Dataset Information

Identification of m6A-Related lncRNAs Associated With Prognoses and Immune Responses in Acute Myeloid Leukemia.

ABSTRACT: Background: Acute myeloid leukemia (AML) remains the most common type of hematopoietic malignancy in adults and has an unfavorable outcome. Herein, we aimed to construct an N6-methylandenosine (m6A)-related long noncoding RNAs (lncRNAs) signature to accurately predict the prognosis of patients with AML using the data downloaded from The Cancer Genome Atlas (TCGA) database. Methods: The RNA-seq and clinical data were obtained from the TCGA AML cohort. First, Pearson correlation analysis was performed to identify the m6A-related lncRNAs. Next, univariate Cox regression analysis was used to determine the candidate lncRNAs with prognostic value. Then, feature selection was carried out by Least absolute shrinkage and selection operator (LASSO) analysis, and seven eligible m6A-related lncRNAs were included to construct the prognostic risk signature. Kaplan-Meier and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive capacity of the risk signature both in the training and testing datasets. A nomogram was used to predict 1-year, 2-year, and 3-year overall survival (OS) of AML patients. Next, the expression levels of lncRNAs in the signature were validated in AML samples by qRT-PCR. Functional enrichment analyses were carried out to identify probable biological processes and cellular pathways. The ceRNA network was developed to explore the downstream targets and mechanisms of m6A-related lncRNAs in AML. Results: Seven m6A-related lncRNAs were identified as a prognostic signature. The low-risk group hold significantly prolonged OS. The nomogram showed excellent accuracy of the signature for predicting 1-year, 2-year and 3-year OS (AUC = 0.769, 0.820, and 0.800, respectively). Moreover, the risk scores were significantly correlated with enrichment in cancer hallmark- and malignancy-related pathways and immunotherapy response in AML patients. Conclusion: We developed and validated a novel risk signature with m6A-related lncRNAs which could predict prognosis accurately and reflect the immunotherapy response in AML patients.

SUBMITTER: Li D

PROVIDER: S-EPMC8637120 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAcute myeloid leukemia (AML) is a highly heterogeneous cluster of hematologic malignancies. Leukemic stem cells (LSCs) are one of the culprits for the persistence and relapse of AML. The discovery of copper-induced cell death, namely cuproptosis, gives bright insights into the treatment of AML. Analogous to copper ions, long non-coding RNAs (lncRNAs) are not bystanders for AML progression, especially for LSC physiology. Uncovering the involvement of cuproptosis-related lncRNAs in AML will benefit clinical management.MethodsDetection of prognostic relevant cuproptosis-related lncRNAs are carried out by Pearson correlation analysis and univariate Cox analysis with RNA sequencing data of The Cancer Genome Atlas-Acute Myeloid Leukemia (TCGA-LAML) cohort. After the least absolute shrinkage and selection operator (LASSO) regression and the subsequent multivariate Cox analysis, a cuproptosis-related risk score (CuRS) system was derived to weigh the risk of AML patients. Thereafter, AML patients were classified into two groups by their risk property which was validated with principal component analysis (PCA), risk curves, Kaplan-Meier survival analysis, the combined receiver operating characteristic (ROC) curves, and nomogram. Variations in biological pathways and divergences in immune infiltration and immune-related processes between groups were resolved by GSEA and CIBERSORT algorism, respectively. Response to chemotherapies were scrutinized as well. The expression profiles of the candidate lncRNAs were examined by real-time quantitative polymerase chain reaction (RT-qPCR) and the specific mechanisms of lncRNA FAM30A were determined by transcriptomic analysis.ResultsWe fabricated an efficient prognostic signature named CuRS incorporating 4 lncRNAs (TRAF3IP2-AS1, NBR2, TP53TG1, and FAM30A) relevant to immune environment and chemotherapy responsiveness. The relevance of lncRNA FAM30A with proliferation, migration ability, Daunorubicin resistance and its reciprocal action with AUF1 were demonstrated in an LSC cell line. Transcriptomic analysis suggested correlations between FAM30A and T cell differentiation and signaling, intercellular junction genes.ConclusionsThe prognostic signature CuRS can guide prognostic stratification and personalized AML therapy. Analysis of FAM30A offers a foundation for investigating LSC-targeted therapies.

