Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

Zhang, Li; Tang, Xianzhe; Wan, Jia; Zhang, Xianghong; Zheng, Tao; Lin, Zhengjun

doi:10.3389/fmolb.2021.715764

Cited by 4 publications

(4 citation statements)

References 59 publications

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“…The correlation between the expression levels of mRNAs and lncRNAs was fully considered. Meanwhile, this method was widely used in the computational genomics field of tumors, such as hepatocellular carcinoma ( Zhou et al, 2021 ), bladder cancer (Ma et al, 2021), soft tissue sarcomas ( Zhang J et al, 2021 ), and breast cancer ( Zhang L et al, 2021 ). There were 601 DELs associated with hypoxia between normal and tumor samples.…”

Section: Discussionmentioning

confidence: 99%

An Effective Hypoxia-Related Long Non-Coding RNA Assessment Model for Prognosis of Lung Adenocarcinoma

Sun

2022

Front. Genet.

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Background: Lung adenocarcinoma (LUAD) represents one of the highest incidence rates worldwide. Hypoxia is a significant biomarker associated with poor prognosis of LUAD. However, there are no definitive markers of hypoxia-related long non-coding RNAs (lncRNAs) in LUAD.Methods: From The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), we acquired the expression of hypoxia-related lncRNAs and corresponding clinical information of LUAD patients. The hypoxia-related prognostic model was constructed by univariable COX regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariable Cox regression analysis. To assess the performance of the model, the Kaplan–Meier (KM) survival and receiver operating characteristic (ROC) curve analyses were performed.Results: We found seven lncRNAs, AC022613.1, AC026355.1, GSEC, LINC00941, NKILA, HSPC324, and MYO16-AS1, as biomarkers of the potential hypoxia-related prognostic signature. In the low-risk group, patients had a better overall survival (OS). In addition, the results of ROC analysis indicated that the risk score predicted LUAD prognosis exactly. Furthermore, combining the expression of lncRNAs with clinical features, two predictive nomograms were constructed, which could accurately predict OS and had high clinical application value.Conclusion: In summary, the seven-lncRNA prognostic signature related to hypoxia might be useful in predicting clinical outcomes and provided new molecular targets for the research of LUAD patients.

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Section: Discussionmentioning

confidence: 99%

An Effective Hypoxia-Related Long Non-Coding RNA Assessment Model for Prognosis of Lung Adenocarcinoma

Sun

2022

Front. Genet.

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“…A recent study also reveals 13 m6A-related lncRNAs in STS [ 12 ]. The previous study demonstrated that cluster 1 had higher abundance of M0 macrophages and activated dendritic cells and lower abundance of CD8 + T cells [ 22 ]. The authors suggested the positive association of risk score and M0 macrophages.…”

Section: Discussionmentioning

confidence: 99%

N6-Methyladenosine-Related lncRNAs Are Anticipated Biomarkers for Sarcoma Patients

Zhang

Zhou

et al. 2022

Journal of Oncology

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Background. Soft 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. Methods. The 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. Results. Following 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. Conclusions. Here, 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.

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“…SARC are characterized by a low incidence rate (approximately 1% of all malignancies in adults and 10–15% of all malignancies in pediatric cancers) but a poor prognosis in most cases ( Hui, 2016 ; Siegel et al, 2019 ). Despite advances in the treatment of STS and osteosarcoma, with comprehensive treatment strategies including surgery, chemotherapy, radiotherapy, and targeted therapy, the survival rate of patients with advanced STSs and BSs needs to be improved ( Zhu et al, 2019a ; Zhang et al, 2021a ; Eaton et al, 2021 ). Although it is difficult to determine the survival for each subtype of sarcoma due to the heterogeneity of the disease, the 5-year survival rates for patients with STS and for patients with bone sarcomas are generally larger than (>) 80% and about 70%, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Their analysis indicated that CNVs and mutations of KIAA1429, YTHDF3, and IGF2BP1 were independent risk factors predicting OS and DFS. Zhang et al (2021a ) constructed and validated a signature based on m 6 A-related lncRNAs which could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m 6 A-related lncRNAs in STS. Noncoding RNAs (ncRNAs), such as long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), function as key regulators of gene expression, and the competitive endogenous RNA (ceRNA) network is a transcriptional regulatory network at the molecular level, consisting of lncRNA, miRNA, and mRNA ( Salmena et al, 2011 ; Zhu et al, 2019b ; Cava et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

Lin

Wang

et al. 2022

Front. Genet.

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Background: N6 methyladenosine (m6A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m6A regulators and m6A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required.Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m6A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m6A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed.Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance.Conclusion: This m6Ascore risk model based on m6A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.

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Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

Cited by 4 publications

References 59 publications

An Effective Hypoxia-Related Long Non-Coding RNA Assessment Model for Prognosis of Lung Adenocarcinoma

An Effective Hypoxia-Related Long Non-Coding RNA Assessment Model for Prognosis of Lung Adenocarcinoma

N6-Methyladenosine-Related lncRNAs Are Anticipated Biomarkers for Sarcoma Patients

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

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