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. 2021 Feb 1;13(4):5104–5119. doi: 10.18632/aging.202431

Prognostic value of an autophagy-related long-noncoding-RNA signature for endometrial cancer

Xiufang Wang 1, Chenyang Dai 1, Minqing Ye 2, Jingyun Wang 1, Weizhao Lin 1, Ruiman Li 1,✉
PMCID: PMC7950257  PMID: 33534780

Abstract

This study retrieved the transcriptome profiling data of 552 endometrial cancer (EC) patients from the TCGA (The Cancer Genome Atlas) database, and identified 1297 lncRNAs (long noncoding RNAs) related to autophagy genes using Pearson correlation analysis. Univariate Cox regression analysis of the training data set revealed that 14 autophagy-related lncRNAs had significantly prognostic value for endometrial cancer (P < 0.01). Multivariate Cox regression analysis of these autophagy-related lncRNAs established the following autophagy-related lncRNA prognosis signature for endometrial cancer: PI = (0.255 × AC005229.4 expression) + (0.405 × BX322234.1 expression) + (0.169 × FIRRE expression value) + (–0.122 × RAB11B-AS1 expression) + (–0.338 × AC003102.1 expression). This signature was validated in both the testing data set and the entire data set. The areas under the receiver operating characteristics curves for the 1-, 3-, and 5-year overall survival rates in the entire data set were 0.772, 0.733, and 0.714, respectively. In addition, a gene set enrichment analysis confirmed that cancer-related and autophagy-related pathways were significantly up-regulated in the high-risk group. In summary, this study has demonstrated that a signature comprising five autophagy-related lncRNAs has potential as an independent prognostic indicator of endometrial cancer, and also that these lncRNAs may play a key role in the development of endometrial cancer.

Keywords: endometrial cancer, autophagy, long-noncoding-RNA, TCGA, prognostic signature

INTRODUCTION

Endometrial cancer (EC) is a common malignant tumor in gynecology that seriously threatens the physical and mental health of females. The latest data from the American Cancer Society indicate that EC is the most-common malignant tumor of the female reproductive system in the United States [1]. EC has also become the second-most-common gynecological malignant tumor (after cervical cancer) in China, where its incidence is increasing and the mean onset age is decreasing. Although surgery is effective for treating most patients with early-stage EC, the prognosis of cases at stages III and IV remains very poor, with 5-year overall survival (OS) rates of 47~69% and 15~17%, respectively [2], and there remains a risk of recurrence or metastasis even after surgery in some EC patients. This situation indicates the importance of the early identification of EC patients at high risk of recurrence and metastasis.

The indicators that are commonly used in clinical risk assessments of EC are mainly based on clinicopathological characteristics such as the pathological tissue type, tumor pathological grade, FIGO stage, muscle invasion depth, and tumor size [3]. Advanced age and lymphatic vascular space invasion are also predictors of a poor prognosis in patients with EC [4, 5]. However, these prognosis criteria and classifications of EC have limitations in clinical practice that make them unsuitable for accurately predicting the prognosis of EC patients [6, 7]. This may lead to inaccurate assessments of the condition of EC patients, and hence either undertreatment or overtreatment. There is therefore an urgent need for effective indicators of the prognosis to be identified in order to help EC patients with more-targeted treatment options so as to improve their prognosis. In short, the development of new predictive biomarkers is essential for the pathogenesis, prognosis, evaluation, and biological treatment of EC.

Autophagy is a degradation pathway that is highly conserved during the evolution of eukaryotes. The formation of a double-layer membrane structure allows the transportation of damaged organelles, misfolded and aggregated proteins, and other macromolecular substances to the lysosome for degradation or recycling [8]. Autophagy plays very complex roles in tumors, including inhibiting or promoting them in different environments and stages of cancer development [9, 10]. Autophagy is generally beneficial during the normal state of the body and the early stages of tumors, by eliminating oncogenic protein substrates, misfolded proteins, and damaged organelles, maintaining cell homeostasis, and either preventing tumors from occurring or inhibiting their progression [11]. However, once tumor develop to an advanced stage, autophagy—as a dynamic degradation and recycling system—promotes their survival and growth by enhancing the living ability of cancer cells in an environment characterized by nutrient starvation and hypoxia [12, 13]. Autophagy can also enhance the resistance of tumors to anticancer treatments such as radiotherapy, chemotherapy, and targeted therapy [14].

