The OncoPPi Portal: an integrative resource to explore and prioritize protein–protein interactions for cancer target discovery

Ivanov, Andrey A.; Revennaugh, Brian; Rusnak, Lauren; González-Pecchi, Valentina; Mo, Xiulei; Johns, Margaret A.; Du, Yuhong; Cooper, Lee; Moreno, Carlos S.; Khuri, Fadlo R.; Fu, Haian

doi:10.1093/bioinformatics/btx743

Cited by 45 publications

(52 citation statements)

References 58 publications

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“…Bioinformatics analyses, including usage of microarray expression datasets (Stuart, Segal, Koller, & Kim, 2003), protein/gene-protein/gene interaction networks (Ivanov et al, 2018), and the annotation of genes (Phuong & Nhung, 2013), are being utilized as a powerful tool to study the cancer progression and to identify serum biomarkers (Hormigo et al, 2006;Huddleston, Wong, Welch, Berkowitz, & Mok, 2005) as well as potential therapeutic targets (Armstrong et al, 2003;Ye et al, 2003). Large amounts of data generated by this tool are collected in public archives such as the major public projects The Cancer Genome Atlas (TCGA) (DeSantis, Ma, Bryan, & Jemal, 2014), Oncomine (Rhodes et al, 2004), Gene Expression Omnibus (GEO) (Barrett et al, 2013), and so on.…”

Section: Introductionmentioning

confidence: 99%

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Liu

Ouyang

Zhou

et al. 2018

Molec Gen & Gen Med

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Background Lung adenocarcinoma (LUAD) accounts for approximately 40% of all lung cancer patients. There is an urgent need to understand the mechanisms of cancer progression in LUAD and to identify useful biomarkers to predict prognosis. Methods In this study, Oncomine database was used to identify potential genes contributed to cancer progression. Bioinformatics analysis including pathway enrichment and text mining was used to explain the potential roles of identified genes in LUAD. The Cancer Genome Atlas database was used to analyze the association of gene expression with survival result. Results Our results indicated that 80 genes were significantly dysregulated in LUAD according to four microarrays covering 356 cases of LUAD and 164 cases of normal lung tissues. Twenty genes were consistently and stably dysregulated by more than twofold. Ten of 20 genes had a relationship with overall survival or disease‐free survival in a cohort of 516 LUAD patients, and 19 genes were associated with tumor stage, gender, age, lymph node, or smoking. Low expression of AGER and high expression of CCNB1 were specifically associated with poor survival. Conclusion Our findings implicate AGER and CCNB1 might be potential biomarkers for diagnosis and prognosis targets for LUAD.

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Section: Introductionmentioning

confidence: 99%

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Liu

Ouyang

Zhou

et al. 2018

Molec Gen & Gen Med

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“…Regarding the OncoPrint genes, 65 (89%) nodes integrated this network (Figure 5A). On the other hand, out of the 258 genes that make up our String PPi network, 16 (6%) genes and 18 edges were part of the OncoPPi BC network 53,54 . The degree centrality made it possible to establish a significant correlation (Spearman p < 0.05) between our String PPi network and the OncoPPi BC network (Figure 5B).…”

Section: Resultsmentioning

confidence: 99%

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

López‐Cortés

Paz‐y‐Miño

Guerrero

et al. 2019

Preprint

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SUMMARYBreast cancer (BC) is a heterogeneous disease where each OncoOmics approach needs to be fully understood as a part of a complex network. Therefore, the main objective of this study was to analyze genetic alterations, signaling pathways, protein-protein interaction networks, protein expression, dependency maps and enrichment maps in 230 previously prioritized genes by the Consensus Strategy, the Pan-Cancer Atlas, the Pharmacogenomics Knowledgebase and the Cancer Genome Interpreter, in order to reveal essential genes to accelerate the development of precision medicine in BC. The OncoOmics essential genes were rationally filtered to 144, 48 (33%) of which were hallmarks of cancer and 20 (14%) were significant in at least three OncoOmics approaches: RAC1, AKT1 CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, PLCG1, GRB2, MED1, TOP2A, GATA3, BCL2, CTNNB1, EGFR and CDK2. According to the Open Targets Platform, there are 111 drugs that are currently being analyzed in 3151 clinical trials in 39 genes. Lastly, there are more than 800 clinical annotations associated with 94 genes in BC pharmacogenomics.

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“…In our previous study [44], we revealed essential proteins associated with BC pathogenesis through deep analyses of genetic alterations, signaling pathways [52], protein-protein interaction networks [55, 56], protein expression [57, 58], dependency maps [57], and enrichment maps [49] of previously prioritized genes by the Consensus Strategy [59], the Pan-Cancer Atlas project [60–64], the Pharmacogenomics Knowledgebase (PharmGKB) [2], and the Cancer Genome Interpreter [65]. The druggable prediction of BC proteins through ML approaches will provide us relevant information regarding potential biomarkers, therapeutic targets and future clinical trials, avoiding ethnicity bias and improving cancer pharmacogenomics and precision medicine worldwide [66–76].…”

Section: Resultsmentioning

confidence: 99%

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

López‐Cortés

Cabrera-Andrade

Cruz-Segundo

et al. 2019

Preprint

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BackgroundDruggable proteins are a trending topic in drug design. The druggable proteome can be defined as the percentage of proteins that have the capacity to bind an antibody or small molecule with adequate chemical properties and affinity. The screening and in silico modeling are critical activities for the reduction of experimental costs.MethodsThe current work proposes a unique prediction model for druggable proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. After feature selection, the best classifier was obtained using the support vector machine method and 200 tri-amino acid composition descriptors.ResultsThe high performance of the model is determined by an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and accuracy of 0.929 ± 0.006 (3-fold cross-validation). Regarding the prediction of cancer-associated proteins using this model, the best ranked druggable predicted proteins in the breast cancer protein set were CDK4, AP1S1, POLE, HMMR, RPL5, PALB2, TIMP1, RPL22, NFKB1 and TOP2A; in the cancer-driving protein set were TLL2, FAM47C, SAGE1, HTR1E, MACC1, ZFR2, VMA21, DUSP9, CTNNA3 and GABRG1; and in the RNA-binding protein set were PLA2G1B, CPEB2, NOL6, LRRC47, CTTN, CORO1A, SCAF11, KCTD12, DDX43 and TMPO.ConclusionsThis powerful model predicts several druggable proteins which should be deeply studied to find better therapeutic targets and thus improve clinical trials. The scripts are freely available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

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The OncoPPi Portal: an integrative resource to explore and prioritize protein–protein interactions for cancer target discovery

Abstract: andrey.ivanov@emory.edu or hfu@emory.edu.

Cited by 45 publications

References 58 publications

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

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