Screening and identification of key candidate genes and pathways in myelodysplastic syndrome by bioinformatic analysis

Ying, Le

doi:10.7717/peerj.8162

Cited by 11 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the above two genes also showed upregulated expression in the two data sets which we downloaded for validating hub genes. A study conducted recently also reported the DEGs identification in MDS, in accordance with our study [35]. The study used two of the same data sets (GSE19429 and GSE58831) as ours and identifying DEGs that may be involved in MDS progression and prognosis including IRF4, IFIT1, ISG20, IFITM1 etc., which were as also observed in this study.…”

Section: Discussionsupporting

confidence: 81%

“…Despite the intersection of the data sets used for analysis, unlike Le' s study [35], our study combined enrichment analysis and GSEA to more accurately interpret the DEGs that may be involved in biological processes and signal pathways. In addition, the network analysis revealed hub genes that were not only verified through additional data sets, but also their role in AML were explored through TCGA online tools.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

"Comprehensive Identification of Hub Genes and Signaling Pathways for Myelodysplastic Syndrome by Bioinformatics Analysis"

Guo

2021

BJSTR

View full text Add to dashboard Cite

Purpose: Myelodysplastic syndrome (MDS) is a group of tumor diseases derived from hematopoietic stem cells. It has a tendency to progress to acute myeloid leukemia (AML), but the mechanism is not clear due to complicated pathogenesis. Based on the integrated analysis of gene microarray data sets, the present study established a gene expression model related to the pathogenesis of MDS, and screened target molecules which have an impact on disease progression. Methods:We downloaded three gene microarray data sets (including 397 MDS patients and 45 normal controls) from Gene Expression Omnibus (GEO) database (http:// www.ncbi.nlm.nih.gov/geo). Then differential expressed genes (DEGs) from each data set was screened and integrated for obtaining co-expression DEGs. Enrichment analysis, network construction were performed to elucidate core genes and pathways related to the pathogenesis of MDS. Moreover, the DEGs were used to validate in extra data sets and for further exploration on online Gene Expression Profiling Interactive Analysis (GEPIA) tool (http://gepia.cancer-pku.cn/).Results: In our study, 325 co-expression DEGs including 141 up-regulated and 184 down-regulated were identified. And we found that these DEGs are enriched in interferonrelated signaling pathways, which also involve participation in antiviral responses. In addition, up-regulated hub genes such as IFIT3 and ITITM have been validated in extra data sets and had an important impact on the prognosis of patients with AML. Conclusion:Our findings will improve our understanding of the cause and underlying molecular events in MDS and may provide new research directions for treatment strategies.

show abstract

Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

"Comprehensive Identification of Hub Genes and Signaling Pathways for Myelodysplastic Syndrome by Bioinformatics Analysis"

Guo

2021

BJSTR

View full text Add to dashboard Cite

show abstract

“…A huge amount of corresponding data from a cDNA microarray and RNA sequencing is stored in several public databases such as ENCODE (https://www.encodeproject.org/), TCGA, ICGC (https://dcc.icgc.org/) and GEO. Integration of multiple HTS datasets (cDNA microarray and RNA sequencing) is considered a better approach of enhancing the reliability of results than an individual HTS dataset (Yan et al, 2018;Le, 2019;Huang et al, 2019). In the present study, an in-silico analysis using several bioinformatics approaches for screening and identification novel molecular targets for CCA treatment was performed using three microarray datasets of patient CCA tissues.…”

Section: Discussionmentioning

confidence: 99%

“…The Gene Expression Omnibus (GEO) database and The Cancer Genomics Atlas (TCGA) offer tools for screening and discovery of novel molecular pathways, promising biomarkers, genetic alterations, prognosis and novel effective target molecules for several cancers. By this approach, several potential key genes, biomarkers and pathways involved with gastric cancer (Yan et al, 2018); lung adenocarcinoma (Guo, Ma & Zhou, 2019) and myelodysplastic syndrome (Le, 2019) have been identified.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis identified CDC20 as a potential drug target for cholangiocarcinoma

