Mapping the Multi-Organ miRNA-mRNA Regulatory Network in LPS-Mediated Endotoxemic Mice: Exploring the Shared Underlying Key Genes and Mechanisms

BackgroundTo this day, the molecular mechanism of endotoxin-induced multi-organ failure has not been completely clarified. This study aimed to construct an miRNA-mRNA regulatory network and identify main pathways and key genes in multi-organ of LPS-mediated endotoxemic mice.MethodsPublic datasets from six mRNA and three miRNA microarray datasets were downloaded from the GEO website to screen final differentially expressed genes (FDEGs) and hub genes in the heart, lung, liver, and kidney of LPS-mediated endotoxemic mice. Functional and pathway enrichment analysis of FDEGs was used to identify the main pathways in multi-organ damage of LPS-treated mice. Finally, hub genes of each organ were intersected to obtain the key genes of multi-organ.ResultsFirstly, 158, 358, 299, and 91 FDEGs were identified in the heart, lung, liver, and kidney, respectively. The pathway enrichment analysis of the FDEGs then showed that the TNF signaling pathway, Toll-like receptor signaling pathway, and some viral-infection-related pathways (influenza A, measles, and herpes simplex) were the main pathways in multi-organ damage of LPS-mediated endotoxemic mice. Moreover, miRNA-mRNA or PPI regulatory networks were constructed based on FDEGs. According to these networks, 31, 34, 34, and 31 hub genes were identified in the heart, lung, liver, and kidney, respectively. Among them, nine key genes (Cd274, Cxcl1, Cxcl9, Icam1, Ifit2, Isg15, Stat1, Tlr2, and Usp18) were enriched in Toll-like receptor signaling pathway and chemokine signaling pathway. Finally, seven potential drugs were predicted based on these key genes.ConclusionThe shared underlying molecular pathways in endotoxin-induced multi-organ damage that have been identified include Toll-like receptor signaling pathway and TNF signaling pathway. Besides, nine key genes (Cd274, Cxcl1, Cxcl9, Icam1, Ifit2, Isg15, Stat1, Tlr2, and Usp18) and seven potential drugs were identified. Our data provide a new sight and potential target for future therapy in endotoxemia-induced multi-organ failure.

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Comprehensive Analysis of the Immune and Prognostic Implication of MMP14 in Lung Cancer

Disease Markers ◽

10.1155/2021/5917506 ◽

2021 ◽

Vol 2021 ◽

pp. 1-21

Author(s):

Chun-long Zheng ◽

Qiang Lu ◽

Nian Zhang ◽

Peng-yu Jing ◽

Ji-peng Zhang ◽

...

Keyword(s):

Lung Cancer ◽

Signaling Pathway ◽

Lung Squamous Cell Carcinoma ◽

Enrichment Analysis ◽

Cell Receptor ◽

Risk Scores ◽

Pathway Enrichment Analysis ◽

Receptor Signaling ◽

Immune Infiltration ◽

Receptor Signaling Pathway

More and more studies have indicated an association between immune infiltration in lung cancer and clinical outcomes. Matrix metalloproteinase 14 (MMP14) has been reported to be dysregulated in many types of tumors and involved in the development and progression of tumors. However, its contribution to cancer immunity was rarely reported. In the study, we found that MMP14 expression was distinctly upregulated in lung cancer specimens compared with nontumor lung specimens. High MMP14 expression predicted a poor prognosis of lung squamous cell carcinoma (LUSC) patients. Increased MMP14 expressions were observed to be positively related to high immune infiltration levels in most of the immune cells. A pathway enrichment analysis of 32 MMP14-associated immunomodulators indicated the involvement of T cell receptor signaling pathway and Toll-like receptor signaling pathway. Based on MMP14-associated immunomodulators, we applied multivariate assays to construct multiple-gene risk prediction signatures. We observed that risk scores were independently associated with overall survival. These data highlighted that MMP14 was involved in tumor immunity, indicating that MMP14 could serve as a novel prognostic biomarker and therapeutic target for lung cancer. Our data suggest that the four genes identified in this study may serve as valuable biomarkers of lung cancer patient outcomes.

