Dataset Information

Identification and Validation of a Potent Multi-miRNA Signature for Prediction of Prognosis of Osteosarcoma Patients.

ABSTRACT: BACKGROUND Osteosarcoma, the most common solid malignancy, has high incidence and mortality rates. We constructed a miRNA-based signature that can be used to assess the prognosis of osteosarcoma patients. MATERIAL AND METHODS The miRNA profile was derived from the Gene Expression Omnibus (GEO) website, with matched clinical records. The miRNA-based overall survival (OS)-predicting signature was established by LASSO Cox regression analysis. Receiver operating characteristic (ROC) curve and Kaplan-Meier (K-M) analyses were performed to examine the stability and discriminatory ability of the OS-predicting signatures. Pathway enrichment analyses were performed to uncover potential mechanisms. RESULTS Three miRNAs (miR-153, miR-212, and miR-591) independently related to the OS were extracted to build a risk score formula. The ROC curve and K-M analyses revealed good discrimination ability of the OS signature for osteosarcoma patients in both the training cohort (P=0.00015, AUC=0.962) and the validation cohort (P=0.0065, AUC=0.793). As shown in multivariate analysis, the classifier showed favorable predictive accuracy similar to the recurrence status to be an independent risk factor for osteosarcoma. Furthermore, the nomogram showed a synergistic effect by combining the clinicopathological features with our classifier. Also, the enrichment analyses of the target genes may contribute to improved treatment of osteosarcoma. CONCLUSIONS The 3-miRNA-based classifier serves as an effective prognosis-predicting signature for osteosarcoma patients.

SUBMITTER: Luo X

PROVIDER: S-EPMC7060510 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Identification and Validation of a Potent Multi-miRNA Signature for Prediction of Prognosis of Osteosarcoma Patients.

Luo Xinle X Tang Jiuyang J Xuan Huabing H Liu Jianlin J Li Xi X

Medical science monitor : international medical journal of experimental and clinical research 20200226

BACKGROUND Osteosarcoma, the most common solid malignancy, has high incidence and mortality rates. We constructed a miRNA-based signature that can be used to assess the prognosis of osteosarcoma patients. MATERIAL AND METHODS The miRNA profile was derived from the Gene Expression Omnibus (GEO) website, with matched clinical records. The miRNA-based overall survival (OS)-predicting signature was established by LASSO Cox regression analysis. Receiver operating characteristic (ROC) curve and Kaplan ...[more]

PMID: 32098942

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Project description:BackgroundOsteosarcoma is a frequent bone malignancy in children and young adults. Despite the availability of some prognostic biomarkers, most of them fail to accurately predict prognosis in osteosarcoma patients. In this study, we used bioinformatics tools and machine learning algorithms to establish an autophagy-related long non-coding RNA (lncRNA) signature to predict the prognosis of osteosarcoma patients.MethodsWe obtained expression and clinical data from osteosarcoma patients in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We acquired an autophagy gene list from the Human Autophagy Database (HADb) and identified autophagy-related lncRNAs by co-expression analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the autophagy-related lncRNAs were conducted. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of the autophagy-related lncRNA signature and validate the relationship between the signature and osteosarcoma patient survival in an independent cohort. We also investigated the relationship between the signature and immune cell infiltration.ResultsWe initially identified 69 autophagy-related lncRNAs, 13 of which were significant predictors of overall survival in osteosarcoma patients. Kaplan-Meier analyses revealed that the 13 autophagy-related lncRNAs could stratify patients based on their outcomes. Receiver operating characteristic curve analyses confirmed the superior prognostic value of the lncRNA signature compared to clinically used prognostic biomarkers. Importantly, the autophagy-related lncRNA signature predicted patient prognosis independently of clinicopathological characteristics. Furthermore, we found that the expression levels of the autophagy-related lncRNA signature were significantly associated with the infiltration levels of different immune cell subsets, including T cells, NK cells, and dendritic cells.ConclusionThe autophagy-related lncRNA signature established here is an independent and robust predictor of osteosarcoma patient survival. Our findings also suggest that the expression of these 13 autophagy-related lncRNAs may promote osteosarcoma progression by regulating immune cell infiltration in the tumor microenvironment.

Project description:Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan-Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature's predictive ability was further examined by the Kaplan-Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells' roles in the gene signature's prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature's prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

Dataset Information

Identification and Validation of a Potent Multi-miRNA Signature for Prediction of Prognosis of Osteosarcoma Patients.

Publications

Identification and Validation of a Potent Multi-miRNA Signature for Prediction of Prognosis of Osteosarcoma Patients.

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