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. 2019 Mar 5;9(4):653–667. doi: 10.1002/2211-5463.12601

An autophagy‐related long non‐coding RNA signature for glioma

Fangkun Luan 1, Wenjie Chen 2, Miao Chen 1, Jun Yan 1, Hao Chen 1, Haiyue Yu 1, Tieqi Liu 1, Ligen Mo 1,✉
PMCID: PMC6443865  PMID: 30984540

Abstract

Glioma is one of the most common types of malignant primary central nervous system tumor, and prognosis for this disease is poor. As autophagic drugs have been reported to induce glioma cell death, we investigated the potential prognostic role of autophagy‐associated long non‐coding RNA (lncRNA) in glioma patients. In this study, we obtained 879 lncRNAs and 216 autophagy genes from the Chinese Glioma Genome Atlas microarray, and found that 402 lncRNAs are correlated with the autophagy genes. Subsequently, 10 autophagy‐associated lncRNAs with prognostic value (PCBP1‐AS1, TP53TG1, DHRS4‐AS1, ZNF674‐AS1, GABPB1‐AS1, DDX11‐AS1, SBF2‐AS1, MIR4453HG, MAPKAPK5‐AS1 and COX10‐AS1) were identified in glioma patients using multivariate Cox regression analyses. A prognostic signature was then established based on these prognostic lncRNAs, dividing patients into low‐risk and high‐risk groups. The overall survival time was shorter in the high‐risk group than that in the low‐risk group [hazard ratio (HR) = 5.307, 95% CI: 4.195–8.305; P < 0.0001]. Gene set enrichment analysis revealed that the gene sets were significantly enriched in cancer‐related pathways, including interleukin (IL) 6/Janus kinase/signal transducer and activator of transcription (STAT) 3 signaling, tumor necrosis factor α signaling via nuclear factor κB, IL2/STAT5 signaling, the p53 pathway and the KRAS signaling pathway. The Cancer Genome Atlas dataset was used to validate that high‐risk patients have worse survival outcomes than low‐risk patients (HR = 1.544, 95% CI: 1.110–2.231; P = 0.031). In summary, our signature of 10 autophagy‐related lncRNAs has prognostic potential for glioma, and these autophagy‐related lncRNAs may play a key role in glioma biology.

Keywords: autophagy, CCGA, glioma, long non‐coding RNA, prognostic signature, TCGA

Abbreviations

CGGA

Chinese Glioma Genome Atlas

GBM

glioblastoma

GSEA

gene set enrichment analysis

HCC

hepatocellular carcinoma

HOTAIR

HOX (homeobox) transcript antisense RNA

HR

hazard ratio

IFN

interferon

IL

interleukin

lncRNA

long non‐coding RNA

miRNA

microRNA

MMP

matrix metalloproteinase

OS

overall survival

STAT

signal transducer and activator of transcription

TCGA

The Cancer Genome Atlas

Glioma is one of the most common types of malignant primary central nervous system tumors with poor prognosis, comprising approximately 44% of central nervous system tumors 1. The prognosis of glioblastoma (GBM) is the worst among gliomas, in which the median overall survival (OS) for patients with GBM is 15–23 months and the 5‐year survival rate is less than 6% 2, 3. Upon diagnosis, the standard treatment of glioma includes maximal surgical resection, chemotherapy, such as temozolomide, and radiation. Treatment options may vary in different stages of the disease and by the age of the patients. Various factors affect the prognosis of GBM including EGFR amplifications, and mutations of IDH1, TP53 and PTEN 4, 5, 6. However, the survival outcome is unfavorable due to the complex genetic mechanism.

Autophagy is the physiological process that directs degradation of proteins and whole organelles in cells. The activation of autophagy is divided into normal and pathological conditions. Under normal circumstances, autophagy represents a response to several stresses by providing the necessary circulating metabolic substrates for survival. In addition, autophagy is active in some pathological processes in order to maintain cellular homeostasis, such as neurodegenerative diseases, pathogenic inflammation, aging and cancer 7. In recent years, many studies have sought to find new potential targeted therapies by investigating autophagy pathways 8, 9, 10. In addition, autophagic drugs induce cell autophagic death (type II cell death) and cause glioma cell death. Whether this is an alternative and emerging concept for the study of novel glioma therapies remains largely unknown 11.

Long non‐coding RNAs (lncRNAs) have a wide range of functional activities 12. They play a significant role in physiological processes, including RNA decay, genetic regulation of gene expression, RNA splicing, microRNA (miRNA) regulation and protein folding 13. lncRNA regulates many proteins that are important for autophagy. Impaired functioning of lncRNAs participates in glioma pathogenesis, such as cellular apoptosis and proliferation 14. HOX (homeobox) transcript antisense RNA (HOTAIR) is a lncRNA that plays an important role in the regulation of cancer transformation, mainly due to extensive miRNA–HOTAIR interactions and its effect on matrix metalloproteinases (MMPs) 15. There may be an involvement of HOTAIR‐interacting miRNAs and MMPs in autophagy regulation 16, 17, 18. Sufficient evidence shows that lncRNAs mediate transcriptional and post‐transcriptional levels of autophagy‐related genes to regulate the autophagy regulatory network 19, 20. This paper proposes to construct a coexpression network of autophagy‐related lncRNAs using bioinformatics methods, providing a theoretical basis for the treatment of gliomas 21.

Therefore, autophagy‐related lncRNAs may have potential value in the prognosis of glioma patients and may serve as potential therapeutic targets. Here, we aimed to establish an autophagy‐related lncRNA signature in glioma and to advance the targeted treatment of glioma.

