A seven-lncRNA signature for predicting Ewing’s sarcoma

Introduction

Ewing’s sarcoma (EWS) is a rare but clinically significant solid tumor that primarily affects children, adolescents and young adults (AYAs), with an estimated 1.5 cases per million children and AYAs worldwide (Grünewald et al., 2018). About one in four EWS cases occur in soft tissue rather than bone, and about one in four patients with EWS have detectable metastases at the time of diagnosis (Toomey, Schiffman & Lessnick, 2010). Generally, most EWS patients exhibit tumor-related symptoms such as pain or tissue mass (Balamuth & Womer, 2010). The long-term survival rate of patients has not significantly improved (Li et al., 2015) despite efforts to advance EWS treatment strategies, including irritation therapy and surgery. EWS patients’ 5-year survival rate is less than 30% once metastasis has occurred and this rate has not changed significantly over the past 30 years (Amaral et al., 2014). It is vital to identify novel biomarkers to diagnose the disease and predict EWS cases’ prognosis.

Long non-coding RNAs (lncRNAs) are a recently-defined family of transcripts greater than 200 nucleotides in length (Hu et al., 2014). More than 60,000 lncRNAs have been identified in humans and the number is rapidly increasing (Chi et al., 2019). LncRNAs play a crucial role in regulating gene expression though chromatin modification and remodeling, histone modification, and nucleosome localization changes (Deans & Maggert, 2015). Only a few lncRNA functions have been annotated to date. LncRNAs can regulate the survival, proliferation, invasion, metastasis, and angiogenesis of cancer cells (Sahu, Singhal & Chinnaiyan, 2015). Many lncRNAs are reportedly related to cancer progression and patient prognosis. Colon cancer-associated transcript 1 (CCAT1) is highly expressed in colon cancer tissues when compared to adjacent normal tissues. Increased CCAT1 expression has been associated with clinical stage, lymph nodes metastasis, and survival time after surgery (He et al., 2014). HOX transcript antisense RNA (HOTAIR) expression is prognostically significant in patients with gallbladder cancer (Ma et al., 2014), small cell lung cancer (Ono et al., 2014), and breast cancer (Zhang et al., 2016). LncRNAs have gene specific regulatory functions in living cells, and also have a cell type-specific expression pattern corresponding to their mRNA targets, reflecting the cell type’s characteristic biological functions (Chen et al., 2018). Thus, lncRNAs are a type of non-coding RNAs with unique characteristics and tissue specificity, which may have the potential to become diagnostic and prognostic markers. They may also be potential targets for innovative treatment strategies. For example, lncRNA PCA3s can be detected in urine and are specific to prostate cancer. Detecting this RNA has advantages over the widely used serum-based prostate cancer biomarker PSA (prostate-specific antigen) because it is a noninvasive method for finding prostate cancer (Wu et al., 2017). The specificity of lncRNA tissue has been used to selectively kill tumors without affecting normal tissue. Several small-molecule inhibitors created from lncRNAs have been approved by the FDA and have well defined toxicity, body distribution, pharmacokinetics, and pharmacodynamics data (Chandra Gupta & Nandan Tripathi, 2017).

In this study, we analyzed EWS patients’ expression profile data and clinical information using data from GEO. The Kaplan–Meier (KM) estimator and receiver operating characteristic (ROC) curves were applied to screen out differentially expressed lncRNAs related to the diagnosis and survival of EWS patients. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to determine key lncRNAs. Finally, we developed an independent prognostic prediction model with seven key lncRNAs and further validated the utility of the lncRNA signature using an external dataset.

Materials and Methods

Results

DELncs identification

We downloaded expression profile data from the GEO database and obtained 1,146 lncRNAs, which were used to identify candidate EWS-related lncRNAs. We used the “limma” R package to analyze their differential expressions (—log2FC—>1, adjusted P < 0.05) and our analysis resulted in 78 DELncs, among which 44 DELncs were upregulated and 34 DELncs were downregulated. We plotted all DELncs in heat maps and volcano maps (Figs. 1A and 1B).

