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Gong L, Lv Y, Li S, Feng T, Zhou Y, Sun Y, Mi D. Changes in transcriptome profiling during the acute/subacute phases of contusional spinal cord injury in rats. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1682. [PMID: 33490194 PMCID: PMC7812200 DOI: 10.21037/atm-20-6519] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background Spinal cord injuries (SCIs), along with subsequent secondary injuries, often result in irreversible damage to both sensory and motor functions. However, a thorough view of the underlying pathological mechanisms of SCIs, especially in a temporal-spatial manner, is still lacking. Methods To obtain a comprehensive, real-time view of multiple subsets of the cellular mechanisms involved in SCIs, we applied RNA-sequencing technology to characterize the temporal changes in gene expression around the lesion site of contusion SCI in rats. First, we identified the differentially expressed genes (DEGs) in contrast to sham controls at 1, 4, and 7 days post SCI. Through bioinformatics analysis, including Pathway analysis, Gene-act-net, and Pathway-act-net, we screened and verified potential key pathways and genes associated with either the acute or subacute stages of SCI pathology. Results The top three overrepresented pathways were associated with cytokine-cytokine receptor interaction, TNF signaling pathway, and cell cycle at day 1; lysosome, cytokine-cytokine receptor interaction, phagosome at day 4; and phagosome, lysosome, cytokine-cytokine receptor interaction at day 7 post injury. Further, we identified uniquely enriched genes at each time point, such as Ccr1 and Nos2 at day 1; as well as Mgst2, and Pla2g3 at 4 and 7 days post-injury. Conclusions Our pathway analysis suggested a transition from inflammatory responses to multiple forms of cell death processes from the acute to subacute stages of SCI. Further, our results revealed a continuous transformation from a more inflammatory to an apoptotic/self-repairing transcriptome following the time-course of SCIs. Our research provides novel insights into the molecular mechanisms of SCI pathophysiology and identifies potential targets for therapeutic intervention after SCI.
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Bian Y, Yang L, Zhao M, Li Z, Xu Y, Zhou G, Li W, Zeng L. Identification of Key Genes and Pathways in Post-traumatic Stress Disorder Using Microarray Analysis. Front Psychol 2019; 10:302. [PMID: 30873067 PMCID: PMC6403462 DOI: 10.3389/fpsyg.2019.00302] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 01/30/2019] [Indexed: 12/17/2022] Open
Abstract
Introduction: Post-traumatic stress disorder (PTSD) is characterized by impaired fear extinction, excessive anxiety, and depression. However, the potential pathogenesis and cause of PTSD are not fully understood. Hence, the purpose of this study was to identify key genes and pathway involved in PTSD and reveal underlying molecular mechanisms by using bioinformatics analysis. Methods: The mRNA microarray expression profile dataset was retrieved and downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R. Gene ontology (GO) was used for gene function annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was performed for enrichment analysis. Subsequently, protein-protein interaction (PPI) network and module analysis by the plugin MCODE were mapped by Cytoscape software. Finally, these key genes were verified in stress-exposed models by Real-Time quantitative (qRT-PCR). In addition, we performed text mining among the key genes and pathway with PTSD by using COREMINE. Results: A total of 1004 DEGs were identified. Gene functional annotations and enrichment analysis indicated that the most associated pathway was closely related to the Wnt signaling pathway. Using PPI network and module analysis, we identified a group of "seed" genes. These genes were further verified by qRT-PCR. In addition, text mining indicated that the altered CYP1A2, SYT1, and NLGN1 affecting PTSD might work via the Wnt signaling pathway. Conclusion: By using bioinformatics analysis, we identified a number of genes and relevant pathway which may represent key mechanisms associated with PTSD. However, these findings require verification in future experimental studies.
