Development and validation of m6A RNA methylation regulators-based signature in lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: Guo W
PROVIDER: S-EPMC8440000 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAdrenocortical carcinoma (ACC) is an aggressive and rare neoplasm that originates from the cortex of the adrenal gland. N6-methyladenosine (m6A) RNA methylation, the most common form of mRNA modification, has been reported to be correlated with the occurrence and development of the malignant tumor. This study aims to identify the significance of m6A RNA methylation regulators in ACC and construct a m6A based signature to predict the prognosis of ACC patients.Materials and methodsRNA-seq data from The Cancer Genome Atlas (TCGA) database was used to identify the expression level of m6A RNA methylation regulators in ACC. An m6A based signature was further constructed and its prognostic and predictive values were assessed by survival analysis and nomogram.Results11 m6A RNA regulators were differentially expressed in ACC and three m6A RNA regulators were finally selected in a signature to predict the prognosis of ACC patients. Survival analysis indicated that high risk scores were closely related to poor survival outcomes in ACC patients. Univariate and multivariate Cox regression analyses demonstrated that the m6A based signature was an independent prognostic factor for ACC patients. A nomogram with clinical factors and the m6A based signature was also constructed to superiorly predict the prognosis of ACC patients. The expression levels of m6A RNA methylation regulators, which were contained in the signature, were also verified in human ACC tissues and normal tissues by using vitro experiments.ConclusionWe identified and validated an m6A based signature, which can be used as an independent prognostic factor in evaluating the prognosis of ACC patients. Further clinical trials and experimental explorations are needed to confirm our observations and mechanisms underlying prognostic values of these m6A RNA methylation regulators in ACC.
| S-EPMC7937949 | biostudies-literature
Project description:Purpose:N6-methyladenosine (m6A) is reported to play a critical role in cancer through various mechanisms. We aimed to construct and validate an m6A RNA methylation regulators-based prognostic signature for Esophageal cancer (ESCA). Materials and Methods:The RNA sequencing transcriptome data of 13 m6A RNA methylation regulators as well as clinical data were obtained from The Cancer Genome Atlas (TCGA) ESCA database. The differential expression of the regulators between ESCA tissues and normal tissues was assessed. Consensus clustering was conducted to explore the different ESCA clusters based on the expression of these regulators. LASSO Cox regression analysis was used to generate a prognostic signature based on m6A RNA methylation regulators expression. Results:Eight regulators (KIAA1429, HNRNPC, RBM15, METTL3, WTAP, YTHDF1, YTHDC1, and YTHDF2) were found to be significantly upregulated in ESCA tissues. Significant differences of survival rate and clinicopathological features were found between the two clusters. A prognostic signature, which consists of HNRNPC and ALKBH5, was constructed based on the TCGA ESCA cohort, which can serve as an independent prognostic predictor. The results of bioinformatics analysis were further successfully validated in the clinical ESCA cohort by qRT-PCR and immunohistochemistry staining. Conclusion:Our study constructed and validated an m6A RNA methylation regulators-based prognostic signature. This might provide important information for developing diagnostic and therapeutic strategies.
| S-EPMC7352013 | biostudies-literature
Project description:N6-methyladenosine (m6A) RNA modification is the most abundant modification method in mRNA, and it plays an important role in the occurrence and development of many cancers. This paper mainly discusses the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) to identify novel prognostic biomarkers. The gene expression data of 19 m6A methylation regulators in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. We selected three significantly differentially expressed m6A regulators in LUAD to construct the risk signature, and evaluated its prognostic prediction efficiency using the receiver operating characteristic (ROC) curve. Kaplan-Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of the risk signature. The ROC curve indicated that the area under the curve (AUC) was 0.659, which means that the risk signature had a good prediction efficiency. The results of the Kaplan-Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD. In addition, we explored the differential signaling pathways and cellular processes related to m6A methylation regulators in LUAD.
| S-EPMC8306618 | biostudies-literature
Project description:Existing studies suggest that m6A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m6A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six GEO data sets as external validation cohorts. The first model (mRNA model) was developed based on m6A-related mRNA. LASSO and stepwise regression were used to screen genes and the prognostic model was developed from multivariate Cox regression model. The second model (lncRNA model) was constructed based on m6A related lncRNAs. The four steps of random survival forest, LASSO, best subset selection and stepwise regression were used to screen genes and develop a Cox regression prognostic model. The third model combined the risk scores of the first two models with clinical variable. Variables were screened by stepwise regression. The mRNA model included 11 predictors. The internal validation C index was 0.736. The lncRNA model has 15 predictors. The internal validation C index was 0.707. The third model combined the risk scores of the first two models with tumor stage. The internal validation C index was 0.794. In validation sets, all C-indexes of models were about 0.6, and three models had good calibration accuracy. Freely online calculator on the web at https://lhj0520.shinyapps.io/LUAD_prediction_model/.
| S-EPMC9243308 | biostudies-literature
Project description:Background: Cervical squamous cell carcinoma (CESC) is one of the most common causes of cancer-related death worldwide. N6-methyladenosine (m6A) plays an important role in various cellular responses by regulating mRNA biology. This study aimed to develop and validate an m6A RNA methylation regulator-based signature for prognostic prediction in CESC. Methods: Clinical and survival data as well as RNA sequencing data of 13 m6A RNA methylation regulators were obtained from The Cancer Genome Atlas (TCGA) CESC database. Consensus clustering was performed to identify different CESC clusters based on the differential expression of the regulators. LASSO Cox regression analysis was used to generate a prognostic signature based on m6A RNA methylation regulator expression. The effect of the signature was further explored by univariate and multivariate Cox analyses. Results: Four regulators (RBM15, METTL3, FTO, and YTHDF2) were identified to be aberrantly expressed in CESC tissues. A prognostic signature that includes ZC3H13, YTHDC1, and YTHDF1 was developed, which can act as an independent prognostic indicator. Significant differences of survival rate and clinicopathological features were found between the high- and low-risk groups. The results of bioinformatics analysis were then validated in the clinical CESC cohort by qRT-PCR and immunohistochemistry staining. Conclusion: In the present study, we developed and validated an m6A RNA methylation regulator-based prognostic signature, which might provide useful insights regarding the development and prognosis of CESC.
