scholarly journals Immune-Related Genes: Potential Regulators and Drug Therapeutic Targets in Hypertrophic Cardiomyopathy

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wei Liu ◽  
Ju Ye ◽  
Jinqiang Cai ◽  
Feng Xie ◽  
Mengjie Tang ◽  
...  

Background. Accumulating evidence shows that the innate immune system is a key player in cardiovascular repair and regeneration, but little is known about the role of immune-related genes (IRGs) in hypertrophic cardiomyopathy (HCM). Methods. The differential mRNA expression profiles of HCM samples were downloaded from the Gene Expression Omnibus (GEO) dataset (GSE89714), and the IRG expression profile was obtained from the ImmPort database. The regulatory pathways of IRGs in HCM were screened out through discrepantly expressive genes (DEGs) analysis, enrichment of gene function/pathway analysis, and protein-protein interaction (PPI) network. Besides, hub IRGs in the PPI network were selected for drug prediction. Results. A total of 854 genes were differentially expressed in HCM, of which 88 were IRGs. Functional enrichment analysis revealed that 88 IRGs were mainly involved in the biological processes (BP) of SMAD protein pathway, smooth muscle cell proliferation, protein serine/threonine kinase, and mitogen-activated protein kinase (MAPK) cascade. Cytokine-cytokine receptor interaction, TGFβ signaling pathway, PI3K-Akt signaling pathway, and MAPK signaling pathway were enriched in the pathway enrichment analysis of these 88 IRGs. Furthermore, the PPI regulatory network of IRGs was constructed, and 10 hub IRGs were screened out to construct a regulatory network for HCM. 4 transcription factors (TFs) were the major regulator of 10 hub IRGs. Finally, these 10 hub IRGs were entered into the pharmacogenomics database for prediction, and the relevant drugs were obtained. Conclusions. In this study, 10 hub IRGs were coexpressed with 4 TFs to construct a regulatory network for HCM. Drug prediction of these 10 hub IRGs proposed potential therapeutic agents that could be used in HCM. These results indicate that IRGs are potential regulators and drug therapeutic targets in HCM.

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2624-2624
Author(s):  
Xin Zhang ◽  
Ya Zhang ◽  
Yang Han ◽  
Zheng Tian ◽  
Xinting Hu ◽  
...  