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He W, Fu L, Yan Q, Zhou Q, Yuan K, Chen L, Han Y. Gene set enrichment analysis and meta-analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma. Oncol Lett 2019; 17:5608-5618. [PMID: 31186783 PMCID: PMC6507356 DOI: 10.3892/ol.2019.10236] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 03/01/2019] [Indexed: 12/14/2022] Open
Abstract
The aim of the present study was to analyze lung adenocarcinoma-associated microarray data and identify potentially crucial genes. The gene expression profiles were downloaded from the Gene Expression Omnibus database and 6 datasets, of which 2 were discarded and 4 were retained, were preprocessed using packages in the R computing language. Subsequently, Gene Set Enrichment Analysis (GSEA) and meta-analysis was used to screen the common pathways and differentially expressed genes at the transcriptional level. The genes detected from GSEA through The Cancer Genome Atlas databases were subsequently examined, and the crucial genes by survival data were identified. Pathways of the crucial genes were obtained using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the online website Database for Annotation, Visualization and Integrated Discovery (DAVID) tool, and the pathways of crucial genes that were upregulated or downregulated were matched using the Venn method to identify the common crucial pathways. Furthermore, on the basis of the common crucial pathways, key genes that are closely associated with the development and progression of lung adenocarcinoma were identified with the KEGG pathway of DAVID. Additional information was obtained through Gene Ontology annotation. A total of two key pathways, including cell cycle and DNA replication, as well as 12 key genes [DNA polymerase δ subunit 2, DNA replication licensing factor MCM4, MCM6, mitotic checkpoint serine/threonine-protein kinase BUB1, BUB1β, mitotic spindle assembly checkpoint protein MAD2A, dual specificity protein kinase TTK, M-phase inducer phosphatase 1, cell division control protein 45 homolog, cyclin-dependent kinase inhibitor 1C, pituitary tumor-transforming gene 1 protein and polo-like kinase 1] were identified. These key pathways and genes may be studied in future studies involving gene transfection/knockdown, which may provide insights into the prognosis of lung adenocarcinoma. Additional studies are required to confirm their biological function.
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Ma LL, Bai Y, Liu WH, Diao ZL. Bioinformatics analysis of potential key ferroptosis-related genes involved in tubulointerstitial injury in patients with diabetic nephropathy. Ren Fail 2023; 45:2199095. [PMID: 37038746 PMCID: PMC10101677 DOI: 10.1080/0886022x.2023.2199095] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023] Open
Abstract
Diabetic nephropathy (DN) is the primary complication of diabetes mellitus. Ferroptosis is a form of cell death that plays an important role in DN tubulointerstitial injury, but the specific molecular mechanism remains unclear. Here, we downloaded the DN tubulointerstitial datasets GSE104954 and GSE30529 from the Gene Expression Omnibus database. We examined the differentially expressed genes (DEGs) between DN patients and healthy controls, and 36 ferroptosis-related DEGs were selected. Pathway-enrichment analyses showed that many of these genes are involved in metabolic pathways, phosphoinositide 3-kinase/Akt signaling, and hypoxia-inducible factor-1 signaling. Ten of the 36 ferroptosis-related DEGs (CD44, PTEN, CDKN1A, DPP4, DUSP1, CYBB, DDIT3, ALOX5, VEGFA, and NCF2) were identified as key genes. Expression patterns for six of these (CD44, PTEN, DDIT3, ALOX5, VEGFA, and NCF2) were validated in the GSE30529 dataset. Nephroseq data indicated that the mRNA expression levels of CD44, PTEN, ALOX5, and NCF2 were negatively correlated with the glomerular filtration rate (GFR), while VEGFA and DDIT3 mRNA expression levels were positively correlated with GFR. Immune infiltration analysis demonstrated altered immunity in DN patients. Real-time quantitative PCR (qPCR) analysis showed that ALOX5, PTEN, and NCF2 mRNA levels were significantly upregulated in high-glucose-treated human proximal tubular (HK-2) cells, while DDIT3 and VEGFA mRNA levels were significantly downregulated. Immunohistochemistry analysis of human renal biopsies showed positive staining for ALOX5 and NCF2 protein in DN samples but not the controls. These key genes may be involved in the molecular mechanisms underlying ferroptosis in patients with DN, potentially through specific metabolic pathways and immune/inflammatory mechanisms.
