Identification of key pathways and genes in &lt;em&gt;TP53 &lt;/em&gt;mutation acute myeloid leukemia: evidence from bioinformatics analysis

Huang, Rui; Liao, Xiwen; Li, Qiaochuan

doi:10.2147/ott.s156003

Cited by 19 publications

(14 citation statements)

References 68 publications

(66 reference statements)

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“…Garczyk S et al have demonstrated that NMU may contribute to the progression of NMUR2-positive breast cancer [21] and enhance resistance to tumor immune responses in breast cancers with HER2 overexpression [22], suggesting NMU is a potential drug target for personalized strategies. Furthermore, NMU is involved in the development of endometrial, colorectal and gastric cancers, as well as acute myeloid leukemia caused by TP53 mutations [23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Shen

Dong

Liu

et al. 2020

BioMed Research International

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Background. The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods. The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results. By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion. The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

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Section: Discussionmentioning

confidence: 99%

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Shen

Dong

Liu

et al. 2020

BioMed Research International

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“…P value < 0.05 and a false discovery rate (FDR) < 0.25 were considered statistically significant enrichment results. 30 3 | RESULTS Figure 1C). Just as existing research, RA is a disorder of the autoimmune system disease.…”

Section: Gene Set Enrichment Analysismentioning

confidence: 94%

Comparison of rheumatoid arthritis (RA) and osteoarthritis (OA) based on microarray profiles of human joint fibroblast‐like synoviocytes

Cai

Jiang

et al. 2018

Cell Biochemistry & Function

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The purpose of the present study was to investigate the underlying molecular mechanism of osteoarthritis (OA) and rheumatoid arthritis (RA) based on microarray profiles. Three human joint fibroblast-like synoviocytes (FLSs) microarray profiles including 26 OA samples, 33 RA samples, and 20 healthy control (HC) samples were downloaded from the GEO database. Differentially expressed genes (DEGs) between OA and HC (DEGsOA) and RA and HC (DEGsRA) were identified. Co-expressed and specific genes were analysed between DEGsOA and DEGsRA. Gene ontology, KEGG pathway enrichment, PPI network, and GSEA were performed to predict the function of DEGs. Two hundred seventy-six and 410 differential genes in DEGsOA and DEGsRA were observed. One hundred fifty coexpressed genes and 126 OA-specific genes (SELE, SERPINE1, and NFKBIA were the key genes) between DEGsOA and DEGsRA were enriched in the tumour necrosis factor (TNF) signalling pathway. However, 260 RA-specific genes of which the key genes were CCR5, CCR7, CXCR4, CCL5, and CCR4 were enriched in chemokine signalling pathway. Therefore, FLSs might exert an inflammatory effect by regulating TNF signalling pathway, targeting SELE, SERPINE1, and NFKBIA during the process of OA. Although TNF signalling pathway was also involved in the synovitis of RA, chemokine signalling pathway played the key role in RA FLSs mediating cell migration, invasion, and release of chemotaxis. In addition, CCR5, CCR7, CXCR4, CCL5, and CCR4 might be hub genes in RA. The different biomarkers and pathways identified in OA and RA may provide references for further study.List of abbreviations: CCL5, C-C motif chemokine ligand 5; CCRs, C-C motif chemokine receptors; CD95, FAS cell surface death receptor; Co-expressed genes, the common genes both in DEGsOA and DEGsRA groups; CXCR4, C-X-C motif chemokine receptor 4; DAVID, Database for Annotation, Visualization, and Integrated Discovery; DEGs, differently expressed genes; DEGsOA, differently expressed genes between osteoarthritis and healthy fibroblast-like synoviocytes; DEGsRA, differently expressed genes between rheumatoid arthritis and healthy fibroblast-like synoviocytes; DLL1, delta-like protein 1

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“…In recent years, large-scale genome sequencing, such as high-throughput data including The Cancer Genome Atlas (TCGA) database, provides a new method to help researchers explore the complex relationship between genetic molecules and disease (Huang & Li, 2017;Zhai et al, 2019). So, in this study, we screened the transcriptome sequencing dataset of appropriate BRCA mutant and wild-type BC patients from the TCGA database, and thereby identified differentially expressed genes (DEGs) through analysis of these two sets of data to reflect gene expression profiles influenced by BRCA1/2 mutations, combined with Gene set enrichment analysis (GSEA), survival analysis and diagnostic value assessment.…”

Section: Introductionmentioning

confidence: 99%

Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

Zhou

Liu

et al. 2020

PeerJ

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Background BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis. Methods Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein–protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations. Results The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer. Conclusion Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients.

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Identification of key pathways and genes in <em>TP53 </em>mutation acute myeloid leukemia: evidence from bioinformatics analysis

Cited by 19 publications

References 68 publications

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Comparison of rheumatoid arthritis (RA) and osteoarthritis (OA) based on microarray profiles of human joint fibroblast‐like synoviocytes

Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

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