Materials and methods

Information extraction of glioma patients

The Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn/, freely available) microarray was used as a training set to establish an autophagy‐associated lncRNA signature of glioma patients. CGGA is the largest glioma tissue database with follow‐ups in China. Thousands of samples have been subjected to whole‐exome sequencing, DNA methylation microarray detection and whole‐genome sequencing, miRNA, mRNA and circRNA sequencing. The training dataset includes CGGA mRNA expression (FPKM) in 325 glioma patients together with relevant clinical data. The patients were diagnosed based on the 2007 WHO classification guidelines. We downloaded clinical information from the dataset website. The prognostic signature was further validated based on The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/) GBM dataset. TCGA GBM dataset (FPKM level 3) was included in our analysis as a validation dataset with 160 GBM patients.

LncRNA and autophagy gene screening

The profiles of lncRNAs and autophagy genes were obtained from the CGGA ALL mRNAseq dataset. Specifically, the autophagy gene list was obtained from the Human Autophagy Database (HADb, http://autophagy.lu/clustering/index.html). All of the mRNA expression data were normalized by log2 transformation. Pearson correlation was applied to calculate the correlation between the lncRNAs and autophagy‐related genes. A lncRNA with a correlation coefficient |R 2| > 0.3 and P < 0.05 was considered to be an autophagy‐related lncRNA.

Signature development

First, univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of autophagy‐related lncRNAs. The lncRNAs with a P‐value < 0.01 by univariate analysis were included in the multivariate stepwise regression Cox analysis to establish the risk score. We used the previous report to determine the risk score for each patient using the following formula: Risk score = βgene1 × exprgene1 + βgene2 × exprgene2 + ··· +  βgenen × exprgenen. Cox analysis was performed to build a signature for predicting survival. For more detail, we assigned risk scores by a linear combination of the expression levels of lncRNAs weighted by regression coefficients (β). The β value was calculated by log transformation of the hazard ratio (HR) from the multivariate Cox regression analysis. High‐risk and low‐risk groups were established based on the median risk score. The lncRNA expression is defined as exprgenen.

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was used to interpret gene expression data. This method derives its function by analyzing gene sets, so it can be used to determine whether the gene set shows a statistically significant difference between the two biological states. In this study, we verified whether genes that are differentially expressed between two groups are enriched during autophagy.

Statistical analysis

The expression levels of autophagy‐related lncRNAs were elevated (P ≤ 0.05). Construction of the autophagy–lncRNA coexpression network was completed using cytoscape software 22 (version 3.4.0; The Cytoscape Consortium, San Diego, CA, USA). Pearson correlation analysis and Cox regression analysis were performed using spss statistics software (version 24; IBM Corp., Armonk, NY, USA). Survival status was the basis for univariate cox regression analysis. prism 7 (GraphPad Software Inc., La Jolla, CA, USA) was used to generate Kaplan–Meier curves. GSEA ( http://www.broadinstitute.org/gsea/index.jsp) was used to distinguish between two sets of functional annotations. Statistical significance was set at a threshold of a two‐tailed P < 0.05.

Results

Construction of a coexpression network for autophagy–lncRNAs

We identified a total of 878 lncRNAs in the CGGA dataset, which was extracted from the CGGA database. A total of 215 autophagy‐related genes were extracted from the Human Autophagy Database (HADb, http://autophagy.lu/clustering/index.html). We constructed an autophagy–lncRNA coexpression network to identify autophagy‐related lncRNAs. Finally, 402 lncRNAs were identified (|R 2| > 0.3 and P ≤ 0.05).

Identification of a signature of 10 autophagy‐related lncRNAs in patients with glioma

First, we identified autophagy‐related lncRNAs by constructing autophagy–lncRNA coexpression networks (P ≤ 0.05). In addition, we used univariate Cox regression analysis based on 402 autophagy‐associated lncRNAs to screen prognostic genes. We ranked the prognostic autophagy‐related lncRNAs in ascending order by their P values. We used a P value of 0.05 as the cutoff value, and the lncRNAs that satisfied this were used for signature development. Our training set was a collection of 325 glioma patients from the CGGA dataset. A total of 19 lncRNAs have prognostic value for glioma patients (P < 0.01). Subsequently, 10 autophagy‐related lncRNAs were found to be independent prognostic factors for glioma patients (Table 1 and Fig. 1), of which five lncRNAs were unfavorable factors (TP53TG1, ZNF674‐AS1, COX10‐AS1, DDX11‐AS1 and SBF2‐AS1) and five lncRNAs were confirmed to be favorable prognostic factors for glioma (PCBP1‐AS1, DHRS4‐AS1, GABPB1‐AS1, MAPKAPK5‐AS1 and MIR4453HG) (Table 2 and Fig. 2).

Table 1.

