Identification of N6-Methyladenosine-Related LncRNAs for Predicting Overall Survival and Clustering of a Potentially Novel Molecular Subtype of Breast Cancer
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SUBMITTER: Zhong X
PROVIDER: S-EPMC8554333 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveGastric cancer (GC) is one of the most prevalent malignant tumors in Asian countries. Studies have proposed that lncRNAs can be used as diagnostic and prognostic indicators of GC due to the high specificity of lncRNAs expression involvement in GC. Recently, N6-methyladenosine (m6A) has also emerged as an important modulator of the expression of lncRNAs in GC. This study aimed at establishing a novel m6A-related lncRNAs prognostic signature that can be used to construct accurate models for predicting the prognosis of GC in the Asian population.MethodsFirst, the levels of m6A modification and m6A methyltransferases expression in GC samples were determined using dot blot and western blot analyses. Next, we evaluated the lncRNAs expression profiles and the corresponding clinical data of 88 Asian GC patients retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of m6A-related lncRNAs between GC and normal tissues was investigated. The relationship between these target lncRNAs and potential immunotherapeutic signatures was also analyzed. Gene set enrichment analysis (GSEA) was performed to identify the malignancy-associated pathways. Univariate Cox regression, LASSO regression, and multivariate Cox regression analyses were performed to establish a novel prognostic m6A-related lncRNAs prognostic signature. Moreover, we constructed a predictive nomogram and determined the expression levels of nine m6A-related lncRNAs in 12 pairs of clinical samples.ResultsWe found that m6A methylation levels were significantly increased in GC tumor samples compared to adjacent normal tissues, and the increase was positively correlated with tumor stage. Patients were then divided into two clusters (cluster 1 and cluster 2) based on the differential expression of the m6A-related lncRNAs. Results showed that there was a significant difference in survival probability between the two clusters (p = 0.018). Notably, the low survival rate in cluster 2 may be associated with high expression of immune cells (resting memory CD4+ T cells, p = 0.027; regulatory T cells, p = 0.0018; monocytes, p = 0.00095; and resting dendritic cells, p = 0.015), and low expression of immune cells (resting NK cells, p = 0.033; and macrophages M1, p = 0.045). Enrichment analysis indicated that malignancy-associated biological processes were more common in the cluster 2 subgroup. Finally, the risk model comprising of six m6A-related lncRNAs was identified as an independent predictor of prognoses, which could divide patients into high- or low-risk groups. Time-dependent ROC analysis suggested that the risk score could accurately predict the prognosis of GC patients. Patients in the high-risk group had worse outcomes compared to patients in the low-risk group, and the risk score showed a positive correlation with immune cells (resting memory CD4+ T cells, R = 0.31, P = 0.038; regulatory T cells, R = 0.42, P = 0.0042; monocytes, R = 0.42, P = 0.0043). However, M1 macrophages (R = -0.37, P = 0.012) and resting NK cells (R = -0.31, P = 0.043) had a negative correlation with risk scores. Furthermore, analysis of clinical samples validated the weak positive correlation between the risk score and tumor stage.ConclusionsThe risk model described here, based on the six m6A-related lncRNAs signature, and may predict the clinical prognoses and immunotherapeutic response in Asian GC patients.
| S-EPMC9248105 | biostudies-literature
Project description:Background: N6-methyladenosine (m6A) is the most common form of mRNA- and long noncoding RNA (lncRNA)-specific internal modification encountered in eukaryotes, with important effects on mRNA stability, translation, and splicing. The role of m6A-modified lncRNAs (m6A-lncRNAs) in bladder cancer (BLCA) is rarely reported. This study aimed to evaluate an efficient prognostic model of BLCA in patients, based on m6A-lncRNAs, and to discover potential biological targets. Methods: Differentially expressed lncRNAs were investigated in 433 BLCA samples derived from The Cancer Genome Atlas (TCGA) database. Kaplan–Meier and univariate Cox regression analyses were performed to screen for m6A-lncRNAs with prognostic roles in BLCA. We implemented Pearson correlation analysis to analyze 18 potentially prognostic lncRNAs and 20 known m6A-associated genes. Next, the data were imputed using least absolute shrinkage and selection operator (LASSO) Cox regression to establish an m6A-lncRNA prognostic signature. Results: We established an integrated risk score (RS) containing five m6A-lncRNAs and constructed a nomogram that had the ability to forecast the overall survival (OS) of patients with BLCA. We showed that the predictive accuracy of the RS for BLCA prognosis was high, which was confirmed by the area under the receiver operating characteristic (ROC) curve. We analyzed the correlation between tumor immune infiltrating cells and RS in high- and low-risk patients with BLCA and used tumor immune dysfunction and exclusion to predict the effect of immunotherapy. We screened out the most relevant modules of RS through the weighted gene co-expression network analysis network and explored their potential biological functions using GO and KEGG analyses. Conclusion: Our findings demonstrate that, compared with nomograms constructed using a single prognostic factor, the integrated RS represents a superior model for predicting survival in patients with BLCA, which may improve the clinical management of BLCA.
