scholarly journals Identification of a Novel Ferroptosis-Related Gene Prognostic Signature in Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiale Sun ◽  
Wenchang Yue ◽  
Jiawei You ◽  
Xuedong Wei ◽  
Yuhua Huang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Experimental Studies ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Molecular Alteration

BackgroundFerroptosis is a newly found non-apoptotic forms of cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRG) in bladder cancer (BLCA) have not been well examined.MethodsFRG data and clinical information were collected from The Cancer Genome Atlas (TCGA). Then, significantly different FRGs were investigated by functional enrichment analyses. The prognostic FRG signature was identified by univariate cox regression and least absolute shrinkage and selection operator (LASSO) analysis, which was validated in TCGA cohort and Gene Expression Omnibus (GEO) cohort. Subsequently, the nomogram integrating risk scores and clinical parameters were established and evaluated. Additionally, Gene Set Enrichment Analyses (GSEA) was performed to explore the potential molecular mechanisms underlying our prognostic FRG signature. Finally, the expression of three key FRGs was verified in clinical specimens.ResultsThirty-two significantly different FRGs were identified from TCGA–BLCA cohort. Enrichment analyses showed that these genes were mainly related to the ferroptosis. Seven genes (TFRC, G6PD, SLC38A1, ZEB1, SCD, SRC, and PRDX6) were then identified to develop a prognostic signature. The Kaplan–Meier analysis confirmed the predictive value of the signature for overall survival (OS) in both TCGA and GEO cohort. A nomogram integrating age and risk scores was established and demonstrated high predictive accuracy, which was validated through calibration curves and receiver operating characteristic (ROC) curve [area under the curve (AUC) = 0.690]. GSEA showed that molecular alteration in the high- or low-risk group was closely associated with ferroptosis. Finally, experimental results confirmed the expression of SCD, SRC, and PRDX6 in BLCA.ConclusionHerein, we identified a novel FRG prognostic signature that maybe involved in BLCA. It showed high values in predicting OS, and targeting these FRGs may be an alternative for BLCA treatment. Further experimental studies are warranted to uncover the mechanisms that these FRGs mediate BLCA progression.

scholarly journals Prognostic Value and Underlying Mechanism of Autophagy-related Genes in Bladder Cancer

2021 ◽  
Author(s):  
Shiyuan Peng ◽  
Shanjin Ma ◽  
Fa Yang ◽  
Chao Xu ◽  
Hongji Li ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Present Model ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Therapeutic Targets ◽  
Underlying Mechanism ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Clinical Variables

Abstract Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure a better prognosis. However, the predictive accuracy of commonly used predictors, including patients’ general condition, histological grade, and pathological stage, is insufficient to identify the patients who need invasive treatment. Autophagy is regarded as a vital factor in maintaining mitochondrial function and energy homeostasis in cancer cells. Whether autophagy-related genes (ARGs) can predict the prognosis of BLCA patients deserves to be investigated. Based on BLCA data retrieved from the Cancer Genome Atlas (TCGA) and ARGs list obtained from the Human Autophagy Database (HADb) website, we identified prognosis-related differentially expressed ARGs (PDEARGs) through Wilcox text and constructed a PDEARGs-based prognostic model through multivariate Cox regression analysis. The predictive accuracy, independent forecasting capability, and the correlation between present model and clinical variables or tumor microenvironment (TME) were evaluated through R software. Enrichment analysis of PDEARGs was performed to explore the underlying mechanism, and a systematic prognostic signature with nomogram was constructed by integrating clinical variables and the aforementioned PDEARGs-based model. We found that the risk score generated by PDEARGs-based model could effectively reflect deteriorated clinical variables and tumor-promoting microenvironment. Additionally, several immune-related gene ontology (GO) terms were significantly enriched by PDEARGs, which might provide insights for present model and propose potential therapeutic targets for BLCA patients. Finally, a systematic prognostic signature with promoted clinical utility and predictive accuracy was constructed to assist clinician decision. PDEARGs are valuable prognostic predictors and potential therapeutic targets for BLCA patients.

scholarly journals The prediction of survival and analysis of microenvironment in patients with sarcoma

2020 ◽  
Author(s):  
Jiarui Chen ◽  
Chong Liu ◽  
Tuo Liang ◽  
Zide Zhang ◽  
Zhaojun Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Data ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Biomarkers