Project description:Background: Acute myelocytic leukemia (AML) is one of the hematopoietic cancers with an unfavorable prognosis. However, the prognostic value of N 6-methyladenosine-associated long non-coding RNAs (lncRNAs) in AML remains elusive. Materials and Methods: The transcriptomic data of m6A-related lncRNAs were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. AML samples were classified into various subgroups according to the expression of m6A-related lncRNAs. The differences in terms of biological function, tumor immune microenvironment, copy number variation (CNV), and drug sensitivity in AML between distinct subgroups were investigated. Moreover, an m6A-related lncRNA prognostic model was established to evaluate the prognosis of AML patients. Results: Nine prognosis-related m6A-associated lncRNAs were selected to construct a prognosis model. The accuracy of the model was further determined by the Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curve. Then, AML samples were classified into high- and low-risk groups according to the median value of risk scores. Gene set enrichment analysis (GSEA) demonstrated that samples with higher risks were featured with aberrant immune-related biological processes and signaling pathways. Notably, the high-risk group was significantly correlated with an increased ImmuneScore and StromalScore, and distinct immune cell infiltration. In addition, we discovered that the high-risk group harbored higher IC50 values of multiple chemotherapeutics and small-molecule anticancer drugs, especially TW.37 and MG.132. In addition, a nomogram was depicted to assess the overall survival (OS) of AML patients. The model based on the median value of risk scores revealed reliable accuracy in predicting the prognosis and survival status. Conclusion: The present research has originated a prognostic risk model for AML according to the expression of prognostic m6A-related lncRNAs. Notably, the signature might also serve as a novel biomarker that could guide clinical applications, for example, selecting AML patients who could benefit from immunotherapy.

Project description:BackgroundThe current study probed prognosis-related potential for m6A-related lncRNAs signatures within colon tumor immune microenvironment (TIM).MethodsAfter downloading transcriptomic datasets for colon cancer (CC) patients from The Cancer Genome Atlas (TCGA), they were divided, in a 1:1 ratio, within training or test datasets. m6A-related lncRNAs were then scrutinized across such dataset using Pearson correlation assessment before generating a m6A-related lncRNAs prognosis-related model using the training dataset. The latter was then validated with the test and the whole dataset. In addition, we compared the differences of TIM and the estimated IC50 of drug response between the high- and low-risk groups.ResultsOverall survival (OS) resulted as linked with 11 m6A-related lncRNAs, while within the developed prognosis-related model, areas-under-curves were as follows: within training dataset, values at 3-, 4-, and 5-years were 0.777, 0.819, and 0.805, accordingly, and for test one, they were 0.697, 0.682, and 0.706, respectively. Finally, the values for the whole dataset were 0.675 (3-year), 0.682 (4-years), and 0.679 (5-years), accordingly. Moreover, CC cases categorized within low-risk cohort demonstrated enhanced OS (p < .0001), lower metastasis (p = 2e-06) and lower T stage (p = .0067), more instability for microsatellite status (p = .012), and downregulation for PD-L1, PD-1, CTLA-4, LAG3, and HAVCR2 (p < .05). In addition, risk scorings were significantly linked to the degree of infiltrative intensity for CD8 and CD4 (memory resting) T-cells, T-regulatory (Tregs), and Mast cells triggering (p < .05). Patients with low infiltrative propensity for CD4 T-cells also had better OS (p = .016). Moreover, six representative drugs were found to be sensitive for treating CC patients.ConclusionA robust m6A-related prognostic model with great performances was developed before exploring the TIM characteristics and its potential therapeutic drugs, which might improve the prognosis and therapeutic efficacy.

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.

Dataset Information

Identification of m6A-Related lncRNAs Associated With Prognoses and Immune Responses in Acute Myeloid Leukemia.

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