scholarly journals Delineating the underlying molecular mechanisms and key genes involved in metastasis of colorectal cancer via bioinformatics analysis

2018 ◽  
Author(s):  
Chao Qi ◽  
Yuanlei Chen ◽  
Yixuan Zhou ◽  
Xucheng Huang ◽  
Guoli Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Key Genes

scholarly journals Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis

2019 ◽  
Author(s):  
Gong‑Peng Dai ◽  
Li‑Ping Wang ◽  
Yu‑Qing Wen ◽  
Xue‑Qun Ren ◽  
Shu‑Guang Zuo
Keyword(s):  
Colorectal Cancer ◽  
Bioinformatics Analysis ◽  
Cancer Prognosis ◽  
Key Genes

Bioinformatics Analysis of Key Genes and Pathways in Colorectal Cancer

2019 ◽  
Vol 26 (4) ◽  
pp. 364-375 ◽  
Author(s):  
Yuewen Qi ◽  
Haowen Qi ◽  
Zeyuan Liu ◽  
Peiyuan He ◽  
Bingqing Li
Keyword(s):  
Colorectal Cancer ◽  
Bioinformatics Analysis ◽  
Key Genes

scholarly journals Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Immune System ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory ◽  
Key Genes

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

scholarly journals Hub Genes and Key Pathway Identification in Colorectal Cancer Based on Bioinformatic Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jian Lv ◽  
Lili Li
Keyword(s):  
Colorectal Cancer ◽  
Molecular Mechanisms ◽  
Malignant Tumors ◽  
Disease Free Survival ◽  
Bioinformatic Analysis ◽  
Receptor Interaction ◽  
Estrogen Metabolism ◽  
Hub Genes ◽  
Extracellular Region ◽  
Key Genes

Colorectal cancer (CRC) is one of the most common malignant tumors. The aim of the present study was to identify key genes and pathways to improve the understanding of the mechanism of CRC. GSE87211, including 203 CRC samples and 160 control samples, was screened to identify differentially expressed genes (DEGs). In total, 853 DEGs were obtained, including 363 upregulated genes and 490 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were performed to obtain enrichment datasets. GO analysis showed that DEGs were significantly enriched in the extracellular region, cell-cell signaling, hormone activity, and cytokine activity. KEGG pathway analysis revealed that the DEGs were mainly enriched in the cytokine-cytokine receptor interaction, drug metabolism, androgen and estrogen metabolism, and neuroactive ligand-receptor interaction. The Protein-Protein Interaction (PPI) network of DEGs was constructed by using Search Tool for the Retrieval of Interacting Genes (STRING). The app MCODE plugged in Cytoscape was used to explore the key modules involved in disease development. 43 key genes involved in the top two modules were identified. Six hub genes (CXCL2, CXCL3, PTGDR2, GRP, CXCL11, and AGTR1) were statistically associated with patient overall survival or disease-free survival. The functions of six hub genes were mainly related to the hormone and chemokine activities. In conclusion, the present study may help understand the molecular mechanisms of CRC development.

scholarly journals Bioinformatics analysis of key genes and pathways in Hashimoto thyroiditis tissues

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Long Zheng ◽  
Xiaojie Dou ◽  
Huijia Song ◽  
Pengwei Wang ◽  
Wei Qu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Interaction Analysis ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Hashimoto Thyroiditis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Abstract Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, only a few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, protein–protein interaction analysis, and gene set enrichment analysis were performed. In total, 85 DEGs, containing 76 up-regulated and 9 down-regulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3, and inflammatory pathways may promote the origin and progression of HT. The present study indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

Effect of cortisol on neurophysin I/oxytocin and peptidyl glycine-α-amidating mono-oxygenase mRNA expression in bovine luteal and granulosa cells

2013 ◽  
Vol 16 (2) ◽  
pp. 231-239
Author(s):  
A. Ziolkowska ◽  
J. Mlynarczuk ◽  
J. Kotwica
Keyword(s):  
Mrna Expression ◽  
Granulosa Cells ◽  
Molecular Mechanisms ◽  
Hormone Secretion ◽  
Oestrous Cycle ◽  
Luteal Cells ◽  
Ru 486 ◽  
Ovarian Cells ◽  
Key Genes ◽  
Ovary Function

Abstract Cortisol stimulates the synthesis and secretion of oxytocin (OT) from bovine granulosa and luteal cells, but the molecular mechanisms of cortisol action remain unknown. In this study, granulosa cells or luteal cells from days 1-5 and 11-15 of the oestrous cycle were incubated for 4 or 8 h with cortisol (1x10-5, 1x10-7 M). After testing cell viability and hormone secretion (OT, progesterone, estradiol), we studied the effect of cortisol on mRNA expression for precursor of OT (NP-I/OT) and peptidyl glycine-α-amidating mono-oxygenase (PGA). The influence of RU 486 (1x10-5 M), a progesterone receptor blocker and inhibitor of the glucocorticosteroid receptor (GR), on the expression for both genes was tested. Cortisol increased the mRNA expression for NP-I/OT and PGA in granulosa cells and stimulated the expression for NP-I/OT mRNA in luteal cells obtained from days 1-5 and days 11-15 of the oestrous cycle. Expression for PGA mRNA was increased only in luteal cells from days 11-15 of the oestrous cycle. In addition, RU 486 blocked the cortisol-stimulated mRNA expression for NP-I/OT and PGA in both types of cells. These data suggest that cortisol affects OT synthesis and secretion in bovine ovarian cells, by acting on the expression of key genes, that may impair ovary function.

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