| S-EPMC9428265 | biostudies-literature
Project description:The prognostic value of N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) was investigated in 646 lower-grade glioma (LGG) samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, and then univariate Cox regression analysis was performed to screen their prognostic roles in LGG patients. Twenty-four prognostic m6A-related lncRNAs were identified as prognostic lncRNAs and they were inputted in a least absolute shrinkage and selection operator (LASSO) Cox regression to establish a m6A-related lncRNA prognostic signature (m6A-LPS, including 9 m6A-related prognostic lncRNAs) in the TCGA dataset. Corresponding risk scores of patients were calculated and divided LGG patients into low- and high-risk subgroups by the median value of risk scores in each dataset. The m6A-LPS was validated in the CGGA dataset and it showed a robust prognostic ability in the stratification analysis. Principal component analysis showed that the low- and high-risk subgroups had distinct m6A status. Enrichment analysis indicated that malignancy-associated biological processes, pathways and hallmarks were more common in the high-risk subgroup. Moreover, we constructed a nomogram (based on m6A-LPS, age and World Health Organization grade) that had a strong ability to forecast the overall survival (OS) of the LGG patients in both datasets. We also establish a competing endogenous RNA (ceRNA) network based on seven of the twenty-four m6A-related lncRNAs. Besides, we also detected five m6A-related lncRNA expression levels in 22 clinical samples using quantitative real-time polymerase chain reaction assay.
| S-EPMC7390977 | biostudies-literature
Project description:N6-methyladenosine (m6A) modification has been demonstrated to exhibit a crucial prognostic effect on colorectal cancer (CRC). Nonetheless, potential mechanism of m6A in survival rate and immunotherapeutic response remains unknown. Here we investigated the genes associated with m6A regulators and developed a risk score for predicting the overall survival (OS) of CRC patients. RNA-seq transcriptomic profiling data of COAD/READ samples were obtained from The Cancer Genome Atlas (TCGA) database. Absolute Shrinkage and Selection Operator (LASSO)- Cox regression analysis was conducted to identify the m6A-related gene expression signatures and the selected genes were inputted into stepwise regression to develop a prognostic risk score in TCGA, and its predictive performance of CRC survival was further validated in Gene Expression Omnibus (GEO) datasets. According to our results, the risk score comprising 18 m6A-related mRNAs was significantly associated with CRC survival in both TCGA and GEO datasets. And the stratified analysis also confirmed that high-risk score acted as a poor factor in different age, sex, T stage, and tumour, node, metastasis (TNM) stages. The m6A-related prognostic score in combination with clinical characteristics yielded time-dependent area under the receiver operating characteristic curve (AUCs) of 0.85 (95%CI: 0.79-0.91), 0.84 (95%CI: 0.79-0.90) and 0.80 (95%CI: 0.71-0.88) for the prediction of the 1-, 3-, 5-year OS of CRC in TCGA cohort. Furthermore, mutation of oncogenes occurred more frequently in the high-risk group and the composition of immune cells in tumour microenvironment (TME) was significantly distinct between the low- and high-risk groups. The low-risk group had a lower microsatellite instability (MSI) score, T-cell exclusion score and dysfunction score, implying that low-risk patients may have a better immunotherapy response than high-risk patients. In summary, a prognostic risk score derived from m6A-related gene expression signatures could serve as a potential prognostic predictor for CRC survival and indicator for predicting immunotherapy response in CRC patients.