Correlation between the prognostic lncRNAs and autophagy genes in glioma

LncRNA Autophagy gene Correlation P
PCBP1‐AS1 CCL2 −0.360049202 2.2072E‐11
PCBP1‐AS1 ATG2B 0.304817007 2.0467E‐08
PCBP1‐AS1 KIF5B 0.305876376 1.818E‐08
PCBP1‐AS1 ATG2A 0.311692494 9.4056E‐09
PCBP1‐AS1 WDFY3 0.31817447 4.4365E‐09
PCBP1‐AS1 MLST8 0.322140176 2.7765E‐09
PCBP1‐AS1 PIK3C3 0.324642153 2.0586E‐09
PCBP1‐AS1 TSC1 0.330291284 3.8213E‐09
PCBP1‐AS1 TSC2 0.332514623 8.3751E‐10
PCBP1‐AS1 NCKAP1 0.339888292 3.3348E‐10
PCBP1‐AS1 GRID2 0.364527555 5.5069E‐11
PCBP1‐AS1 SIRT2 0.366890173 8.5922E‐12
PCBP1‐AS1 MTOR 0.371080649 4.7675E‐12
PCBP1‐AS1 SIRT1 0.385311738 6.0485E‐13
PCBP1‐AS1 NBR1 0.3900753 3.5039E‐13
PCBP1‐AS1 ERBB2 0.398520286 8.1268E‐14
PCBP1‐AS1 BIRC6 0.401737956 4.9072E‐14
PCBP1‐AS1 HDAC6 0.410137987 1.2879E‐14
PCBP1‐AS1 KIAA0226 0.417146773 3.9968E‐15
PCBP1‐AS1 RPTOR 0.426641207 8.8818E‐16
TP53TG1 GABARAP 0.44046552 0
TP53TG1 TM9SF1 0.440120961 0
TP53TG1 VAMP3 0.43087095 4.4409E‐16
TP53TG1 ITGB4 0.379361437 1.4515E‐12
TP53TG1 LAMP1 0.362507507 1.5766E‐11
TP53TG1 RHEB 0.338549143 3.7103E‐10
TP53TG1 FADD 0.322540605 2.6472E‐09
TP53TG1 RAB7A 0.320497198 3.3743E‐09
TP53TG1 HDAC1 0.316709215 5.2662E‐09
TP53TG1 ATG4B 0.310888419 1.0312E‐08
TP53TG1 DIRAS3 0.305503826 1.8954E‐08
TP53TG1 RGS19 0.300279741 3.5506E‐08
DHRS4‐AS1 PRKAR1A 0.304280356 2.6545E‐08
DHRS4‐AS1 BIRC6 0.307830991 1.7911E‐08
DHRS4‐AS1 RPTOR 0.313316207 9.6535E‐09
DHRS4‐AS1 WDFY3 0.314945434 8.0148E‐09
DHRS4‐AS1 GOPC 0.319627801 5.2128E‐09
DHRS4‐AS1 MAPK1 0.320002607 4.4667E‐09
DHRS4‐AS1 ST13 0.322382713 3.3795E‐09
DHRS4‐AS1 PIK3R4 0.323543353 2.9471E‐09
DHRS4‐AS1 NCKAP1 0.367547056 1.0542E‐11
DHRS4‐AS1 SIRT1 0.40099047 7.8826E‐14
DHRS4‐AS1 NBR1 0.430691637 6.6613E‐16
ZNF674‐AS1 ITPR1 −0.319258764 1.3176E‐08
ZNF674‐AS1 TP53INP2 −0.313961307 7.2452E‐09
ZNF674‐AS1 EIF2S1 0.304321171 2.163E‐08
ZNF674‐AS1 PARP1 0.311082339 1.0086E‐08
ZNF674‐AS1 HDAC1 0.311370172 9.7591E‐09
ZNF674‐AS1 EEF2K 0.319838206 3.6476E‐09
ZNF674‐AS1 TM9SF1 0.321374147 3.0412E‐09
ZNF674‐AS1 GNAI3 0.331509862 8.9278E‐10
ZNF674‐AS1 FKBP1A 0.339350907 3.3524E‐10
ZNF674‐AS1 ATG4B 0.34228221 2.308E‐10
ZNF674‐AS1 PELP1 0.342564645 2.226E‐10
ZNF674‐AS1 FADD 0.344531987 1.7285E‐10
ZNF674‐AS1 ENSG00000177993.3 0.344786314 1.6727E‐10
ZNF674‐AS1 HGS 0.349662833 8.8614E‐11
ZNF674‐AS1 WDR45 0.350583974 7.8496E‐11
ZNF674‐AS1 CAPN10 0.357666059 3.0499E‐11
ZNF674‐AS1 RHEB 0.357722928 3.0266E‐11
ZNF674‐AS1 MAP1LC3C 0.358011106 2.9109E‐11
ZNF674‐AS1 BIRC5 0.365800568 3.2339E‐11
ZNF674‐AS1 MAP2K7 0.37311006 3.5731E‐12
ZNF674‐AS1 STK11 0.375183168 2.6561E‐12
ZNF674‐AS1 GNB2L1 0.402865027 4.1078E‐14
ZNF674‐AS1 PRKAB1 0.411848633 9.77E‐15
ZNF674‐AS1 EIF4EBP1 0.441387008 0
ZNF674‐AS1 RAF1 0.471527103 0
ZNF674‐AS1 HDAC6 0.529212582 0
MAPKAPK5‐AS1 DLC1 −0.37599408 2.7613E‐12
MAPKAPK5‐AS1 CTSB −0.321021809 3.1711E‐09
MAPKAPK5‐AS1 RGS19 −0.304801176 2.1559E‐08
MAPKAPK5‐AS1 PRKCD −0.303327412 7.978E‐08
MAPKAPK5‐AS1 APOL1 −0.301552957 5.851E‐08
MAPKAPK5‐AS1 EIF2S1 0.306873043 1.6255E‐08
MAPKAPK5‐AS1 STK11 0.316360528 5.4847E‐09
MAPKAPK5‐AS1 ATG3 0.317029326 5.073E‐09
MAPKAPK5‐AS1 GNB2L1 0.325707112 1.8109E‐09
MAPKAPK5‐AS1 PRKAB1 0.332894214 7.5251E‐10
MAPKAPK5‐AS1 MAP2K7 0.334240467 6.3674E‐10
MAPKAPK5‐AS1 ATG4B 0.342782716 2.1647E‐10
MAPKAPK5‐AS1 GABARAPL2 0.344580777 1.7176E‐10
MAPKAPK5‐AS1 RAF1 0.368047755 7.3084E‐12
MAPKAPK5‐AS1 HDAC6 0.370823221 4.9445E‐12
MAPKAPK5‐AS1 HGS 0.373857161 3.2117E‐12
MAPKAPK5‐AS1 BID 0.396105786 1.1813E‐13
MAPKAPK5‐AS1 RAB24 0.404333356 3.2641E‐14
MAPKAPK5‐AS1 GABARAP 0.420904922 2.2204E‐15
MAPKAPK5‐AS1 PELP1 0.451556383 0
MAPKAPK5‐AS1 CDKN1B 0.456366201 0
COX10‐AS1 MAP1LC3A −0.375521773 2.53E‐12
COX10‐AS1 TP53INP2 −0.369627107 5.854E‐12
COX10‐AS1 PINK1 −0.367447805 7.9483E‐12
COX10‐AS1 GABARAPL1 −0.345745019 1.4775E‐10
COX10‐AS1 HDAC1 0.30053166 3.2895E‐08
COX10‐AS1 FKBP1A 0.305650956 1.8644E‐08
COX10‐AS1 RB1 0.307329731 1.544E‐08
COX10‐AS1 ATF6 0.308265668 1.3892E‐08
COX10‐AS1 HGS 0.312518629 8.5551E‐09
COX10‐AS1 NAF1 0.313398471 7.7313E‐09
COX10‐AS1 PIK3C3 0.323065939 2.4864E‐09
COX10‐AS1 ITGB1 0.330112011 1.0601E‐09