The dynamic role of autophagy in tumor progression has received considerable attention in research into clinical treatments. Regulating autophagy activity to inhibit tumor development has emerged as a new direction for tumor treatments. Autophagy and EC are closely related, with studies showing that autophagy plays a vital role in the development and survival mechanism of EC [15]. Giatromanolaki et al. and Deng et al. found that certain autophagy-related factors are overexpressed in EC tissues and can promote the occurrence and development of these tumors [16, 17]. The PI3K-Akt-mTOR signal transduction pathway is often overactivated in EC [18], and autophagy inhibitors such as rapamycin and chloroquine can inhibit the proliferation of EC cells [19, 20]. Autophagy is therefore a potential target for exploring the pathogenesis of EC.

Long noncoding RNA (lncRNA) is a noncoding RNA longer than 200 nucleotides that has no protein coding function. This type of RNA can participate in regulation via various mechanism, such as epigenetic regulation, transcription regulation, and posttranscriptional regulation. Gene expression plays an important role in various biological processes such as cell proliferation, differentiation, and apoptosis [21–23]. lncRNAs have been shown to be closely related to human diseases, especially those involving tumors [24, 25]. lncRNAs are abnormally expressed in a broad spectrum of tumors, and they play a key role in tumor occurrence, metastasis, and chemotherapy resistance, including in EC [26, 27]. lncRNAs the proliferation, migration, and invasion of EC cells by participating in various signal pathways, and they are potential targets for EC therapy and biomarkers for early diagnoses [28].

Autophagy is an important regulatory pathway for tumors that is closely related to lncRNA. Autophagy and lncRNA work together in tumors and other human diseases [29]. Many lncRNAs are involved in the dynamic process of autophagy, and can regulate the progression of most tumors by regulating the transcription and posttranscriptional autophagy-related genes [30, 31]. Example of this include AC023115.3 lncRNA, which increases the chemosensitivity of glioma cells to cisplatin by inhibiting autophagy [32]. Conversely, Li et al. found that MALAT1 lncRNA promotes the progression of pancreatic cancer by enhancing autophagy [33], while AC023115.3 lncRNA improves the chemosensitivity of glioma cells to cisplatin by regulating the miR-26a-GSK3β-Mcl1 pathway. Long-chain noncoding MEG3 interacts with ATG3 so as to increase the level of autophagy, resulting in inhibition of the occurrence and development of epithelial ovarian cancer [34]. LncRNAs, specifically HOTAIR, contribute to the cisplatin resistance of EC cells by enhancing autophagy [35]. Since these autophagy-related lncRNAs play important regulatory roles in the proliferation, metastasis, and chemotherapy resistance of tumor cells, they may be useful for prognosis evaluations of EC patients and as potential therapeutic targets for EC.

This study analyzed the lncRNAs data of EC patients in the TCGA (The Cancer Genome Atlas) database, identified autophagy-related lncRNAs related to the prognosis of EC, and constructed a novel autophagy-related lncRNA prognosis signature for EC. The present findings provide new ideas and directions for future investigations of the pathogenesis and prognosis of EC.

RESULTS

Identification of autophagy-related lncRNAs in EC

We extracted 14,142 lncRNA data sets and 210 autophagy-related genes from the TCGA database. The coefficients for the correlations between lncRNAs and autophagy-related genes were calculated using Pearson correlation. Applying screening criteria of a correlation coefficient of >0.3 and P<0.001 resulted in the identification of 1297 autophagy-related lncRNAs.

Construction of a signature of five autophagy-related lncRNAs for patients with EC

We used the caret package in R software to randomly divide the EC samples into the training and testing data sets. Applying univariate Cox regression analysis to the training data set revealed 14 autophagy-related lncRNAs that had a significant prognostic value for EC (P<0.01). The detailed information of 14 autophagy-related lncRNA significantly related to OS are presented in Table 1. The following autophagy-related lncRNA prognosis signature was established for EC: PI = (0.255 × AC005229.4 expression) + (0.405 × BX322234.1 expression) + (0.169 × FIRRE expression) + (–0.122 × RAB11B-AS1 expression) + (–0.338 × AC003102.1 expression). The positive coefficients for AC005229.4, BX322234.1, and FIRRE in this signature indicate that patients with high expression levels of these lncRNAs had worse survival, whereas those with high expression levels of RAB11B-AS1 and AC003102.1 had better survival. The correlations between these five lncRNAs and autophagy genes are presented in Table 2 and Figure 1.

