The RNA-binding protein HuR is a negative regulator in adipogenesis

Siang, Diana Teh Chee; Lim, Yen Ching; Kyaw, Aung Maung Maung; Win, Khaing Nwe; Chia, Sook Yoong; Degirmenci, Ufuk; Hu, Xiang; Tan, Bryan C.; Walet, Arcinas Camille Esther; Sun, Li; Xu, Dan

doi:10.1038/s41467-019-14001-8

Cited by 78 publications

(68 citation statements)

References 40 publications

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“…Preadipocytes’ isolation, culture, and differentiation were conducted as our previous studies ( Siang et al, 2020 ; Sun et al, 2013 ). Briefly, iWATs from ~3-week-old mice were minced, and digested in 0.2% collagenase (Sigma), which were subsequently filtered by 40 μm cell strainer and centrifuged to collect stromal vascular fraction (SVF) cells in the pellets.…”

Section: Star⋆methodsmentioning

confidence: 99%

A Landscape of Murine Long Non-Coding RNAs Reveals the Leading Transcriptome Alterations in Adipose Tissue during Aging

et al. 2020

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SUMMARY Aging is an inevitable process that involves profound physiological changes. Long non-coding RNAs (lncRNAs) are emerging as important regulators in various biological processes but are not systemically studied in aging. To provide an organism-wide lncRNA landscape during aging, we conduct comprehensive RNA sequencing (RNA-seq) analyses across the mouse lifespan. Of the 1,675 aging-regulated lncRNAs (AR-lncRNAs) identified, the majority are connected to inflammation-related biological pathways. AR-lncRNAs exhibit high tissue specificity; conversely, those with higher tissue specificity are preferentially regulated during aging. White adipose tissue (WAT) displays the highest number of AR-lncRNAs and develops the most dynamic crosstalk between AR-lncRNA and AR-mRNA during aging. An adipose-enriched AR-lncRNA, lnc-adipoAR1, is negatively correlated with aging, and knocking it down inhibits adipogenesis, phenocopying the compromised adipogenic capacity of aged fat. Our works together reveal AR-lncRNAs as essential components in aging and suggest that although each tissue ages in a distinct manner, WAT is a leading contributor to aging-related health decline.

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Section: Star⋆methodsmentioning

confidence: 99%

A Landscape of Murine Long Non-Coding RNAs Reveals the Leading Transcriptome Alterations in Adipose Tissue during Aging

et al. 2020

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“…Functional enrichment analysis results reveal that the DERBPs were mainly enriched in translation initiation, mRNA catabolic processes, RNA catabolic processes, nuclear-transcribed mRNA catabolic processes, SRP-dependent co-translational protein targeting to membrane, and cotranslational protein targeting to membrane, etc. Previous studies have demonstrated that regulation of translation, RNA processing, and the RNA metabolism process were the causes of the occurrence and development of the human disease ( Jain et al, 2019 ; Siang et al, 2020 ). The KEGG analysis results indicate that the dysregulated RBPs were enriched in Ribosome and Legionellosis, which is consistent with previous studies ( Li et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Nine-RNA Binding Protein Signature Predicting Overall Survival for Kidney Renal Clear Cell Carcinoma

et al. 2020

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Cumulative studies have shown that RNA binding proteins (RBPs) play an important role in numerous malignant tumors and are related to the occurrence and progression of tumors. However, the role of RBPs in kidney renal clear cell carcinoma (KIRC) is not fully understood. In this study, we first downloaded gene expression data and corresponding clinical information of KIRC from the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) database, respectively. A total of 137 differentially expressed RBPs (DERBPs) were then identified between normal and tumor tissue, including 38 downregulated and 99 upregulated RBPs. Nine RBPs (EIF4A1, RPL36A, EXOSC5, RPL28, RPL13, RPS19, RPS2, EEF1A2, and OASL) were served as prognostic genes and exploited to construct a prognostic model through survival analysis. Kaplan-Meier curves analysis showed that the low-risk group had a better survival outcome when compared with the high-risk group. The area under the curve (AUC) value of the prognostic model was 0.713 in the TCGA data set (training data set), 0.706 in the ICGC data set, and 0.687 in the GSE29609 data set, respectively, confirming a good prognostic model. The prognostic model was also identified as an independent prognostic factor for KIRC survival by performing cox regression analysis. In addition, we also built a nomogram relying on age and the prognostic model and internal validation in the TCGA data set. The clinical benefit of the prognostic model was revealed by decision curve analysis (DCA). Gene set enrichment analysis revealed several crucial pathways (ERBB signaling pathway, pathways in cancer, MTOR signaling pathway, WNT signaling pathway, and TGF BETA signaling pathway) that may explain the underlying mechanisms of KIRC. Furthermore, potential drugs for KIRC treatment were predicted by the Connectivity Map (Cmap) database based on DERBPs, including several important drugs, such as depudecin and vorinostat, that could reverse KIRC gene expression, which may provide reference for the treatment of KIRC. In summary, we developed and validated a robust nine-RBP signature for KIRC prognosis prediction. A nomogram with risk score and age can be applied to promote the individualized prediction of overall survival in patients with KIRC. Moreover, the two drugs depudecin and vorinostat may contribute to KIRC treatment.