Diagnostic and prognostic analysis of all lncRNAs in EWS

We performed the Kaplan Meier survival analysis and ROC curve analysis to evaluate the prognostic and diagnostic value of all DELncs in EWS patients. Ten overlapping lncRNAs were found between DELncs of survival analysis (P < 0.05) and ROC analysis (AUC >0.6) (Fig. 1C). Our results showed that 10 key lncRNAs were significantly associated with overall survival (OS) (Figs. 2A–2J) (SNHG17, log-rank test P = 0.044; LINC00943, P = 0.010; C10orf71-AS1, P = 0.018; LINC00623, P = 0.004; STAU2-AS1, P = 0.050; FOXP1-IT1, P = 0.024; ERVH-1, P = 0.00022; SSBP3-AS1, P = 0.034; WAC-AS1, P = 0.004; TDRG1, P = 0.004). These key lncRNAs had the following 3-year AUC values: 0.624, 0.649, 0.629, 0.687, 0.687, 0.687, 0.704, 0.724, 0.671 and 0.727, respectively.

Construction of the lncRNA signature of EWS

We used univariate Cox regression analysis of the key lncRNAs to screen nine lncRNAs associated with OS (P < 0.05). In the process of constructing the lncRNA prognostic model by LASSO regression analysis, we found that the number of independent coefficients approached zero as lambda increased (Figs. 3B–3D). We performed a ten-fold cross validation procedure for model validation. The confidence interval (CI) under each lambda was analyzed as shown in Fig. 3C. The following prognostic risk score was calculated: (0. 281712273 × SNHG17 expression) + (3. 255312454 × C10orf71-AS1 expression) + (0. 421134052 × LINC00- 623 expression) + (2. 197657636 × STAU2-AS1 expression) + (0. 845771530 × SSBP3-AS1 expression) + (1. 051868412 × WAC-AS1 expression) + (0. 230113692 × TDRG1 expression). EWS patients from the training cohort were categorized into a low-risk group or a high-risk group based on the median value of the risk score. We used the model formula and the seven lncRNAs’ expression to obtain risk scores for each of the 57 EWS samples in the training set. We then calculated the median value of all samples’ risk scores. We compared the median value with the risk score of each sample to obtain a high- and low-risk group. Similarly, we used the model formula and the expression of seven lncRNAs in the validation set to produce a high- and low-risk group. We compared the survival of the low-risk group with that of the high-risk group and found that the survival time of the high-risk group was significantly shorter than the low-risk group (Fig. 3E). We generated ROC curves to assess the prognostic accuracy of the model. The AUC was 0.8 (1-year), 0.905 (3-year), and 0.922 (5-year) (Fig. 3F). The distribution of EWS patients’lncRNA expression, risk score and survival duration were analyzed independently for the training set (Figs. 4A, 4C, and 4E).

Evaluation of the risk model as independent prognostic factor for EWS

Univariate and multivariate Cox regression analyses were performed to assess whether the lncRNA signature-based risk score was an independent prognostic factor for EWS. The HR of the risk score and 95% CI were 4.293, 1.473, and 2.971–6.203, 1.059–2.050 (p < 0.001, p = 0.021) in the univariate Cox regression analysis of the training cohort (Fig. 5A and 5C). The HR of the risk score and 95% CI in multivariate Cox regression analysis were 4.309, 1.507 and 2.982–6.227, 1.072–2.119 (p < 0.001, p = 0.018 (Fig. 5B and 5D) for the validation cohort. The risk model of the seven lncRNAs was the most significant prognostic factor for EWS, independent of clinicopathological parameters such as age and sex. We estimated the area under the ROC curve of the risk score to evaluate the predictive specificity and sensitivity of the risk score on the prognosis of EWS patients. The 3-year AUC values of the risk score for the training and validation cohorts were 0.912 and 0.697, respectively (Fig. 5E and 5G). The 5-year AUC values of the risk score were 0.927 and 0.694, respectively (Fig. 5F and 5H), followed by the AUC of age and sex. These results indicate that the prognostic risk model for the seven lncRNAs for EWS was reliable. The results indicate that the seven-lncRNA signature was a significant independent prognostic factor for EWS patients. Two nomograms of the training group (Fig. 6A) and validation group (Fig. 6D) were constructed based on the prognostic signature and clinical factors, such as age and sex. The calibration plots showed good agreement in predicting OS with the actual probability of OS at 3- and 5-year in the training (Fig. 6B and 6C) and validation cohorts (Fig. 6E and 6F).