Table 1. Detailed information of 14 autophagy-related lncRNA significantly related to OS in EC.

lncRNA KM B SE HR HR.95L HR.95H P-value
LINC00662 0.002 0.266 0.074 1.305 1.129 1.508 0.000
AC017074.1 0.001 0.047 0.018 1.049 1.012 1.086 0.008
AC079807.1 0.008 0.805 0.204 2.236 1.499 3.334 0.000
LNCTAM34A 0.001 -0.318 0.123 0.727 0.571 0.926 0.010
AC107057.1 0.000 0.096 0.033 1.101 1.033 1.174 0.003
AC003102.1 0.006 -0.418 0.148 0.658 0.493 0.879 0.005
RAB11B-AS1 0.010 -0.204 0.076 0.815 0.703 0.945 0.007
AC005229.4 0.001 0.274 0.088 1.316 1.107 1.564 0.002
KRT7-AS 0.004 0.170 0.052 1.185 1.071 1.312 0.001
BX322234.1 0.002 0.578 0.140 1.783 1.356 2.345 0.000
AC006329.1 0.004 0.106 0.034 1.112 1.040 1.189 0.002
LINC01224 0.005 0.142 0.046 1.153 1.054 1.261 0.002
FIRRE 0.005 0.226 0.063 1.254 1.108 1.419 0.000
AC010894.2 0.003 0.275 0.091 1.317 1.101 1.575 0.003
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Table 2. Expression correlations between autophagy genes and OS-associated lncRNAs in EC.

LncRNA ARG gene Correlation P-value
AC005229.4 RHEB 0.365765623 6.46E-19
BX322234.1 WIPI2 0.312898199 5.29E-14
BX322234.1 UVRAG 0.446190163 2.32E-28
BX322234.1 SPNS1 0.402240936 6.98E-23
BX322234.1 RPTOR 0.369415813 2.73E-19
BX322234.1 CDKN2A 0.327356943 2.97E-15
BX322234.1 ATG4D 0.313754433 4.48E-14
FIRRE WDFY3 0.499216145 3.96E-36
FIRRE VAMP7 0.330521098 1.55E-15
FIRRE SIRT1 0.328338857 2.43E-15
FIRRE RB1CC1 0.536857049 1.57E-42
FIRRE RAB33B 0.383946076 7.87E-21
FIRRE PTEN 0.401258287 9.07E-23
FIRRE PIK3R4 0.511169817 4.47E-38
FIRRE PIK3C3 0.389171215 2.10E-21
FIRRE PEX3 0.340283484 1.98E-16
FIRRE NCKAP1 0.51556263 8.23E-39
FIRRE NAMPT 0.329353888 1.97E-15
FIRRE NAF1 0.305954124 2.00E-13
FIRRE MBTPS2 0.405194041 3.17E-23
FIRRE KLHL24 0.635974705 6.71E-64
FIRRE GOPC 0.35438881 8.88E-18
FIRRE GNAI3 0.427794497 5.71E-26
FIRRE FOXO1 0.318055813 1.93E-14
FIRRE EIF2AK3 0.408316782 1.36E-23
FIRRE EIF2AK2 0.408328314 1.36E-23
FIRRE CHMP2B 0.335022602 6.05E-16
FIRRE BIRC6 0.626085477 2.04E-61
FIRRE ATG2B 0.522143589 6.22E-40
RAB11B-AS1 VAMP7 -0.350040404 2.35E-17
RAB11B-AS1 USP10 -0.324970779 4.83E-15
RAB11B-AS1 SIRT1 -0.358295007 3.65E-18
RAB11B-AS1 RAB7A -0.321930194 8.91E-15
RAB11B-AS1 PIK3R4 -0.424176953 1.62E-25
RAB11B-AS1 PEX3 -0.352755027 1.28E-17
RAB11B-AS1 NFKB1 -0.371001989 1.87E-19
RAB11B-AS1 NFE2L2 -0.367478845 4.32E-19
RAB11B-AS1 NCKAP1 -0.386736941 3.90E-21
RAB11B-AS1 NAF1 -0.339333786 2.42E-16
RAB11B-AS1 MTOR -0.319280676 1.51E-14
RAB11B-AS1 MBTPS2 -0.382826961 1.04E-20
RAB11B-AS1 MAPK1 -0.38722407 3.45E-21
RAB11B-AS1 MAP2K7 0.30129363 4.78E-13
RAB11B-AS1 MAP1LC3A 0.377692003 3.70E-20
RAB11B-AS1 ITGB1 -0.419554687 6.04E-25
RAB11B-AS1 HSPA8 -0.336012079 4.91E-16
RAB11B-AS1 GNAI3 -0.415554018 1.86E-24
RAB11B-AS1 FOXO3 -0.302776485 3.63E-13
RAB11B-AS1 EIF4G1 -0.353547373 1.07E-17
RAB11B-AS1 EIF2S1 -0.322388095 8.13E-15
RAB11B-AS1 EIF2AK3 -0.304404232 2.67E-13
RAB11B-AS1 EIF2AK2 -0.338353032 2.99E-16
RAB11B-AS1 EDEM1 -0.364453974 8.79E-19
RAB11B-AS1 BIRC6 -0.305110956 2.34E-13
RAB11B-AS1 BECN1 -0.31318785 5.00E-14
RAB11B-AS1 ATG16L2 0.324723157 5.08E-15
RAB11B-AS1 ATG16L1 -0.301721876 4.41E-13
RAB11B-AS1 ATF6 -0.306838338 1.69E-13
RAB11B-AS1 ARNT -0.322264444 8.34E-15
AC003102.1 ULK3 0.355676499 6.63E-18
AC003102.1 ATG16L2 0.375825357 5.83E-20
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Figure 1.