Abstract Introduction Chronic lymphocytic leukemia (CLL) is a highly heterogeneous disease characterized by malignant clonal expansion of mature B lymphocytes. Competitive endogenous RNAs(ceRNAs) such as long noncoding RNAs (lncRNAs) and circular RNAs (circ RNAs) have miRNA response elements (MREs) and can bind to miRNAs to influence mRNA expression. An increasing number of studies have shown that the ceRNA network played an important role in the initiation and progression of tumors. However, the roles and functions of the ceRNA network in chronic lymphocytic leukemia (CLL) are still unclear. This study aims to explore the molecular mechanism of CLL and provide potential prognostic markers and therapeutic targets through the integrated analysis of the ceRNA network in CLL. Methods The expression profile of RNAs of CLL patients, CLL cell lines (MEC1 and EHEB) and healthy group were obtained by the illumina sequencing. R software was used for functional enrichment analysis. The data in the genome microarray map GSE22762 was used for survival analysis. The circRNA-miRNA-mRNA ceRNA networks were visualized by Cytoscape 3.7.2. The expression of the circRNA hsa_circ_0007675/hsa-miR-185-3p/TCF7L1 axis were verified by Quantitative real-time PCR and the correlation between hsa_circ_0007675 and TCF7L1 was analyzed. Results In total, we identified 57 differentially expressed mRNAs (DEmRNAs), 1391 DElncRNAs, 335 DEmiRNAs and 2413 DEcircRNAs by comparing CLL patients with healthy donors. Meanwhile, 482 mRNAs, 6085 lncRNAs, 302 miRNAs and 1847 circRNAs were explored differently expressed between CLL cell lines and healthy donors. GO analysis results showed that the functions of differentially expressed genes (DEGs) between CLL patients and control are mainly enriched in sequence−specific DNA binding, chromatin and gene expression (Figure 1A) while between CLL cell lines and control they were mainly enriched in oxidoreductase activity, ribosomal subunit and lipid metabolism (Figure 1C). KEGG pathway analysis revealed that the DEGs between CLL patients and control were mainly enriched in Notch signaling pathway, JAK-STAT signaling pathway and cGMP-PKG signaling pathway (Figure 1B). Meanwhile between CLL cell lines and control, DEGs were mainly enriched in mTOR signaling pathway, cell cycle and p53 signaling pathway (Figure 1D). The survival analyses showed that 15 DEGs (INIP, IL3RA, CHD1, NLRP12, IL20RB, HNRNPC, B3GALT4, SIT1, ACOT8, PCLAF, C19orf18, SELENOS, OR7A17, PCDH7, PHGDH) were significantly differentially expressed in the survival analyses. The overall survival of the high expression group of INIP, IL3RA, CHD1, NLRP12, IL20RB and HNRNPC were higher than that of the low expression group (Figure 2A-F) while the overall survival of the low expression group of B3GALT4, SIT1, ACOT8, PCLAF, C19orf18, SELENOS, OR7A17, PCDH7 and PHGDH were higher than that of the high expression group (Figure 2G-O). The ceRNA network were built by Cytoscape3.7.2. In total, 11 mRNA nodes, 19 miRNA nodes, 251 circRNA nodes were identified as differentially expressed profiles between CLL patients and control (Figure 3A). We verified the circRNA hsa_circ_0007675/hsa-miR-185-3p/TCF7L1 axis. Compared with normal people, the expression of TCF7L1 and hsa_circ_0007675 in patient specimens were significantly increased (p<0.05; Figure 3B, D) whereas the expression of hsa-miR-185-3p was downregulated (p<0.05; Figure 3C). TCF7L1 and hsa_circ_0007675 were positively correlated (p<0.001, R=0.7834; Figure 3E). The correlation analysis of TCF7L1 and other genes were shown in Figure 3F. The interaction mechanism between them is that hsa_circ_0007675 can sponge hsa-miR-185-3p, thereby inhibiting the inhibitory effect of hsa-miR-185-3p on TCF7L1 and finally upregulate the expression of TCF7L1(Figure 3G). Conclusions In this study, we identified the expression profile of RNAs in CLL patients and CLL cell lines. Functional enrichment analysis and survival analysis revealed the potential functions of DEGs. The ceRNA network we established can help to further understand the pathogenesis of CLL and provide potential prognostic biomarkers and novel therapeutic targets. Keywords: Chronic lymphocytic leukemia; Competing endogenous RNA; Non-coding RNAs; Prognostic biomarkers; Therapeutic targets Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


2022 ◽  
Vol 12 ◽  
Author(s):  
Shenghua Pan ◽  
Tingting Tang ◽  
Yanke Wu ◽  
Liang Zhang ◽  
Zekai Song ◽  
...  

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan–Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein–protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients’ overall survival (log rank p < 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan–Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.


2020 ◽  
Author(s):  
Song Wang ◽  
Yi Quan ◽  
Hongying Lyu ◽  
Jian Deng

Abstract Background: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is correlated with a poor prognosis. Thus, the study objective was to reveal target genes and key pathways in HER-2 subtype breast cancer. Methods: The gene expression dataset (GSE29431) was downloaded from the Gene Expression Omnibus database(GEO), and the differentially expressed genes (DEGs) were determined using LIMMA package in R software. Subsequently, Functional enrichment analysis were performed in ClusterProfiler package of R platform. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction (PPI) network of DEGs. Module analysis and target genes were identified by Cytoscape software. Further more, The influence of target genes on overall survival (OS) was assessed using the Kaplan-Meier plotter database.Results: The differential expression analysis revealed 96 genes were up-regulated while 407 genes were down-regulated in HER-2 positive breast cancer tissue compared to normal breast tissue. Functional enrichment analysis showed that the DEGs were mainly involved in regulation of lipid metabolic process, PPAR signaling pathway and PI3K-Akt signaling pathway. PPI network construction revealed a total of 199 nodes and 560 edges, and 12 target genes were identified by the highest value of degree. In addition, target genes were associated with worse overall prognosis, including NUSAP1, PTTG1, CEP55, TOP2A, CCNB1, CENPF, MELK, AURKA, UBE2C, BUB1B, KIF20A and RRM2.Conclusion: The present study identified 12 target genes associated with the development of HER-2 subtype breast cancer, which may help to provide new biomarkers and therapeutic targets.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Dongyang Li ◽  
Xuanyu Hao ◽  
Yongsheng Song