COX10‐AS1 PRKAB1 0.333014795 7.4136E‐10
COX10‐AS1 GNB2L1 0.344926977 1.6425E‐10
COX10‐AS1 PARP1 0.345318807 1.5614E‐10
COX10‐AS1 EIF2S1 0.3460972 1.4116E‐10
COX10‐AS1 FADD 0.361593998 1.7871E‐11
COX10‐AS1 MYC 0.375188431 2.6541E‐12
COX10‐AS1 EEF2K 0.377661311 1.8581E‐12
COX10‐AS1 EIF4EBP1 0.405855635 2.5757E‐14
COX10‐AS1 EIF2AK3 0.419195952 2.8866E‐15
COX10‐AS1 HDAC6 0.428787718 4.4409E‐16
COX10‐AS1 GNAI3 0.439816001 0
COX10‐AS1 BIRC5 0.448936102 0
COX10‐AS1 RAF1 0.572860513 0
GABPB1‐AS1 BID 0.420118584 2.4425E‐15
GABPB1‐AS1 BIRC6 0.36038314 2.1089E‐11
GABPB1‐AS1 CASP4 −0.343935388 5.2362E‐10
GABPB1‐AS1 CCL2 −0.373849011 3.2156E‐12
GABPB1‐AS1 CCR2 −0.368177579 3.6858E‐11
GABPB1‐AS1 CDKN1B 0.364553409 1.1889E‐11
GABPB1‐AS1 CTSB −0.372819935 3.7241E‐12
GABPB1‐AS1 CTSD −0.43970168 0
GABPB1‐AS1 DAPK1 0.323366578 2.3986E‐09
GABPB1‐AS1 DIRAS3 −0.300618621 3.2582E‐08
GABPB1‐AS1 DLC1 −0.313694777 8.3115E‐09
GABPB1‐AS1 DNAJB1 −0.308644775 1.3309E‐08
GABPB1‐AS1 EEF2 0.36428838 1.2333E‐11
GABPB1‐AS1 GNB2L1 0.304868539 2.0349E‐08
GABPB1‐AS1 GRID2 0.358164661 1.2447E‐10
GABPB1‐AS1 HDAC6 0.583737387 0
GABPB1‐AS1 KLHL24 0.37211066 4.1194E‐12
GABPB1‐AS1 MAP1LC3A −0.335686931 5.3165E‐10
GABPB1‐AS1 MAP2K7 0.351129758 7.3042E‐11
GABPB1‐AS1 MYC 0.350474546 7.9636E‐11
GABPB1‐AS1 NAMPT −0.317006934 5.0863E‐09
GABPB1‐AS1 PARP1 0.318853967 4.0962E‐09
GABPB1‐AS1 PEA15 0.306793364 1.6401E‐08
GABPB1‐AS1 PELP1 0.350174812 8.2843E‐11
GABPB1‐AS1 PPP1R15A −0.432497553 4.4409E‐16
GABPB1‐AS1 PRKCD −0.354522637 2.4201E‐10
GABPB1‐AS1 RAF1 0.518711717 0
GABPB1‐AS1 SERPINA1 −0.35246404 9.1958E‐11
GABPB1‐AS1 SIRT1 0.377399334 1.9298E‐12
GABPB1‐AS1 SQSTM1 −0.300514253 3.2958E‐08
GABPB1‐AS1 VAMP3 −0.349193236 9.4249E‐11
GABPB1‐AS1 WIPI1 −0.425794454 8.8818E‐16
DDX11‐AS1 PINK1 −0.380737586 1.1873E‐12
DDX11‐AS1 PRKCD −0.337496704 1.8761E‐09
DDX11‐AS1 MAP1LC3A −0.321966423 2.8346E‐09
DDX11‐AS1 TP53INP2 −0.315547251 6.0291E‐09
DDX11‐AS1 EIF2AK3 0.301487968 2.9609E‐08
DDX11‐AS1 EIF2S1 0.34484361 1.6603E‐10
DDX11‐AS1 FKBP1A 0.36028176 2.1383E‐11
DDX11‐AS1 MAP2K7 0.361662635 1.7704E‐11
DDX11‐AS1 MYC 0.36701736 8.4412E‐12
DDX11‐AS1 PRKAB1 0.367277985 8.1393E‐12
DDX11‐AS1 HGS 0.398721365 7.8604E‐14
DDX11‐AS1 PARP1 0.399014394 7.5051E‐14
DDX11‐AS1 GNB2L1 0.446121011 0
DDX11‐AS1 HDAC6 0.4630659 0
DDX11‐AS1 BIRC5 0.494242839 0
DDX11‐AS1 EIF4EBP1 0.494327951 0
DDX11‐AS1 RAF1 0.595959165 0
SBF2‐AS1 SIRT1 −0.359724478 2.6578E‐11
SBF2‐AS1 BID −0.359318057 2.8079E‐11
SBF2‐AS1 EEF2 −0.345315983 1.7787E‐10
SBF2‐AS1 HDAC6 −0.30180643 3.1545E‐08
SBF2‐AS1 HSPA5 0.303106799 2.7341E‐08
SBF2‐AS1 NAMPT 0.305876474 2.0115E‐08
SBF2‐AS1 DLC1 0.313900704 9.0314E‐09
SBF2‐AS1 PPP1R15A 0.315700878 6.5975E‐09
SBF2‐AS1 FKBP1B 0.330524677 1.1347E‐09
SBF2‐AS1 WIPI1 0.347602864 1.3239E‐10
SBF2‐AS1 DIRAS3 0.34895202 1.111E‐10
SBF2‐AS1 CFLAR 0.353930283 5.7749E‐11
SBF2‐AS1 CCR2 0.365426608 6.1051E‐11
SBF2‐AS1 CASP4 0.387535164 1.7764E‐12
SBF2‐AS1 CTSD 0.39811287 1.0303E‐13
SBF2‐AS1 RAB33B 0.41262521 1.0436E‐14
SBF2‐AS1 MAP1LC3A 0.466078431 0
MIR4453HG BIRC6 0.482298774 0
MIR4453HG CCL2 −0.32967133 1.1188E‐09
MIR4453HG CCR2 −0.306263916 5.3174E‐08
MIR4453HG CDKN1B 0.37854434 1.6347E‐12
MIR4453HG CTSD −0.36405224 1.2743E‐11
MIR4453HG EEF2 0.339277778 3.3836E‐10
MIR4453HG EIF2AK3 0.30731381 1.5468E‐08
MIR4453HG EIF2S1 0.300378118 3.3454E‐08
MIR4453HG ERBB2 0.31509245 6.356E‐09
MIR4453HG GRID2 0.33900455 1.3033E‐09
MIR4453HG HDAC6 0.530273615 0
MIR4453HG MBTPS2 0.372080137 4.1376E‐12
MIR4453HG MLST8 0.336179553 4.9987E‐10
MIR4453HG NAF1 0.343120261 2.0729E‐10
MIR4453HG NBR1 0.332149604 9.3012E‐10
MIR4453HG NCKAP1 0.315032585 6.7536E‐09
MIR4453HG PIK3C3 0.398066735 8.7041E‐14
MIR4453HG PIK3R4 0.383400705 8.0247E‐13
MIR4453HG RAF1 0.446543819 0
MIR4453HG SERPINA1 −0.316157599 7.7713E‐09
MIR4453HG SIRT1 0.394985589 1.4033E‐13
MIR4453HG ST13 0.343209924 2.0492E‐10
MIR4453HG TSC2 0.324952539 2.1001E‐09
MIR4453HG USP10 0.330643906 1.3364E‐09
MIR4453HG VAMP3 −0.34420749 1.8024E‐10
MIR4453HG WDFY3 0.321627435 2.9511E‐09
MIR4453HG WIPI1 −0.324415393 2.1154E‐09
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Figure 1.

