Bioinformatics Analysis of Key Genes and Pathways Associated with Thrombosis in Essential Thrombocythemia

Guo, Chao; Li, Zhenling

doi:10.12659/msm.918719

Cited by 11 publications

(13 citation statements)

References 36 publications

(41 reference statements)

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“…Research on prognosis-related genes in recent years usually took the gene connectivity in a PPI network into consideration (Guo and Li, 2019 ; Li et al, 2020 ). Here, all up-regulated genes were firstly calculated for their MCC values (Chin et al, 2014 ), and then the top 100 genes based on the sorted MCC values were defined as central nodes to cross-validate the functional association among these prognostic genes, together with the PPI network.…”

Section: Resultsmentioning

confidence: 99%

“…Research on prognosis-related genes in recent years usually took the gene connectivity in a PPI network into consideration (Guo and Li, 2019;Li et al, 2020). Here, all up-regulated genes FIGURE 5 | The protein-protein interaction (PPI) network of 32 candidate genes, where eight genes were selected as predictors in the prognostic model.…”

Section: Cross-validation By the Protein-protein Interaction Networkmentioning

confidence: 99%

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A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

Tang

Wang

Chen

et al. 2021

Front. Cell Dev. Biol.

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Carcinoma diagnosis and prognosis are still hindered by the lack of effective prediction model and integration methodology. We proposed a novel feature selection with orthogonal regression (FSOR) method to resolve predictor selection and performance optimization. Functional enrichment and clinical outcome analyses with multi-omics information validated the method's robustness in the early-stage prognosis of lung adenocarcinoma. Furthermore, compared with the classic least absolute shrinkage and selection operator (LASSO) regression method [the averaged 1- to 4-years predictive area under the receiver operating characteristic curve (AUC) measure, 0.6998], the proposed one outperforms more accurately by 0.7208 with fewer predictors, particularly its averaged 1- to 3-years AUC reaches 0.723, vs. classic 0.6917 on The Cancer Genome Atlas (TCGA). In sum, the proposed method can deliver better prediction performance for early-stage prognosis and improve therapy strategy but with less predictor consideration and computation burden. The self-composed running scripts, together with the processed results, are available at https://github.com/gladex/PM-FSOR.

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Section: Resultsmentioning

confidence: 99%

“…Research on prognosis-related genes in recent years usually took the gene connectivity in a PPI network into consideration (Guo and Li, 2019;Li et al, 2020). Here, all up-regulated genes FIGURE 5 | The protein-protein interaction (PPI) network of 32 candidate genes, where eight genes were selected as predictors in the prognostic model.…”

Section: Cross-validation By the Protein-protein Interaction Networkmentioning

confidence: 99%

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

Tang

Wang

Chen

et al. 2021

Front. Cell Dev. Biol.

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“…LYZ encodes human lysozyme and acts as a macrophage marker, its expression levels positively correlate with the numbers of CD68 + pSTAT1 + macrophages [24]. In addition, it could interact with CD34 + cells and neutrophils that may predict an increased risk of thrombosis in essential thrombocythemia patients [25]. MEDAG was involved in the processes of lipid accumulation, adipocyte differentiation, and glucose uptake in mature adipocytes [26].…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis reveals the ceRNA co-expression network in tumor microenvironment and prognostic biomarkers in Sarcomas

Zou¹,

Wang²,

Zhao³

et al. 2020

Preprint

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Background Sarcomas (SARCs) are rare, heterogeneous mesenchymal neoplasia. To understand the tumor microenviroment (TME) and identify potential biomarkers for prognosis that associated with TME of SARCs might provide effective clues for immune therapy. Methods We evaluated the immune scores and stromal scores by using the RNA sequencing dataset of SARCs patients from The Cancer Genome Atlas (TCGA) database and the ESTIMATE algorithm. Then the differentially expressed mRNAs (DEGs), miRNAs (DEMs) and lncRNAs (DELs) were filtered after comparing the two high- and low- scores groups. Next, based on these DERNAs, we established a competing endogenous RNA (ceRNA) network and explored the prognostic roles of biomarkers involved in the network with the help of bioinformatics analysis. Results High immune scores were significantly associated with favorable overall survival of SARCs patients. Next, a total of 328 DEGs, 18 DEMs and 67 DELs that commonly regulated in the immune and stromal scores groups were obtained. And for the DEGs, some of the Gene Ontology (GO) terms and pathways were mainly associated with immune processes. A ceRNA network were constructed with 142 nodes and 424 edges, in which hsa-miR-9-5p, hsa-miR-490-3p, hsa-miR-133a-3p, hsa-miR-133b and hsa-miR-129-5p were the top 5 nodes. Additionally, the protein-protein Interaction (PPI) network identified MMP9, TYROBP, CSF1, CXCR4, FBN1, FLNA, PDGFRB, CYBB, FCGR3A and MYH11 as hub nodes with considerable importance that functioned in the network. Finally, the Kaplan-Meier survival analysis demonstrated that 9 mRNAs (APOL1, EFEMP1, LYZ, MEDAG, MYH11, RARRES1, TNFAIP2, TNFSF10 and ZNF385A), 2 miRNAs (hsa-miR-9-5p and hsa-miR-183-5p) and 3 lncRNAs (CTD-2228K2.7, HOTAIRM1 and NCF1C) were closely associated with the overall survival of SARCs patients. Conclusions Taken together, our study confirmed that the prognosis value of immune scores for SARCs patients, also we identified a list of TME-related biomarkers which might contribute to prognostic prediction and help improve the efficacy of immune therapy.

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“…They used Cytoscape software with cytoHubba and MCODE plugins. With their analysis, they identified DEGs and hub genes that interacted with CD34+ cells and neutrophils that may predict an increased risk of thrombosis in patients with ET [ 122 ].…”

Section: Machine Based Learning and Its Role In Mpnmentioning

confidence: 99%

The Contemporary Approach to CALR-Positive Myeloproliferative Neoplasms

Mikič

Pajič

Zver

et al. 2021

IJMS

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CALR mutations are a revolutionary discovery and represent an important hallmark of myeloproliferative neoplasms (MPN), especially essential thrombocythemia and primary myelofibrosis. To date, several CALR mutations were identified, with only frameshift mutations linked to the diseased phenotype. It is of diagnostic and prognostic importance to properly define the type of CALR mutation and subclassify it according to its structural similarities to the classical mutations, a 52-bp deletion (type 1 mutation) and a 5-bp insertion (type 2 mutation), using a statistical approximation algorithm (AGADIR). Today, the knowledge on the pathogenesis of CALR-positive MPN is expanding and several cellular mechanisms have been recognized that finally cause a clonal hematopoietic expansion. In this review, we discuss the current basis of the cellular effects of CALR mutants and the understanding of its implementation in the current diagnostic laboratorial and medical practice. Different methods of CALR detection are explained and a diagnostic algorithm is shown that aids in the approach to CALR-positive MPN. Finally, contemporary methods joining artificial intelligence in accordance with molecular-genetic biomarkers in the approach to MPN are presented.

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Bioinformatics Analysis of Key Genes and Pathways Associated with Thrombosis in Essential Thrombocythemia

Cited by 11 publications

References 36 publications

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

Bioinformatics analysis reveals the ceRNA co-expression network in tumor microenvironment and prognostic biomarkers in Sarcomas

The Contemporary Approach to CALR-Positive Myeloproliferative Neoplasms

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