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Gene Reports Jun 2022The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying...
The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying pathological mechanisms remain elusive. To determine common transcriptional signatures and pathways between sarcoidosis and COVID-19, we investigated the whole-genome transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and sarcoidosis and conducted bioinformatic analysis, including gene ontology and pathway enrichment, protein-protein interaction (PPI) network, and gene regulatory network (GRN) construction. We identified 33 abnormally expressed genes that were common between COVID-19 and sarcoidosis. Functional enrichment analysis showed that these differentially expressed genes were associated with cytokine production involved in the immune response and T cell cytokine production. We identified several hub genes from the PPI network encoded by the common genes. These hub genes have high diagnostic potential for COVID-19 and sarcoidosis and can be potential biomarkers. Moreover, GRN analysis identified important microRNAs and transcription factors that regulate the common genes. This study provides a novel characterization of the transcriptional signatures and biological processes commonly dysregulated in sarcoidosis and COVID-19 and identified several critical regulators and biomarkers. This study highlights a potential pathological association between COVID-19 and sarcoidosis, establishing a theoretical basis for future clinical trials.
PubMed: 35317263
DOI: 10.1016/j.genrep.2022.101597 -
Biomolecules Dec 2022Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk...
Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data were performed to investigate the clonal evolution characteristics and to find novel prognostic targets in MM. The scRNA-seq data were analyzed by the Seurat pipeline and Monocle 2 to identify MM cell branches with different differentiation states. Marker genes in each branch were uploaded to the STRING database to construct the Protein-Protein Interaction (PPI) network, followed by the detection of hub genes by Cytoscape software. Using bulk RNA-seq data, Kaplan-Meier (K-M) survival analysis was then carried out to determine prognostic biomarkers in MM. A total of 342 marker genes in two branches with different differentiation states were identified, and the top 20 marker genes with the highest scores in the network calculated by the MCC algorithm were selected as hub genes in MM. Furthermore, K-M survival analysis revealed that higher NDUFB8, COX6C, NDUFA6, USMG5, and COX5B expression correlated closely with a worse prognosis in MM patients. Moreover, ssGSEA and Pearson analyses showed that their expression had a significant negative correlation with the proportion of Tcm (central memory cell) immune cells. Our findings identified NDUFB8, COX6C, NDUFA6, USMG5, and COX5B as novel prognostic biomarkers in MM, and also revealed the significance of genetic heterogeneity during cell differentiation in MM prognosis.
Topics: Humans; Biomarkers, Tumor; Multiple Myeloma; Prognosis; RNA-Seq; Single-Cell Gene Expression Analysis; Transcription Factors
PubMed: 36551283
DOI: 10.3390/biom12121855 -
Computational and Mathematical Methods... 2022Adrenal cortical carcinoma (ACC) is a severe malignant tumor with low early diagnosis rates and high mortality. In this study, we used a variety of bioinformatic...
Adrenal cortical carcinoma (ACC) is a severe malignant tumor with low early diagnosis rates and high mortality. In this study, we used a variety of bioinformatic analyses to find potential prognostic markers and therapeutic targets for ACC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets were used to perform differential expressed analysis. WebGestalt was used to perform enrichment analysis, while String was used for protein-protein analysis. Our study first detected 28 up-regulation and 462 down-regulation differential expressed genes through the GEO and TCGA databases. Then, GO functional analysis, four pathway analyses (KEGG, REACTOME, PANTHER, and BIOCYC), and protein-protein interaction network were performed to identify these genes by WebGestalt tool and KOBAS website, as well as String database, respectively, and finalize 17 hub genes. After a series of analyses from GEPIA, including gene mutations, differential expression, and prognosis, we excluded one candidate unrelated to the prognosis of ACC and put the remaining genes into pathway analysis again. We screened out CCNB1 and NDC80 genes by three algorithms of Degree, MCC, and MNC. We subsequently performed genomic analysis using the TCGA and cBioPortal databases to better understand these two hub genes. Our data also showed that the CCNB1 and NDC80 genes might become ACC biomarkers for future clinical use.
Topics: Adrenal Cortex Neoplasms; Adrenocortical Carcinoma; Biomarkers, Tumor; Computational Biology; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Prognosis
PubMed: 35983531
DOI: 10.1155/2022/2465598 -
Frontiers in Immunology 2022The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).
PURPOSE
The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).
METHODS
Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI).
RESULTS
The nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes.
CONCLUSION
Lymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.