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Identification of Key Genes and Prognostic Analysis between Chromophobe Renal Cell Carcinoma and Renal Oncocytoma by Bioinformatic Analysis

BioMed Research International ◽

10.1155/2020/4030915 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Hongwei Wu ◽

Lijing Fan ◽

Haiping Liu ◽

Baozhang Guan ◽

Bo Hu ◽

...

Keyword(s):

Overall Survival ◽

Signaling Pathway ◽

Enrichment Analysis ◽

Cell Receptor ◽

Renal Oncocytoma ◽

Receptor Signaling ◽

Kaplan Meier ◽

Receptor Signaling Pathway ◽

Key Genes ◽

Cell Receptor Signaling

The present techniques of clinical and histopathological diagnosis hardly distinguish chromophobe renal cell carcinoma (ChRCC) from renal oncocytoma (RO). To identify differentially expressed genes (DEGs) as effective biomarkers for diagnosis and prognosis of ChRCC and RO, three mRNA microarray datasets (GSE12090, GSE19982, and GSE8271) were downloaded from the GEO database. Functional enrichment analysis of DEGs was performed by DAVID. STRING and Cytoscape were applied to construct the protein-protein interaction (PPI) network and key modules of DEGs. Visualized plots were conducted by the R language. We downloaded clinical data from the TCGA database and the influence of key genes on the overall survival of ChRCC was performed by Kaplan–Meier and Cox analyses. Gene set enrichment analysis (GSEA) was utilized in exploring the function of key genes. A total of 79 DEGs were identified. Enrichment analyses revealed that the DEGs are closely related to tissue invasion and metastasis of cancer. Subsequently, 14 hub genes including ESRP1, AP1M2, CLDN4, and CLDN7 were detected. Kaplan–Meier analysis indicated that the low expression of CLDN7 and GNAS was related to the worse overall survival in patients with ChRCC. Univariate Cox analysis showed that CLDN7 might be a helpful biomarker for ChRCC prognosis. Subgroup analysis revealed that the expression of CLDN7 showed a downtrend with the development of the clinical stage, topography, and distant metastasis of ChRCC. GSEA analysis identified that cell adhesion molecules cams, B cell receptor signaling pathway, T cell receptor signaling pathway, RIG-I like receptor signaling pathway, Toll-like receptor signaling pathway, and apoptosis pathway were associated with the expression of CLDN7. In conclusion, ESRP1, AP1M2, CLDN4, PRSS8, and CLDN7 were found to distinguish ChRCC from RO. Besides, the low expression of CLDN7 was closely related to ChRCC progression and could serve as an independent risk factor for the overall survival in patients with ChRCC.

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DHX36, BAX, and ARPC1B May Be Critical for the Diagnosis and Treatment of Tuberculosis

Canadian Respiratory Journal ◽

10.1155/2020/4348371 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yunli Zhang ◽

Yanming Li ◽

Hongling Li ◽

Qingxia Liu ◽

Wei Wang ◽

...

Keyword(s):

Signaling Pathway ◽

Enrichment Analysis ◽

Hierarchical Cluster ◽

Diagnosis And Treatment ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Peripheral Blood Mononuclear ◽

Type Group ◽

Pathway Enrichment ◽

Key Genes

Background. Tuberculosis (TB) is usually caused by Mycobacterium tuberculosis, which has the highest mortality rate among infectious diseases. This study is designed to identify the key genes affecting the diagnosis and treatment of TB. Methods. GSE54992, which included 39 peripheral blood mononuclear cell (PBMC) samples, was extracted from the Gene Expression Omnibus database. After the samples were classified into type and time groups by limma package, the differentially expressed genes (DEGs) were analyzed using the Analysis of Variance. Using pheatmap package, hierarchical cluster analysis was performed for the DEGs. Then, the key modules correlated with TB were selected using the WGCNA package. Finally, functional and pathway enrichment analyses were carried out using clusterProfiler package. Results. The DEGs in subclusters 3, 6, 7, and 8 were chosen for further analyses. Based on WGCNA analysis, blue and green modules in type group and pink module in time group were selected as key modules. From the key modules, 9 (including BAX and ARPC1B) hub genes in type group and 6 (including DHX36) hub genes in time group were screened. Through pathway enrichment analysis, the TNF signaling pathway was enriched for the green module. Conclusion. DHX36, BAX, and ARPC1B might be key genes acting in the mechanisms of TB. Besides, the TNF signaling pathway might also be critical for the diagnosis and therapy of the disease.