Abstract Background: Sarcomas were rare, aggressive, and heterogeneous group of tumors. The degree of tumor microenvironment cells, infiltrating immune cells and stromal cells in the tumor had an important impact on prognosis of sarcoma. The aim of this study was to identify the differentially expressed genes (DEGs) association with immune of sarcoma and potential prognostic immune biomarkers for predicting survival of sarcoma patients. Methods: The gene expression data and clinical data of sarcoma was downloaded from The Cancer Genome Atlas (TCGA) dataset. The immune scores and stromal scores were calculated by ESTIMATE algorithm. The limma package was used to identify the immune DEGs. ClusterProfiler package and STRING were further to analysis the immune DEGs. A prognostic signature was built based the immune gene and clinical data by univariate Cox regression analysis and multivariable Cox analysis. Finally, the prognostic signature was evaluated by functional assessment and the Gene Expression Omnibus (GEO) database. Results: The functional enrichment showed that the up immune DEGs was associated with immune cell activation, proliferation, and adhesion. A single prognostic signature was constructed with seven immune genes, and patient with high risk scores had a worse survival than those with low risk scores. Evaluating showed that the prognostic signature performed well and served as an independent factor in sarcoma. Conclusions: The chemokine receptors or chemokine ligands and immune related genes played a significant role in the mechanism of sarcoma, and the novel immune gene expression had a significant clinical guidance for the prognosis of patient in sarcoma.

scholarly journals Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hao Guo ◽  
Jing Zhou ◽  
Yanjun Zhang ◽  
Zhi Wang ◽  
Likun Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Model ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Prognostic Predictor ◽  
Key Genes

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

scholarly journals A Three Protein-Coding Gene Prognostic Model Predicts Overall Survival in Bladder Cancer Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiang-hui Ning ◽  
Yuan-yuan Qi ◽  
Fang-xin Wang ◽  
Song-chao Li ◽  
Zhan-kui Jia ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Zinc Finger Protein ◽  
Risk Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Lasso Regression ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Differentially Methylated Genes

Bladder cancer (BLCA) is the most common urinary tract tumor and is the 11th most malignant cancer worldwide. With the development of in-depth multisystem sequencing, an increasing number of prognostic molecular markers have been identified. In this study, we focused on the role of protein-coding gene methylation in the prognosis of BLCA. We downloaded BLCA clinical and methylation data from The Cancer Genome Atlas (TCGA) database and used this information to identify differentially methylated genes and construct a survival model using lasso regression. We assessed 365 cases, with complete information regarding survival status, survival time longer than 30 days, age, gender, and tumor characteristics (grade, stage, T, M, N), in our study. We identified 353 differentially methylated genes, including 50 hypomethylated genes and 303 hypermethylated genes. After annotation, a total of 227 genes were differentially expressed. Of these, 165 were protein-coding genes. Three genes (zinc finger protein 382 (ZNF382), galanin receptor 1 (GALR1), and structural maintenance of chromosomes flexible hinge domain containing 1 (SMCHD1)) were selected for the final risk model. Patients with higher-risk scores represent poorer survival than patients with lower-risk scores in the training set ( HR = 2.37 , 95% CI 1.43-3.94, p = 0.001 ), in the testing group ( HR = 1.85 , 95% CI 1.16-2.94, p = 0.01 ), and in the total cohort ( HR = 2.06 , 95% CI 1.46-2.90, p < 0.001 ). Further univariate and multivariate analyses using the Cox regression method were conducted in these three groups, respectively. All the results indicated that risk score was an independent risk factor for BLCA. Our study screened the different methylation protein-coding genes in the BLCA tissues and constructed a robust risk model for predicting the outcome of BLCA patients. Moreover, these three genes may function in the mechanism of development and progression of BLCA, which should be fully clarified in the future.

scholarly journals A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zaisheng Ye ◽  
Miao Zheng ◽  
Yi Zeng ◽  
Shenghong Wei ◽  
He Huang ◽  
...  
Keyword(s):  
Risk Score ◽  
Molecular Mechanisms ◽  
Cancer Metabolism ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Stomach Adenocarcinoma ◽  
Prognostic Prediction

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.

scholarly journals The construction and validation of an RNA binding protein-related prognostic model for bladder cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fengxia Chen ◽  
Qingqing Wang ◽  
Yunfeng Zhou
Keyword(s):  
Bladder Cancer ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Sequence Data ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Depth Analysis