Figure 1

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The co-expression network of OS-associated lncRNAs and autophagy genes in endometrial cancer. Among them, the pink node represents the lncRNA, and the blue node represents the co-expressed autophagy gene.

Prognosis evaluation of the autophagy-related lncRNA signature in patients with EC in the training data set

We used the above formula to calculate the prognosis risk score for each patient in the training data set. The patients were divided into high- and low-risk groups by using the median score as the cutoff. The distributions of the risk scores, survival status, and survival duration of the 372 EC patients and the expression heatmap for the 5 lncRNAs are shown in Figure 2A. The K-M survival curve showed that OS was significantly worse for EC patients in the high-risk group than for those in the low-risk group (P<0.001, Figure 2B). ROC curves of the 1-, 3-, and 5-year OS rates drawn to evaluate the sensitivity and specificity of the prognosis signature revealed AUCs of 0.767, 0.727, and 0.730, respectively (Figure 2C). This indicates that the prognosis signature could be used to predict the prognosis of EC patients in the training data set.

Figure 2.

Figure 2

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The evaluation of the autophagy-related lncRNA signature in the training dataset. (A) Autophagy-related lncRNA risk score analysis (Risk score distribution of the EC patients; survival status and duration of the EC patients; Heatmap of the 5 lncRNAs expression). (B) Kaplan-Meier survival analysis for EC patients in the training dataset; (C) Time-dependent ROC curve analysis for EC patients in the training dataset.

Validation of the autophagy-related lncRNA signature in the testing and entire data sets

We also tested the predictive power of the prognosis signature in the testing data set (n=156) and the entire data set (n=528). The formula was used to calculate the risk scores for EC patients in the testing data set and in the entire data set, and then the EC patients were divided into high- and low-risk groups using the cutoff for the training data set. K-M survival curves for the testing data set and the entire data set showed that the OS remained lower for EC patients in the high-risk group than for those in the low-risk group (Figure 3A, 3B). The AUCs for 1-, 3-, and 5-year OS rates were 0.849, 0.748, and 0.669, respectively, in the testing data set, and 0.772, 0.733, and 0.714 in the entire data set (Figure 3C, 3D). This reverification process showed that the prognosis signature had good accuracy and robustness.

Figure 3.

Figure 3

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The validation of the autophagy-related lncRNA signature in the testing dataset and entire dataset. (A) Kaplan-Meier survival analysis for EC patients in the testing dataset; (B) Kaplan-Meier survival analysis for EC patients in the entire dataset; (C) Time-dependent ROC curve analysis for EC patients in the testing dataset. (D) Time-dependent ROC curve analysis for EC patients in the entire dataset.

Independence of the autophagy-related lncRNA signature for EC patients

The independent value of the autophagy-related lncRNA prognosis signature was evaluated by performing univariate and multivariate Cox regression analyses of the model and the clinical prognostic factors in the entire data set. The clinical prognostic factors comprised age, pathological type (endometrioid adenocarcinoma versus mixed and serous adenocarcinoma), FIGO stage (stage I + stage II versus stage III + stage IV), and pathological grade (grade 1 + grade 2 versus grade 3). The univariate Cox regression analysis showed that the autophagy-related lncRNA prognosis signature and the pathological type, age, FIGO stage, and tumor pathological grade were associated with the prognosis of EC patients (P<0.05) (Figure 4A). Meanwhile, the multivariate Cox regression analysis showed that the autophagy-related lncRNA prognosis signature and age, FIGO stage, and tumor pathological grade were independent prognostic factors for EC patients, whereas the pathological type was not (Figure 4B).

Figure 4.

Figure 4

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The forest plots of univariate (A) and multivariate (B) Cox regression analysis of the prognostic value in the entire dataset.