| S-EPMC7472601 | biostudies-literature
Project description:Background:The mortality rate of clear cell renal cell carcinoma (ccRCC) remains high. The aim of this study was to identify novel prognostic biomarkers by using m6A RNA methylation regulators capable of improving the risk-stratification criteria of survival for ccRCC patients. Methods:The gene expression data of 16 m6A methylation regulators and its relevant clinical information were extracted from The Cancer Genome Atlas (TCGA) database. The expression pattern of these m6A methylation regulators were evaluated. Consensus clustering analysis was conducted to identify clusters of ccRCC patients with different prognosis. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were performed to construct multiple-gene risk signature. A survival analysis was carried out to determine the independent prognostic significance of the signature. Results:Five m6A-related genes (ZC3H13, METTL14, YTHDF2, YTHDF3 and HNRNPA2B1) showed significantly downregulated in tumor tissue, while seven regulators (YTHDC2, FTO, WTAP, METTL3, ALKBH5, RBM15 and KIAA1429) was remarkably upregulated in ccRCC. Consensus clustering analysis identified two clusters of ccRCC with significant differences in overall survival (OS) and tumor stage between them. We also constructed a two-gene signature, METTL3 and METTL14, serving as an independent prognostic indicator for distinguishing ccRCC patients with different prognosis both in training, validation and our own clinical datasets. The receiver operator characteristic (ROC) curve indicated the area under the curve (AUC) in these three datasets were 0.721, 0.684 and 0.828, respectively, demonstrated that the prognostic signature had a good prediction efficiency. Conclusions:m6A methylation regulators exert as potential biomarkers for prognostic stratification of ccRCC patients and may assist clinicians achieving individualized treatment for this patient population.
| S-EPMC7206820 | biostudies-literature
Project description:Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n?=?255) and validation (n?=?157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n?=?67; HR?=?2.05, 95% CI?=?1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR?=?3.05, 95% CI?=?2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR?=?2.78, 95% CI?=?1.91 to 11.13), EGFR wild-type (HR?=?3.01, 95% CI?=?1.73 to 14.98), and EGFR mutant (HR?=?8.99, 95% CI?=?62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.
| S-EPMC5051943 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky.MethodsThe gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model.ResultConsensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094).ConclusionOur study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.
| S-EPMC8379743 | biostudies-literature
Project description:PurposeN6-methyladenosine (m6A) modifications represent one of the most common methylation modifications, and they are mediated by m6A RNA methylation regulators. However, their functions in renal cell carcinoma (RCC) are not completely understood. The aim of this study was to investigate the effects of the regulators in RCC.Materials and methodsThe expression levels of the 13 main m6A RNA methylation regulators in RCC were detected and consensus clustering was performed to explore their relationships with RCC. Thereafter, a risk signature based on the regulators was established. This risk model was fully verified by conducting prognostic analyses using two datasets (The Cancer Genome Atlas [TCGA] and Gene Expression Omnibus [GEO] datasets) and a ROC curve analysis.ResultsOf the 13 main m6A regulators, six were significantly upregulated and four were significantly downregulated in 893 RCC cases compared to 128 normal controls in the TCGA database. Consensus clustering based on the regulators identified two clusters of RCC cases, which were significantly associated with a pathological characteristic (T status). Thus, these results indicated that m6A RNA methylation regulators were associated with RCC. Thereafter, a risk model involving two of the regulators (METTL14 and WTAP) was established. The alterations in the mRNA and protein expression levels of these two regulators were further confirmed based on Human Protein Atlas data and real-time PCR in RCC and normal cell lines. The results indicated that the risk model may serve as an independent prognostic marker of overall survival, and it was also associated with clinicopathological characteristics (T status, M status, pathological stage, and gender) in RCC.ConclusionCollectively, the results of this study indicated that the risk model (based on two m6A RNA methylation regulators) may serve as an independent prognostic indicator of RCC, which may aid further investigation into m6A RNA modification in RCC.
| S-EPMC7814279 | biostudies-literature
Project description:BackgroundThe modification 6-methyladenine (m6 A) is the most common type in RNA methylation. Our study aims to explore the bioinformatic analysis of m6 A in endometrial cancer.MethodsThe expression of 23 m6 A RNA methylation regulators was compared through The Cancer Genome Atlas (TCGA) database among 406 endometrial tissue and 19 normal tissue samples. The Wilcoxon test was applied to compare the relationship between the clinicopathological characteristics and expression. Cox regressions were performed to identify the prognostic factors associated with overall survival. Gene ontology (GO) and Gene Set Enrichment Analysis (GSEA) were performed to evaluate the potential pathways.ResultsYTHDF2, HNRNPA2B1, HDRNPA2B1, YTHDF1, FMR1, IGF2BP3, METTL13, RBM15B, IGF2BP1, YTHDF3, YTHDC1, ZC3H13 IGF2BP2, KIAA1429, METTL14, RBMX, FTO, ALKBH5, and METTL16 were significantly abnormally expressed in endometrial cancer tissue samples. Both univariate and multivariate Cox regression analyses indicated that age, grade, and risk score were independent risk factors. High expression of FTO was associated with worse overall survival.ConclusionM6 A RNA methylation regulators play vital role in endometrial cancer.
| S-EPMC8418492 | biostudies-literature