Discussion

EWS is one of the most common malignancies in children. Approximately 70% of EWS children can be cured by surgery and chemotherapy, regardless of whether they receive radiation therapy, however, only 30% of metastatic cancers can be cured (Sun et al., 2017). LncRNAs were initially considered to be transcription noise. The role of lncRNA in human diseases has been recognized recently, especially in human cancer (Peng, Koirala & Mo, 2017). LncRNA is involved in tumor differentiation, proliferation, metastasis, and transcriptional regulation. A large number of studies have reported that lncRNA signatures were related to the development and prognosis of cancer (Li et al., 2019; Dinescu et al., 2019). However, the research on lncRNAs in EWS is limited and we sought to screen the lncRNA signature to aid in the diagnosis and treatment of this disease.

We used the training cohort to screen survival-related DELncs in the EWS, and 10 lncRNAs were significantly-associated with the survival of patients with EWS (log-rank test P < 0.05; AUC >0.6). Univariate COX regression and LASSO analyses were used to identify seven prognosis-related lncRNAs (SNHG17, C10orf71-AS1, LINC- 00623, STAU2-AS1, SSBP3-AS1, WAC-AS1, TDRG1) (P < 0.05). The seven-lncRNA signature had a high predictive value of overall survival in the training and validation cohorts. The 1-, 3-, 5-year AUC values of ROC showed that this model had superior accuracy. Two nomograms, including the seven-lncRNA signature, age, and sex were established to predict EWS prognosis in the two cohorts. The ROC analysis results showed that the prognostic risk model of the seven lncRNAs for EWS was reliable. We found that the high expression group had a low probability of survival. Increased expression of SNHG17, WAC-AS1, LINC00623, SSBP3-AS1, and TDRG1 caused the risk score of patients to increase, indicating a poor prognosis. Of these seven lncRNAs, none has been related to EWS disease and additional studies are needed to verify them in the future. The small nucleolar RNA host gene 17 (SNHG17) has a length of 1,186 bp and belongs to a large family of noncoding genes hosting small RNAs (Zhang et al., 2019; Han et al., 2020). Previous studies have proven that SNHG17 is highly-expressed and carcinogenic in cancers, including melanoma, gastric cancer, and colorectal cancer (Wu et al., 2020; Gao, Liu & Sun, 2019). The lncRNA testis developmental related gene 1 (TDRG1) has been identified as a proto-oncogene for many tumor types, including gastric carcinoma, cervical cancer, epithelial ovarian cancer, endometrial cancer, and testicular germ cell tumors (Ma et al., 2020; Peng et al., 2019). LINC00623 also plays a role in hormone-related cancers (Wang et al., 2020).

Our study was limited by its reliance on a GEO dataset with a small sample size to establish the lncRNA prediction model. The small sample size may indicate that the expression levels between the high- and low-risk groups are similar. The expression levels of some lncRNAs (such as LINC00623, SSBP3-AS1, and TDRG1) are different between the training group and the validation group. The number of samples may not produce consistent results for bioinformatics analysis and additional samples are needed to validate the prognostic performance of our proposed lncRNA signature for EWS. In addition, a luciferase reporter assay and chromatin immunoprecipitation should be applied to the lncRNAs to verify their expression levels, biological functions, and specific regulatory mechanisms in EWS.

We constructed and validated a seven-lncRNA signature to predict the prognosis of patients with EWS. Our findings provide a new reference for the prognostic assessment of EWS and may help in the diagnosis and treatment of EWS in the future.

Supplemental Information