The prognostic effects of the autophagy-related gene prognosis signature were compared with those of other clinical factors by drawing ROC curves for the 1-year OS. The AUC was 0.772 for the autophagy-related lncRNA prognosis signature, and 0.555, 0.592, 0.740, and 0.649 for the pathological type, age, FIGO stage, and pathological grade, respectively. These values indicate that our autophagy-related lncRNA prognosis signature has better prognostic potential than the other clinical factors (Figure 5).

Figure 5.

Figure 5

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ROC curve analysis for 1-year OS in the entire dataset.

Clinical utility of the autophagy-related lncRNA signature

We further analyzed the relationships between the autophagy-related lncRNA prognosis signature and age, pathological grade, FIGO grade, and pathological type of EC patients. The results show that, the difference of the risk score for our signature was observed between age > 60 and age ≤ 60 (P <0.001). Besides, the risk score for our signature was higher in Stage III-IV than in Stage I-II (P <0.001), and higher in G3 than G1-2 (P <0.001), and higher in mixed and serous adenocarcinoma than endometrioid adenocarcinoma(P < 0.001) (Figure 6). The above results fully prove that the signature is closely related to EC progression.

Figure 6.

Figure 6

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Clinical significance of the prognostic signature of EC. (A) age; (B) pathological grade; (C) FIGO stage; (D) histological type (1 endometrioid adenocarcinoma, 2 mixed and serous adenocarcinoma).

Gene set enrichment analysis

GSEA was applied to the high- and low-risk groups of the autophagy-related lncRNA prognosis signature. The results revealed that 69 pathways were significantly enriched in the high-risk group, including those related to axon guidance, progesterone-mediated oocyte maturation, cancer, ErbB signaling, DNA replication, EC, MAPK, and the cell cycle (false discovery rate: q<0.05) (Table 3). Figure 7 shows that there was partial pathway enrichment in the high-risk group, including in landmark-cancer-related pathways. We similarly found that autophagy-related signaling pathways were also enriched in the high-risk group (Figure 8), further confirming that the identified autophagy-related lncRNAs contribute to important cancer and autophagy pathways, which might represent strong evidence for its usefulness in the development of targeted therapies for EC.

Table 3. Results of gene set enrichment analysis based on the autophagy-related lncRNA signature.