| S-EPMC10020527 | biostudies-literature
Project description:The N6-methyladenosine (m6A) RNA modification can modify long non-coding RNAs (lncRNAs), thereby affecting the tumorigenesis and progression of tumors. However, the underlying role of m6A-modified lncRNAs in colorectal cancer (CRC) remains largely unknown. Therefore, our aim was to assess the prognostic value of m6A-modified lncRNAs in CRC patients. The gene expression and clinicopathological data of CRC were extracted from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was used to investigate the m6A-modified lncRNAs. Consensus clustering was conducted to identify molecular subtypes of CRC, and the clinical significance of molecular subtypes was identified. The least absolute shrinkage and selection operator analysis (LASSO) was applied to establish a risk signature. Finally, a prognostic nomogram with risk score and clinicopathological variables was established. In total, 29 m6A-modified lncRNAs were identified as prognostic lncRNAs. Two molecular clusters were identified and significant differences were found with respect to clinicopathological features and prognosis. Cluster1 is associated with poor overall survival (OS), down-regulation of Programmed cell death ligand-1 (PD-L1) expression, lower immune score, and less immune cell infiltration. Then, an m6A-modified lncRNA signature for predicting OS was constructed in the TCGA training cohort. The signature demonstrated favorable prediction performance in both training and validation sets. Compared with low-risk patients, patients with high risk showed worse clinical outcomes, lower immune scores, and downregulated PD-L1 expression. Further analysis indicated that the signature was an independent prognostic indicator, and then a prognostic nomogram based on risk score, tumor location, and tumor stage was established. Our study identified a seven m6A-modified lncRNA signature and established a prognostic nomogram that reliably predicts OS in CRC. These findings may improve the understanding of m6A modifications in CRC and provide insights into the prognosis and treatment strategy of CRC.
| S-EPMC8472391 | biostudies-literature
Project description:BackgroundThe connection between m6A-assiociateed lncRNAs and prognosis has been demonstrated in multiple types of tumors. However, potential roles of m6A-assiociateed lncRNAs in glioma is still rare.MethodsWe implemented consensus cluster analysis to group the downloaded samples into two subtypes. The least absolute shrinkage and selection operator (LASSO) analysis was used to create a risk model. Additionally, the conjunction between m6A-related lncRNAs and immune cells infiltration was explored by conducting the R package. Ultimately, we inspected the underlying downstream pathways of the two subtypes by performing Gene Set Enrichment Analysis (GSEA). The expression level of m6A-connected lncRNAs in glioma were examined by conducting in vitro experiments.ResultsWe ascertained two subtypes of glioma in line with the consensus clustering of m6A-associated lncRNAs. We confirmed that age, grade, and IDH are related to the two subtypes. Additionally, the immune cells infiltration and immune checkpoint molecules of the two clusters were discussed. A risk signature including AL359643.3, AL445524.1, AL162231.2, AL117332.1, AP001486.2, POLR2J4, AC120036.4, LINC00641, LINC00900, CRNDE, and AL158212.3, was identified using the Cox regression and LASSO analyses. We also verified the prognostic value and discussed the immune cells infiltration and immune checkpoint molecules of the risk signature. In vitro experiments verified that the m6A-associated lncRNAs was abnormally expressed in glioma.ConclusionWe elaborated the significant role of m6A-connected lncRNAs in glioma prognosis and immune infiltration and suggest that these key regulators may serve as underlying therapeutic targets to build up the efficacy of glioma immunotherapy.
| S-EPMC10170299 | biostudies-literature
Project description:BackgroundWith a lack of specific symptoms, ovarian cancer (OV) is often diagnosed at an advanced stage. This coupled with inadequate prognostic indicators and treatments with limited therapeutic effect make OV the deadliest type of gynecological tumor. Recent research indicates that N6-methyladenosine (m6A) and long-chain non-coding RNA (lncRNA) play important roles in the prognosis of OV and the efficacy of immunotherapy.ResultsUsing the Cancer Genome Atlas (TCGA) OV-related data set and the expression profiles of 21 m6A-related genes, we identified two m6A subtypes, and the differentially expressed genes between the two. Based on the differentially expressed lncRNAs in the two m6A subtypes and the lncRNAs co-expressed with the 21 m6A-related genes, single-factor cox and LASSO regression were used to further isolate the 13 major lncRNAs. Finally, multi-factor cox regression was used to construct a m6A-related lncRNA risk score model for OV, with good performance in patient prognosis. Using risk score, OV tumor samples are divided into with high- and low-score groups. We explored the differences in clinical characteristics, tumor mutational burden, and tumor immune cell infiltration between the two groups, and evaluated the risk score's ability to predict the benefit of immunotherapy.ConclusionOur m6A-based lncRNA risk model could be used to predict the prognosis and immunotherapy response of future OV patients.
