A Five Immune-Related lncRNA Signature as a Prognostic Target for Glioblastoma

Li, Yongming; Sun, Li; Wang, Xue; Wang, Nan; Xu, Kanghong; Jiang, Xinquan; Xu, Shuo

doi:10.3389/fmolb.2021.632837

Cited by 20 publications

(13 citation statements)

References 57 publications

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“…It is concentrated in the cytoplasm of CRC cells and reversely regulates miR-1287-5p expression, which indicates adverse outcomes [49]. Recent studies have also shown that PRKCQ-AS1, as an immune-related lncRNA, provides a reliable prognostic indicator for glioblastoma patients [50]. (4) LINC00957 is signi cantly related to poor OS of CRC patients upon increased expression (P < 0.05), and inhibiting LINC00957 expression can reverse 5-FU resistance via P-gP down regulation [51].…”

Section: Discussionmentioning

confidence: 99%

A Innovative Prognostic Symbol Based on Autophagy-Related Long Noncoding RNAs Signature Predicts in Low-Grade Glioma

Maimaiti

Tuerhong

Wang

et al. 2021

Preprint

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Autophagy is a highly conserved lysosomal degradation process essential in tumorigenesis. However, the involvement of autophagy-related lncRNA in low-grade gliomas (LGG) remains a pending question. Efforts were made to establish an autophagy-related lncRNA signature prognostic in LGG patients, and to explore the behind potential function. We used Univariate Cox, Least Absolute Shrinkage and Selection Operator (Lasso), and Multivariate Cox regression models were designed to establish an autophagy-related lncRNA prognostic signature. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, nomogram, C-index, calibration curve and clinical decision-making curve were adopted to assess the predictive capability of the identified signature.A signature comprising 9 autophagy-related lncRNAs (AL136964.1, ARHGEF26-AS1, PCED1B-AS1, AS104072.1, PRKCQ-AS1, LINC00957, AS125616.1, PSMB8-AS1, and AC087741.1) was identified as a prognostic model. LGG patients were allocated into high- and low-risk cohorts depending on the median model-based Riskscore. The survival analysis showed a 10-year survival rate of 9.3% (95% CI: 1.91-45.3%) in high-risk LGG patients and 48.4% (95% CI: 24.7-95.0%) in low-risk individuals in the training set. While those of patients in the validation set were 13.48% (95% CI: 4.52-40.2%) for high-risk LGG patients and 48.4% (95% CI: 28.04-83.4%) for low-risk LGG patients, respectively. This suggested a relatively low survival in high-risk cases compared to low-risk individuals. In addition, LncRNA signature was independently prognostic and potentially associated with the progression of LGG. Taken together, our constructed 9 autophagy-related lncRNA signature may play a crucial part in the diagnosis and treatment for LGG, which may guide to open up a new avenues for tumor targeted therapy.

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Section: Discussionmentioning

confidence: 99%

A Innovative Prognostic Symbol Based on Autophagy-Related Long Noncoding RNAs Signature Predicts in Low-Grade Glioma

Maimaiti

Tuerhong

Wang

et al. 2021

Preprint

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“…Pearson coefficient (r) was applied to indicate the correlation between genes and reflect the biological relationship between the two genes. MTFR2 co-expressed genes were screened in 535 cases of LUAD tissues from TCGA via the R (version 3.6.1) [29]. Screening criteria: P < 0.001 and Pearson coefficient (r > 0.4 or r < − 0.4), were defined as moderate or above.…”

Section: Screening Of Mtfr2 Co-expressed Genesmentioning

confidence: 99%

“…The genes were arranged according to the degree of differential expression by GSEA (version 4.0.1) to explore the effect of MTFR2 on each gene. Each analysis was repeated 1000 times [29,31]. Screening criteria: NOM p < 0.05.…”

Section: Go Kegg and Gseamentioning

confidence: 99%

Evaluation of clinical value and potential mechanism of MTFR2 in lung adenocarcinoma via bioinformatics

