Identification and validation of a novel eight mutant-derived long non-coding RNAs signature as a prognostic biomarker for genome instability in low-grade glioma

Maimaiti, Aierpati; Wang, Xixian; Pei, Yinan; Nuermaimaiti, Nuerbiye; Tuersunniyazi, Abudireheman; Abula, Yaeraili; Feng, Zhaohai; Jiang, Lei; Shi, Xin; Kasimu, Maimaitijiang

doi:10.18632/aging.203079

Cited by 13 publications

(15 citation statements)

References 49 publications

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“…To compare the predictability of our GInLncSig model and the other three existing lncRNA-related signatures for LGG patients’ survival, we conducted ROC curve analyses using the same samples of the entire TCGA cohort. The other three lncRNA models were 8-lncRNA prognostic model documented by Maimaiti’s study (MaimaitiLncSig) ( Maimaiti et al, 2021 ), 5-lncRNA predictive model derived from Wang’s study (WangLncSig) ( Wang et al, 2020 ), and 6-lncRNA prognostic model reported by Lin’s study (LinLncSig) ( Lin et al, 2020 ). As outlined in Figure 9A , our GInLncSig model (AUC = 0.871, 0.851, and 0.723 for 1, 3, and 5 years) outperformed the MaimaitiLncSig (AUC = 0.743, 0.693, and 0.626 for 1, 3, and 5 years), WangLncSig (AUC = 0.866, 0.773, and 0.647 for 1, 3, and 5 years), and LinLncSig (AUC = 0.841, 0.766, and 0.721 for 1, 3, and 5 years) in predicting 1-, 3- and 5-year OS of LGG patients.…”

Section: Resultsmentioning

confidence: 99%

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Cao

Zhu²,

Liu

et al. 2022

Front. Genet.

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Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

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Section: Resultsmentioning

confidence: 99%

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Cao

Zhu²,

Liu

et al. 2022

Front. Genet.

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“…LncRNAs play a critical role in maintaining genomic instability ( Nair et al, 2020 ; Jianfeng et al, 2021 ). Increasing evidence has revealed the prognostic significance of GIRlncRNAs for cancers ( Fang et al, 2021 ; Liang et al, 2021 ; Maimaiti et al, 2021 ; Yan et al, 2021 ). Since NSCLC possesses a poor survival prognosis due to limited diagnosis and treatment ( Zappa and Mousa, 2016 ; Li C. et al, 2019 ), we developed a prognostic GIRlncSig to support the clinical stratification and treatment decision for NSCLC patients.…”

Section: Discussionmentioning

confidence: 99%

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

et al. 2022

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Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

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“…FLG-AS1 and AQP4-AS1 are two other DElncRNAs that were identified by our analysis. Dysregulation of FLG-AS1 was reported in some cancers, but the specific function and detailed mechanisms of FLG‐AS1 are still unknown 45 , 46 . A previous integrative analysis showed that FLG‐AS1 acts as a ceRNA in adipose tissue from obese individuals with type 2 diabetes 47 .…”

Section: Discussionmentioning

confidence: 99%

Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis

Sabaie

Moghaddam

et al. 2021

Sci Rep

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The etiology of schizophrenia (SCZ), as a serious mental illness, is unknown. The significance of genetics in SCZ pathophysiology is yet unknown, and newly identified mechanisms involved in the regulation of gene transcription may be helpful in determining how these changes affect SCZ development and progression. In the current work, we used a bioinformatics approach to describe the role of long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) in the olfactory epithelium (OE) samples in order to better understand the molecular regulatory processes implicated in SCZ disorders in living individuals. The Gene Expression Omnibus database was used to obtain the OE microarray dataset (GSE73129) from SCZ sufferers and control subjects, which contained information about both lncRNAs and mRNAs. The limma package of R software was used to identify the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). RNA interaction pairs were discovered using the Human MicroRNA Disease Database, DIANA-LncBase, and miRTarBase databases. In this study, the Pearson correlation coefficient was utilized to find positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Eventually, lncRNA-associated ceRNA axes were developed based on co-expression relations and DElncRNA-miRNA-DEmRNA interactions. This work found six potential DElncRNA-miRNA-DEmRNA loops in SCZ pathogenesis, including, SNTG2-AS1/hsa-miR-7-5p/SLC7A5, FLG-AS1/hsa-miR-34a-5p/FOSL1, LINC00960/hsa-miR-34a-5p/FOSL1, AQP4-AS1/hsa-miR-335-5p/FMN2, SOX2-OT/hsa-miR-24-3p/NOS3, and CASC2/hsa-miR-24-3p/NOS3. According to the findings, ceRNAs in OE might be promising research targets for studying SCZ molecular mechanisms. This could be a great opportunity to examine different aspects of neurodevelopment that may have been hampered early in SCZ patients.

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Identification and validation of a novel eight mutant-derived long non-coding RNAs signature as a prognostic biomarker for genome instability in low-grade glioma

Cited by 13 publications

References 49 publications

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis

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