| S-EPMC8933961 | biostudies-literature
Project description:BackgroundSoft tissue sarcomas (STSs) are rare tumors and occur at any site in the body. Our goal was to identify a putative molecular mechanism for N6-methyladenosine (m6A) lncRNA alteration and to develop predictive biomarkers for sarcoma.MethodsThe lncRNA levels were obtained from TCGA datasets. Pearson correlation analysis was used to select all the lncRNAs that are connected to m6A. An m6A-related lncRNA model was built using LASSO Cox regression. To assess the prognostic efficiency of the model and potential lncRNAs, we performed univariate survival analysis and receiver operating characteristic (ROC) analysis. We also performed enrichment analysis to evaluate the roles of the potential genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to confirm m6A-related lncRNA expression in tissues.ResultsFollowing Pearson correlation analysis on TCGA datasets, we identified 78 m6A-related lncRNAs. Next, we used LASSO Cox regression analysis and identified 13 m6A-related lncRNAs as prognostic lncRNAs. After calculating risk scores, sarcoma patients were divided into high- and low-risk groups depending on the median of risk scores. We also found that these lncRNAs were immune associated via enrichment analysis.ConclusionsHere, we found that SNHG1, FIRRE, and YEATS2-AS1 could serve as biomarkers to predict overall survival of sarcoma patients, which provides a new insight into treatment of STS.
| S-EPMC9166981 | biostudies-literature
Project description:BackgroundSeveral recent studies have confirmed epigenetic regulation of the immune response. However, the potential role of RNA N6-methyladenosine (m6A) modifications in cervical cancer and tumour microenvironment (TME) cell infiltration remain unclear.ResultsWe evaluated and analysed m6A modification patterns in 307 cervical cancer samples from The Cancer Genome Atlas (TCGA) dataset based on 13 m6A regulators. Pearson correlation analysis was used to identify lncRNAs associated with m6A, followed by univariate Cox regression analysis to screen their prognostic role in cervical cancer patients. We also correlated TME cell infiltration characteristics with modification patterns. We screened six m6A-associated lncRNAs as prognostic lncRNAs and established the prognostic profile of m6A-associated lncRNAs by least absolute shrinkage and choice of operator (LASSO) Cox regression. The corresponding risk scores of the patients were derived based on their prognostic features, and the correlation between this feature model and disease prognosis was analysed. The prognostic model constructed based on the TCGA-CESC (The Cancer Genome Cervical squamous cell carcinoma and endocervical adenocarcinoma) dataset showed strong prognostic power in the stratified analysis and was confirmed as an independent prognostic indicator for predicting the overall survival of patients with CESC. Enrichment analysis showed that biological processes, pathways, and markers associated with malignancy were more common in the high-risk subgroup. Risk scores were strongly correlated with the tumour grade. ECM receptor interactions and pathways in cancer were enriched in Cluster 2, while oxidative phosphorylation and other biological processes were enriched in Cluster 1. The expression of immune checkpoint molecules, including programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1), was significantly increased in the high-risk subgroup, suggesting that this prognostic model could be a predictor of immunotherapy.ConclusionsThis study reveals that m6A modifications play an integral role in the diversity and complexity of TME formation. Assessing the m6A modification patterns of individual tumours will help improve our understanding of TME infiltration characteristics and thus guide immunotherapy more effectively. We also developed an independent prognostic model based on m6A-associated lncRNAs as a predictor of overall survival, which can also be used as a predictor of immunotherapy.
| S-EPMC8767716 | biostudies-literature
Project description:Accumulating evidence indicates the role of N 6-methyladenosine (m6A) regulator-mediated RNA methylation in cancer progression and metastasis; yet its potential clinical significance, if any, remains unclear. In this first-of-its-kind study, we systematically evaluated the role of m6A regulators as potential disease biomarkers based on comprehensive analysis of gene expression profiles of 9770 cancer cell lines and clinical specimens from 25 publicly available datasets, encompassing 13 human cancers. We developed and established RNAMethyPro-a gene expression signature of seven m6A regulators, which robustly predicted patient survival in multiple human cancers. Pan-cancer analysis identified activated epithelial-mesenchymal transition (EMT), as a highly conserved pathway in high-risk patients predicted by RNAMethyPro in 10 of the 13 cancer types. A network-based analysis revealed an intimate functional interplay between m6A regulators and EMT-associated factors via druggable targets such as XPO1 and NTRK1. Finally, the clinical significance of RNAMethyPro was further exemplified in colorectal cancer, where high-risk patients demonstrated strong associations with a mesenchymal subtype, activated stromal infiltration, and poor therapeutic response to targeted anti-EGFR therapy. In summary, RNAMethyPro is a novel, EMT-associated prognostic gene-expression signature in multiple human cancers and may offer an important clinical decision-making tool in the future.
| S-EPMC6494854 | biostudies-literature