Name Size ES NES NOM p-val FDR q-val FWER p-val
KEGG_AXON_GUIDANCE 129 0.609 2.315 0.000 0.002 0.002
KEGG_CELL_CYCLE 124 0.689 2.223 0.002 0.004 0.010
KEGG_PROGESTERONE_MEDIATED_OOCYTE_MATURATION 85 0.606 2.201 0.000 0.005 0.013
KEGG_PANCREATIC_CANCER 70 0.644 2.229 0.000 0.006 0.009
KEGG_CHRONIC_MYELOID_LEUKEMIA 73 0.621 2.142 0.000 0.006 0.023
KEGG_OOCYTE_MEIOSIS 112 0.594 2.108 0.004 0.007 0.032
KEGG_ERBB_SIGNALING_PATHWAY 87 0.566 2.115 0.000 0.007 0.031
KEGG_PATHWAYS_IN_CANCER 325 0.525 2.146 0.000 0.007 0.023
KEGG_SMALL_CELL_LUNG_CANCER 84 0.592 2.124 0.000 0.008 0.029
KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS 134 0.588 2.077 0.004 0.008 0.039
KEGG_NEUROTROPHIN_SIGNALING_PATHWAY 126 0.548 2.072 0.002 0.008 0.044
KEGG_ADHERENS_JUNCTION 73 0.614 2.082 0.000 0.008 0.038
KEGG_ENDOCYTOSIS 181 0.508 2.058 0.000 0.008 0.051
KEGG_NON_SMALL_CELL_LUNG_CANCER 54 0.617 2.153 0.000 0.009 0.023
KEGG_GLIOMA 65 0.554 2.014 0.000 0.009 0.075
KEGG_TIGHT_JUNCTION 132 0.500 2.016 0.002 0.009 0.072
KEGG_MAPK_SIGNALING_PATHWAY 267 0.481 2.025 0.000 0.009 0.067
KEGG_REGULATION_OF_ACTIN_CYTOSKELETON 213 0.518 2.047 0.000 0.009 0.055
KEGG_BASAL_TRANSCRIPTION_FACTORS 35 0.675 2.026 0.002 0.009 0.066
KEGG_COLORECTAL_CANCER 62 0.585 2.016 0.002 0.009 0.072
KEGG_MISMATCH_REPAIR 23 0.804 2.001 0.002 0.009 0.085
KEGG_INSULIN_SIGNALING_PATHWAY 137 0.502 2.026 0.002 0.009 0.065
KEGG_RNA_DEGRADATION 59 0.654 2.031 0.002 0.009 0.063
KEGG_RENAL_CELL_CARCINOMA 70 0.576 2.037 0.000 0.009 0.06
KEGG_INOSITOL_PHOSPHATE_METABOLISM 54 0.583 1.978 0.002 0.012 0.102
KEGG_GAP_JUNCTION 90 0.523 1.963 0.002 0.013 0.116
KEGG_SPLICEOSOME 127 0.647 1.963 0.014 0.013 0.115
KEGG_ONE_CARBON_POOL_BY_FOLATE 17 0.749 1.939 0.004 0.015 0.146
KEGG_FOCAL_ADHESION 199 0.516 1.943 0.008 0.015 0.14
KEGG_DNA_REPLICATION 36 0.819 1.948 0.004 0.015 0.137
KEGG_TGF_BETA_SIGNALING_PATHWAY 85 0.542 1.910 0.008 0.017 0.176
KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY 75 0.560 1.914 0.012 0.017 0.172
KEGG_PURINE_METABOLISM 157 0.478 1.919 0.002 0.017 0.166
KEGG_ENDOMETRIAL_CANCER 52 0.569 1.914 0.002 0.018 0.171
KEGG_TYPE_II_DIABETES_MELLITUS 47 0.556 1.897 0.002 0.018 0.186
KEGG_PROSTATE_CANCER 89 0.521 1.920 0.000 0.018 0.165
KEGG_FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS 96 0.531 1.900 0.008 0.018 0.183
KEGG_WNT_SIGNALING_PATHWAY 150 0.496 1.902 0.000 0.018 0.179
KEGG_PYRIMIDINE_METABOLISM 98 0.538 1.872 0.008 0.021 0.214
KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC 74 0.516 1.866 0.004 0.021 0.218
KEGG_THYROID_CANCER 29 0.587 1.862 0.010 0.021 0.221
KEGG_RNA_POLYMERASE 29 0.644 1.838 0.015 0.024 0.25
KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION 56 0.557 1.840 0.012 0.024 0.249
KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY 108 0.529 1.834 0.004 0.024 0.256
KEGG_HOMOLOGOUS_RECOMBINATION 28 0.696 1.841 0.022 0.024 0.247
KEGG_DILATED_CARDIOMYOPATHY 90 0.498 1.844 0.006 0.024 0.245
KEGG_LYSINE_DEGRADATION 44 0.567 1.812 0.027 0.027 0.287
KEGG_DORSO_VENTRAL_AXIS_FORMATION 24 0.610 1.813 0.008 0.028 0.283
KEGG_MELANOGENESIS 101 0.471 1.805 0.008 0.028 0.297
KEGG_ACUTE_MYELOID_LEUKEMIA 57 0.518 1.799 0.016 0.029 0.299
KEGG_ECM_RECEPTOR_INTERACTION 84 0.541 1.777 0.016 0.031 0.324
KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY 67 0.486 1.779 0.010 0.032 0.322
KEGG_MTOR_SIGNALING_PATHWAY 52 0.482 1.781 0.020 0.032 0.32
KEGG_PHOSPHATIDYLINOSITOL_SIGNALING_SYSTEM 76 0.496 1.766 0.006 0.033 0.337
KEGG_NOTCH_SIGNALING_PATHWAY 47 0.519 1.755 0.012 0.034 0.352
KEGG_BASAL_CELL_CARCINOMA 55 0.537 1.755 0.012 0.034 0.352
KEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY 102 0.491 1.757 0.028 0.035 0.351
KEGG_LONG_TERM_POTENTIATION 70 0.476 1.747 0.014 0.035 0.364
KEGG_BLADDER_CANCER 42 0.494 1.729 0.016 0.039 0.387
KEGG_PROXIMAL_TUBULE_BICARBONATE_RECLAMATION 23 0.575 1.725 0.012 0.040 0.393
KEGG_CYTOSOLIC_DNA_SENSING_PATHWAY 54 0.503 1.722 0.028 0.040 0.401
KEGG_NUCLEOTIDE_EXCISION_REPAIR 44 0.607 1.710 0.035 0.043 0.424
KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM 30 0.500 1.696 0.010 0.045 0.444
KEGG_MELANOMA 71 0.449 1.697 0.015 0.046 0.439
KEGG_PYRUVATE_METABOLISM 40 0.516 1.685 0.029 0.046 0.466
KEGG_SELENOAMINO_ACID_METABOLISM 25 0.539 1.688 0.031 0.047 0.466
KEGG_JAK_STAT_SIGNALING_PATHWAY 155 0.428 1.685 0.026 0.047 0.466
KEGG_REGULATION_OF_AUTOPHAGY 35 0.499 1.673 0.026 0.049 0.489
KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM 83 0.451 1.675 0.015 0.049 0.486
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* SIZE indicates the number of genes in the gene set; ES represents enrichment score; NES represents normalized enrichment score; NOM p-val represents nominal p value; FDRq-val represents false discovery rate; FWERp-val is Family-wise error rate.

