Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis

Li, Haoyu

doi:10.1155/2021/8127145

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…The expression level of TMEM176B was higher in gastric cancer tissues than in normal tissues, and according to survival analysis, higher expression levels were related to poorer prognosis ( Sun et al, 2018 ). The expression of TMEM176B in lymphoma and its control tissues also differed significantly ( Cuajungco et al, 2012 ; Ma and Li, 2021 ). The expression of TMEM176B is up-regulated in the tumor blood vessels of human renal cell carcinoma specimens, suggesting that it is implicated in tumor angiogenesis and could be a target of anti-angiogenesis therapy for cancer patients ( Otsubo et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

A Potential Diagnostic and Prognostic Biomarker TMEM176B and Its Relationship With Immune Infiltration in Skin Cutaneous Melanoma

Jiang

Yang

Liu

et al. 2022

Front. Cell Dev. Biol.

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Background: Melanoma is a highly malignant and aggressive tumor. The search for new and effective biomarkers facilitates early diagnosis and treatment, ultimately improving the prognosis of melanoma patients. Although the transmembrane protein TMEM176B has been linked to a number of cancers, its role in cancer immunity remains unknown.Methods: Expression levels of TMEM176B in normal tissues and several cancers, including Skin Cutaneous Melanoma (SKCM), were collected from TCGA and GTEx. We used Receiver operating characteristic and Kaplan–Meier survival curves and performed regression analysis to elucidate the link between TMEM176B and clinicopathological features of SKCM in order to determine the prognostic significance of TMEM176B in SKCM. We then used the GEPIA and STRING websites to search for proteins and associated top genes that may interact with TMEM176B and enriched them for analysis. The link between TMEM176B and immune cells infiltration was then investigated using TIMER, CIBERSORT algorithm and GSVA package of R (v3.6.3). Finally, animal tests were conducted to confirm the expression of Tmem176b and its influence on T-cell immune infiltration.Results:TMEM176B expression was considerably elevated in SKCM compared to normal tissues. Particularly, TMEM176B expression was also linked to pathological stage, tumor ulceration and radiation therapy. Patients with elevated TMEM176B expression had a better prognosis, according to the survival analysis. The majority of tumor infiltrating lymphocytes (TILs) especially T cells in SKCM was positively linked with TMEM176B expression. Our animal experiments also verified that the T-cell infiltration was significantly inhibited in local melanoma tissue of Tmem176b knockout mice. At the same time deleting Tmem176b accelerated tumor progress and impaired T cells effector function.Conclusion: Upregulated expression of TMEM176B in SKCM is associated with a better prognosis and it has the potential to serve as a diagnostic and prognostic marker for the disease. It may serve as a target for SKCM immunotherapy by regulating CD8+ T cells although it requires more evidence.

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Section: Introductionmentioning

confidence: 99%

A Potential Diagnostic and Prognostic Biomarker TMEM176B and Its Relationship With Immune Infiltration in Skin Cutaneous Melanoma

Jiang

Yang

Liu

et al. 2022

Front. Cell Dev. Biol.

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“…In addition, WGCNA implements methods for both weighted and unweighted correlation networks and provides a more effective mean to explore the hub genes that regulate critical biological processes ( Barabási et al, 2011 ). To date, WGCNA has been successfully applied for identifying potential biomarkers with diagnostic and prognostic values in a wide range of cancer types ( Liu et al, 2021 ; Ma and Li, 2021 ; Yang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers

Liu

Chen

et al. 2022

Front. Genet.

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In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein–protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.

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“…A previous study has suggested that IGFBP4 is associated with poor outcomes in glioblastoma ( 42 ). Interestingly, in diffuse large B cell lymphoma (DLBCL) patients, the C1qA [276] polymorphism of the A/A allele is an independent favorable prognostic factor for rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) as first-line therapy ( 43 ), while the expression level of LGALS2 was not associated with OS but was lower in tumors than in normal samples ( 44 ). LAMP3 is over-expressed in various tumors and is correlated with the poor or good prognosis of different patients ( 45 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma

Zhang¹,

Shi²,

Wang³

et al. 2022

Ann Transl Med

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Background: The immune landscape, prognostic model, and molecular variations of mantle cell lymphoma (MCL) remain unclear. Hence, an integrated bioinformatics analysis of MCL datasets is required for the development of immunotherapy and the optimization of targeted therapies.Methods: Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules.Results: Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with downregulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis.Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes.Conclusions: Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.

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Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis

Cited by 3 publications

References 41 publications

A Potential Diagnostic and Prognostic Biomarker TMEM176B and Its Relationship With Immune Infiltration in Skin Cutaneous Melanoma

A Potential Diagnostic and Prognostic Biomarker TMEM176B and Its Relationship With Immune Infiltration in Skin Cutaneous Melanoma

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers

Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma

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