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Query: UNIPROT:P10145 (IL-8)
 23,849 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

In India, oral cancer has consistently ranked among top three causes of cancer-related deaths, and it has emerged as a top cause for the cancer-related deaths among men. Lack of effective therapeutic options is one of the main challenges in clinical management of oral cancer patients. We interrogated large pool of samples from oral cancer gene expression studies to identify potential therapeutic targets that are involved in multiple cancer hallmark events. Therapeutic strategies directed towards such targets can be expected to effectively control cancer cells. Datasets from different gene expression studies were integrated by removing batch-effects and was used for downstream analyses, including differential expression analysis. Dependency network analysis was done to identify genes that undergo marked topological changes in oral cancer samples when compared with control samples. Causal reasoning analysis was carried out to identify significant hypotheses, which can explain gene expression profiles observed in oral cancer samples. Text-mining based approach was used to detect cancer hallmarks associated with genes significantly expressed in oral cancer. In all, 2365 genes were detected to be differentially expressed genes, which includes some of the highly differentially expressed genes like matrix metalloproteinases (MMP-1/3/10/13), chemokine (C-X-C motif) ligands (IL8, CXCL-10/-11), PTHLH, SERPINE1, NELL2, S100A7A, MAL, CRNN, TGM3, CLCA4, keratins (KRT-3/4/13/76/78), SERPINB11 and serine peptidase inhibitors (SPINK-5/7). XIST, TCEAL2, NRAS and FGFR2 are some of the important genes detected by dependency and causal network analysis. Literature mining analysis annotated 1014 genes, out of which 841 genes were statistically significantly annotated. The integration of output of various analyses, resulted in the list of potential therapeutic targets for oral cancer, which included targets such as ADM, TP53, EGFR, LYN, CTLA4, SKIL, CTGF and CD70.
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PMID:Potential therapeutic targets for oral cancer: ADM, TP53, EGFR, LYN, CTLA4, SKIL, CTGF, CD70. 2502 26

Colorectal cancer, a malignant neoplasm that occurs in the colorectal mucosa, is one of the most common types of gastrointestinal cancer. Colorectal cancer has been studied extensively, but the molecular mechanisms of this malignancy have not been characterized. This study identified and verified core genes associated with colorectal cancer using integrated bioinformatics analysis. Three gene expression profiles (GSE15781, GSE110223, and GSE110224) were downloaded from the Gene Expression Omnibus (GEO) databases. A total of 87 common differentially expressed genes (DEGs) among GSE15781, GSE110223, and GSE110224 were identified, including 19 upregulated genes and 68 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for common DEGs using clusterProfiler. These common DEGs were significantly involved in cancer-associated functions and signaling pathways. Then, we constructed protein-protein interaction networks of these common DEGs using Cytoscape software, which resulted in the identification of the following 10 core genes: SST, PYY, CXCL1, CXCL8, CXCL3, ZG16, AQP8, CLCA4, MS4A12, and GUCA2A. Analysis using qRT-PCR has shown that SST, CXCL8, and MS4A12 were significant differentially expressed between colorectal cancer tissues and normal colorectal tissues (P < 0.05). Gene Expression Profiling Interactive Analysis (GEPIA) overall survival (OS) has shown that low expressions of AQP8, ZG16, CXCL3, and CXCL8 may predict poor survival outcome in colorectal cancer. In conclusion, the core genes identified in this study contributed to the understanding of the molecular mechanisms involved in colorectal cancer development and may be targets for early diagnosis, prevention, and treatment of colorectal cancer.
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PMID:Identification and Verification of Core Genes in Colorectal Cancer. 3246 20