Figure 7.

Figure 7

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Some pathways were enriched in the high-risk group, among which the landmark cancer-related pathways were enriched.

Figure 8.

Figure 8

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Gene set enrichment analysis showed that the autophagy pathway was enriched in the high-risk group.

DISCUSSION

lncRNA has been shown to play an important role in the development and progression of tumors, including EC [36], and can be used as a biomarker for the diagnosis, prognosis, and potential therapeutic targets in various cancers. Recent studies of lncRNAs have identified that many are involved in the regulation of autophagy in tumors, and that most autophagy-related lncRNAs affect the occurrence and development of tumors [37]. Therefore, autophagy-related lncRNAs are a potential and promising target for tumor treatments and prognosis evaluations. Zhou et al. developed a signature based on 13 autophagy-related lncRNAs that could serve as an independent prognosis indicator for lung adenocarcinoma [38], and Luan et al. identified 10 prognostic autophagy-related lncRNAs and validated an autophagy-related-lncRNA-based index for predicting the OS in glioma [39]. However, the prognostic significance of autophagy-related lncRNAs in EC has not been reported previously.

The present study collected expression data of lncRNAs and autophagy-related genes of EC patients in the TCGA database, and evaluated the correlations between lncRNAs and autophagy-related genes using Pearson correlation analysis in order to identify autophagy-related lncRNAs. The obtained samples were randomly divided into training and testing data sets at the proportion of 7:3. In the training data set, we constructed a novel autophagy-related lncRNA prognosis signature using univariate and multivariate Cox regression analyses. After dividing the EC patients into high- and low-risk groups, those in the high-risk group had a worse OS. In addition, our signature was found to be a more-effective independent prognostic factor for EC compared with traditional clinical prognostic factors, and have a good AUC (i.e., higher prognosis resolution). This study also analyzed the relationships between the autophagy-related lncRNA prognosis signature and clinical features, with the results showing that the risk score for the signature tended to increase at higher levels, suggesting that the signature reflects the progression of EC.

Our signature indicates that EC patients with high expression levels of AC005229.4, BX322234.1, and FIRRE have worse survival, while those with high expression levels of RAB11B-AS1 and AC003102.1 have better survival. RAB11B-AS1 can inhibit the development of osteosarcoma via its natural antisense transcript RAB11B, and its low expression level is associated with a poor prognosis of osteosarcoma patients [40]. Shi et al. found that FIRRE lncRNA was overexpressed in diffuse large-B-cell lymphoma (DLBCL) tissue and cells. FIRRE lncRNA can promote the proliferation of tumor cells, reduce cell apoptosis, and is associated with poor OS in DLBCL patients [41]. However, there have been no previous reports on the other three lncRNAs identified in the present study: AC005229.4, BX322234.1, and AC003102.1.

Our GSEA also showed that cancer-related pathways were significantly enriched in the high-risk group, including those related to pancreatic cancer, small-cell lung cancer, EC, cancer, ErbB signaling, MAPK, and other common cancers [42, 43]. Moreover, the autophagy-related signaling pathways were also enriched in the high-risk group. This suggests that the five autophagy-related lncRNAs that we have identified are related to the occurrence and development of EC.

This study was subject to some limitations. First, all of the analyzed data were collected from the TCGA database, and so our novel signature needs to be further validated in other prospective cohorts in order to ensure its robustness. Second, the potential and molecular correlations between our autophagy-related lncRNAs and autophagy need to be studied further. Third, the role and mechanism of these autophagy-related lncRNAs in EC also need to be further validated.

In summary, we have constructed an autophagy–lncRNA coexpression network to explore the molecular markers related to the progression and prognosis of EC, and have developed a signature based on five autophagy-related lncRNAs that has independent prognostic value for EC patients.

MATERIALS AND METHODS

Collection of data on EC patients

The transcriptome profiling data of EC and corresponding clinical information were extracted from the TCGA database at https://portal.gdc.cancer.gov/. The EC data set totaled 552 tumor samples, with clinical follow-up data being available for 528 of the samples. We randomly divided EC patients with clinical follow-up data at the proportion of 7:3 into a training data set (n=372) and a testing data set (n=156). The training data set was used to identify autophagy-related lncRNAs related to the prognosis of EC and to establish a prognosis signature, whose validity and stability were verified in the testing data set (Table 4).

