bims-lorfki 2021-10-24 papers

Issue of 2021‒10‒24
three papers selected by
Nikita Dewani
Max Delbrück Centre for Molecular Medicine

Int J Mol Sci. 2021 Oct 17. pii: 11193. [Epub ahead of print]22(20):

LncRNAs in the Regulation of Genes and Signaling Pathways through miRNA-Mediated and Other Mechanisms in Clear Cell Renal Cell Carcinoma.

Eleonora A Braga, Marina V Fridman, Elena A Filippova, Vitaly I Loginov, Irina V Pronina, Alexey M Burdennyy, Alexander V Karpukhin, Alexey A Dmitriev, Sergey G Morozov.

The fundamental novelty in the pathogenesis of renal cell carcinoma (RCC) was discovered as a result of the recent identification of the role of long non-coding RNAs (lncRNAs). Here, we discuss several mechanisms for the dysregulation of the expression of protein-coding genes initiated by lncRNAs in the most common and aggressive type of kidney cancer-clear cell RCC (ccRCC). A model of competitive endogenous RNA (ceRNA) is considered, in which lncRNA acts on genes through the lncRNA/miRNA/mRNA axis. For the most studied oncogenic lncRNAs, such as HOTAIR, MALAT1, and TUG1, several regulatory axes were identified in ccRCC, demonstrating a number of sites for various miRNAs. Interestingly, the LINC00973/miR-7109/Siglec-15 axis represents a novel agent that can suppress the immune response in patients with ccRCC, serving as a valuable target in addition to the PD1/PD-L1 pathway. Other mechanisms of action of lncRNAs in ccRCC, involving direct binding with proteins, mRNAs, and genes/DNA, are also considered. Our review briefly highlights methods by which various mechanisms of action of lncRNAs were verified. We pay special attention to protein targets and signaling pathways with which lncRNAs are associated in ccRCC. Thus, these new data on the different mechanisms of lncRNA functioning provide a novel basis for understanding the pathogenesis of ccRCC and the identification of new prognostic markers and targets for therapy.

Keywords: alternative mechanisms; clear cell renal cell carcinoma; competitive endogenous RNA model; lncRNA; protein targets and signaling pathways

DOI: https://doi.org/10.3390/ijms222011193

Front Oncol. 2021 ;11 728181

Identification of a Somatic Mutation-Derived Long Non-Coding RNA Signatures of Genomic Instability in Renal Cell Carcinoma.

Xisheng Fang, Xia Liu, Lin Lu, Guolong Liu.

Background: Renal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.Methods: Clinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.
Results: A total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, 'PRDM16-dt', AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients' overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.
Conclusion: Our study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

Keywords: genome instability-related lncRNAs (GInLncRNAs); genomic instability; lncRNA; prognostic risk model; renal cell carcinoma

DOI: https://doi.org/10.3389/fonc.2021.728181

Cancer Cell Int. 2021 Oct 18. 21(1): 545

A novel prognostic cancer-related lncRNA signature in papillary renal cell carcinoma.

Binghai Chen, Di Dong, Qin Yao, Yuanzhang Zou, Wei Hu.

BACKGROUND: Papillary renal cell carcinoma (pRCC) ranks second in renal cell carcinoma and the prognosis of pRCC remains poor. Here, we aimed to screen and identify a novel prognostic cancer-related lncRNA signature in pRCC.METHODS: The RNA-seq profile and clinical feature of pRCC cases were downloaded from TCGA database. Significant cancer-related lncRNAs were obtained from the Immlnc database. Differentially expressed cancer-related lncRNAs (DECRLs) in pRCC were screened for further analysis. Cox regression report was implemented to identify prognostic cancer-related lncRNAs and establish a prognostic risk model, and ROC curve analysis was used to evaluate its precision. The correlation between RP11-63A11.1 and clinical characteristics was further analyzed. Finally, the expression level and role of RP11-63A11.1 were studied in vitro.
RESULTS: A total of 367 DECRLs were finally screened and 26 prognostic cancer-related lncRNAs were identified. Among them, ten lncRNAs (RP11-573D15.8, LINC01317, RNF144A-AS1, TFAP2A-AS1, LINC00702, GAS6-AS1, RP11-400K9.4, LUCAT1, RP11-63A11.1, and RP11-156L14.1) were independently associated with prognosis of pRCC. These ten lncRNAs were incorporated into a prognostic risk model. In accordance with the median value of the riskscore, pRCC cases were separated into high and low risk groups. Survival analysis indicated that there was a significant difference on overall survival (OS) rate between the two groups. The area under curve (AUC) in different years indicated that the model was of high efficiency in prognosis prediction. RP11-63A11.1 was mainly expressed in renal tissues and it correlated with the tumor stage, T, M, N classifications, OS, PFS, and DSS of pRCC patients. Consistent with the expression in pRCC tissue samples, RP11-63A11.1 was also down-regulated in pRCC cells. More importantly, up-regulation of RP11-63A11.1 attenuated cell survival and induced apoptosis.
CONCLUSIONS: Ten cancer-related lncRNAs were incorporated into a powerful model for prognosis evaluation. RP11-63A11.1 functioned as a cancer suppressor in pRCC and it might be a potential therapeutic target for treating pRCC.

Keywords: LncRNA; Papillary renal cell carcinoma; Prognosis; RP11-63A11.1; TCGA

DOI: https://doi.org/10.1186/s12935-021-02247-6