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An innovative prognostic model based on autophagy-related long noncoding RNA signature for low-grade glioma

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Abstract

Autophagy is a highly conserved lysosomal degradation process essential in tumorigenesis. However, the involvement of autophagy-related long noncoding RNAs (lncRNAs) in low-grade glioma (LGG) remains unclear. In this study, we established an autophagy-related lncRNA prognostic signature for patients with LGG and assess its underlying functions. We used univariate Cox, least absolute shrinkage and selection operator and multivariate Cox regression models to establish an autophagy-related lncRNA prognostic signature. Kaplan–Meier survival analysis, receiver operating characteristic curve, nomogram, C-index, calibration curve and clinical decision-making curve were used to assess the predictive capability of the identified signature. A signature comprising nine 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. Patients with LGG were divided into the high- and low-risk cohorts based on the median model-based risk score. The survival analysis revealed a 10-year survival rate of 9.3% (95% CI 1.91–45.3%) and 13.48% (95% CI 4.52–40.2%) in high-risk patients in the training and validation sets, respectively, and 48.4% (95% CI 24.7–95.0%) and 48.4% (95% CI 28.04–83.4%) in low-risk patients in the training and validation sets, respectively. This finding suggested a relatively low survival in high-risk patients. In addition, the lncRNA signature was independently prognostic and potentially associated with the progression of LGG. Therefore, the 9-autophagy-related-lncRNA signature may play a crucial role in the diagnosis and treatment of LGG, which may offer new avenues for tumour-targeted therapy.

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  1. Aierpati Maimaiti and Mirezhati Tuerhong equally contributed to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi District, Urumchi, Xinjiang, China

    Aierpati Maimaiti, Yongxin Wang, Maimaitili Aisha, Lei Jiang, Xixian Wang, Yusufu Mahemuti, Yirizhati Aili, Zhaohai Feng & Maimaitijiang Kasimu

  2. Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China

    Mirezhati Tuerhong

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Contributions

AM and MT: Conceptualization, methodology, validation, investigation, supervision, software, visualization, writing—original draft, writing—reviewing and editing. YW: Validation, supervision, investigation. MA: Visualization, methodology. LJ: Data curation, software, validation. XW: Methodology, formal analysis, visualization. YM: Software, validation. YA: Data curation, visualization, validation. ZF: Investigation, validation. MK: Investigation, writing—reviewing and editing.

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Correspondence to Maimaitijiang Kasimu.

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Supplementary Information

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Supplementary Figure 1 Sankey charts for autophagy-related lncRNAs, mRNAs and risk types (TIF 1871 kb)

Supplementary Figure 2 The PH assumption of the Cox regression model (TIF 1479 kb)

11010_2022_4368_MOESM3_ESM.tif

Supplementary Figure 3 Kaplan–Meier curves of OS differences stratified by age (A), tumour grade(B), sex(C), new tumour events(D), radiation therapy(E), tumour type(F), IDH1 R132 mutation status(G) and TP53 mutation status(H) between the high- and low-risk groups in the TCGA entire set (TIF 3914 kb)

11010_2022_4368_MOESM4_ESM.tif

Supplementary Figure 4 A. Estimation of immune and stromal cells in low-grade glioma tissues using expression data in training set. Violin plots of tumour purity for the low- and high-risk groups based on the immune score, stromal score and ESTIMATE score. B. Differences in infiltration levels of immune cell types in the high- and low-risk groups in training set. C. Comparison of immune-related functions between the high- and low-risk groups in training set. D. Comparison of the expression of immune checkpoints between the high- and low-risk groups in training set (TIF 3874 kb)

11010_2022_4368_MOESM5_ESM.tif

Supplementary Figure 5 A–D Differences in estimation of stromal and immune cells, immune cell infiltration, immune-related functions, immune checkpoints and risk scores in validation set (TIF 6324 kb)

11010_2022_4368_MOESM6_ESM.tif

Supplementary Figure 6 A–E. In the CGGA mRNA-seq-693 dataset, the overall survival probability of patients with low expression levels of ARHGEF26-AS1 and PRKCQ-AS1 was significantly lower than that of patients with high expression levels (P < 0.05). C. In the CGGA mRNA-seq-693 dataset, the overall survival probability of patients with high expression levels of PCED1B-AS1 was significantly lower than that of patients with low expression levels (P < 0.05). D. In the CGGA mRNA-seq-693 dataset, no statistical difference was observed in the overall survival probability between patients with high and low expression levels of LINC00957. E. In the GSE16011 dataset, the overall survival probability was significantly higher in patients with high PRKCQ-AS1 expression levels than in patients with low expression levels (P < 0.001) (TIF 2887 kb)

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Maimaiti, A., Tuerhong, M., Wang, Y. et al. An innovative prognostic model based on autophagy-related long noncoding RNA signature for low-grade glioma. Mol Cell Biochem 477, 1417–1438 (2022). https://doi.org/10.1007/s11010-022-04368-6

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