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“…These differentially expressed RBPs were signi cantly enriched in mRNA surveillance pathway, miRNAs in cancer, RNA transport, and hepatitis Cand in uenza A-related signaling pathways. RBPs interact with miRNAs, mRNAs, long ncRNAs, and cRNAs to form ribonucleoprotein complexes, thereby improving the stability of target genes, promoting gene expression, and playing a key role in the tumorigenesis and progression of many tumors [23][24][25][26][27] . For example, the abnormal expression of RBP-eIF3c promotes the proliferation of HCC, which is positively correlated with KRAS, vascular endothelial growth factor, and Hedgehog signaling pathways [28] .…”

Section: Discussionmentioning

confidence: 99%

Systematic Analysis of the Function and Prognostic Value of RNA-Binding Proteins in Hepatocellular Carcinoma

Wu¹,

Yang²,

Yang³

et al. 2020

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Background: RNA-binding proteins (RBPs) are abnormally expressed in a variety of malignant tumors and are closely related to tumorigenesis, tumor progression, and prognosis. The role of RBPs in hepatocellular carcinoma (HCC) is unclear. Based on the cancer genome atlas (TCGA) database, we conducted a systematic bioinformatics analysis of abnormally expressed RBPs in HCC, with the aim of identifying the prognostic markers and potential therapeutic targets.Methods: HCC RNA sequencing data downloaded from TCGA database were used to determine the differentially expressed RBPs in livery cancer and normal tissues, followed by performing functional enrichment analysis and visualization of interaction relationships. Univariate and multivariate Cox regression analyses were subsequently used to identify RBPs that were significantly related to the prognosis to construct a prognostic model. The predictive performance of the prognostic model was evaluated by survival analysis and receiver operating characteristic (ROC) curve analysis and verified in the test cohort. Human protein atlas online database was used to verify the expression level of RBPs in the prognostic model.Results: In total, 82 differentially expressed RBPs were identified, including 55 upregulated and 27 downregulated RBPs. Further functional enrichment and interaction analyses showed that the differentially expressed RBPs were mainly related to regulating of mRNA metabolic process, RNA catabolic, mRNA catabolic process, and macromolecule methylation. Five RBP genes, LIN28B, SMG5, PPARGC1A, LARP1B, and ANG were identified as prognostic-related genes and used to construct the prognostic model. The predictive ability of the prognostic model was verified in the test cohort. ROC curve analysis showed that the prognostic model had good sensitivity and specificity. Independent prognostic analysis showed that the risk score may be an independent prognostic factor for HCC.Conclusion: This study constructed a reliable prognostic prediction model by analyzing the differentially expressed RBPs of HCC, facilitating the identification of HCC prognostic biomarkers and therapeutic targets.

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The RNA-binding protein HuR is a negative regulator in adipogenesis

Cited by 78 publications

References 40 publications

A Landscape of Murine Long Non-Coding RNAs Reveals the Leading Transcriptome Alterations in Adipose Tissue during Aging

A Landscape of Murine Long Non-Coding RNAs Reveals the Leading Transcriptome Alterations in Adipose Tissue during Aging

Development and Validation of Nine-RNA Binding Protein Signature Predicting Overall Survival for Kidney Renal Clear Cell Carcinoma

Systematic Analysis of the Function and Prognostic Value of RNA-Binding Proteins in Hepatocellular Carcinoma

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