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Morgan SL, Wyant GA, Dinulescu DM. "Take it up a NOTCH": novel strategies for cancer therapy. Cell Cycle 2012; 12:191-2. [PMID: 23287472 PMCID: PMC3575440 DOI: 10.4161/cc.23375] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
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Du G, Geng D, Zhou K, Fan Y, Su R, Zhou Q, Liu B, Duysenbi S. Identification of potential key pathways, genes and circulating markers in the development of intracranial aneurysm based on weighted gene co-expression network analysis. ARTIFICIAL CELLS NANOMEDICINE AND BIOTECHNOLOGY 2020; 48:999-1007. [PMID: 32589050 DOI: 10.1080/21691401.2020.1770264] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Background: Intracranial aneurysm (IA) is a disease resulted from weak brain control, characterized by local expansion or dilation of brain artery. This study aimed to construct a gene co-expression network by Weighted Gene Correlation Network Analysis (WGCNA) to explore the potential key pathways and genes for the development of IA.Method: Six IA-related gene expression data sets were downloaded from the Gene Expression Omnibus (GEO) database for identifying differentially expressed genes (DEGs). WGCNA was used to identify modules associated with IA. Functional enrichment analysis was used to explore the potential biological functions. ROC analysis was used to find markers for predicting IA.Results: Purple, greenyellow and yellow modules were significantly associated with unruptured intracranial aneurysms, while blue and turquoise modules were significantly associated with ruptured intracranial aneurysms. Functional modules significantly related to IA were enriched in Ribosome, Glutathione metabolism, cAMP signalling pathway, Lysosome, Glycosaminoglycan degradation and other pathways. CD163, FCEREG, FPR1, ITGAM, NLRC4, PDG, and TYROBP were up-regulated ruptured intracranial aneurysms and serum, these genes were potential circulating markers for predicting IA rupture.Conclusions: Potential IA-related key pathways, genes and circulating markers were identified for predicting IA rupture by WGCNA analysis.
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Farsi Z, Allahyari Fard N. The identification of key genes and pathways in glioblastoma by bioinformatics analysis. Mol Cell Oncol 2023; 10:2246657. [PMID: 37593751 PMCID: PMC10431734 DOI: 10.1080/23723556.2023.2246657] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 08/19/2023]
Abstract
GBM is the most common and aggressive type of brain tumor. It is classified as a grade IV tumor by the WHO, the highest grade. Prognosis is generally poor, with most patients surviving only about a year. Only 5% of patients survive longer than 5 years. Understanding the molecular mechanisms that drive GBM progression is critical for developing better diagnostic and treatment strategies. Identifying key genes involved in GBM pathogenesis is essential to fully understand the disease and develop targeted therapies. In this study two datasets, GSE108474 and GSE50161, were obtained from the Gene Expression Omnibus (GEO) to compare gene expression between GBM and normal samples. Differentially expressed genes (DEGs) were identified and analyzed. To construct a protein-protein interaction (PPI) network of the commonly up-regulated and down-regulated genes, the STRING 11.5 and Cytoscape 3.9.1 were utilized. Key genes were identified through this network analysis. The GEPIA database was used to confirm the expression levels of these key genes and their association with survival. Functional and pathway enrichment analyses on the DEGs were conducted using the Enrichr server. In total, 698 DEGs were identified, consisting of 377 up-regulated genes and 318 down-regulated genes. Within the PPI network, 11 key up-regulated genes and 13 key down-regulated genes associated with GBM were identified. NOTCH1, TOP2A, CD44, PTPRC, CDK4, HNRNPU, and PDGFRA were found to be important targets for potential drug design against GBM. Additionally, functional enrichment analysis revealed the significant impact of Epstein-Barr virus (EBV), Cell Cycle, and P53 signaling pathways on GBM.
