Genome-Wide Transcriptomic Analysis of Non-Tumorigenic Tissues Reveals Aging-Related Prognostic Markers and Drug Targets in Renal Cell Carcinoma

Oh, Euiyoung; Kim, Jun-Hyeong; Um, JungIn; Jung, Dawoon; Williams, Darren R.; Lee, Hyunju

doi:10.3390/cancers13123045

Cited by 12 publications

(10 citation statements)

References 62 publications

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“…Through statistical analyses, Soh et al [ 34 ] identified cancer-related miRNAs associated with somatic copy number alterations and validated them in vitro by measuring the viability and proliferation rates of cells transfected with their inhibitors. Oh et al [ 35 ] identified aging-related genes in the kidney tissue using a regression method and validated them in vivo using mouse and zebrafish models. Joehanes et al [ 36 ] selected genes related to coronary heart disease based on DE analysis and conducted a validation study using real time-qPCR.…”

Section: Methodsmentioning

confidence: 99%

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Lee

Hwang

Seo

et al. 2022

Cells

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Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

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Section: Methodsmentioning

confidence: 99%

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Lee

Hwang

Seo

et al. 2022

Cells

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“…Aging-related genes (ARGs) play a vital role in cell senescence and regulate the biological activity of neoplastic tissue [19]. They are associated with many types of cancer, such as renal cell carcinoma and malignant melanoma, and have been shown to hold great promise for clinical application [20,21]. Nonetheless, few studies about BLCA have worked out an effective prognostic model based on ARGs to predict the survival of patients and determine personalized therapy.…”

Section: Introductionmentioning

confidence: 99%

Construction and evaluation of a prognostic risk model of aging-related genes in bladder cancer

Wang

Ning

et al. 2022

Preprint

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Background: Bladder cancer (BLCA) is the most common malignant tumor of the urinary system. With the aging of the population, the incidence of bladder cancer is increasing year by year. Thus, the current research aimed to determine the role of aging-related biomarkers in bladder cancer.Methods: Based on TCGA, we downloaded gene expression data of 410 BLCA samples and 19 control samples. Differentially expressed genes (DEGs) between BLCA and normal samples were intersected with human aging-related genes (ARGs). TCGA cohort was divided into train set and test set according to the proportion of 7:3. The univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic aging-related signatures, followed by the construction of the risk score model and nomogram for predicting the survival of BLCA patients. Kaplan-Meier (K-M) analysis and receiver operating characteristic (ROC) analysis were conducted to assess its prognostic power. The CIBERSORT algorithm was used to explore the tumor immune microenvironment characteristics.Results: We identified 6194 DEGs closely related to BLCA. A total of 83 differentially expressed aging-related genes (DEARGs) were found in BLCA. Subsequently, seven genes (IGF1, NGF, GCLM, PYCR1, EFEMP1, APOC3, IFNB1) were determined and used to build a prognostic risk model. In the training and test dataset, survival analysis demonstrated that the overall survival of high-risk patients was worse compared with the patients in the low-risk group (P <0.05). The AUC of the ROC curve reached 0.66, 0.675, and 0.684 at 1, 2, and 3 years in the training dataset. Also, AUC of 1, 2, and 3 years were 0.625, 0.675, and 0.534 in the test dataset. T4, N3 or female BLCA patients had a significantly increased risk score. However, M stage had no significant difference in the risk score. We further found seven immune cells and many immune checkpoints with significant differences between the low- and high-risk groups.Conclusion: The seven aging biomarkers can be used for prognostic prediction in bladder cancer. Additionally, the immune microenvironment and immune checkpoints can provide a potential target for individualized therapy.

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“…Many aging-related cellular events (genomic instability, inflammatory response, immunity, etc.) are hallmarks of cancer ( 9 ). Despite the widespread study of the aging microenvironment in cancers, few studies focused on the overall characteristics of the transcriptional landscape of aging-associated genes in ccRCC.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the widespread study of the aging microenvironment in cancers, few studies focused on the overall characteristics of the transcriptional landscape of aging-associated genes in ccRCC. Previously, several aging-associated genes have been determined to be linked to unfavorable survival outcomes of RCC ( 9 ). Additionally, experimental evidence demonstrates that aging-associated genes participate in RCC progression.…”

Section: Introductionmentioning

confidence: 99%

Aging-based molecular classification and score system in ccRCC uncovers distinct prognosis, tumor immunogenicity, and treatment sensitivity

Zhu

Huang

et al. 2022

Front. Immunol.

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ObjectiveAging is a complex biological process and a major risk factor for cancer development. This study was conducted to develop a novel aging-based molecular classification and score system in clear cell renal cell carcinoma (ccRCC).MethodsIntegrative analysis of aging-associated genes was performed among ccRCC patients in the TCGA and E-MTAB-1980 cohorts. In accordance with the transcriptional expression matrix of 173 prognostic aging-associated genes, aging phenotypes were clustered with the consensus clustering approach. The agingScore was generated to quantify aging phenotypes with principal component analysis. Tumor-infiltrating immune cells and the cancer immunity cycle were quantified with the ssGSEA approach. Immunotherapy response was estimated through the TIDE algorithm, and a series of tumor immunogenicity indicators were computed. Drug sensitivity analysis was separately conducted based on the GDSC, CTRP, and PRISM analyses.ResultsThree aging phenotypes were established for ccRCC, with diverse prognosis, clinical features, immune cell infiltration, tumor immunogenicity, immunotherapeutic response, and sensitivity to targeted drugs. The agingScore was developed, which enabled to reliably and independently predict ccRCC prognosis. Low agingScore patients presented more undesirable survival outcomes. Several small molecular compounds and three therapeutic targets, namely, CYP11A1, SAA1, and GRIK4, were determined for the low agingScore patients. Additionally, the high agingScore patients were more likely to respond to immunotherapy.ConclusionOverall, our findings introduced an aging-based molecular classification and agingScore system into the risk stratification and treatment decision-making in ccRCC.

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Genome-Wide Transcriptomic Analysis of Non-Tumorigenic Tissues Reveals Aging-Related Prognostic Markers and Drug Targets in Renal Cell Carcinoma

Cited by 12 publications

References 62 publications

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Construction and evaluation of a prognostic risk model of aging-related genes in bladder cancer

Aging-based molecular classification and score system in ccRCC uncovers distinct prognosis, tumor immunogenicity, and treatment sensitivity

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