Analysis of m6A-Related Signatures in the Tumor Immune Microenvironment and Identification of Clinical Prognostic Regulators in Adrenocortical Carcinoma

Jin, Y.; Wang, Zhanwang; He, Dong; Zhu, Yuxing; Hu, Xiaofang; Gong, Lian; Xiao, Mengqing; Chen, Xingyu; Cheng, Ying; Cao, Ke

doi:10.3389/fimmu.2021.637933

Cited by 56 publications

(48 citation statements)

References 53 publications

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“…In order to assess the prognostic value of the six hub genes, and compare the prognostic value of the best model/the nomogram with the prognostic biomarkers given by other studies, we collected several biomarkers from previous studies. In total, eight biomarkers including CTNNB1 ( Assié et al, 2014 ), IGF2 ( Catalano et al, 2021 ), TP53 ( Assié et al, 2014 ), MKI67 ( Beuschlein et al, 2015 ), SF1 ( Tian et al, 2021 ), IPRPs model ( Tian et al, 2021 ), m6A-related signature ( Jin et al, 2021 ), and m6A-based signature ( Shen et al, 2021 ) were used in the present study. According to the result ( Table 6 ), all the hub genes could effectively predict the OS of ACC patients (ASPM: AUC = 0.840, C-index = 0.827; AURKA: AUC = 0.786, C-index = 0.794; CCNB2: AUC = 0.830, C-index = 0.821; CDC20: AUC = 0.833, C-index = 0.838; KIF2C: AUC = 0.819, C-index = 0.823; NUF2: AUC = 0.811, C-index = 0.843).…”

Section: Resultsmentioning

confidence: 99%

“…Tian et al constructed a robust prognostic model for ACC ( Tian et al, 2021 ). Two m6A-related signatures, which might be used as independent prognostic factors in evaluating the prognosis of ACC patients, were also established ( Jin et al, 2021 ; Shen et al, 2021 ). Among the six hub genes we identified, two, including ASPM ( Yuan et al, 2018 ) and CCNB2 ( Gao et al, 2019 ; Guo et al, 2020 ), were already regarded as prognostic biomarkers in previous studies.…”

Section: Discussionmentioning

confidence: 99%

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Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Yan

Guo

et al. 2021

Front. Cell Dev. Biol.

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Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Yan

Guo

et al. 2021

Front. Cell Dev. Biol.

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“…According to previous publications, a total of 23 m6A RNA methylation regulators (METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, RBM15B, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGF2BP1, IGF2BP2, IGF2BP3, RBMX, FT0, and ALKBH5) were obtained [15,16]. lncRNAs were defined using the long noncoding RNA annotation file of the GENCODE website.…”

Section: Identification Of M6a Methylation-related Lncrnamentioning

confidence: 99%

m6A-Related lncRNA to Develop Prognostic Signature and Predict the Immune Landscape in Bladder Cancer

Zhong

et al. 2021

Journal of Oncology

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Abnormal m6A methylation plays a significant role in cancer progression. Increasingly, researchers have focused on developing lncRNA signatures to evaluate the prognosis of cancer patients. The specific function of m6A-related lncRNAs in the prognosis of bladder cancer patients and the immune microenvironment of bladder cancer remains elusive. Herein, we performed a comprehensive analysis of m6A-related lncRNA prognostic values and their association with the immune microenvironment in bladder cancer using the TCGA dataset. A total of 9 m6A-related lncRNAs were dramatically correlated with overall survival outcomes in bladder cancer. Two molecular subtypes (cluster 1 and cluster 2) were identified by consensus clustering for 9 m6A-related prognostic lncRNAs. Cluster 1 was significantly correlated with poor prognosis, advanced clinical stage, higher PD-L1 expression, a higher ESTIMATEScore and immuneScore, and distinct immune cell infiltration. GSEA revealed the enrichment of apoptosis and the JAK-STAT signaling pathway in cluster 2. A prognostic risk score was constructed using 9 m6A-related prognostic lncRNAs, which functioned as an independent prognostic factor for bladder cancer. Moreover, bladder cancer patients in the low-risk score group had a higher pN stage, pT stage, and clinical stage and a lower tumor grade and immuneScore. The risk score was correlated with the infiltration levels of certain immune cells, including B cells, plasma cells, follicular helper T cells, regulatory T cells, resting NK cells, neutrophils, M0 macrophages, M1 macrophages, and M2 macrophages. Collectively, our study elucidated the important role of m6A-related lncRNAs in the prognosis of bladder cancer patients and in the bladder cancer immune microenvironment. The results suggest that the components of the m6A-related prognostic lncRNA signature might serve as a crucial mediator of the immune microenvironment in bladder cancer, representing promising therapeutic targets for improving immunotherapeutic efficacy.

