A seven-lncRNA signature for predicting Ewing’s sarcoma

Abstract

Background Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs with unique characteristics. These RNA can regulate cancer cells’ survival, proliferation, invasion, metastasis, and angiogenesis and are potential diagnostic and prognostic markers. We identified a seven-lncRNA signature related to the overall survival (OS) of patients with Ewing’s sarcoma (EWS). Methods We used an expression profile from the Gene Expression Omnibus (GEO) database as a training cohort to screen out the OS-associated lncRNAs in EWS and further established a seven-lncRNA signature using univariate Cox regression, the least absolute shrinkage, and selection operator (LASSO) regression analysis. The prognostic lncRNA signature was validated in an external dataset from the International Cancer Genome Consortium (ICGC) as a validation cohort. Results We obtained 10 survival-related lncRNAs from the Kaplan-Meier and ROC curve analysis (log-rank test P < 0.05; AUC >0.6). Univariate Cox regression and LASSO regression analyses confirmed seven key lncRNAs and we established a lncRNA signature to predict an EWS prognosis. EWS patients in the training cohort were categorized into a low-risk group or a high-risk group based on their median risk score. The high-risk group’s survival time was significantly shorter than the low-risk group’s. This seven-lncRNA signature was further confirmed by the validation cohort. The area under the curve (AUC) for this lncRNA signature was up to 0.905 in the training group and 0.697 in the 3-year validation group. The nomogram’s calibration curves demonstrated that EWS probability in the two cohorts was consistent between the nomogram prediction and actual observation. Conclusion We screened a seven-lncRNA signature to predict the EWS patients’ prognosis. Our findings provide a new reference for the current prognostic evaluation of EWS and new direction for the diagnosis and treatment of EWS.

Identification of Key Iron Metabolism-Related Long Non-Coding Rnas in Hepatocellular Carcinoma Based on Bioinformatics Analysis

“…For example, Liu et al suggested that fetal-lethal noncoding developmental regulatory RNA (FENDRR) as an anti brotic long noncoding RNA can reduced pulmonary brosis by inhibiting broblast activation by reducing iron concentration, which may act as a potential therapeutic target for pulmonary brosis 16 . Aberrant expression of lncRNAs related with iron metabolism has been participated in regulating critical physiological functions which were linked to different types of cancer in present studies [17][18][19] . Moreover, a review revealed that hepatic iron accumulation can lead to brotic responses, and the signi cance and potential of iron-related proteins in the progression and development of liver brosis 20 .…”

The Landscape of Regulatory Noncoding RNAs in Ewing’s Sarcoma

“…The increased expression of these seven lncRNAs was statistically correlated with poor overall survival [111]. These seven lncRNA, including SNHG17, WAC-AS1, LINC00623, SSBP3-AS1, and TDRG1, are yet to tested experimentally for their association and role in ES, although some have functional roles in other cancers [111]. This study opened a new research direction to explore the use of a set of ncRNAs as prognostic markers.…”

“…Just last month, a new study by Chen et al used machine learning and training models on existing RNA sequencing data sets from ES patients to identify a set of seven lncRNAs as prognostic risk marker for ES. The increased expression of these seven lncRNAs was statistically correlated with poor overall survival [111]. These seven lncRNA, including SNHG17, WAC-AS1, LINC00623, SSBP3-AS1, and TDRG1, are yet to tested experimentally for their association and role in ES, although some have functional roles in other cancers [111].…”

Establishment and Verification of Prognostic Model and ceRNA Network Analysis for Colorectal Cancer Liver Metastasis

“…It should be noted that AUC greater than 0.7 is considered to have high accuracy of diagnostic model, while AUC greater than 0.6 is considered to have high accuracy of prognostic model. Several studies also showed that the accuracy of the prediction model is good based on AUC > 0.6 [30][31] . The area under the ROC curve of 1-5-year overall survival predicted in our study is greater than 0.6, indicating that the accuracy of the prediction model is good.…”