Dataset Information

Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N⁶-Methyladenosine RNA Methylation Regulators.

ABSTRACT: As the most prevalent internal eukaryotic modification, N⁶-methyladenosine (m⁶A) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of m⁶A in clear cell renal cell carcinoma (ccRCC). In this study, we researched the RNA-seq transcriptome data of ccRCC in the TCGA dataset and used bioinformatics analyses to detect the relationship between m⁶A RNA methylation regulators and ccRCC. First, we compared the expression of 18 m⁶A RNA methylation regulators in ccRCC patients and normal tissues. Then, data from ccRCC patients were divided into two clusters by consensus clustering. LASSO Cox regression analysis was used to build a risk signature to predict the prognosis of patients with ccRCC. An ROC curve, univariate Cox regression analysis, and multivariate Cox regression analysis were used to verify this risk signature's predictive ability. Then, we internally validated this signature by random sampling. Finally, we explored the role of the genes in the signature in some common pathways. Gene distribution between the two subgroups was different; cluster 2 was gender-related and had a worse prognosis. IGF2BP3, IGF2BP2, HNRNPA2B1, and METTL14 were chosen to build the risk signature. The overall survival of the high- and low-risk groups was significantly different (p = 7.47e - 12). The ROC curve also indicated that the risk signature had a decent predictive significance (AUC = 0.72). These results imply that the risk signature has a potential value for ccRCC treatment.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7895564 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N6-Methyladenosine RNA Methylation Regulators.

Zhang Yan Y Yao Yao Y Qi Xiaochen X Li Jianyi J Liu Meihong M Che Xiangyu X Xu Yingkun Y Wu Guangzhen G

Journal of immunology research 20210212

As the most prevalent internal eukaryotic modification, N6-methyladenosine (m6A) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of m6A in clear cell renal cell carcinoma (ccRCC). In this study, we researched the RNA-seq transcriptome data of ccRCC in the TCGA dataset and used bioinformatics analyses to detect the relationship between m6A RNA methylation regulators and c ...[more]

PMID: 33628845

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Project description:N6‑methyladenosine (m6A) RNA methylation is the most prevalent type of mRNA modification; however, little is known about its function in clear cell renal cell carcinoma (ccRCC). The present study aimed to establish and validate a m6A‑related risk signature as a prognostic factor for patients with ccRCC. Consensus clustering was used to divide patients with ccRCC from The Cancer Genome Atlas (TCGA) cohort (n=489) into three clusters (cluster 1/2/3) based on 19 m6A RNA methylation regulators. In addition, a m6A‑related risk signature was constructed using TCGA data, and its accuracy was validated using data from the International Cancer Genome Consortium (n=91). The prognostic performance of the risk signature was evaluated by Kaplan‑Meier analyses, least absolute shrinkage and selection operator Cox regression, multivariate Cox regression, receiver operating characteristic curves and nomograms. The results revealed that the majority of the 19 m6A RNA methylation regulators were differentially expressed among ccRCC stratified by different clinicopathological features. The cluster 1 group exhibited a higher frequency of metastasis and poorer overall survival compared with the cluster 2/cluster 3 group. The hallmarks of RNA metabolism, transcription misregulation in cancer and regulation of autophagy, were significantly enriched in the cluster 1 group. A m6A‑related risk signature was constructed and validated with high prognostic accuracy for the prediction of 5‑year survival and recurrence (area under the curve, 0.736 and 0.728, respectively). The present study also established robust nomograms for evaluating the risk of mortality and recurrence for patients with ccRCC (c‑index, 0.783 and 0.819, respectively). The dysregulation of hub m6A RNA methylation regulator expression levels and m6A RNA methylation levels were also validated in multiple RCC cells using in vitro experiments. Taken together, the m6A RNA methylation regulators promoted the malignant progression of ccRCC and exhibited good performance in prognostic predictions. These results provided insight into the development of m6A‑targeted treatments for ccRCC.

Project description:BackgroundKidney renal clear cell carcinoma (KIRC) lacks effective prognostic biomarkers and the role and mechanism of N6-methyladenosine (m6A) modification of long noncoding RNAs (lncRNAs) in KIRC remain unclear.MethodsWe extracted standard mRNA-sequencing and clinical data from the TCGA database. The prognostic risk model was obtained by Lasso regression and Cox regression. We randomly divided the samples into training and test sets, each taking half of the cases. Based on Lasso regression and Cox regression for training set, the prognostic risk signature was constructed; risk scores were calculated with the R package "glmnet." Based on the median value of the prognostic risk score, risk scores were calculated for each patient and we divided all KIRC samples into high-risk and low-risk groups. Then, high- and low-risk subtypes were established and their prognosis, clinical features, and immune infiltration microenvironment were evaluated in test set and the entire sampled data set. The reliability of the prognostic model was confirmed by receiver operating characteristic curve analysis.ResultsWe found 28 prognostic m6A-related lncRNAs and established a m6A-related lncRNAs prognostic signature. Risk score=AC015813.1∗(0.0086)+EMX2OS∗(-0.0101)+LINC00173∗(0.0309)+PWAR5∗(-0.0146)+SNHG1∗(0.0043). The signature showed a better predictive ability than other clinical indicators, including tumor node metastasis classification (TNM), histological, and pathological stages. In the high-risk group, M0 macrophages, CD8+ T cells, and regulatory T cells had significantly higher scores. Contrarily, in the low-risk group, activated dendritic cells, M1 macrophages, mast resting cells, and monocytes had significantly higher scores. In the high-risk group, LSECtin was overexpressed. In the low-risk group, PD-L1 was overexpressed. Moreover, high-risk patients may benefit more from AZ628.ConclusionsIn conclusion, prognosis prediction of patients with KIRC and new insights for immunotherapy are provided by the m6A-related lncRNA prognostic signature.