Table 4. Clinical characteristics of EC patients from each database.

Characteristics Training dataset (n=372) Testing dataset (n=156) Entire dataset (n=528) P-value
n % n % n %
Age (year) 0.902
≤60 140 37.63% 62 39.74% 202 38.26%
>60 232 62.37% 94 60.26% 326 61.74%
FIGO stage
I 234 62.90% 98 62.82% 332 62.88% 0.967
II 33 8.87% 18 11.54% 51 9.66%
III 85 22.85% 34 21.79% 119 22.54%
IV 20 5.38% 6 3.85% 26 4.92%
Histological type 0.194
Endometrioid 292 78.49% 111 71.15% 403 76.33%
Mixed and serous 80 21.51% 45 28.85% 125 23.67%
Tumor grade 0.198
G1 73 19.62% 25 16.02% 98 18.56%
G2 93 25.00% 27 17.31% 120 22.73%
G3 206 55.38% 104 66.67% 310 58.71%
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Identification of autophagy-related lncRNA

The lncRNA data and autophagy-related genes were extracted from the transcriptome profiling data of EC obtained from the TCGA database. The list of autophagy genes was obtained from the Human Autophagy Database at http://autophagy.lu/clustering/index.html. Pearson correlation analysis was used to calculate the correlations between lncRNAs and autophagy-related genes. Any lncRNA with a correlation coefficient of >0.3 and P<0.001 was regarded as being related to autophagy.

Construction of a prognosis signature based on autophagy-related lncRNAs

Univariate Cox regression analyses were applied to the training data set to evaluate the prognostic value of autophagy-related lncRNAs. lncRNAs for which P<0.01 were then analyzed by stepwise multivariate Cox regression. According to the principle of the minimum Akaike information criterion, a prognosis signature based on autophagy-related lncRNA was constructed using the following formula: PI=∑i=1n(βi∗lncRNAi), where βi and [lncRNAi] are the regression coefficient and expression value of the i-th autophagy-related lncRNA, respectively, and n is the number of autophagy-related lncRNAs included in the prognosis signature. This formula was used to calculate the risk score for each EC patient, and then all of the EC patients were divided into high- and low-risk groups using the median risk score as the cutoff. Kaplan-Meier (K-M) survival analysis was then used to compare the OS rate between the high- and low-risk groups, with a log-rank P of <0.05 for the survival difference between the two groups considered to be statistically significant.

The receiver operating characteristics (ROC) curve and the area under the ROC (AUC) were used to evaluate the sensitivity and specificity of the autophagy-related lncRNA prognosis signature. We also analyzed the relationship between this signature and other clinical factors related to the prognosis of EC, and further compared the survival prediction capabilities of the prognostic factors.

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was applied to the high- and low-risk groups of the autophagy-related lncRNA prognosis signature. This study verified whether the genes that were differentially expressed between the two groups are enriched during autophagy. In addition, we analyzed whether the autophagy pathway was enriched in the GSEA high-risk group.

Statistical analysis

Statistical analyses were implemented using R software (version 3.6.2). Pearson correlation analysis was used to evaluate the correlations between autophagy genes and lncRNA. Survival analysis was performed by the K-M method, with the log-rank test used for comparisons. The ROC curve analysis was performed using the survivalROC package, while Cytoscape software (version 3.71) was used to construct an autophagy–lncRNA coexpression network. The Gene Set Enrichment Analysis software (version 4.0.3) was used for the GSEA.

ACKNOWLEDGMENTS

We would like to acknowledge TCGA and the Human Autophagy Database for free use.

Abbreviations

EC

endometrial cancer

TCGA

The Cancer Genome Atlas

lncRNA

long non-coding RNA

OS

overall survival

LVSI

lymph-vascular space invasion

ARG

autophagy-related gene

FDR

false discovery rate

KEGG

Kyoto Encyclopedia of Genes and Genomes

PI

prognostic index

ROC

receiver operating characteristic curve

AUC

area under curve

ES

enrichment score

NES

normalized enrichment score

NOM p-val

nominal p-value

Footnotes

AUTHOR CONTRIBUTIONS: WXF designed the study, collected and preliminary analyzed data. DCY, YMQ, WJY and LWZ interpreted the data; WXF and DCY drafted the manuscripts. LRM was in charge of the entire study. The final draft was read and approved by all authors.

CONFLICTS OF INTEREST: The authors declare that there are no conflicts of interest.

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