Methods: The expression of BUB1B was determined using immunohistochemistry and bioinformatics analysis in RCC. The effects of BUB1B knockdown on cell growth and invasion were evaluated. We analyzed the interaction between BUB1B, cancer stem cell markers, p53, and PD-L1 in RCC. Results: In 121 cases of RCC, immunohistochemistry showed that 30 (25%) of the RCC cases were positive for BUB1B. High BUB1B expression was significantly correlated with high nuclear grade, T stage, and M stage. A Kaplan-Meier analysis showed that the high expression of BUB1B was associated with poor overall survival after nephrectomy. High BUB1B expression was associated with CD44, p53, and PD-L1 in RCC. Knockdown of BUB1B suppressed cell growth and invasion in RCC cell lines. Knockdown of BUB1B also suppressed the expression of CD44 and increased the expression of phospho-p53 (Ser15). In silico analysis showed that BUB1B was associated with inflamed CD8+, exhausted T-cell signature, IFN-γ signature, and the response to nivolumab. Conclusion: These results suggest that BUB1B plays an oncogenic role and may be a promising predictive biomarker for survival in RCC.

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Section: Methodsmentioning

confidence: 99%

BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

et al. 2021

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“…As shown, ERG is directly connected to 11 genes within the same module and maps to five different peptides. In addition, users can click on the Heatmap link (Figure 2B), which will direct them to ProTrack tool, [ 18 ] which provides a web‐based interface to explore ccRCC multi‐omics data through interactive heatmaps. To query whether other genes have similar association to tumor grade as ERG, users can easily construct a simple query for those genes (Figure 2C) by specifying the phenotype of interest (grade) and the FDR cutoff value (FDR = 20%).…”

Section: Figurementioning

confidence: 99%

ProNetView‐ccRCC: A Web‐Based Portal to Interactively Explore Clear Cell Renal Cell Carcinoma Proteogenomics Networks

et al. 2020

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To better understand the molecular basis of cancer, the NCI's Clinical Proteomics Tumor Analysis Consortium (CPTAC) has been performing comprehensive large-scale proteogenomic characterizations of multiple cancer types. Gene and protein regulatory networks are subsequently being derived based on these proteogenomic profiles, which serve as tools to gain systems-level understanding of the molecular regulatory factories underlying these diseases. On the other hand, it remains a challenge to effectively visualize and navigate the resulting network models, which capture higher order structures in the proteogenomic profiles. There is a pressing need to have a new open community resource tool for intuitive visual exploration, interpretation, and communication of these gene/protein regulatory networks by the cancer research community. In this work, ProNetView-ccRCC (http://ccrcc.cptac-network-view.org/), an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data is introduced. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade. Recent advances in molecular profiling technologies [1,2] enable the large-scale integrative proteomic and genomic (proteogenomic) studies of cancers. For example, the National Cancer Institute's Clinical Proteomics Tumor Analysis Consortium (CPTAC) has recently performed comprehensive proteogenomic characterizations of tumor samples from breast, [3,4] colon, [5]

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“…As shown, ERG is directly connected to 11 genes within the same module and maps to 5 different peptides. In addition, users can click on the Heatmap link ( Figure 2B), which will direct them to ProTrack tool, [16] which provides a web-based interface to explore ccRCC multi-omics data through interactive heatmaps. To query whether other genes have similar association to tumor grade as ERG, users can easily construct a simple query for those genes ( Figure 2C) by specifying the phenotype of interest (grade) and the FDR cutoff value (FDR= 20%).…”

Section: Use Protocolsmentioning

confidence: 99%

ProNetView-ccRCC: A web-based portal to interactively explore clear cell renal cell carcinoma proteogenomics networks

Kalayci

Petralia

Wang

et al. 2020

Preprint

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ABSTRACTTo better understand the molecular basis of cancer, the NCI’s Clinical Proteomics Tumor Analysis Consortium (CPTAC) has been performing comprehensive large-scale proteogenomic characterizations of multiple cancer types. Gene/protein regulatory networks has subsequently been derived based on these proteogenomic profiles, which serve as useful tools to gain system-level understanding of the molecular regulatory factories underlying the diseases. On the other hand, it remains a challenge to effectively visualize and navigate the resulting network models, which capture higher order structures in the proteogenomic profiles. There is a pressing need to have a new open community resource tool for intuitive visual exploration, interpretation and communication of these gene/protein regulatory networks by the cancer research community. In this work, we introduce ProNetView-ccRCC (http://ccrcc.cptac-network-view.org/), an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3,614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade.

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ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies

Cited by 4 publications

References 6 publications

BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

ProNetView‐ccRCC: A Web‐Based Portal to Interactively Explore Clear Cell Renal Cell Carcinoma Proteogenomics Networks

ProNetView-ccRCC: A web-based portal to interactively explore clear cell renal cell carcinoma proteogenomics networks

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