Objective. To identify key microRNAs (miRNAs) and their regulatory networks in prostate cancer.Methods. Four miRNA and three gene expression microarray datasets were downloaded for analysis from Gene Expression Omnibus database. The differentially expressed miRNA and genes were accessed by a GEO2R. Functional and pathway enrichment analyses were performed using the DAVID program. Protein-protein interaction (PPI) and miRNA-mRNA regulatory networks were constructed using the STRING and Cytoscape tool. Moreover, the results and clinical significance were validated in TCGA data.Results. We identified 26 significant DEMs, 633 upregulated DEGs, and 261 downregulated DEGs. Functional enrichment analysis indicated that significant DEGs were related to TGF-beta signaling pathway and TNF signaling pathway in PCa. Key DEGs such as HSPA8, PPP2R1A, CTNNB1, ADCY5, ANXA1, and COL9A2 were found as hub genes in PPI networks. TCGA data supported our results and the miRNAs were correlated with clinical stages and overall survival.Conclusions. We identified 26 miRNAs that may take part in key pathways like TGF-beta and TNF pathways in prostate cancer regulatory networks. MicroRNAs like miR-23b, miR-95, miR-143, and miR-183 can be utilized in assisting the diagnosis and prognosis of prostate cancer as biomarkers. Further experimental studies are required to validate our results.


2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
YunXia Liu ◽  
YeFeng Xu ◽  
Feng Xiao ◽  
JianFeng Zhang ◽  
YiQing Wang ◽  
...  

Gastric cancer (GC) is the most common malignancy of the stomach. This study was aimed at elucidating the regulatory network of circRNA-miRNA-mRNA and identifying the precise inflammation-related targets in GC. The expression profiles of GSE83521, GSE78091, and GSE33651 were obtained from the GEO database. Interactions between miRNAs and circRNAs were investigated by the Circular RNA Interactome, and targets of miRNAs were predicted with miRTarBase. Then, a circRNA/miRNA/mRNA regulatory network was constructed. Also, functional enrichment analysis of selected differentially expressed genes (DEGs) was performed. The inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. And a protein-protein interaction (PPI) network of DE mRNAs was constructed with STRING and Cytoscape to identify hub genes. The genetic alterations, neighboring gene networks, expression levels, and the poor prognosis of hub genes were investigated in cBioPortal, Oncomine, and Human Protein Atlas databases and Kaplan-Meier plotter, respectively. A total of 10 DE miRNAs and 33 DEGs were identified. The regulatory network contained 26 circRNAs, 10 miRNAs, and 1459 mRNAs. Functional enrichment analysis revealed that the selected 33 DEGs were involved in negative regulation of fat cell differentiation, response to wounding, extracellular matrix- (ECM-) receptor interaction, and regulation of cell growth pathways. THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were selected as inflammation-related hub genes of GC in the PPI network. The genetic alterations in these hub genes were related to amplification and missense mutations. Furthermore, the genes RYR2, ERBB2, PI3KCA, and HELZ2 were connected to hub genes in this study. The hub gene levels in clinical specimens were markedly upregulated in GC tissues and correlated with poor overall survival (OS). Our results suggest that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were associated with the pathogenesis of gastric carcinogenesis and may serve as biomarkers and inflammation-related targets for GC.


2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.


2020 ◽  
Vol 17 (5) ◽  
pp. 647-660 ◽  
Author(s):  
Shivananda Kandagalla ◽  
Sharath Belenahalli Shekarappa ◽  
Gollapalli Pavan ◽  
Umme Hani ◽  
Manjunatha Hanumanthappa

Background: Capsaicin is an active alkaloid /principal component of red pepper responsible for the pungency of chili pepper. Capsaicin by changing the intracellular redox homeostasis regulate a variety of signaling pathways ultimately producing a divergent cellular outcome. Several reports showed the potential of capsaicin against cancer metastasis, however unexplored molecular mechanism is still an active part of the research. Several growth factors have a critical role during cancer metastasis among them TGF- β signaling play a vital role. Methods: The present study aimed at analyzing capsaicin modulation of TGF-β signaling using network pharmacology approach. The chemical and protein interaction data of capsaicin was curated and abstracted using STITCH4.0, PubChem and ChEMBL database. Further, the compiled data set was subjected to the pathway and functional enrichment analysis using Protein Analysis THrough Evolutionary Relationship (PANTHER) and, Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Meanwhile, the pattern of amino acid composition across the capsaicin targets was analyzed using the EMBOSS Pepstat tool. Capsaicin targets involved in TGF- β were identified and their Protein-Protein Interaction (PPI) network constructed using STRING v10 and Cytoscape (v 3.2.1). From the above-constructed network, the clusters were mined using the MCODE clustering algorithm and finally binding affinity of capsaicin with its targets involved in TGF-β signaling pathway was analyzed using Autodock Vina. Results: The analysis explored capsaicin targets and, their associated functional and pathway annotations. Besides, the analysis also provides a detailed distinct pattern of amino acid composition across the capsaicin targets. The capsaicin targets described as MAPK14, JUN, SMAD3, MAPK3, MAPK1 and MYC involved in TGF-β signaling pathway through pathway enrichment analysis. The binding mode analysis of capsaicin with its targets has shown high affinity with MAPK3, MAPK1, JUN and MYC. Conclusion: The study explores the potential of capsaicin as a potent modulator of TGF-β signaling pathway during cancer metastasis and proposes new methodology and mechanism of action of capsaicin against TGF- β signaling pathway.