Sungwan

Lert-itthiporn

Silsirivanit

et al. 2021

PeerJ

View full text Add to dashboard Cite

Background Cholangiocarcinoma (CCA) is a malignancy that originates from bile duct cells. The incidence and mortality of CCA are very high especially in Southeast Asian countries. Moreover, most CCA patients have a very poor outcome. Presently, there are still no effective treatment regimens for CCA. The resistance to several standard chemotherapy drugs occurs frequently; thus, searching for a novel effective treatment for CCA is urgently needed. Methods In this study, comprehensive bioinformatics analyses for identification of novel target genes for CCA therapy based on three microarray gene expression profiles (GSE26566, GSE32225 and GSE76297) from the Gene Expression Omnibus (GEO) database were performed. Based on differentially expressed genes (DEGs), gene ontology and pathway enrichment analyses were performed. Protein-protein interactions (PPI) and hub gene identifications were analyzed using STRING and Cytoscape software. Then, the expression of candidate genes from bioinformatics analysis was measured in CCA cell lines using real time PCR. Finally, the anti-tumor activity of specific inhibitor against candidate genes were investigated in CCA cell lines cultured under 2-dimensional and 3-dimensional cell culture models. Results The three microarray datasets exhibited an intersection consisting of 226 DEGs (124 up-regulated and 102 down-regulated genes) in CCA. DEGs were significantly enriched in cell cycle, hemostasis and metabolism pathways according to Reactome pathway analysis. In addition, 20 potential hub genes in CCA were identified using the protein-protein interaction (PPI) network and sub-PPI network analysis. Subsequently, CDC20 was identified as a potential novel targeted drug for CCA based on a drug prioritizing program. In addition, the anti-tumor activity of a potential CDC20 inhibitor, namely dinaciclib, was investigated in CCA cell lines. Dinaciclib demonstrated huge anti-tumor activity better than gemcitabine, the standard chemotherapeutic drug for CCA. Conclusion Using integrated bioinformatics analysis, CDC20 was identified as a novel candidate therapeutic target for CCA.

show abstract

“…MDS, also known as pre-leukemia, is characterized by a high risk of transformation to acute myeloid leukemia (AML). The exact cause of MDS is uncertain in many cases, although preceding exposure to radiation therapy, chemotherapy, poisonous solvents, tobacco, or agricultural chemicals are risk factors in some cases [ 1 ]. Apart from allogeneic hematopoietic stem cell transplantation (HSCT), effective therapies are absent; thus, MDS has long been considered a difficult to control disease.…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Profiles Identify Biomarkers of Resistance to Decitabine in Myelodysplastic Syndromes

Kim

Shin

Kim

et al. 2021

Cells

View full text Add to dashboard Cite

Myelodysplastic syndrome (MDS) is a clonal hematopoietic stem cell disease characterized by inefficient hematopoiesis and the potential development of acute leukemia. Among the most notable advances in the treatment of MDS is the hypomethylating agent, decitabine (5-aza-2′deoxycytidine). Although decitabine is well known as an effective method for treating MDS patients, only a subset of patients respond and a tolerance often develops, leading to treatment failure. Moreover, decitabine treatment is costly and causes unnecessary toxicity. Therefore, clarifying the mechanism of decitabine resistance is important for improving its therapeutic efficacy. To this end, we established a decitabine-resistant F-36P cell line from the parental F-36P leukemia cell line, and applied a genetic approach employing next-generation sequencing, various experimental techniques, and bioinformatics tools to determine differences in gene expression and relationships among genes. Thirty-eight candidate genes encoding proteins involved in decitabine-resistant-related pathways, including immune checkpoints, the regulation of myeloid cell differentiation, and PI3K-Akt signaling, were identified. Interestingly, two of the candidate genes, AKT3 and FOS, were overexpressed in MDS patients with poor prognoses. On the basis of these results, we are pursuing development of a gene chip for diagnosing decitabine resistance in MDS patients, with the goal of ultimately improving the power to predict treatment strategies and the prognosis of MDS patients.

show abstract

Screening and identification of key candidate genes and pathways in myelodysplastic syndrome by bioinformatic analysis

Cited by 11 publications

References 36 publications

"Comprehensive Identification of Hub Genes and Signaling Pathways for Myelodysplastic Syndrome by Bioinformatics Analysis"

"Comprehensive Identification of Hub Genes and Signaling Pathways for Myelodysplastic Syndrome by Bioinformatics Analysis"

Bioinformatics analysis identified CDC20 as a potential drug target for cholangiocarcinoma

Gene Expression Profiles Identify Biomarkers of Resistance to Decitabine in Myelodysplastic Syndromes

Contact Info

Product

Resources

About