Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes

Wang, Linhui; Wang, Yutao; Wang, Jianfeng; Li, Luanfeng; Bi, Jianbin

doi:10.1155/2021/3236384

Cited by 19 publications

(16 citation statements)

References 87 publications

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“…Notably, we first found MIF in nonimmune cells bound to the receptor TNFRSF14 in other types of cells. TNFRSF14, known as herpesvirus entry mediator (HVEM), encodes members of the tumor necrosis factor (TNF) receptor superfamily and serves as a molecular switch by interacting with different ligands to regulate a series of immune responses ( Yu-Di and Ming-Yue, 2018 ; Wang L. et al, 2021 ). TNFRSF14 mediates apoptosis and prevents tumor cells from immune escape.…”

Section: Discussionmentioning

confidence: 99%

Single-Cell Sequencing Unveils the Heterogeneity of Nonimmune Cells in Chronic Apical Periodontitis

Lin

Chi

Meng

et al. 2022

Front. Cell Dev. Biol.

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Chronic apical periodontitis (CAP) is a unique dynamic interaction between microbial invasions and host defense mechanisms, resulting in infiltration of immune cells, bone absorption, and periapical granuloma formation. To help to understand periapical tissue pathophysiology, we constituted a single-cell atlas for 26,737 high-quality cells from inflammatory periapical tissue and uncovered the complex cellular landscape. The eight types of cells, including nonimmune cells and immune cells, were identified in the periapical tissue of CAP. Considering the key roles of nonimmune cells in CAP, we emphasized osteo-like cells, basal/stromal cells, endothelial cells, and epithelial cells, and discovered their diversity and heterogeneity. The temporal profiling of genomic alterations from common CAP to typical periapical granuloma provided predictions for transcription factors and biological processes. Our study presented potential clues that the shift of inflammatory cytokines, chemokines, proteases, and growth factors initiated polymorphic cell differentiation, lymphangiogenesis, and angiogenesis during CAP.

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Section: Discussionmentioning

confidence: 99%

Single-Cell Sequencing Unveils the Heterogeneity of Nonimmune Cells in Chronic Apical Periodontitis

Lin

Chi

Meng

et al. 2022

Front. Cell Dev. Biol.

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“…Several bioinformatics studies in recent years have established prognostic models, including an immune genes-related model [ 23 ] and an 11-gene model based on 5 cohorts [ 11 ], for clinical decision-making. Furthermore, hypoxia-related risk factors [ 9 ] and immune landscapes [ 24 ] have also been associated with bladder cancer prognosis. In this study, we successfully established a reliable 7-gene focal adhesion-related prognostic model for BLCA using RNA-seq data from the TCGA-BLCA cohort.…”

Section: Discussionmentioning

confidence: 99%

A novel focal adhesion-related risk model predicts prognosis of bladder cancer —— a bioinformatic study based on TCGA and GEO database

Wang

et al. 2022

BMC Cancer

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Background Bladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present. Methods The prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan–Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model. Results VCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts’ high- and low-risk groups. The higher risk score was associated with a worse prognosis. Conclusions The 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making.

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“…Zhang et al identified a glycolysis-based 4-mRNA signature to promote therapeutic options in BC [ 34 ]. Wang et al used univariate and robust methods to establish a 5-gene risk model in BC [ 35 ]. To further examine the advantages of our signature, we compared these risk models simultaneously.…”

Section: Discussionmentioning

confidence: 99%

In Silico Establishment and Validation of Novel Lipid Metabolism-Related Gene Signature in Bladder Cancer

Sun

Zhang

Chen

et al. 2022

Oxidative Medicine and Cellular Longevity

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Background. Aberrant lipid metabolism is an alteration common to many types of cancer. Dysregulation of lipid metabolism is considered a major risk factor for bladder cancer. Accordingly, we focused on genes related to lipid metabolism and screened novel markers for predicting the prognosis of bladder cancer. Methods. RNA-seq data for bladder cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The nonnegative matrix factorization (NMF) algorithm was used to classify the molecular subtypes. Weighted correlation network analysis (WGCNA) was applied to identify coexpressed genes, and least absolute shrinkage and selection operator (LASSO) multivariate Cox analysis was used to construct a prognostic risk model. External validation data and in vitro experiments were used to verify the results from in silico analysis. Results. Bladder cancer samples were grouped into two clusters based on the NMF algorithm. A total of 1467 genes involved in coexpression modules were identified in WGCNA. We finally established a 5-gene signature (TM4SF1, KCNK5, FASN, IMPDH1, and KCNJ15) that exhibited good stability across different datasets and was also an independent risk factor for prognosis. Furthermore, the predictive efficacy of our model was generally higher than the predictive efficacy of other published models. Distinct risk groups of patients also showed significantly different immune infiltration cell patterns and associations with clinical variables. Moreover, the 5 signature genes were verified in clinical samples by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry, which were in agreement with the in silico analysis. For in vitro experiments, knockdown of IMPDH1 markedly inhibited cell proliferation in bladder cancer. Conclusion. We established a 5-gene prognosis signature based on lipid metabolism in bladder cancer, which could be an effective prognostic indicator.

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Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes

Cited by 19 publications

References 87 publications

Single-Cell Sequencing Unveils the Heterogeneity of Nonimmune Cells in Chronic Apical Periodontitis

Single-Cell Sequencing Unveils the Heterogeneity of Nonimmune Cells in Chronic Apical Periodontitis

A novel focal adhesion-related risk model predicts prognosis of bladder cancer —— a bioinformatic study based on TCGA and GEO database

In Silico Establishment and Validation of Novel Lipid Metabolism-Related Gene Signature in Bladder Cancer

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