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HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-03958-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zhixin Wu ◽

Yinxian Wen ◽

Guanlan Fan ◽

Hangyuan He ◽

Siqi Zhou ◽

...

Keyword(s):

Femoral Head ◽

Roc Curve ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Overlapping Genes ◽

Hub Genes ◽

Diagnostic Biomarkers ◽

Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

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Transcriptome Analysis for Genes Associated with Small Ruminant Lentiviruses Infection in Goats of Carpathian Breed

Viruses ◽

10.3390/v13102054 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2054

Author(s):

Monika Olech ◽

Katarzyna Ropka-Molik ◽

Tomasz Szmatoła ◽

Katarzyna Piórkowska ◽

Jacek Kuźmak

Keyword(s):

Gene Expression ◽

Immune Response ◽

Signaling Pathways ◽

Signaling Pathway ◽

Proviral Load ◽

Cytokine Production ◽

Small Ruminant ◽

Receptor Signaling ◽

Toll Like Receptor ◽

Receptor Signaling Pathway

Small ruminant lentiviruses (SRLV) are economically important viral pathogens of sheep and goats. SRLV infection may interfere in the innate and adaptive immunity of the host, and genes associated with resistance or susceptibility to infection with SRLV have not been fully recognized. The presence of animals with relatively high and low proviral load suggests that some host factors are involved in the control of virus replication. To better understand the role of the genes involved in the host response to SRLV infection, RNA sequencing (RNA-seq) method was used to compare whole gene expression profiles in goats carrying both a high (HPL) and low (LPL) proviral load of SRLV and uninfected animals. Data enabled the identification of 1130 significant differentially expressed genes (DEGs) between control and LPL groups: 411 between control and HPL groups and 1434 DEGs between HPL and LPL groups. DEGs detected between the control group and groups with a proviral load were found to be significantly enriched in several gene ontology (GO) terms, including an integral component of membrane, extracellular region, response to growth factor, inflammatory and innate immune response, transmembrane signaling receptor activity, myeloid differentiation primary response gene 88 (MyD88)-dependent toll-like receptor signaling pathway as well as regulation of cytokine secretion. Our results also demonstrated significant deregulation of selected pathways in response to viral infection. The presence of SRLV proviral load in blood resulted in the modification of gene expression belonging to the toll-like receptor signaling pathway, the tumor necrosis factor (TNF) signaling pathway, the cytokine-cytokine receptor interaction, the phagosome, the Ras signaling pathway, the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) (PI3K-Akt) signaling pathway and rheumatoid arthritis. It is worth mentioning that the most predominant in all pathways were genes represented by toll-like receptors, tubulins, growth factors as well as interferon gamma receptors. DEGs detected between LPL and HPL groups were found to have significantly enriched regulation of signaling receptor activity, the response to toxic substances, nicotinamide adenine dinucleotide (NADH) dehydrogenase complex assembly, cytokine production, vesicle, and vacuole organization. In turn, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway tool classified DEGs that enrich molecular processes such as B and T-cell receptor signaling pathways, natural killer cell-mediated cytotoxicity, Fc gamma R-mediated phagocytosis, toll-like receptor signaling pathways, TNF, mammalian target of rapamycin (mTOR) signaling and forkhead box O (Foxo) signaling pathways, etc. Our data indicate that changes in SRLV proviral load induced altered expression of genes related to different biological processes such as immune response, inflammation, cell locomotion, and cytokine production. These findings provide significant insights into defense mechanisms against SRLV infection. Furthermore, these data can be useful to develop strategies against SRLV infection by selection of animals with reduced SRLV proviral concentration that may lead to a reduction in the spread of the virus.

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