2021 ◽  
Vol 22 (5) ◽  
pp. 2442
Author(s):  
Qun Wang ◽  
Aurelia Vattai ◽  
Theresa Vilsmaier ◽  
Till Kaltofen ◽  
Alexander Steger ◽  
...  

Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.


2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Shuxin Chen ◽  
Zepeng Du ◽  
Bingli Wu ◽  
Huiyang Shen ◽  
Chunpeng Liu ◽  
...  

Background. In our previous study, mouse double minute 2 homolog (MDM2), insulin-like growth factor 1 (IGF1), signal transducer and activator of transcription 1 (STAT1), and Rac family small GTPase 1 (RAC1) were correlated with the recurrence of giant cell tumor of bone (GCT). The aim of this study is to use a large cohort study to confirm the involvement of these four genes in GCT recurrence. Methods. The expression of these four genes was detected and compared between GCT patients with or without recurrence. The correlation between the expression of these four genes and clinical characteristics was evaluated. Protein-protein interaction (PPI) network was constructed for functional enrichment analysis. Results. It showed that the expression levels of MDM2, IGF1, STAT1, and RAC1 in GCT patients with recurrence were significantly higher than those in GCT patients without recurrence (P<0.05). Multivariate logistic regression analysis suggested that several clinical characteristics may influence prognosis. A PPI network was constructed using the four genes as hub genes. Functional enrichment analysis showed that this network involves many important biological progress mediated by these four genes, including immune response. Conclusion. MDM2, IGF1, STAT1, and RAC1 are associated with GCT recurrence, which might serve as biomarkers for GCT recurrence.


2020 ◽  
Author(s):  
Liucheng Xiao ◽  
Zonghuan Li ◽  
Chongyuan Fan ◽  
Chenggong Zhu ◽  
Xingyu Ma ◽  
...  

Abstract Background: Xiao-Xian-Xiong decoction is a useful formula in the treatment of atherosclerosis in traditional Chinese medicine. In this study, we aimed to investigate the function of Xiao-Xian-Xiong decoction in the treatment of atherosclerosis. Methods: In this study, we conducted the method of network pharmacology and molecular docking to discover the mechanism of Xiao-Xian-Xiong decoction against atherosclerosis. Then, we validated the function of Xiao-Xian-Xiong decoction in atherosclerosis in vitro. We investigated the function and mechanism of Xiao-Xian-Xiong decoction in RAW264.7 macrophage-derived foam cells.Results: We identified 213 targets of Xiao-Xian-Xiong decoction and 331 targets of atherosclerosis. The PPI networks of Xiao-Xian-Xiong decoction and atherosclerosis were constructed. Furthermore, the two PPI networks were merged and the core PPI network was obtained. Then, functional enrichment analysis was conducted with GO and KEGG signaling pathway analysis. KEGG analysis indicated Xiao-Xian-Xiong decoction was correlated with ubiquitin mediated proteolysis pathway, PI3K-AKT pathway, MAPK pathway, Notch signaling pathway, and TGF-β signaling pathway. At last, we validated the function of Xiao-Xian-Xiong decoction with atherosclerosis in vitro. Xiao-Xian-Xiong decoction reduced lipid accumulation and promoted the outflow of cholesterol in RAW264.7-derived foam cells. Xiao-Xian-Xiong decoction increased the expression of ABCA1 and ABCG1 protein in foam cells. ABCA1 and ABCG1 were related with regulation of the inflammatory pathway and cell proliferation in atherosclerosis.Conclusions: Combined the mechanism of available treatments of atherosclerosis, we inferred Xiao-Xian-Xiong decoction could alleviate atherosclerosis by inhibiting inflammatory response and cell proliferation.


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