Figure 1

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Network of prognostic lncRNAs with co‐expressed autophagy genes in glioma. In the centric position, grey blue nodes indicate lncRNAs and the sky blue indicates autophagy genes. The coexpression network is visualized by cytoscape 3.4 software.

Table 2.

Detailed information for 10 autophagy‐related lncRNAs significantly associated with OS in glioma

LncRNA Ensemble ID β SE P HR Lower Upper
PCBP1‐AS1 ENSG00000179818 −0.363 0.184 0.049 0.696 0.485 0.998
TP53TG1 ENSG00000182165 0.443 0.16 0.006 1.558 1.139 2.131
DHRS4‐AS1 ENSG00000215256 −0.253 0.099 0.01 0.776 0.639 0.942
ZNF674‐AS1 ENSG00000230844 0.448 0.199 0.024 1.565 1.06 2.31
MAPKAPK5‐AS1  ENSG00000234608 −0.64 0.245 0.009 0.527 0.326 0.852
COX10‐AS1 ENSG00000236088 0.829 0.194 < 0.001 2.29 1.565 3.351
GABPB1‐AS1 ENSG00000244879 −0.403 0.154 0.009 0.668 0.494 0.904
DDX11‐AS1  ENSG00000245614 0.296 0.128 0.021 1.344 1.046 1.726
SBF2‐AS1  ENSG00000246273 0.134 0.064 0.036 1.143 1.009 1.295
MIR4453HG ENSG00000268471 −0.551 0.155 < 0.001 0.577 0.426 0.781
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Figure 2.