Abstract Background RNA-binding proteins (RBPs) play crucial and multifaceted roles in post-transcriptional regulation. While RBPs dysregulation is involved in tumorigenesis and progression, little is known about the role of RBPs in bladder cancer (BLCA) prognosis. This study aimed to establish a prognostic model based on the prognosis-related RBPs to predict the survival of BLCA patients. Methods We downloaded BLCA RNA sequence data from The Cancer Genome Atlas (TCGA) database and identified RBPs differentially expressed between tumour and normal tissues. Then, functional enrichment analysis of these differentially expressed RBPs was conducted. Independent prognosis-associated RBPs were identified by univariable and multivariable Cox regression analyses to construct a risk score model. Subsequently, Kaplan–Meier and receiver operating characteristic curves were plotted to assess the performance of this prognostic model. Finally, a nomogram was established followed by the validation of its prognostic value and expression of the hub RBPs. Results The 385 differentially expressed RBPs were identified included 218 and 167 upregulated and downregulated RBPs, respectively. The eight independent prognosis-associated RBPs (EFTUD2, GEMIN7, OAS1, APOBEC3H, TRIM71, DARS2, YTHDC1, and RBMS3) were then used to construct a prognostic prediction model. An in-depth analysis showed lower overall survival (OS) in patients in the high-risk subgroup compared to that in patients in the low-risk subgroup according to the prognostic model. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve were 0.795 and 0.669 for the TCGA training and test datasets, respectively, showing a moderate predictive discrimination of the prognostic model. A nomogram was established, which showed a favourable predictive value for the prognosis of BLCA. Conclusions We developed and validated the performance of a prognostic model for BLCA that might facilitate the development of new biomarkers for the prognostic assessment of BLCA patients.

scholarly journals A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

2021 ◽  
Author(s):  
Renjie Liu ◽  
Guifu Wang ◽  
Chi Zhang ◽  
Dousheng Bai
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Risk Scores

Abstract Background: Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods: To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results: Compared to normal tissues, 43 highly up-regulated and 8 down-regulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated two-year or five-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion: Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

scholarly journals Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254368
Author(s):  
Gang Liu ◽  
Jian-ying Ma ◽  
Gang Hu ◽  
Huan Jin
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Critical Role ◽  
Clinicopathological Features ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Background Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). Methods Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. Results An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. Conclusions The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

scholarly journals Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer

2020 ◽  
Author(s):  
Keying Zhang ◽  
Jingwei Wang ◽  
Chao Xu ◽  
Jingliang Zhang ◽  
Shaojie Liu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Prognostic Model ◽  
Present Model ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Therapeutic Targets ◽  
Underlying Mechanism ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Clinical Variables

Abstract Background Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure better prognosis. However, the predictive accuracy of commonly used predictors, including patients’ general condition, histological grade and pathological stage, is insufficient to identify the patients who need invasive treatment. Autophagy is regarded as a vital factor in maintaining mitochondrial function and energy homeostasis in cancer cells. Whether autophagy-related genes (ARGs) can predict the prognosis of BLCA patients deserves to be investigated. Methods Based on BLCA data retrieved from the Cancer Genome Atlas (TCGA) and ARGs list obtained from the Human Autophagy Database (HADb) website, we identified prognosis-related differentially expressed ARGs (PDEARGs) through Wilcox text and constructed a PDEARGs-based prognostic model through multivariate Cox regression analysis. The predictive accuracy, independent forecasting capability, and the correlation between present model and clinical variables or tumor microenvironment (TME) were evaluated through R software. Enrichment analysis of PDEARGs was performed to explore the underlying mechanism, and a systematic prognostic signature with nomogram was constructed by integrating clinical variables and aforementioned PDEARGs-based model. Results We identified several PDEARGs and constructed a PDEARGs-based prognostic model, which could precisely predict the prognosis of BLCA patients. Then, we found that the risk score generated by PDEARGs-based model could effectively reflect deteriorated clinical variables and tumor-promoting microenvironment. Additionally, several immune-related gene ontology (GO) terms were significantly enriched by PDEARGs, which might provide insights for present model and propose potential therapeutic targets for BLCA patients. Finally, a systematic prognostic signature with promoted clinical utility and predictive accuracy was constructed to assist clinician decision. Conclusion PDEARGs are valuable prognostic predictor and potential therapeutic targets for BLCA patients.

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Ding Li ◽  
Jiaming Liang ◽  
Cheng Cheng ◽  
Wenbin Guo ◽  
Shuolei Li ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Cox Regression ◽  
Pearson Correlation ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Hematopoietic Malignancy ◽  
Cox Regression Analysis ◽  
Acute Myeloid

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.

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