Topics: COVID-19; Computational Biology; Gene Ontology; Humans; Protein Interaction Maps; Tuberculosis, Spinal
PubMed: 35720320
DOI: 10.3389/fimmu.2022.882651 -
Journal of Gastrointestinal Oncology Apr 2022The incidence of liver cancer is increasing every year. Hepatocellular carcinoma (HCC) accounts for nearly 90% of liver cancer, and the overall 5-year survival rate of...
BACKGROUND
The incidence of liver cancer is increasing every year. Hepatocellular carcinoma (HCC) accounts for nearly 90% of liver cancer, and the overall 5-year survival rate of become of Hepatocellular carcinoma patients less than 20%. However, the molecular mechanism of HCC progression and prognosis still requires further exploration.
METHODS
In this study, we downloaded the gene expression data from the Cancer Genome Atlas (TCGA) Genomic Data and the official website of GEO database. Weighted gene co-expression network analysis (WGCNA) and Pearson's correlation coefficient were utilized to detect the gene modules. The shared differentially-expressed genes (DEGs) were screened out by a Venn diagram, and the hub genes were identified through protein-protein interaction (PPI) network analyses. GO and KEGG enrichment analyses were constructed for these hub genes. Overall survival (OS) and correlation analysis were conducted to investigate the relationship between the hub genes and clinical features.
RESULTS
We screened out 27 shared DEGs, and the mainly enriched GO terms were mitotic nuclear division, chromosomal region, and tubulin binding. Furthermore, the top three enriched KEGG pathways were "cell cycle", "oocyte meiosis", and "p53 signaling pathway". According to the Maximal Clique Centrality (MCC) algorithm, the top 10 candidate hub genes were , , , , , , , , , and , among which BIRC5, CDC20, and UBE2C showed a strong correlation with the OS.
CONCLUSIONS
Three hub genes (, , and ) were identified and found to be correlated to the progression and prognosis of HCC. These may become potential targets for HCC therapy.
PubMed: 35557563
DOI: 10.21037/jgo-22-303 -
Cancers Jul 2022Merkel cell carcinoma (MCC) is a rare but highly aggressive neuroendocrine carcinoma of the skin with a poor prognosis. Improving the prognosis of MCC by means of...
BACKGROUND
Merkel cell carcinoma (MCC) is a rare but highly aggressive neuroendocrine carcinoma of the skin with a poor prognosis. Improving the prognosis of MCC by means of targeted therapies requires further understanding of the mechanisms that drive tumor progression. In this study, we aimed to identify the genes, processes, and pathways that play the most crucial roles in determining MCC outcomes.
METHODS
We investigated transcriptomes generated by RNA sequencing of formalin-fixed paraffin-embedded tissue samples of 102 MCC patients and identified the genes that were upregulated among survivors and in patients who died from MCC. We subsequently cross-referenced these genes with online databases to investigate the functions and pathways they represent. We further investigated differential gene expression based on viral status in patients who died from MCC.
RESULTS
We found several novel genes associated with MCC-specific survival. Genes upregulated in patients who died from MCC were most notably associated with angiogenesis and the PI3K-Akt and MAPK pathways; their expression predominantly had no association with viral status in patients who died from MCC. Genes upregulated among survivors were largely associated with antigen presentation and immune response.
CONCLUSION
This outcome-based discrepancy in gene expression suggests that these pathways and processes likely play crucial roles in determining MCC outcomes.
PubMed: 35892849
DOI: 10.3390/cancers14153591 -
Oncotarget Jul 2018Merkel cell carcinoma (MCC) is a rare, highly aggressive neuroendocrine skin cancer. In more than 80% of the cases, Merkel cell polyomavirus (MCPyV) is a causal factor....
Merkel cell carcinoma (MCC) is a rare, highly aggressive neuroendocrine skin cancer. In more than 80% of the cases, Merkel cell polyomavirus (MCPyV) is a causal factor. The oncogenic potential of MCPyV is mediated through its viral oncoproteins, large T antigen (LT) and small t antigen (sT). To investigate the role of cytokines in MCC, a PCR array analysis for genes encoding inflammatory cytokines and receptors was performed on MCPyV-negative and MCPyV-positive MCC cell lines, respectively. We detected an increased expression of CCL17/TARC in the MCPyV-positive MKL2 cell line compared to the MCPyV-negative MCC13 cell line. Transfection studies in MCC13 cells with LT expression plasmid, and a luciferase reporter plasmid containing the CCL17/TARC promoter, exhibited stimulated promoter activity. Interestingly, the ectopic expression of CCL17/TARC upregulated MCPyV early and late promoter activities in MCC13 cells. Furthermore, recombinant CCL17/TARC activated both the mitogen-activated protein kinase and the NF-κB pathways. Finally, immunohistochemical staining on human MCC tissues showed a strong staining of CCL17/TARC and its receptor CCR4 in both LT-positive and -negative MCC. Taken together, CCL17/TARC and CCR4 may be a potential target in MCC therapy providing MCC patients with a better overall survival outcome.
