bims-lorfki 2022-01-02 papers

Aging (Albany NY). 2021 Dec 26. 13(undefined):

Cheng Tang, GenYi Qu, Yong Xu, Guang Yang, Jiawei Wang, Maolin Xiang.

OBJECTIVE: Using model algorithms, we constructed an immune-related long non-coding RNAs (lncRNAs) risk coefficient model to predict outcomes for patients with clear cell renal cell carcinoma (ccRCC) to understand the infiltration of tumor immune cells and the sensitivity to immune-targeted drugs.
METHODS: Open genes data were downloaded from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, and immune-related lncRNAs were obtained through Pearson correlation analysis. R language software was used to obtain differentially expressed immune-related lncRNAs and immune-related lncRNA pairs. The model was constructed using least absolute shrinkage and selector operation regression analysis, and receiver operator characteristic curves were drawn. The Akaike information criterion was used to distinguish the high-risk from the low-risk group. We also conducted correlation analysis for the high- and low-risk subgroups.
RESULTS: We identified 27 immune-related lncRNAs pairs, 16 of which were included in the model construction. After merging clinical data, the areas under the curve of 1 -year, 3-year, and 5-year survival times of ccRCC patients were 0.867, 0.832, and 0.838, respectively. Subgroup analyses were conducted according to the cut-off value. We found that the high-risk group was associated with poor outcomes. The risk score and tumor stage were independent predictors of the outcome of ccRCC. The risk model predicted specific immune cell infiltration, immune checkpoint gene expression levels, and high-risk groups more sensitive to sunitinib targeted therapy.
CONCLUSION: We obtained prognostic-related novel ccRCC markers and risk model that predicts the outcome of patients with ccRCC and helps identify those who can benefit from sunitinib.

Keywords: TCGA database; clear cell renal cell carcinoma; immune-related lncRNA pairs; prognosis; risk coefficient model; targeted therapy; tumor immune infiltration

DOI: https://doi.org/10.18632/aging.203797

Front Cell Dev Biol. 2021 ;9 777349

Targeted Methylation of the LncRNA NEAT1 Suppresses Malignancy of Renal Cell Carcinoma.

Jieqing Chen, Xinhui Liao, Jianli Cheng, Ganglin Su, Fen Yuan, Zhongfu Zhang, Jianting Wu, Hongbing Mei, Wanlong Tan.

Long-chain non-coding RNA (LncRNA) has been found to play an important role in the regulation of the occurrence and progression of renal cell carcinoma (RCC). In this study, we demonstrated that LncRNA NEAT1 expression and m6A methylation level was decreased in RCC tissues. Further, the downregulated expression level of LncRNA NEAT1 was associated with poor prognosis for RCC patients. Then we used CRIPSR/dCas13b-METTL3 to methylate LncRNA NEAT1 in RCC cells. The results showed that the expression level of LncRNA NEAT1 was upregulated after methylated by dCas13b-METTL3 in RCC cells. And the proliferation and migration ability of RCC cells was decreased after methylated LncRNA NEAT1. Finally, we examined the effect of LncRNA NEAT1 hypermethylation on the transcriptome. We found differentially expressed genes in RCC cells were associated with "cGMP-PKG signaling pathway", "Cell adhesion molecules" and "Pathways in cancer". In conclusion, CRISPR/Cas13b-METTL3 targeting LncRNA NEAT1 m6A methylation activates LncRNA NEAT1 expression and provides a new target for treatment of RCC.

Keywords: NEAT1; RCC = renal cell cancer; dCas13b; lncRNA; m6A (N6-methyladenosine)

DOI: https://doi.org/10.3389/fcell.2021.777349

Aging (Albany NY). 2021 Dec 27. 13(undefined):

Identification of a competing endogenous RNA network related to immune signature in clear cell renal cell carcinoma.

Yuke Zhang, Jiangwen Dai, Weifeng Huang, Qingsong Chen, Wei Chen, Qiying He, Feng Chen, Peng Zhang.

Clear cell renal cell carcinoma (ccRCC) is a fatal cancer of the urinary system. Long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) involving the ccRCC progression. However, the relationship between the ceRNA network and immune signature is largely unknown. In this study, the ccRCC-related gene expression profiles retrieved from the TCGA database were used first to identify the differentially expressed genes through differential gene expression analysis and weighted gene co-expression network analysis. The interaction among differentially expressed lncRNAs, miRNAs, and mRNAs were matched using public databases. As a result, a ceRNA network was developed that contained 144 lncRNAs, 23 miRNAs, as well as 62 mRNAs. Four of 144 lncRNAs including LINC00943, SRD5A3-AS1, LINC02345, and U62317.3 were identified through LASSO regression and Cox regression analyses, and were used to create a prognostic risk model. Then, the ccRCC samples were divided into the high- and low-risk groups depending on their risk scores. ROC curves, Kaplan-Meier survival analysis, and the survival risk plots indicated that the predictive performance of our developed risk model was accurate. Moreover, the CIBERSORT algorithm was used to measure the infiltration levels of immune cells in the ccRCC samples. The further genomic analysis illustrated a positive correlation between most immune checkpoint blockade-related genes and the risk score. In conclusion, the present findings effectually contribute to the comprehensive understanding of the ccRCC pathogenesis, and may offer a reference for developing novel therapeutic and prognostic biomarkers.

Keywords: clear cell renal cell carcinoma; competing endogenous RNA; immune cell infiltration; prognosis; tumor microenvironment

DOI: https://doi.org/10.18632/aging.203784

Front Cell Dev Biol. 2021 ;9 811734

A Prognostic Pyroptosis-Related lncRNAs Risk Model Correlates With the Immune Microenvironment in Colon Adenocarcinoma.

Fada Xia, Yuanliang Yan, Cong Shen.

Recent studies have indicated that long non-coding RNAs (lncRNAs) may participate in the regulation of tumor cell proptosis. However, the connection between lncRNA expression and pyroptosis remains unclear in colon adenocarcinoma (COAD). This study aims to explore and establish a prognostic signature of COAD based on the pyroptosis-related lncRNAs. We identify 15 prognostic pyroptosis-related lncRNAs (ZNF667-AS1, OIP5-AS1, AL118506.1, AF117829.1, POC1B-AS1, CCDC18-AS1, THUMPD3-AS1, FLNB-AS1, SNHG11, HCG18, AL021707.2, UGDH-AS1, LINC00641, FGD5-AS1 and AC245452.1) from the TCGA-COAD dataset and use them to construct the risk model. After then, this pyroptosis-related lncRNA signature is validated in patients from the GSE17536 dataset. The COAD patients are divided into low-risk and high-risk groups by setting the median risk score as the cut-off point and represented differences in the immune microenvironment. Hence, we construct the immune risk model based on the infiltration levels of ssGSEA immune cells. Interestingly, the risk model and immune risk model are both independent prognostic risk factors. Therefore, a nomogram combined risk score, immune risk score with clinical information which is meaningful in univariate and multivariate Cox regression analysis is established to predict the overall survival (OS) of COAD patients. In general, the signature consisted of 15 pyroptosis-related lncRNAs and was proved to be associated with the immune landscape of COAD patients.

Keywords: colon adenocarcinoma (COAD); immune microenvironment; lncRNA; prognosis; pyroptosis

DOI: https://doi.org/10.3389/fcell.2021.811734