Dataset Information

An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing's Sarcoma Based on a Machine Learning Iterative Lasso Regression.

ABSTRACT: The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

SUBMITTER: Ren EH

PROVIDER: S-EPMC8187926 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCuproptosis is copper-induced cell death. Copper metabolism related genes (CMRGs) were demonstrated that used to assess the prognosis out of tumors. In the study, CMRGs were tested for their effect on TME cell infiltration in Ewing's sarcoma (ES).MethodsThe GEO and ICGC databases provided the mRNA expression profiles and clinical features for downloading. In the GSE17674 dataset, 22prognostic-related copper metabolism related genes (PR-CMRGs) was identified by using univariate regression analysis. Subsequently, in order to compare the survival rates of groups with high and low expression of these PR-CMRGs,Kaplan-Meier analysis was implemented. Additionally, correlations among them were examined. The study employed functional enrichment analysis to investigate probable underlying pathways, while GSVA was applied to evaluate enriched pathways in the ES (Expression Set). Through an unsupervised clustering algorithm, samples were classified into two clusters, revealing significant differences in survival rates and levels of immune infiltration.ResultsUsing Lasso and step regression methods, five genes (TFRC, SORD, SLC11A2, FKBP4, and AANAT) were selected as risk signatures. According to the Kaplan-Meier survival analysis, the high-risk group had considerably lower survival rates than the low-risk group(p=6.013e-09). The area under the curve (AUC) values for the receiver operating characteristic (ROC) curve were 0.876, 0.883, and 0.979 for 1, 3, and 5 years, respectively. The risk model was further validated in additional datasets, namely GSE63155, GSE63156, and the ICGC datasets. To aid in outcome prediction, a nomogram was developed that incorporated risk levels and clinical features. This nomogram's performance was effectively validated through calibration curves.Additionally, the study evaluated the variations in immune infiltration across different risk groups, as well as high-expression and low-expression groups. Importantly, several drugs were identified that displayed sensitivity, offering potential therapeutic options for ES.ConclusionThe findings above strongly indicate that CMRGs play crucial roles in predicting prognosis and immune status in ES.

Project description:Ewing's sarcoma is a high-grade malignancy bone and soft tissue tumor that most commonly occurs in children and adolescents. Although the overall prognosis of Ewing's sarcoma has improved, the 5-year survival rate has not improved significantly. The study aimed to determine the risk factors independently associated with the prognosis of Ewing's sarcoma and to construct a nomogram to predict patient survival. Patients diagnosed with Ewing's sarcoma were collected from the Surveillance, Epidemiology, and End Results program database between 2004 and 2015 and further divided into training and validation cohort. Univariate and multivariate Cox regression analyses were used to identify meaningful independent prognostic factors. The nomogram was used to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS). Finally, the nomogram was verified internally and externally through the training and validation cohorts, and the predictive capability was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve and compared with that of the 7th TNM stage. A total of 1120 patients were divided into training (n = 713) and validation (n = 407) cohorts. Based on the multivariate analysis of the training cohort, a nomogram that integrated age, tumor size, primary site, N stage, and M stage was constructed (P < 0.05). The predicted C-indexes of OS and CSS of the training cohort were 0.744 (95% CI 0.717-0.771) and 0.743 (95% CI 0.715-0.770), respectively. However, the TNM stage had a C-index of 0.695 (95% CI 0.666-0.724) and 0.698 (95% CI 0.669-0.727) for predicting OS and CSS, respectively. The nomogram showed higher C-indexes than those in the TNM stage. Furthermore, the internal and external calibration curves showed good consistency between the predicted and observed values. Age, tumor size, primary site, N stage, and M stage are independent risk factors affecting the OS and CSS in Ewing's sarcoma patients. Compared with the 7th TNM staging, the nomogram consisting of these factors was more accurate for risk assessment and survival prediction in patients with Ewing's sarcoma, thus providing a novel reliable tool for risk assessment and survival prediction in Ewing's sarcoma patients.

Project description:Background: Increasing evidence has demonstrated that immune-related long non-coding RNAs (irlncRNAs) are critically involved in tumor initiation and progression and associated with the prognosis of various cancers. However, their role in soft tissue sarcoma (STS) remains significantly uninvestigated. Materials and Methods: Gene expression profiles were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) for the identification of irlncRNAs. Univariate analysis and modified least absolute shrinkage and selection operator (LASSO) penalized regression analysis were employed to determine differently expressed irlncRNA (DEirlncRNA) pairs of prognostic value, and subsequently, a risk signature based on DEirlncRNA pairs was established. Furthermore, Kaplan-Meier analysis and the area under the receiver operating characteristic curve (AUC) were used to assess survival differences and the predictive accuracy of the risk signature, respectively. Lastly, the correlation of irlncRNAs with immune characteristics and chemosensitivity in patients with STS were further investigated. Results: A total of 1088 irlncRNAs were identified, and 311 irlncRNAs were distinguished as DEirlncRNAs. A total of 130 DEirlncRNA pairs were further identified as prognostic markers, and 14 pairs were selected for establishing a risk signature. The irlncRNA-based risk signature functioned as an independent prognostic marker for STS. Compared with the patients in the high-risk group, those in the low-risk group exhibited a better prognosis and were more sensitive to several chemotherapeutic agents. In addition, the irlncRNA-based risk signature was significantly associated with immune scores, infiltrating immune cells, and the expression of several immune checkpoints. Conclusion: In conclusion, our data revealed that the irlncRNA-based risk signature resulted in reliable prognosis, effectively predicted the immune landscape of patients with STS and was significantly correlated with chemosensitivity, thus providing insights into the potential role of irlncRNAs as prognostic biomarkers and novel therapeutic targets for STS.

Project description:BackgroundCuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function.MethodsRNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships.ResultsThe cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan-Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed.ConclusionsA novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome.

Dataset Information

An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing's Sarcoma Based on a Machine Learning Iterative Lasso Regression.

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