Figure 2

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Kaplan–Meier survival curves for the 10 prognostic lncRNAs for glioma in CCGA dataset. The 10 autophagy‐related lncRNAs were found to be independent prognostic factors for glioma patients, of which five lncRNAs were unfavorable factors (TP53TG1, ZNF674‐AS1, COX10‐AS1, DDX11‐AS1 and SBF2‐AS1) and five lncRNAs were confirmed to be favorable prognostic factors for glioma (PCBP1‐AS1, DHRS4‐AS1, GABPB1‐AS1, MAPKAPK5‐AS1 and MIR4453HG).

The prognostic impact of an autophagy‐related lncRNA signature for glioma

Next, we use a risk score method to develop an autophagy‐related lncRNA signature. We divided the glioma patients into two groups (low‐risk group and high‐risk group) by median risk score (Fig. 3). As a result, the risk score could significantly predict the OS of glioma patients, in which the OS period is longer in the low‐risk group than that in the high‐risk group (median OS 1211 vs 346 days; log rank P < 0.05). Additionally, the Cox regression analysis also revealed a significant prognostic effect of the risk score on the glioma patients (HR = 5.307, 95% CI: 4.195–8.305; P < 0.0001, Fig. 4). Further, we also explored whether the risk score signature is an independent predictor for the prognosis of glioma patients by multivariate Cox regression analysis. As a consequence, a HR of 2.736 indicated that the risk score could significantly contribute to the prediction of survival of glioma patients, eliminating the influence of other factors such as sex, age, grade, radiotherapy, chemotherapy and the molecular status (IDHDampR, TP53.1, EGFR, ATRX and EZH2) (Table 3).

Figure 3.

Figure 3

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Autophagy‐related lncRNA risk score analysis of glioma patients in CCGA. (A) The low and high score group for the autophagy‐related lncRNA signature in glioma patients. (B) The survival status and duration of glioma cases. (C) Heatmap of the 10 key lncRNAs expression in glioma. The color from blue to red shows an increasing trend from low levels to high levels.

Figure 4.

Figure 4

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Kaplan–Meier survival curves for the autophagy‐related lncRNA risk score for glioma in CCGA dataset. The Kaplan–Meier survival curves showed that the OS period is longer in the low‐risk group than that in the high‐risk group in the CCGA datasets (median OS 1211 vs 346 days; log rank P < 0.05).

Table 3.

Multivariate Cox regression analysis of characteristics and risk score in glioma

Variable β SE Wald P HR Lower Upper
Gender −0.022 0.221 0.01 0.921 0.978 0.635 1.507
Age 0 0.01 0 0.987 1 0.98 1.021
Grade 0.831 0.16 26.965 < 0.001 2.296 1.678 3.143
Radiotherapy −0.932 0.206 20.553 < 0.001 0.394 0.263 0.589
Chemotherapy −0.469 0.207 5.143 0.023 0.626 0.417 0.938
IDHDampR −0.639 0.248 6.641 0.01 0.528 0.324 0.858
TP53.1 −0.334 0.186 3.23 0.072 0.716 0.498 1.031
EGFR −0.165 0.212 0.605 0.437 0.848 0.559 1.285
ATRX −0.817 0.41 3.98 0.046 0.442 0.198 0.986
EZH2 0.477 0.271 3.104 0.078 1.612 0.948 2.742
Risk score 1.006 0.243 17.156 < 0.001 2.736 1.699 4.405
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Clinical value of the lncRNA signature for glioma patients

Subsequently, we also determined the clinical value of the 10‐lncRNA signature regarding the grade, radiotherapy and chemotherapy. As shown in the Table 4, the risk score tends to increase in the higher grades, suggesting that this lncRNA signature might be associated with the progression of glioma. Interestingly, the risk score was lower in patients receiving radiotherapy than that in patients without radiotherapy (t = −2.267, P = 0.025). In contrast to the results of radiotherapy, a higher risk score was found in patients without chemotherapy, while the patients who had received chemotherapy presented a lower risk score. Moreover, we also assessed differences in risk score based on molecular status. As a result, lower risk scores were found in those with the IDH mutation than in those without, indicating a potential association between the lncRNA signature and IDH mutation.

Table 4.

Clinical impact of risk score signature for the CCGA cohort

Clinicopathological feature n Risk score
Mean SD t P
Grade
I–II 216 1.203847424 0.8808 10.898 < 0.001
III–IV 109 0.245607385 0.6717
Radiotherapy
Yes 212 0.777059137 0.9674 −2.267 0.025
No 84 1.029456602 0.8189
Chemotherapy
Yes 158 1.01284085 0.8665 2.604 0.01
No 128 0.727299406 0.9862
IDH (DNA and RNA)
Mutation 171 0.497626264 0.8174 −8.69 < 0.001
Wildtype 154 1.30979323 0.8671
IDH1‐R32
Wildtype 162 0.509472199 0.8242 −7.824 < 0.001
Mutation 163 1.253176395 0.8881
TP53.1
Wildtype 189 0.937494898 0.9505 1.254 0.211
Mutation 136 0.805997888 0.9062
EGFR
Wildtype 110 0.770831973 0.9802 −1.546 0.123
Mutation 215 0.939584798 0.905
ATRX
Wildtype 33 0.856142312 0.9358 −0.171 0.865
Mutation 292 0.885443672 0.9343
EZH2
Wildtype 37 1.182088779 1.1162 1.77 0.084
Mutation 288 0.843975569 0.9019
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Validation in the TCGA dataset

Next, these results were further validated in the additional dataset (TCGA) using the same β value. In total, 160 GBM patients were enrolled for the validation of the lncRNA signature (Fig. 5). We divided these patients into the high‐risk and low‐risk groups on the basis of the median value of the risk score. Consistent with the results derived from the CGGA dataset, the high‐risk patients had a shorter median OS than that of the low‐risk patients (median OS 385 vs 468 days; log rank P = 0.012; Fig. 6). This finding was further validated by Cox regression analysis, in which the high‐risk group tended to have a shorter OS time for GBM patients than that of the low‐risk group (HR = 1.544, 95% CI: 1.110–2.231; P = 0.031). In light of these results, we could confirm that the lncRNA signature provides a robust prediction for the prognosis of glioma patients.

