Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0017638 (glioma)
 30,880 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The oncogenic role of STAT3 has been elucidated in a number of human malignancies including leukemia, lymphoma, malignant glioma and cancers of the breast, lung, and head and neck (HNSCC). Here we show that WP1066 has profound anti-neoplastic effects in HNSCC, mediated in part by suppression of JAK2-STAT3 signaling. WP1066 inhibited constitutive and inducible STAT3 phosphorylation in both dose- and time-dependant manners. Further, the nuclear translocation of STAT3 was completely inhibited, resulting in decreased DNA binding activity. In vivo testing of WP1066 in a nude mouse orthotopic model of HNSCC demonstrated significant anti-tumor effects, with histological evidence of decreased cellular proliferation and angiogenesis. Collectively, these data suggest that WP1066 suppresses squamous cell carcinoma cell growth, in part through its effects on JAK-STAT pathways, and establishes this small molecule as potentially efficacious agent in the treatment of HNSCC.
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PMID:Therapeutic suppression of constitutive and inducible JAK\STAT activation in head and neck squamous cell carcinoma. 2019 18

The emergence of low molecular weight kinase inhibitors as "targeted" drugs has led to remarkable advances in the treatment of cancer patients. The clinical benefits of these tumor therapies, however, vary widely in patient populations and with duration of treatment. Intrinsic and acquired resistance against such drugs limits their efficacy. In addition to the well studied mechanisms of resistance based upon drug transport and metabolism, genetic alterations in drug target structures and the activation of compensatory cell signaling have received recent attention. Adaptive responses can be triggered which counteract the initial dependence of tumor cells upon a particular signaling molecule and allow only a transient inhibition of tumor cell growth. These compensating signaling mechanisms are often based upon the relief of repression of regulatory feedback loops. They might involve cell autonomous, intracellular events or they can be mediated via the secretion of growth factor receptor ligands into the tumor microenvironment and signal induction in an auto- or paracrine fashion. The transcription factors Stat3 and Stat5 mediate the biological functions of cytokines, interleukins and growth factors and can be considered as endpoints of multiple signaling pathways. In normal cells this activation is transient and the Stat molecules return to their non-phosphorylated state within a short time period. In tumor cells the balance between activating and de-activating signals is disturbed resulting in the persistent activation of Stat3 or Stat5. The constant activation of Stat3 induces the expression of target genes, which cause the proliferation and survival of cancer cells, as well as their migration and invasive behavior. Activating components of the Jak-Stat pathway have been recognized as potentially valuable drug targets and important principles of compensatory signaling circuit induction during targeted drug treatment have been discovered in the context of kinase inhibition studies in HNSCC cells [1]. The treatment of HNSCC with a specific inhibitor of c-Src, initially resulted in reduced Stat3 and Stat5 activation and subsequently an arrest of cell proliferation and increased apoptosis. However, the inhibition of c-Src only caused a persistent inhibition of Stat5, whereas the inhibition of Stat3 was only transient. The activation of Stat3 was restored within a short time period in the presence of the c-Src inhibitor. This process is mediated through the suppression of P-Stat5 activity and the decrease in the expression of the Stat5 dependent target gene SOCS2, a negative regulator of Jak2. Jak2 activity is enhanced upon SOCS2 downregulation and causes the reactivation of Stat3. A similar observation has been made upon inhibition of Bmx, bone marrow kinase x-linked, activated in the murine glioma cell lines Tu-2449 and Tu-9648. Its inhibition resulted in a transient decrease of P-Stat3 and the induction of a compensatory Stat3 activation mechanism, possibly through the relief of negative feedback inhibition and Jak2 activation. These observations indicate that the inhibition of a single tyrosine kinase might not be sufficient to induce lasting therapeutic effects in cancer patients. Compensatory kinases and pathways might become activated and maintain the growth and survival of tumor cells. The definition of these escape pathways and their preemptive inhibition will suggest effective new combination therapies for cancer.
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PMID:Resistance of Cancer Cells to Targeted Therapies Through the Activation of Compensating Signaling Loops. 2504 45

Non-coding RNAs occupy a significant fraction of the human genome. Their biological significance is backed up by a plethora of emerging evidence. One of the most robust approaches to demonstrate non-coding RNA's biological relevance is through their prognostic value. Using the rich gene expression data from The Cancer Genome Altas (TCGA), we designed Advanced Expression Survival Analysis (AESA), a web tool which provides several novel survival analysis approaches not offered by previous tools. In addition to the common single-gene approach, AESA computes the gene expression composite score of a set of genes for survival analysis and utilizes permutation test or cross-validation to assess the significance of log-rank statistic and the degree of over-fitting. AESA offers survival feature selection with post-selection inference and utilizes expanded TCGA clinical data including overall, disease-specific, disease-free, and progression-free survival information. Users can analyse either protein-coding or non-coding regions of the transcriptome. We demonstrated the effectiveness of AESA using several empirical examples. Our analyses showed that non-coding RNAs perform as well as messenger RNAs in predicting survival of cancer patients. These results reinforce the potential prognostic value of non-coding RNAs. AESA is developed as a module in the freely accessible analysis suite MutEx. Abbreviation: ACC: Adrenocortical Carcinoma (n = 92); BLCA: Bladder Urothelial Carcinoma (n = 412); BRCA: Breast Invasive Carcinoma (n = 1098); CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (n = 307); CHOL: Cholangiocarcinoma (n = 51); COAD: Colon Adenocarcinoma (n = 461); DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (n = 58); ESCA: Oesophageal Carcinoma (n = 185); GBM: Glioblastoma Multiforme (n = 617); HNSC: Head and Neck Squamous Cell Carcinoma (n = 528); KICH: Kidney Chromophobe (n = 113); KIRC: Kidney Renal Clear Cell Carcinoma (n = 537); KIRP: Kidney Renal Papillary Cell Carcinoma (n = 291); LAML: Acute Myeloid Leukaemia (n = 200); LGG: Brain Lower Grade Glioma (n = 516); LIHC: Liver Hepatocellular Carcinoma (n = 377); LUAD: Lung Adenocarcinoma (n = 585); LUSC: Lung Squamous Cell Carcinoma (n = 504); MESO: Mesothelioma (n = 87); OV: Ovarian Serous Cystadenocarcinoma (n = 608) PAAD: Pancreatic Adenocarcinoma (n = 185); PCPG: Pheochromocytoma and Paraganglioma (n = 179); PRAD: Prostate Adenocarcinoma (n = 500); READ: Rectum Adenocarcinoma (n = 172); SARC: Sarcoma (n = 261); SKCM: Skin Cutaneous Melanoma (n = 470); STAD: Stomach Adenocarcinoma (n = 443); TGCT: Testicular Germ Cell Tumours (n = 150); THCA: Thyroid Carcinoma (n = 507) THYM: Thymoma (n = 124); UCEC: Uterine Corpus Endometrial Carcinoma (n = 560); UCS: Uterine Carcinosarcoma (n = 57); UVM: Uveal Melanoma (n = 80).
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PMID:Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA. 3160 16

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.) and (ii) a flexible boundary between the variable selection and network modeling stages - the boundary that can be adjusted in accordance with the investigators' BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.
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PMID:New Analysis Framework Incorporating Mixed Mutual Information and Scalable Bayesian Networks for Multimodal High Dimensional Genomic and Epigenomic Cancer Data. 3262 38