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“…Both m6A enzymes and lncRNAs are ideal diagnostic and prognostic markers. Accumulating evidence shows that m6Arelated mRNAs and lncRNAs can serve as novel potential targets to predict the prognosis for multiple cancers (Barros-Silva et al, 2020;Li et al, 2020;Meng et al, 2020;Xu et al, 2020Xu et al, , 2021Jin et al, 2021). For instance, METTL14 is a prognosis-associated regulator of m6A RNA methylation in hepatocellular carcinoma (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

N6-Methylandenosine-Related lncRNA Signature Is a Novel Biomarkers of Prognosis and Immune Response in Colon Adenocarcinoma Patients

Zhang

Liu

Lü

2021

Front. Cell Dev. Biol.

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BackgroundColon adenocarcinoma (COAD) is the most common type of colon cancer. To date, however, the prognostic values of m6A RNA methylation-related long non-coding RNAs (lncRNAs) in COAD are largely unknown.Materials and MethodsThe m6A-related lncRNAs were identified from The Cancer Genome Atlas (TCGA) data set. Univariate and multivariate Cox regression analyses were performed to explore the prognostic m6A-related lncRNAs. Consistent clustering analysis was performed to classify the COAD patients into different subgroups based on the expression of m6A-related lncRNAs. The potential biological functions as well as differences in the stemness index and tumor immune microenvironment between different subgroups were analyzed. The prognostic m6A-related lncRNAs were used to establish an m6A-related lncRNA risk model to predict prognosis and survival status.ResultsWe identified 31 m6A-associated lncRNAs with prognostic values from the TCGA data set. Based on the expression of prognostic m6A-associated lncRNAs, TCGA-COAD patients were classified into three clusters using consistent clustering analysis. There was a low correlation of tumor stemness between the three clusters but a significant correlation with the tumor immune microenvironment as well as the tumor mutational load. Thirty-one prognostic-related m6A-associated lncRNAs were used to construct a risk model, which was further determined by survival analysis, receiver operating characteristic (ROC) curve, and univariate and multifactor Cox analysis. The m6A-related risk model demonstrates good performance in predicting prognosis and survival status. The model-based high-risk group exhibited poorer overall survival (OS) compared with the low-risk group.ConclusionIn this study, we construct a risk model that consists of 31 m6A-related lncRNAs with independent prognostic values in COAD. Our study shows the critical roles of these 31 m6A-related lncRNAs in the tumor immune microenvironment, indicating the prospect of informing prognostic stratification and the development of immunotherapeutic strategies for COAD patients.

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Analysis of m6A-Related Signatures in the Tumor Immune Microenvironment and Identification of Clinical Prognostic Regulators in Adrenocortical Carcinoma

Cited by 56 publications

References 53 publications

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

m6A-Related lncRNA to Develop Prognostic Signature and Predict the Immune Landscape in Bladder Cancer

N6-Methylandenosine-Related lncRNA Signature Is a Novel Biomarkers of Prognosis and Immune Response in Colon Adenocarcinoma Patients

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