scholarly journals Transcriptome research identifies four hub genes related to primary myelofibrosis: a holistic research by weighted gene co-expression network analysis

Aging ◽  
2021 ◽  
Author(s):  
Weihang Li ◽  
Yingjing Zhao ◽  
Dong Wang ◽  
Ziyi Ding ◽  
Chengfei Li ◽  
...  
2020 ◽  
Author(s):  
Weihang Li ◽  
Bin Yuan ◽  
Shilei Zhang ◽  
Ziyi Ding ◽  
Yingjing Zhao ◽  
...  

Abstract Background: This study aimed to identify novel targets of diagnosis, therapy as well as prognosis for primary myelofibrosis (PMF).Methods: The gene expression profiles of GSE26049 was obtained from GEO dataset, weighted gene co-expression network analysis (WGCNA) was then performed to identify the most related modules with PMF. Subsequently, GO (Gene Ontology), KEGG (Kyoto Encyclopedia Genes and Genomes), GSEA (Gene Set Enrichment Analysis) and PPI (Protein-Protein Interaction) network were conducted to fully understand the detailed information of the green module.Results: Green module was strongly correlated with PMF disease after WGCNA analysis. 20 genes in green module were identified as hub genes responsible for the progression of PMF. Functional annotation and pathway analysis revealed that these hub genes were primarily enriched in erythrocyte differentiation, transcription factor binding, hemoglobin complex, transcription factor complex and cell cycle et al. Of which, EPB42, CALR, SLC4A1 and MPL had the most correlations with PMF.Conclusions: This study elucidated that genes EPB42, CALR, SLC4A1 and MPL were significantly more highly expressed in PMF samples than in normal samples. These four genes may be considered candidate prognostic biomarkers and potential therapeutic targets for early stage of PMF. Meanwhile, EPB42 and SLC4A1 were firstly found to be highly correlated with the progression of PMF.


2020 ◽  
Vol 8 (21) ◽  
pp. 1348-1348
Author(s):  
Zetao Ma ◽  
Zhida Shen ◽  
Yingchao Gong ◽  
Jiaqi Zhou ◽  
Xiaoou Chen ◽  
...  

Hematology ◽  
2021 ◽  
Vol 26 (1) ◽  
pp. 478-490
Author(s):  
Haotian Ma ◽  
Jincen Liu ◽  
Zilong Li ◽  
Huaye Xiong ◽  
Yulei Zhang ◽  
...  

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
Chun Li ◽  
Bangming Pu ◽  
Long Gu ◽  
Mingwei Zhang ◽  
Hongping Shen ◽  
...  

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bahman Panahi ◽  
Mohammad Amin Hejazi

AbstractDespite responses to salinity stress in Dunaliella salina, a unicellular halotolerant green alga, being subject to extensive study, but the underlying molecular mechanism remains unknown. Here, Empirical Bayes method was applied to identify the common differentially expressed genes (DEGs) between hypersaline and normal conditions. Then, using weighted gene co-expression network analysis (WGCNA), which takes advantage of a graph theoretical approach, highly correlated genes were clustered as a module. Subsequently, connectivity patterns of the identified modules in two conditions were surveyed to define preserved and non-preserved modules by combining the Zsummary and medianRank measures. Finally, common and specific hub genes in non-preserved modules were determined using Eigengene-based module connectivity or module membership (kME) measures and validation was performed by using leave-one-out cross-validation (LOOCV). In this study, the power of beta = 12 (scale-free R2 = 0.8) was selected as the soft-thresholding to ensure a scale-free network, which led to the identification of 15 co-expression modules. Results also indicate that green, blue, brown, and yellow modules are non-preserved in salinity stress conditions. Examples of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in non-preserved modules are Sulfur metabolism, Oxidative phosphorylation, Porphyrin and chlorophyll metabolism, Vitamin B6 metabolism. Moreover, the systems biology approach was applied here, proposed some salinity specific hub genes, such as radical-induced cell death1 protein (RCD1), mitogen-activated protein kinase kinase kinase 13 (MAP3K13), long-chain acyl-CoA synthetase (ACSL), acetyl-CoA carboxylase, biotin carboxylase subunit (AccC), and fructose-bisphosphate aldolase (ALDO), for the development of metabolites accumulating strains in D. salina.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jin-Yu Sun ◽  
Yang Hua ◽  
Hui Shen ◽  
Qiang Qu ◽  
Jun-Yan Kan ◽  
...  

Abstract Background Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear. Methods The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by ‘limma’ and ‘weighted gene co-expression network analysis (WGCNA)’ package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein–protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation. Results We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively. Conclusions This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.


PLoS ONE ◽  
2014 ◽  
Vol 9 (4) ◽  
pp. e94868 ◽  
Author(s):  
Yun Zhou ◽  
Jiucheng Xu ◽  
Yunqing Liu ◽  
Juntao Li ◽  
Cuifang Chang ◽  
...  

Author(s):  
Vahid Mansouri ◽  
◽  
Mostafa Rezaiee Tavirani ◽  
Farshad Okhovatian ◽  
◽  
...  

Aim: Screening of candidate genes related to sural nerve diabetic neuropathy to find the critical ones is the aim of this study. Back Ground: Diabetes mellitus is a chronic disease causes by insulin uptake or deficiency. Side effects of diabetes are numerous according to severity of disease. Diabetes could harm the peripheral nerves with chronic pain, lead to nerve damage entitled diabetic neuropathy (DN). Signs and symptoms of DN are sharp pains, numbness, and tangling. Many patterns of nerve injuries could happen during DN but distal symmetric polyneuropathy (DSP) is most common. On the other hand, network analysis is a useful tool to assess incidences and progression of diseases. Methods: Expression of different genes in diabetic patients with and without progressive neuropathy of surreal nerve (GSE24290) is considered as including data. GEO2R was applied to first step analysis to find the significant differentially expressed genes (DEGs). The queried significant DEGs plus 100 first neighbors were included in a network by Cytoscape software. The network was analyzed by Network analyzed application of Cytoscape and the central nodes were determined. Results: The total 26 significant DEGs plus 100 first neighbors were interacted to form the network. INS, ALB, AKT1, APP, SNAP25, NEFL, GFAP, IL6, NEFM, TNF, MAPT, GAP43, and MBP were identified as 13 hubs of the network. NEFL and NEFM were highlighted as the queried hub genes. Insulin as the top hub node was determined among all interacted genes (the queried and added genes). Conclusions: INS, NEFL, and NEFM are key genes in DN which are involve in metabolism regulation and intra cellular transportation into axons and denderites respectively.


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