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Wei L, Wang Y, Zhou D, Li X, Wang Z, Yao G, Wang X. Bioinformatics analysis on enrichment analysis of potential hub genes in breast cancer. Transl Cancer Res 2021; 10:2399-2408. [PMID: 35116555 PMCID: PMC8797715 DOI: 10.21037/tcr-21-749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/21/2021] [Indexed: 11/07/2022]
Abstract
Background Despite recent advances in screening, treatment, and survival, breast cancer remains the most invasive cancer in women. The development of novel diagnostic and therapeutic markers for breast cancer may provide more information about its pathogenesis and progression. Methods We obtained GSE86374 micro-expression matrix chip data from the Gene Expression Omnibus (GEO) database consisting of 159 samples (124 normal samples and 35 breast cancer samples). The language was then used to perform data processing and differential expression analysis. For all differentially expressed genes (DEGs), “FDR <0.01 and |logFC| ≥1” were selected as thresholds. Results In this study, 173 up-regulated genes and 143 down-regulated genes were selected for GO and KEGG enrichment analysis. These genes are also significantly enriched in the KEGG pathway, including phenylalanine metabolism, staphylococcus aureus infection, and the PPAR signaling pathway. The survival and prognosis of the selected eight key genes (DLGAP5, PRC1, TOP2A, CENPF, RACGAP1, RRM2, PLK1, and ASPM) were analyzed by the Kaplan-Meier plotter database. Conclusions Eight hub genes and pathways closely related to the onset and progression of breast cancer were identified. We found that the PPAR signaling pathway, especially PPARγ, plays an important role in breast cancer and suggest this pathway be the subject of further research.
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Zhang J, Pan Y, Zhao L, Zhao T, Yu S, Cui Y. Identification of key genes and biological pathways in different parts of yak oviduct based on transcriptome analysis. Front Vet Sci 2022; 9:1016191. [PMID: 36504863 PMCID: PMC9727391 DOI: 10.3389/fvets.2022.1016191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/18/2022] [Indexed: 11/24/2022] Open
Abstract
The oviduct consists of three parts: the infundibulum (In), ampulla (Am), and isthmus (Is). These have the same histological structure, but different physiological functions. In this study, transcriptomics was used to analyze mRNA in these three parts of yak oviduct. The results showed that there were 325 up-regulated genes and 282 down-regulated genes in the infundibulum and ampulla. Moreover, there were 234 up-regulated genes and 776 down-regulated genes in the isthmus and ampulla, as well as 873 up-regulated genes and 297 down-regulated genes in the infundibulum and isthmus. The expression of C3 in the infundibulum was significantly higher than that in the ampulla and isthmus. The expression of FAU in the isthmus was significantly lower than that in the ampulla and infundibulum, and the expression of EEF1A1 in the ampulla was significantly higher than that in the ampulla and infundibulum. When the infundibulum was compared with the ampulla and isthmus, it was found that the up-regulated genes were enriched in the lysosome, phagosome, staphylococcus aureus infection, and leishmaniasis pathway. When the isthmus was compared with the ampulla and infundibulum, the up-regulated genes were present in the apoptosis pathway, oxidative phosphorylation, and viral myocarditis pathway. When the isthmus was compared with the infundibulum and ampulla, the down-regulated pathways were protein processing in the endoplasmic reticulum and the endocytosis. The Epstein-Barr virus infection pathway was up-regulated according to a comparison of the isthmus and infundibulum and was down-regulated based on a comparison of the isthmus and ampulla. Transcriptional misregulation in the Middle East pathway was up-regulated based on a comparison of the isthmus and ampulla and was down-regulated based on a comparison of the isthmus and infundibulum. ERBB2, JUP, CTNND1, and KRT7 were defined as the hub genes of the yak oviduct. The results of this study provide sufficient omics data for yak fertilization, which is also of great significance to altitude medicine.
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Exploring the Novel Computational Drug Target and Associated Key Pathways of Oral Cancer. Curr Issues Mol Biol 2022; 44:3552-3572. [PMID: 36005140 PMCID: PMC9406749 DOI: 10.3390/cimb44080244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 08/04/2022] [Accepted: 08/06/2022] [Indexed: 11/17/2022] Open
Abstract
Oral cancer (OC) is a serious health concern that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. The publicly available repository the Gene Expression Omnibus (GEO) was to the source for the collection of OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected for analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to calculate the upregulated and downregulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein−protein interaction (PPI) networks were built to identify the most active genes, and a clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study OC-associated DEGs in-depth. Finally, top gene performers from PPI networks were used to apply drug signature analysis. After applying filtration and cut-off criteria, 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the cell cycle and influenza A. The PPI network holds 88 nodes and 492 edges, and CDC6 had the highest number of connections. Four clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of OC pathophysiology and the development of new therapies for OC.
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