PubMed: 30140381
DOI: 10.18632/oncotarget.25836 -
Bioscience Reports Jul 2020Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they...
BACKGROUND
Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes.
MATERIALS AND METHODS
In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool.
RESULTS
A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis.
CONCLUSION
Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.
Topics: Biomarkers, Tumor; Brain; Brain Neoplasms; Computational Biology; Datasets as Topic; Down-Regulation; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Glioblastoma; Humans; Membrane Glycoproteins; Nerve Tissue Proteins; Oligonucleotide Array Sequence Analysis; Prognosis; Protein Interaction Mapping; Protein Interaction Maps; Survival Analysis; Up-Regulation
PubMed: 32667033
DOI: 10.1042/BSR20201625 -
Evidence-based Complementary and... 2022Sarcopenia is a condition that reduces muscle mass and exercise capacity. Muscle atrophy is a common manifestation of sarcopenia and can increase morbidity and mortality...
Sarcopenia is a condition that reduces muscle mass and exercise capacity. Muscle atrophy is a common manifestation of sarcopenia and can increase morbidity and mortality in specific patient populations. The aim of this study was to identify novel prognostic biomarkers for muscle atrophy and associated pathway analysis using bioinformatics methods. The samples were first divided into different age groups and different muscle type groups, respectively, and each of these samples was analyzed for differences to obtain two groups of differentially expressed genes (DEGs). The two groups of DEGs were intersected using Venn diagrams to obtain 1,630 overlapping genes, and enrichment analysis was performed to observe the Gene Ontology (GO) functional terms of overlapping genes and the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Subsequently, WGCNA (weighted gene coexpression network analysis) was used to find gene modules associated with both the age and muscle type to obtain the lightgreen module. The genes in the key modules were analyzed using PPI, and the top five genes were obtained using the MCC (maximum correntropy criterion) algorithm. Finally, CUL3 and COPS5 were obtained by comparing gene expression levels and analyzing the respective KEGG pathways using gene set enrichment analysis (GSEA). In conclusion, we identified that CUL3 and COPS5 may be novel prognostic biomarkers in muscle atrophy based on bioinformatics analysis. CUL3 and COPS5 are associated with the ubiquitin-proteasome pathway.
PubMed: 35815279
DOI: 10.1155/2022/1488905 -
International Journal of... 2023The ability of transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction in diabetic patients remains unknown....
The ability of transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction in diabetic patients remains unknown. The present study was designed to detect possible biomarkers associated with the incidence of post-infarction complications in diabetes to assist thedevelopment of novel treatments for this condition. Two gene expression datasets, GSE12639 and GSE6880, were downloaded from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) were identified between post-infarction diabetics and healthy samples from the left ventricular wall of rats. These DEGs were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed to explore the functional roles of these genes. In total, 30 DEGs (14 upregulated and 16 downregulated) were shared between these two datasets, as identified through Venn diagram analyses. GO analyses revealed these DEGs to be significantly enriched in ovarian steroidogenesis, fatty acid elongation, biosynthesis of unsaturated fatty acids, synthesis and degradation of ketone bodies, and butanoate metabolism. The PPI network of the DEGs had 14 genes and 70 edges. We identified two key proteins, 3-hydroxymethylglutaryl-CoA synthase 2 (Hmgcs2) and Δ3, Δ2-Enoyl-CoA Delta Isomerase 1 (ECI1), and the upregulated gene Hmgcs2 with the highest score in the MCC method. We generated a co-expression network for the hub genes and obtained the top ten medications suggested for infarction with diabetes. Taken together, the findings of these bioinformatics analyses identified key hub genes associated with the development of myocardial infarction in diabetics. These hub genes and potential drugs may become novel biomarkers for prognosis and precision treatment in the future.
Topics: Humans; Animals; Rats; Gene Regulatory Networks; Biomarkers; Protein Interaction Maps; Gene Expression Profiling; Myocardial Infarction; Diabetes Mellitus
PubMed: 37999626
DOI: 10.1177/03946320231216313