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A support vector machine and a random forest classifier indicates a 15-miRNA set related to osteosarcoma recurrence.

ABSTRACT: Background:Osteosarcoma, which originates in the mesenchymal tissue, is the prevalent primary solid malignancy of the bone. It is of great importance to explore the mechanisms of metastasis and recurrence, which are two primary reasons accounting for the high death rate in osteosarcoma. Data and methods:Three miRNA expression profiles related to osteosarcoma were downloaded from GEO DataSets. Differentially expressed miRNAs (DEmiRs) were screened using MetaDE.ES of the MetaDE package. A support vector machine (SVM) classifier was constructed using optimal miRNAs, and its prediction efficiency for recurrence was detected in independent datasets. Finally, a co-expression network was constructed based on the DEmiRs and their target genes. Results:In total, 78 significantly DEmiRs were screened. The SVM classifier constructed by 15 miRNAs could accurately classify 58 samples in 65 samples (89.2%) in the GSE39040 database, which was validated in another two databases, GSE39052 (84.62%, 22/26) and GSE79181 (91.3%, 21/23). Cox regression showed that four miRNAs, including hsa-miR-10b, hsa-miR-1227, hsa-miR-146b-3p, and hsa-miR-873, significantly correlated with tumor recurrence time. There were 137, 147, 145, and 77 target genes of the above four miRNAs, respectively, which were assigned to 17 gene ontology functionally annotated terms and 14 Kyoto Encyclopedia of Genes and Genomes pathways. Among them, the "Osteoclast differentiation" pathway contained a total of seven target genes and was analyzed further. Conclusion:The 15-miRNAs-based SVM classifier provides a potential useful tool to predict the recurrence of osteosarcoma. Our results suggest the possible mechanisms of osteosarcoma metastasis and recurrence and provide fresh DEmiRs as potential biomarkers or therapeutic targets for osteosarcoma.

SUBMITTER: He Y

PROVIDER: S-EPMC5759858 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A support vector machine and a random forest classifier indicates a 15-miRNA set related to osteosarcoma recurrence.

He Yunfei Y Ma Jun J Wang An A Wang Weiheng W Luo Shengchang S Liu Yaoming Y Ye Xiaojian X

OncoTargets and therapy 20180105

<h4>Background</h4>Osteosarcoma, which originates in the mesenchymal tissue, is the prevalent primary solid malignancy of the bone. It is of great importance to explore the mechanisms of metastasis and recurrence, which are two primary reasons accounting for the high death rate in osteosarcoma.<h4>Data and methods</h4>Three miRNA expression profiles related to osteosarcoma were downloaded from GEO DataSets. Differentially expressed miRNAs (DEmiRs) were screened using MetaDE.ES of the MetaDE pack ...[more]

PMID: 29379305

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Project description:BackgroundMicrosatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size.ResultsWe developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases).ConclusionThese results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes.

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A support vector machine and a random forest classifier indicates a 15-miRNA set related to osteosarcoma recurrence.

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A support vector machine and a random forest classifier indicates a 15-miRNA set related to osteosarcoma recurrence.

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