Comprehensive Analysis of Gene Expression Changes and Validation in Hepatocellular Carcinoma

Zhang, Hao; Liu, Renzheng; Sun, Lin; Guo, Weidong; Ji, Xiaoyue; Hu, Xiao

doi:10.2147/ott.s294500

Cited by 24 publications

(23 citation statements)

References 26 publications

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“…The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) online tool ( http://cibersort.stanford.edu/ ) [ 20 ] was used to explore the state of OA and RA synovial membranes in preestablished 22 types of IICs. The analysis result was filtered complying with the cut-off criteria that adjusted- P value < 0.05, and the IICs composition of each sample was visualized using “barplot”, “corrplot” and “ggplot2” packages in R language version 3.6.1 [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Chen

et al. 2021

Hereditas

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Background Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with similar clinical phenotypes. This study aimed to determine the mechanistic similarities and differences between OA and RA by integrated analysis of multiple gene expression data sets. Methods Microarray data sets of OA and RA were obtained from the Gene Expression Omnibus (GEO). By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein–protein interaction (PPI) network analysis of DEGs were conducted to determine hub genes and pathways. The “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” algorithm was employed to evaluate the immune infiltration cells (IICs) profiles in OA and RA. Moreover, mouse models of RA and OA were established, and selected hub genes were verified in synovial tissues with quantitative polymerase chain reaction (qPCR). Results A total of 1116 DEGs were identified between OA and RA. GO functional enrichment analysis showed that DEGs were enriched in regulation of cell morphogenesis involved in differentiation, positive regulation of neuron differentiation, nuclear speck, RNA polymerase II transcription factor complex, protein serine/threonine kinase activity and proximal promoter sequence-specific DNA binding. KEGG pathway analysis showed that DEGs were enriched in EGFR tyrosine kinase inhibitor resistance, ubiquitin mediated proteolysis, FoxO signaling pathway and TGF-beta signaling pathway. Immune cell infiltration analysis identified 9 IICs with significantly different distributions between OA and RA samples. qPCR results showed that the expression levels of the hub genes (RPS6, RPS14, RPS25, RPL11, RPL27, SNRPE, EEF2 and RPL19) were significantly increased in OA samples compared to their counterparts in RA samples (P < 0.05). Conclusion This large-scale gene analyses provided new insights for disease-associated genes, molecular mechanisms as well as IICs profiles in OA and RA, which may offer a new direction for distinguishing diagnosis and treatment between OA and RA.

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Section: Methodsmentioning

confidence: 99%

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Chen

et al. 2021

Hereditas

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“…After obtaining the expression matrix of immune cells according to the instruction of the CIBERSORT website, we use “ggplot2” package to draw boxplot to depict the distribution of immune cells. The Pearson correlation coefficient between each kind of immune cells was calculated and the results were displayed by correlation heatmap using “corrplot” package [ 24 ] in R software.…”

Section: Methodsmentioning

confidence: 99%

Identification of key genes and immune infiltration modulated by CPAP in obstructive sleep apnea by integrated bioinformatics analysis

et al. 2021

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Patients with obstructive sleep apnea (OSA) experience partial or complete upper airway collapses during sleep resulting in nocturnal hypoxia-normoxia cycling, and continuous positive airway pressure (CPAP) is the golden treatment for OSA. Nevertheless, the exact mechanisms of action, especially the transcriptome effect of CPAP on OSA patients, remain elusive. The goal of this study was to evaluate the longitudinal alterations in peripheral blood mononuclear cells transcriptome profiles of OSA patients in order to identify the hub gene and immune response. GSE133601 was downloaded from Gene Expression Omnibus (GEO). We identified black module via weighted gene co-expression network analysis (WGCNA), the genes in which were correlated significantly with the clinical trait of CPAP treatment. Finally, eleven hub genes (TRAV10, SNORA36A, RPL10, OBP2B, IGLV1-40, H2BC8, ESAM, DNASE1L3, CD22, ANK3, ACP3) were traced and used to construct a random forest model to predict therapeutic efficacy of CPAP in OSA with a good performance with AUC of 0.92. We further studied the immune cells infiltration in OSA patients with CIBERSORT, and monocytes were found to be related with the remission of OSA and partially correlated with the hub genes identified. In conclusion, these key genes and immune infiltration may be of great importance in the remission of OSA and related research of these genes may provide a new therapeutic target for OSA in the future.

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“…After that, the analysis result was ltered according to adjusted-P value < 0.05, and the immune cell composition of each sample was shown in the barplot. As a result, "corrplot" package [22] downloaded in R was used to visualize the correlation of 22 types of in ltrating immune cells (IICs); "ggplot2" script was run to draw violin diagrams to depict the differences in each immune cell types.…”

Section: Immune In Ltrationmentioning

confidence: 99%

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Chen

et al. 2021

Preprint

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Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with partially common phenotypes and genotypes. This study aimed to determine the mechanistic similarities and differences between osteoarthritis and rheumatoid arthritis by analyzing the differentially expressed genes and signaling pathways. Microarray data of osteoarthritis and rheumatoid arthritis were obtained from the Gene Expression Omnibus. By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified in synovial membrane samples from patients and healthy donations. Then, the Gene ontology significant functions annotation, Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction network analysis were conducted. Moreover, CIBERSORT was used to further distinguish OA and RA in immune infiltration. Finally, animal experimentation was conducted and the establishment of model, which was verified using PCR in the mouse. As an overlapping process, we identified 1116 DEGs between OA and RA. It was indicated that specific gene signatures differed significantly between OA and RA connected with the distinct pathways. Of identified DEGs, 9 immune cell types among 22 were identified to distinguish from each other. The qRT-PCR result showed that the eight-tenths expression levels of the hub genes were significantly increased in OA samples (P < 0.05). This large-scale gene expression study provided new insights for disease-associated genes and molecular mechanisms as well as their associated function in osteoarthritis and rheumatoid arthritis, which simultaneously offer a new direction for biomarker development and the distinguishment of gene-level mechanisms between osteoarthritis and rheumatoid arthritis.

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Comprehensive Analysis of Gene Expression Changes and Validation in Hepatocellular Carcinoma

Cited by 24 publications

References 26 publications

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Identification of key genes and immune infiltration modulated by CPAP in obstructive sleep apnea by integrated bioinformatics analysis

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

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