Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.7.10.2 (focal adhesion kinase)
 44,029 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We observed previously that each of seven cancer progression inhibitors suppresses the mRNA expression of some matrix metalloproteinases (MMPs), but stimulates that of others, in breast cancer cells. In the present study we tested the effect of overexpressing other cancer modulators on MMP expression. The MMPs tested are MMP1, MMP2, MMP7, MMP13, MMP14, MMP16, MMP19, and MMP25. The proteins that were overexpressed are cancer inhibitors (NME, DRG1, IL10), enhancers (SOD2, FAK, IL17, and CREB), and proteins that suppress cancer progression in cells of some cancers and promote it in others (FUT1, integrin beta3, serpin E1, TIAM1, and claudin 4). Unexpectedly, all of them only lowered MMP mRNA expression, mainly of MMP16, MMP2, and MMP13, in breast cancer cells. Signaling from SOD2 uncoupled the accumulation of two MMP16 mRNA splice variants, suggesting signaling to a late step in MMP16 mRNA accumulation, such as MMP16 mRNA stabilization or late mRNA processing. Signaling that modulates MMP expression differed widely among the total population of MDA-MB-231 cells and single-cell progenies cloned from that population. It also differed substantially between cells of two metastatic breast basal adenocarcinomas, MDA-MB-231 and MDA-MB-468. The present study detected 37 new signaling pathways from cancer progression modulators located upstream of MMP mRNA expression in human breast cancer cells. Our siRNA-induced MMP knockdown data support the interpretation that signaling from MMP19, MMP1, MMP7, MMP12, MMP14, and MMP11 each stimulates the mRNA expression of other MMPs in breast cancer cells.
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PMID:New signaling pathways from cancer progression modulators to mRNA expression of matrix metalloproteinases in breast cancer cells. 2134 90

Artificial neural networks (ANNs) have been utilized for classification and prediction task with remarkable accuracy. However, its implications for unsupervised data mining using molecular data is under-explored. We found that embedding can extract biologically relevant information from The Cancer Genome Atlas (TCGA) gene expression dataset by learning a vector representation through gene co-occurrence. Ground truth relationship, such as cancer types of the input sample and semantic meaning of genes, were showed to retain in the resulting entity matrices. We also demonstrated the interpretability and usage of these matrices in shortlisting candidates from a long gene list as in the case of immunotherapy response. 73 related genes are singled out while the relatedness of 55 genes with immune checkpoint proteins (PD-1, PD-L1, and CTLA-4) are supported by literature. 16 novel genes (ACAP1, C11orf45, CD79B, CFP, CLIC2, CMPK2, CXCR2P1, CYTIP, FER, MCTO1, MMP25, RASGEF1B, SLFN12, TBC1D10C, TRAF3IP3, TTC39B) related to immune checkpoint proteins were identified. Thus, this method is feasible to mine big volume of biological data, and embedding would be a valuable tool to discover novel knowledge from omics data. The resulting embedding matrices mined from TCGA gene expression data are interactively explorable online (http://bit.ly/tcga-embedding-cancer) and could serve as an informative reference for gene relatedness in the context of cancer and is readily applicable to biomarker discovery of any molecular targeted therapy.
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PMID:Embedding of Genes Using Cancer Gene Expression Data: Biological Relevance and Potential Application on Biomarker Discovery. 3066 51

Bronchopulmonary dysplasia (BPD) is a complex disorder resulting from interactions between genes and the environment. The accurate molecular etiology of BPD remains largely unclear. This study aimed to identify key BPD-associated genes and pathways functionally enriched using weighted gene co-expression network analysis (WGCNA). We analyzed microarray data of 62 pre-term patients with BPD and 38 pre-term patients without BPD from Gene Expression Omnibus (GEO). WGCNA was used to construct a gene expression network, and genes were classified into definite modules. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of BPD-related hub genes were performed. Firstly, we constructed a weighted gene co-expression network, and genes were divided into 10 modules. Among the modules, the yellow module was related to BPD progression and severity and included the following hub genes: MMP25, MMP9, SIRPA, CKAP4, SLCO4C1, and SLC2A3; and the red module included some co-expression molecules that displayed a continuous decline in expression with BPD progression and included the following hub genes: LEF1, ITK, CD6, RASGRP1, IL7R, SKAP1, CD3E, and ICOS. GO and KEGG analyses showed that high expression of inflammatory response-related genes and low expression of T cell receptor activation-related genes are significantly correlated with BPD progression. The present WGCNA-based study thus provides an overall perspective of BPD and lays the foundation for identifying potential pathways and hub genes that contribute to the development of BPD.
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PMID:Weighted Gene Co-expression Network Analysis of Key Biomarkers Associated With Bronchopulmonary Dysplasia. 3303 95