Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis

Qin, Yang; Li, Kaihu; Li, Xin; Liu, Jing

doi:10.7150/ijms.48244

Cited by 27 publications

(18 citation statements)

References 113 publications

(103 reference statements)

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“…The last target of the optimal probes is MARCH11 (targeted by probe cg09017434), regulating the intracellular transport of lysines. As for its correlations with GBM, according to recent publications, such gene has shown to be correlated with the carcinogenic transformation of cells with different expression levels (Yang et al, 2020). Considering the correlations between gene region methylation and gene expression, it is reasonable for us to speculate that the methylation status of such gene may be correlated with potential malignant alterations, supporting its correlations with GBM.…”

Section: Optimal Genes Relevant To Gbmmentioning

confidence: 73%

Distinguishing Glioblastoma Subtypes by Methylation Signatures

Zhang

Zeng

et al. 2020

Front. Genet.

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Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes’ methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). Representative functions, such as nervous system development, intrinsic plasma membrane component, calcium ion binding, systemic lupus erythematosus, and alcoholism, are potential pathogenic functions that participate in the initiation and progression of glioblastoma and its subtypes. With these sites, an efficient model can be built to classify the subtypes of glioblastoma.

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Section: Optimal Genes Relevant To Gbmmentioning

confidence: 73%

Distinguishing Glioblastoma Subtypes by Methylation Signatures

Zhang

Zeng

et al. 2020

Front. Genet.

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“…Puerkaiti et al observed overexpression of ITGB2 in triple negative breast cancer (TNBC), and also that the expression level of ITGB2 was significantly correlated to cancer stage, local metastasis, and prognosis (25). In hematological malignancies, ITGB2 has been correlated with chronic lymphocytic leukemia (CLL) and myeloma (26)(27)(28) This study found that ITGB2 acted as a key immunerelated gene and had an adverse signature in AML patients. We investigated the relationship between ITGB2 and immune cell infiltrates.…”

Section: Discussionmentioning

confidence: 95%

Key immune-related gene ITGB2 as a prognostic signature for acute myeloid leukemia

Wei¹,

Huang²,

Huang³

et al. 2021

Ann Transl Med

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Background: The tumor microenvironment (TME) has an essential role in tumorigenesis, progression, and therapeutic response in many cancers. Currently, the role of TME in acute myeloid leukemia (AML) is unclear.This study investigated the correlation between immune-related genes and prognosis in AML patients.Methods: Transcriptome RNA-Seq data for 151 AML samples were downloaded from TCGA database (https://portal.gdc.cancer.gov/), and the immune related genes (irgs) were selected from Immport database.Bioinformatics screening was used to identify irgs for AML, and genes with a critical role in the prognosis of AML were selected for further analysis. To confirm the prognostic role of irgs in AML, we undertook protein-protein interaction (PPI) network analysis of the top 30 interacting genes. We then investigated associations between immune cell infiltration and prognosis in AML patients. Immunohistochemistry was used to validate protein expression levels between AML and normal bone marrow samples. Analysis of the drug sensitivity of the selected gene was then performed. Results:The integrin lymphocyte function-associated antigen 1 (CD11A/CD18; ITGAL/ITGB2) was identified as the key immune-related gene that significantly influenced prognosis in AML patients.Overexpression of ITGB2 indicated poor prognosis in AML patients (P=0.007). Risk modeling indicated that a high-risk score led to poor outcomes (P=3.076e−08) in AML patients. The risk model showed accuracy for predicting prognosis in AML patients, with area under curve (AUC) at 1 year, 0.816; AUC at 3 years, 0.82; and AUC at 5 years, 0.875. In addition, we found that ITGB2 had a powerful influence on immune cell infiltration into AML TME. The results of immunohistochemistry showed that AML patients had significantly higher ITGB2 protein expression than normal samples. The AML patients were divided into 2 groups based on ITGB2 risk scores. Drug sensitivity test results indicated that the high-risk group was sensitive to cytarabine, axitinib, bosutinib, and docetaxel, but resistant to cisplatin and bortezomib.Conclusions: In the present study, we found that ITGB2 may be able to serve as a biomarker for assessing prognosis and drug sensitivity in AML patients.

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“…Yang et al's group performed a comprehensive bioinformatics analysis on microarray data of myeloma cells. PLEK2 was identified as a potential therapeutic target [ 27 ]. In this study, we utilized FunRich to analyse the biological pathways related to PLEK2.…”

Section: Discussionmentioning

confidence: 99%

Pleckstrin-2 as a Prognostic Factor and Mediator of Gastric Cancer Progression

Wang

Sun

et al. 2021

Gastroenterology Research and Practice

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Pleckstrin-2 (PLEK2) is a crucial mediator of cytoskeletal reorganization. However, the potential roles of PLEK2 in gastric cancer are still unknown. PLEK2 expression in gastric cancer was examined by western blotting and real-time PCR. Survival analysis was utilized to test the clinical impacts of the levels of PLEK2 in gastric cancer patients. In vitro and in vivo studies were used to estimate the potential roles played by PLEK2 in modulating gastric cancer proliferation, self-renewal, and tumourigenicity. Bioinformatics approaches were used to monitor the effect of PLEK2 on epithelial-mesenchymal transition (EMT) signalling pathways. PLEK2 expression was significantly upregulated in gastric cancer as compared with nontumour samples. Kaplan-Meier plotter analysis revealed that gastric cancer patients with higher PLEK2 levels had substantially poorer overall survival compared with gastric cancer patients with lower PLEK2 levels. The upregulation or downregulation of PLEK2 in gastric cancer cell lines effectively enhanced or inhibited cell proliferation and proinvasive behaviour, respectively. Additionally, we also found that PLEK2 enhanced EMT through downregulating E-cadherin expression and upregulating Vimentin expression. Our findings demonstrated that PLEK2 plays a potential role in gastric cancer and may be a novel therapeutic target for gastric cancer.

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Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis

Cited by 27 publications

References 113 publications

Distinguishing Glioblastoma Subtypes by Methylation Signatures

Distinguishing Glioblastoma Subtypes by Methylation Signatures

Key immune-related gene ITGB2 as a prognostic signature for acute myeloid leukemia

Pleckstrin-2 as a Prognostic Factor and Mediator of Gastric Cancer Progression

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