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

2019 ◽  
Author(s):  
Andrés López-Cortés ◽  
César Paz-y-Miño ◽  
Santiago Guerrero ◽  
Alejandro Cabrera-Andrade ◽  
Stephen J. Barigye ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Precision Medicine ◽  
Genetic Alterations ◽  
Essential Genes ◽  
Hallmarks Of Cancer ◽  
Panoramic View ◽  
Protein Protein Interaction ◽  
Consensus Strategy ◽  
Protein Protein Interaction Networks ◽  
Pan Cancer

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.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrés López-Cortés ◽  
César Paz-y-Miño ◽  
Santiago Guerrero ◽  
Alejandro Cabrera-Andrade ◽  
Stephen J. Barigye ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Precision Medicine ◽  
Essential Genes ◽  
Panoramic View

scholarly journals MicroRNA-Regulated Protein-Protein Interaction Networks and Their Functions in Breast Cancer

2013 ◽  
Vol 14 (6) ◽  
pp. 11560-11606 ◽  
Author(s):  
Chia-Hsien Lee ◽  
Wen-Hong Kuo ◽  
Chen-Ching Lin ◽  
Yen-Jen Oyang ◽  
Hsuan-Cheng Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

scholarly journals An Integrative Computational Approach Based on Expression Similarity Signatures to Identify Protein–Protein Interaction Networks in Female-Specific Cancers

2020 ◽  
Vol 11 ◽  
Author(s):  
Katia Pane ◽  
Ornella Affinito ◽  
Mario Zanfardino ◽  
Rossana Castaldo ◽  
Mariarosaria Incoronato ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Regulatory Protein ◽  
Genetic Alterations ◽  
Computational Approach ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Endometrial Cancers ◽  
Female Specific

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein–protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein–protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein–protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.

scholarly journals The Evolutionary Landscape of Pan-Cancer Drives Clinical Aggression

2018 ◽  
Author(s):  
Shichao Pang ◽  
Leilei Wu ◽  
Xin Shen ◽  
Yidi Sun ◽  
Jingfang Wang ◽  
...  
Keyword(s):  
Evolutionary Process ◽  
Protein Interaction Networks ◽  
Cancer Evolution ◽  
Evolutionary Patterns ◽  
Protein Protein Interaction ◽  
Differentially Expressed Protein ◽  
Cancer Types ◽  
Protein Protein Interaction Networks ◽  
The Difference ◽  
Pan Cancer

AbstractAlthough cancer mechanisms differ from occurrence and development, some of them have similar oncogenesis, which leads to similar clinical phenotypes. Most existing genotyping studies look at “omics” data, but intentionally or unintentionally avoided that cancer is a time-dependent evolutionary process, biologically represented by the time evolution of tumor clones. We used the Bayesian mutation landscape approach to reconstruct the evolutionary process of cancer by acquiring somatic mutation data consisting of 21 cancer types. Four representative evolution patterns of pan-cancer have been discovered: trees, chaos, biconvex, and Cambrian, and a strong correlation between these four evolutionary patterns and clinical aggressivity. We further explained the characteristics of the corresponding biological systems in the evolution of pan cancer by analyzing the function of differentially expressed protein-protein interaction networks. Our results explained the difference in clinical aggressivity between cancer evolution patterns from the evolution of tumor clones and exposed the functional mechanism behind.

scholarly journals Label-Free Proteomics Revealed Oxidative Stress and Inflammation as Factors That Enhance Chemoresistance in Luminal Breast Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Bruno R. B. Pires ◽  
Carolina Panis ◽  
Vinícius Dias Alves ◽  
Ana C. S. A. Herrera ◽  
Renata Binato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Luminal Breast Cancer ◽  
Label Free ◽  
Luminal A ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Cancer Chemoresistance ◽  
The Complement System ◽  
And Oxidative Stress

Breast cancer is the leading cause of cancer-associated death among women worldwide. Its high mortality rate is related to resistance towards chemotherapies, which is one of the major challenges of breast cancer research. In this study, we used label-free mass spectrometry- (MS-) based proteomics to investigate the differences between circulating proteins in the plasma of patients with chemoresponsive and chemoresistant luminal A breast cancer. MS analysis revealed 205 differentially expressed proteins. Furthermore, we used in silico tools to build protein-protein interaction networks. Most of the upregulated proteins in the chemoresistant group were closely related and tightly linked. The predominant networks were related to oxidative stress, the inflammatory response, and the complement cascade. Through this analysis, we identified inflammation and oxidative stress as central processes of breast cancer chemoresistance. Furthermore, we confirmed our hypothesis by evaluating oxidative stress and performing cytokine profiling in our cohort. The connections among oxidative stress, inflammation, and the complement system described in our study seem to indicate a pivotal axis in breast cancer chemoresistance. Hence, these findings will have significant clinical implications for improving therapies to bypass breast cancer chemoresistance in the future.

Network-based methods for predicting essential genes or proteins: a survey

2019 ◽  
Vol 21 (2) ◽  
pp. 566-583 ◽  
Author(s):  
Xingyi Li ◽  
Wenkai Li ◽  
Min Zeng ◽  
Ruiqing Zheng ◽  
Min Li
Keyword(s):  
Drug Targets ◽  
Rapid Development ◽  
Essential Genes ◽  
Biological Information ◽  
Future Research ◽  
Dynamic Features ◽  
Laboratory Methods ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Topological Characteristics

Abstract Genes that are thought to be critical for the survival of organisms or cells are called essential genes. The prediction of essential genes and their products (essential proteins) is of great value in exploring the mechanism of complex diseases, the study of the minimal required genome for living cells and the development of new drug targets. As laboratory methods are often complicated, costly and time-consuming, a great many of computational methods have been proposed to identify essential genes/proteins from the perspective of the network level with the in-depth understanding of network biology and the rapid development of biotechnologies. Through analyzing the topological characteristics of essential genes/proteins in protein–protein interaction networks (PINs), integrating biological information and considering the dynamic features of PINs, network-based methods have been proved to be effective in the identification of essential genes/proteins. In this paper, we survey the advanced methods for network-based prediction of essential genes/proteins and present the challenges and directions for future research.

scholarly journals Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bradley A. Maron ◽  
Rui-Sheng Wang ◽  
Sergei Shevtsov ◽  
Stavros G. Drakos ◽  
Elena Arons ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Precision Medicine ◽  
Protein Interaction ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Janus Kinase ◽  
Interaction Networks ◽  
Patient Specific ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

AbstractProgress in precision medicine is limited by insufficient knowledge of transcriptomic or proteomic features in involved tissues that define pathobiological differences between patients. Here, myectomy tissue from patients with obstructive hypertrophic cardiomyopathy and heart failure is analyzed using RNA-Seq, and the results are used to develop individualized protein-protein interaction networks. From this approach, hypertrophic cardiomyopathy is distinguished from dilated cardiomyopathy based on the protein-protein interaction network pattern. Within the hypertrophic cardiomyopathy cohort, the patient-specific networks are variable in complexity, and enriched for 30 endophenotypes. The cardiac Janus kinase 2-Signal Transducer and Activator of Transcription 3-collagen 4A2 (JAK2-STAT3-COL4A2) expression profile informed by the networks was able to discriminate two hypertrophic cardiomyopathy patients with extreme fibrosis phenotypes. Patient-specific network features also associate with other important hypertrophic cardiomyopathy clinical phenotypes. These proof-of-concept findings introduce personalized protein-protein interaction networks (reticulotypes) for characterizing patient-specific pathobiology, thereby offering a direct strategy for advancing precision medicine.

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