Figure 5.

Figure 5

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Autophagy‐related lncRNA risk score analysis of glioma patients in TCGA. (A) The low and high score group for the autophagy‐related lncRNA signature in glioma patients. (B) The survival status and duration of glioma cases. (C) Heatmap of the 10 key lncRNAs expressed in glioma. The color from blue to red shows an increasing trend from low levels to high levels.

Figure 6.

Figure 6

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Kaplan–Meier survival curves for the autophagy‐related lncRNA risk score for glioma in TCGA dataset. Consistent with the results derived from the CGGA dataset, the high‐risk patients had a shorter median OS than that of the low‐risk patients in TCGA datasets (median OS 385 vs 468 days; log rank P = 0.012).

Gene set enrichment analysis

Further functional annotation was conducted through GSEA. The results revealed that the differentially expressed genes between the two groups were enriched in the autophagy‐related and tumor‐related pathways. As result, a total of 19 gene sets were significantly enriched at a nominal P‐value < 5% (Table 5). Among the gene sets, several pathways are well‐established in cancers, including interleukin (IL) 6/Janus kinase/signal transducer and activator of transcription (STAT) 3 signaling, tumor necrosis factor α signaling via nuclear factor‐κB, IL2/STAT5 signaling, the p53 pathway and the KRAS signaling pathway (Fig. 7). Moreover, the gene sets were also found to be involved in the vital functions of tumorigenesis and progression of cancer. For instance, epithelial mesenchymal transition, angiogenesis and hypoxia were closely related to the invasion and metastasis of cancer (Fig. 8). Notably, the GSEA revealed that the gene sets were involved in the reactive oxygen species pathway, interferon (IFN)‐γ response, IFN‐α response and inflammatory response, which are strongly associated with autophagy (Fig. 9). Taken together, the defined autophagy‐related genes contribute to vital cancer and autophagy pathways, which might provide strong evidence for a cancer‐targeted treatment for glioma.

Table 5.

Gene set enrichment analysis results based on the signature of 10 autophagy lncRNAs

Name Size ES NES NOM P‐value FDR q‐value FWER P‐value Rank at max Leading edge
Hallmark_Interferon_gamma_response 194 0.663942 2.002969 0.003831 0.03201 0.019 3784 tags = 62%, list = 18%, signal = 74%
Hallmark_Coagulation 134 0.546977 1.968419 0 0.024199 0.027 4273 tags = 47%, list = 20%, signal = 58%
Hallmark_Allograft_rejection 196 0.606039 1.934347 0.005894 0.02353 0.037 4277 tags = 59%, list = 20%, signal = 73%
Hallmark_Epithelial_mesenchymal_transition 195 0.61046 1.914759 0.01354 0.02293 0.051 4360 tags = 62%, list = 20%, signal = 77%
Hallmark_Interferon_alpha_response 95 0.695722 1.901815 0.007937 0.020948 0.057 3003 tags = 61%, list = 14%, signal = 71%
Hallmark_Il6_jak_stat3_signaling 86 0.627249 1.854703 0.011905 0.02949 0.083 4232 tags = 60%, list = 20%, signal = 75%
Hallmark_Tnfa_signaling_via_nfkb 197 0.609462 1.79719 0.024 0.041906 0.129 4243 tags = 58%, list = 20%, signal = 72%
Hallmark_Angiogenesis 35 0.582243 1.740052 0.005988 0.059463 0.178 4728 tags = 57%, list = 22%, signal = 73%
Hallmark_Complement 192 0.472855 1.732826 0.026263 0.056876 0.188 3996 tags = 45%, list = 19%, signal = 55%
Hallmark_Hypoxia 197 0.484747 1.725543 0.034068 0.053391 0.195 3666 tags = 45%, list = 17%, signal = 54%
Hallmark_Glycolysis 194 0.445341 1.707246 0.014 0.055343 0.223 4979 tags = 49%, list = 23%, signal = 64%
Hallmark_Il2_stat5_signaling 196 0.451793 1.706509 0.017341 0.050981 0.223 5329 tags = 54%, list = 25%, signal = 71%
Hallmark_Reactive_oxigen_species_pathway 46 0.522081 1.675037 0.017964 0.061012 0.263 2719 tags = 39%, list = 13%, signal = 45%
Hallmark_Inflammatory_response 194 0.527449 1.670621 0.037549 0.057958 0.266 5277 tags = 59%, list = 25%, signal = 77%
Hallmark_P53_pathway 195 0.411785 1.645334 0.027559 0.063102 0.302 4075 tags = 38%, list = 19%, signal = 47%
Hallmark_Kras_signaling_up 198 0.419712 1.634183 0.028 0.063116 0.318 4808 tags = 51%, list = 22%, signal = 64%
Hallmark_Apoptosis 159 0.432405 1.616621 0.023529 0.066577 0.339 5324 tags = 53%, list = 25%, signal = 71%
Hallmark_Apical_surface 44 0.402856 1.481723 0.034483 0.132731 0.539 1498 tags = 23%, list = 7%, signal = 24%
Hallmark_Mtorc1_signaling 193 0.417297 1.473019 0.094378 0.130749 0.55 4257 tags = 44%, list = 20%, signal = 54%
Hallmark_Apical_junction 195 0.35423 1.457525 0.066148 0.133971 0.567 3822 tags = 33%, list = 18%, signal = 40%
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ES, enrichment score; FDR, false discovery rate; FWER, familywise‐error rate; NES, normalized enrichment score; NOM P Value, nominal P Value.