Chen

Tang

et al. 2021

BMC Cancer

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Background Mitochondrial fission regulator 2 (MTFR2) was involved in the progression and development of various cancers. However, the relationship between MTFR2 with lung adenocarcinoma (LUAD) had not been reported. Herein, this study analyzed the clinical significance and potential mechanisms of MTFR2 in LUAD via bioinformatics tools. Results We found that the level of MTFR2 was increased, and correlated with sex, age, smoking history, neoplasm staging, histological subtype and TP53 mutation status in LUAD patients. Kaplan-Meier survival analysis showed LUAD patients with increased MTFR2 had a poor prognosis. In addition, univariate COX regression analysis showed neoplasm staging, T stage, distant metastasis and MTFR2 level were risk factors for the prognosis of LUAD. A total of 1127 genes were coexpressed with MTFR2, including 840 positive and 208 negative related genes. KEGG and GSEA found that MTFR2 participated in the progression of LUAD by affecting cell cycle, DNA replication, homologous recombination, p53 signaling pathway and other mechanisms. The top 10 coexpressed genes, namely CDK1, CDC20, CCNB1, PLK1, CCNA2, AURKB, CCNB2, BUB1B, MAD2L1 and BUB1 were highly expressed, and were associated with poor prognosis in LUAD. Conclusions Consequently, we elucidated MTFR2 was a biomarker for diagnosis and poor prognosis in LUAD, and might participate in the progression of LUAD via affecting cell cycle, DNA replication, homologous recombination and p53 signaling pathway.

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“…Wang et al [9] identi ed 4 irlncRNAs to establish a risk model for lung adenocarcinoma, while 8 irlncRNAs were used to construct a prognostic model for melanoma [10]. Moreover, irlncRNAs have also utilized in glioblastoma, head and neck squamous cell carcinoma and bladder cancer [11][12][13]. These models demonstrate the potential clinical signi cance of irlncRNAs and provide novel insights for establishing a clinical prognostic model.…”

Section: Backgroundsmentioning

confidence: 99%

Construction of a Novel Risk Model Based on the Random Forest Algorithm to Distinguish Pancreatic Cancers with Different Prognoses and Immune Microenvironment Features

Lei

Tang

et al. 2021

Preprint

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Background: Immune-related long noncoding RNAs (irlncRNAs) have been demonstrated to be actively involved in the regulation of immune status. With this study, we aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in prognosis prediction and the immune landscape in pancreatic cancer.Methods: The transcriptome profiles and clinical information of pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen the lncRNAs by correlation analysis (R>0.5, p<0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and the minimal depth. Then, Cox regression was used to establish a risk model with 3 irlncRNAs, which was evaluated by Kaplan-Meier analysis. In addition, we performed Cox regression analysis to establish the clinical prognostic model, which showed that the risk score was the only independent prognostic factor (p<0.001). A nomogram was drawn to visualize the clinical features. Wilcoxon signed-rank test and analysis of the correlations between the risk score and immune cells was applied to explore the potential irlncRNAs related to immune status.Results: A total of 176 samples were randomly divided into a training set (n=123) and a test set (n=53). A total of 1903 irlncRNAs were identified after Pearson correlation analysis between irgenes and lncRNAs. A total of 9 irlncRNAs were identified in the survival tree analysis, and 3 irlncRNAs (LINC00462, LINC01887, RP11-706C16.8) were used to establish a risk model. The areas under curve (AUC) of the receiver operating characteristic (ROC) for 36 months, 30 months, 24 months, 18 months, 12 months and 6 months were 0.778, 0.774, 0.751, 0.753, 0.780 and 0.756, respectively, with a concordance index (C-index) of 0.696. In the clinical prognostic model, the C-index increased from 0.599 to 0.682 after combining the risk scores. Furthermore, a high risk was associated with increased infiltration of CD4+ T cells, M0 macrophages and M1 macrophages.Conclusions: We established a novel three-irlncRNA risk model. The clinical evaluation showed its robustness in predicting the prognosis and immune landscape of pancreatic cancer.

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A Five Immune-Related lncRNA Signature as a Prognostic Target for Glioblastoma

Cited by 20 publications

References 57 publications

A Innovative Prognostic Symbol Based on Autophagy-Related Long Noncoding RNAs Signature Predicts in Low-Grade Glioma

A Innovative Prognostic Symbol Based on Autophagy-Related Long Noncoding RNAs Signature Predicts in Low-Grade Glioma

Evaluation of clinical value and potential mechanism of MTFR2 in lung adenocarcinoma via bioinformatics

Construction of a Novel Risk Model Based on the Random Forest Algorithm to Distinguish Pancreatic Cancers with Different Prognoses and Immune Microenvironment Features

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