Project description:In recent years, methylation modification regulators have been found to have essential roles in various tumor mechanisms. However, the relationships between N6-methyladenosine (m6A) and 5-methylcytosine (m5C) regulators and clear cell renal cell carcinoma (ccRCC) remain unknown. This study investigated these relationships using the data from The Cancer Genome Atlas database. We calculated risk scores using a Lasso regression analysis and divided the patient samples into two risk groups (tumor vs. normal tissues). Furthermore, we used univariate and multivariate Cox analyses to determine independent prognostic indicators and explore correlations between the regulatory factors and immune infiltrating cell characteristics. Finally, quantitative reverse transcriptase-polymerase chain reaction (PCR) and The Human Protein Atlas were used to verify signature-related gene expression in clinical samples. We identified expression differences in 35 regulatory factors between the tumor and normal tissue groups. Next, we constructed a five-gene risk score signature (NOP2 nucleolar protein [NOP2], methyltransferase 14, N6-adenosine-methyltransferase subunit [METTL14], NOP2/Sun RNA methyltransferase 5 [NSUN5], heterogeneous nuclear ribonucleoprotein A2/B1 [HNRNPA2B1], and zinc finger CCCH-type containing 13 [ZC3H13]) using the screening criteria (p < 0.01), and then divided the cases into high- and low-risk groups based on their median risk score. We also screened for independent prognostic factors related to age, tumor grade, and risk score. Furthermore, we constructed a Norman diagram prognostic model by combining two clinicopathological characteristics, which demonstrated good prediction efficiency with prognostic markers. Then, we used a single-sample gene set enrichment analysis and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method to evaluate the tumor microenvironment of the regulatory factor prognostic characteristics. Moreover, we evaluated five risk subgroups with different genetic signatures for personalized prognoses. Finally, we analyzed the immunotherapy and immune infiltration response and demonstrated that the high-risk group was more sensitive to immunotherapy than the low-risk group. The PCR results showed that NSUN5 and HNRNPA2B1 expression was higher in tumor tissues than in normal tissues. In conclusion, we identified five m6A and m5C regulatory factors that might be promising biomarkers for future research.

Project description:BackgroundPatients with advanced clear cell renal cell carcinoma (ccRCC) have a poor prognosis and lack effective prognostic biomarkers. N6-methyladenosine-related lncRNAs (m6A-related long noncoding RNAs [lncRNAs]) have been confirmed to be associated with the development of multiple tumors, but its role in ccRCC is not clear.MethodsGene expression data and clinical information of ccRCC patients were extracted from The Cancer Genome Atlas Database. The prognostic m6A-related lncRNAs were obtained by Pearson's correlation analysis and univariate Cox regression analysis. Afterward, the cluster classification and its correlation with prognosis, clinical characteristics, and immunity were analyzed. LASSO regression was used to establish the prognostic risk model. The predictive performance of the prognostic model was evaluated and validated by survival analysis and receiver operating characteristic curve analysis, et al. The expression of immune checkpoints and immune cell infiltration in patients with different risks were systematically analyzed.ResultsA total of 27 prognostic m6A-related lncRNAs were identified. These m6A-related lncRNAs were differentially expressed between tumor and normal tissues. Among them, 24 high-risk m6A-related lncRNAs were overexpressed in Cluster 2 and correlated with poor prognosis, low stromal score, high expression of immune checkpoints, and immunosuppressive cells infiltration. Based upon, a prognostic risk model composed of seven m6A-related lncRNAs was constructed. After a series of analyses, it was proved that this model had good sensitivity and specificity, and could predict the prognosis of patients with different clinical stratification. The expression of PD-1, PD-L1, CTLA-4, LAG-3, TIM-3, and TIGIT were significantly increased in the high-risk patients, and there was a correlation between the risk score and immune cell infiltration.ConclusionsThe seven m6A-related lncRNAs prognostic risk signature showed reliable prognostic predictive power for ccRCC and was associated with the expression of immune checkpoints and immune cell infiltration. This seven m6A-related lncRNAs signature will be helpful in managing ccRCC and guiding individualized immunotherapy.

Dataset Information

Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N⁶-Methyladenosine RNA Methylation Regulators.

Publications

Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N<sup>6</sup>-Methyladenosine RNA Methylation Regulators.

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