Identification of the Shared Gene Signatures and Biological Mechanism in Type 2 Diabetes and Pancreatic Cancer

Hu, Yifang; Zeng, Ni; Ge, Yaoqi; Wang, Dan; Qin, Xiaoxuan; Zhang, Wensong; Jiang, Feng; Liu, Yun

doi:10.3389/fendo.2022.847760

Cited by 32 publications

(28 citation statements)

References 55 publications

(50 reference statements)

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“…Previous studies identified shared gene signatures and biological mechanisms in type 2 diabetes and pancreatic cancer by utilizing the WGCNA method ( Hu et al, 2022 ). To further explore the relationship between gene expression and abundance of CAFs, we performed the WGCNA according to the expression profiles of all genes (17,003 genes) by utilizing the R package WGCNA ( Langfelder and Horvath, 2008 ) and next identified the remarkable gene modules positively correlated with the fraction of CAFs.…”

Section: Methodsmentioning

confidence: 99%

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

Zeng

Wang

et al. 2022

Front. Genet.

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Ovarian cancer (OvCa) is one of the most widespread malignant tumors, which has the highest morbidity and unsatisfactory clinical outcomes among all gynecological malignancies in the world. Previous studies found that cancer-associated fibroblasts (CAFs) play significant roles in tumor growth, progression, and chemoresistance. In the current research, weighted gene co-expression network analysis (WGCNA), univariable COX regression, and the least absolute shrinkage and selection operator (LASSO) analysis were applied to recognize CAF-specific genes. After multiple bioinformatic analyses, four genes (AXL, GPR176, ITGBL1, and TIMP3) were identified as OvCa-specific CAF markers and used to construct the prognostic signature (CAFRS). Furthermore, the specificity of the four genes' expression was further validated at the single-cell level, which was high-selectively expressed in CAFs. In addition, our results showed that CAFRS is an independent significant risk factor affecting the clinical outcomes of OvCa patients. Meanwhile, patients with higher CAFRS were more likely to establish chemoresistance to platinum. Besides, the CAFRS were notably correlated with well-known signal pathways that were related to tumor progression. In summary, our study identifies four CAF-specific genes and constructs a novel prognostic signature, which may provide more insights into precise prognostic assessment in OvCa.

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Section: Methodsmentioning

confidence: 99%

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

Zeng

Wang

et al. 2022

Front. Genet.

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“…As previous studies have done ( Hu et al, 2022 ; Jiang et al, 2022 ), differentially expressed genes (DEGs) were screened in GSE48452 and GSE63067 in a batch-calibrated screening set. DEGs between NAFLD samples and normal samples were screened using the limma program package, with P. adj.…”

Section: Methodsmentioning

confidence: 99%

Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease

Zhang

et al. 2022

Front. Genet.

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Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis.Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells.Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells.Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.

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“…To ensure the high positive correlation between the selected module and the corresponding disease, we selected the two modules with the highest positive correlation coefficient and the highest significance in SLE (Meblue and Metan), Meblack and Mebrown modules were selected in DLBCL using the same method 15,16 . In the SLE and DLBCL highly positivity related modules, there were 47 overlapping genes, which were defined as common genes (CGs) (Supplementary Figure 2).…”

Section: Common Gene Signatures In Sle and Dlbclmentioning

confidence: 99%

Exploration of the molecular mechanisms, shared gene signatures, and MicroRNAs between systemic lupus erythematosus and diffuse large B cell lymphoma by bioinformatics analysis

Peng

Liang

Lin

et al. 2022

Lupus

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Background Systemic lupus erythematosus (SLE) is a complex heterogeneous systemic autoimmune disease. Previous studies have shown that SLE may be related to diffuse large B cell lymphoma (DLBCL), but the mechanism of their relationship is still unclear. The present study aimed to explore the common genetic molecular mechanisms, core shared genes, and miRNAs between SLE and DLBCL as well as to investigate the diagnostic markers of DLBCL. Methods The SLE and DLBCL microarray data were downloaded from the comprehensive Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules. Four core shared genes were screened out by various algorithms and validated in other cohorts. Finally, we constructed a common core gene-miRNA network using the human microRNA disease database (HMDD) and TarBase. Results Using WGCNA, four modules were identified as important modules for SLE and DLBCL. Enrichment analysis of the shared genes showed that the highly activated NF-κB pathway was a common feature of the pathophysiology. Four core shared genes, namely, PSMB10, PSMB4, TAF10, and NFΚBIA, were screened out. These core shared genes were significantly upregulated in both diseases, and they may be potential diagnostic markers of DLBCL. The core gene-miRNA network showed that miR-155–5p, regulating the shared NF-κB pathway, may play an important role in the susceptibility of SLE patients to DLBCL. Conclusion The present study revealed that NF-κB pathway in SLE may be a crucial susceptible factor for DLBCL. In addition, we identified PSMB10, PSMB4, TAF10, NFΚBIA and miR-155 involved in the common pathogenesis as potential biomarkers and therapeutic targets for DLBCL.

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Identification of the Shared Gene Signatures and Biological Mechanism in Type 2 Diabetes and Pancreatic Cancer

Cited by 32 publications

References 55 publications

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease

Exploration of the molecular mechanisms, shared gene signatures, and MicroRNAs between systemic lupus erythematosus and diffuse large B cell lymphoma by bioinformatics analysis

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