Figure 7.

Figure 7

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Gene set enrichment analysis indicated significant enrichment of hallmark cancer‐related pathways in the high‐risk group based on CCGA dataset. JAK, Janus kinase; NFKB, nuclear factor‐κB; TNFA, tumour necrosis factor α.

Figure 8.

Figure 8

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Gene set enrichment analysis indicated significant enrichment of the progression‐ and metastasis‐related pathway in the high‐risk group based on CCGA dataset.

Figure 9.

Figure 9

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Gene set enrichment analysis indicated significant autophagy‐related enrichment based on CCGA dataset.

Discussion

Glioma is the most aggressive and common type of primary brain tumor in humans. With the development of clinical management of glioma, some prognostic factors are well characterized, including tumor size, tumor grade and stage. High‐throughput biological technologies are being widely used to predict cancer recurrence and tumor metastasis by detecting the alteration of miRNAs or genes 23, 24. The major class of lncRNAs, as a complement to genes or miRNAs, provides a promising opportunity to predict the risk of recurrence of glioma 25. However, so far, there has been no systematic process to identify lncRNA signature sets for predicting the survival of glioma patients. Therefore, it is necessary to establish a lncRNA signature to predict the prognosis of glioma patients.

In this study, two datasets (CGGA and TCGA) were collected to explore the prognosis of autophagy‐related lncRNAs for glioma patients. In the first step, we identified 402 lncRNAs through the lncRNA–autophagy gene co‐expression network. Furthermore, we identified 10 autophagy‐associated lncRNA signatures that could divide glioma patients into high‐ and low‐risk groups based on the median risk score. Additionally, it was found that the OS is longer in the low‐risk group than that in the high‐risk group. Through univariate and multivariate Cox regression analyses, we can conclude that the signature is an independent factor that is significantly related to OS.

Although little is known about the role of autophagy in cancer therapy to date, recent studies suggest that autophagy therapy will become a new approach to glioma treatment 26, 27. In recent studies, IFN‐γ was found to influence autophagy and cell growth in human hepatocellular carcinoma (HCC) cells. IFN‐γ is a cytokine with anti‐viral and immune regulation. The cytokine induces autophagosome formation and transformation of microtubule‐associated protein 1 light chain 3 proteins and can inhibit cell growth and non‐apoptotic cell death in Huh7 cells. In addition, autophagy in Huh7 cells is also activated by the overexpression of interferon‐regulatory factor‐1. Eventually, induced autophagy will inhibit IFN‐γ and cell death in HCC 28. Since autophagy can respond to a variety of stresses to promote the survival of cancer cells, it has protumorigenic functions. Glucose metabolism promotes adhesion‐independent conversion driven by oncogene insult‐mutationally active Ras. In human cancer cell lines carrying KRAS mutations and cells ectopically expressing oncogenic H‐Ras, autophagy is induced after the extracellular matrix is isolated. If autophagy is inhibited by RNA interference‐mediated depletion of multiple autophagic regulators or genetic deletion, Ras‐mediated conversion and glycolytic capacity proliferation independent of adhesion will be impaired. In addition, when the availability of glucose is decreased, the conversion and proliferation of autophagy‐deficient cells expressing oncogenic Ras are unaffected, which is just the opposite of that in autophagy‐competent cells. In conclusion, autophagy can promote the unique mechanism of Ras‐driven tumor growth in specific metabolic environments 29.

Among the 10 autophagy‐related lncRNAs, PCBP1‐AS1, DHRS4‐AS1, MAPKAPK5‐AS1 and GABPB1‐AS1 were risk‐associated genes, while TP53TG1, ZNF674‐AS1, DDX11‐AS1, SBF2‐AS1, MIR4453HG and COX10‐AS1 were protective genes. Specifically, we also found that the high‐risk group was enriched in the glycolysis pathway. Consistent with our studies, a recent study revealed that TP53TG1 might affect the expression of glucose metabolism‐related genes under glucose deprivation, leading to cell proliferation and migration of glioma cells 30. Additionally, MAPKAPK5‐AS1 regulates gene expression by acting with miRNAs and is significantly associated with the OS of liver cancer 31. Furthermore, the expression of COX10‐AS1 in oral cavity and oropharyngeal squamous cell carcinoma is more than twice that of normal cells 32.

All of the lncRNAs we identified directly or indirectly regulate autophagy, many by regulating miRNAs; thus, we must perform lncRNA–mRNA co‐expression analyses to assess the function of lncRNAs 33, 34, 35. Therefore, we can conclude that due to the various functions of lncRNAs, the 10 autophagy‐related lncRNAs we identified will be potential therapeutic targets 12, 36.

In conclusion, by constructing an autophagy–lncRNA coexpression network, we identified a signature of 10 autophagy‐related lncRNAs, which has prognostic value for glioma patients. In addition, our study classified low‐risk and high‐risk groups based on the median risk score, and each showed different autophagy states.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

LM designed the study, and revised the manuscript. FL, the main author of study, conceived and designed the analysis and wrote the manuscript. WC and MC took part in analyzing the data and writing the manuscript. JY and HC analyzed the data and conducted the results. HY and TL contributed to writing and revising the manuscript. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by Guangxi Medical Health Appropriate Technology Development and Application Project (S2017099).

Fangkun Luan and Wenjie Chen contributed equally to this article

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