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A validated miRNA signature for the diagnosis of osteoporosis related fractures using SVM algorithm classification.

ABSTRACT: The aim of the present study was to develop a circulating microRNA expression signature for early prediction of osteoporotic fractures and to validate the results using Gene Expression Omnibus (GEO) datasets. The GSE70318 dataset was downloaded from GEO and used to build an osteoporotic fracture prediction model based on the receiver operating characteristic curve and support vector machine (SVM) classification index. The GSE74209 dataset was used as a validation dataset. Additionally, in vitro, alkaline phosphatase (ALP) activity was measured in the presence or absence of microRNA (miRNA/miR) treatments in human osteoblast cells. The expression of two selected genes was detected by western blotting. miR-188-3p, miR-942-3p, miR-576-3p and miR-135a-5p were differentially expressed between controls and osteoporotic patients with fractures. SVM classification using these four miRNAs provided better dichotomization. It was further confirmed that miR-576-3p and 135a-5p in the GSE74209 dataset could also significantly discriminate between the controls and fracture patients, the area under the curve of SVM2 was 0.9722 with 95% CI 0.8885-1.056. Further analysis indicated that the target genes of the two miRNAs participated in the Wingless-related integration site, Hedgehog and transforming growth factor-? signaling pathways and osteoclast differentiation. miR-576-3p and miR-135-5p transfection decreased ALP activity and ALP activity was increased in the presence of blocking antisense oligonucleotides. Western blotting indicated miR-576-3p and miR-135-5p decreased CSNK1A1L and LRP6 levels, respectively. In conclusion, two miRNA signatures were developed and validated for the prediction of osteoporotic fractures.

SUBMITTER: Tang X

PROVIDER: S-EPMC7401749 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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A validated miRNA signature for the diagnosis of osteoporosis related fractures using SVM algorithm classification.

Tang Xiaolin X Bai Yinshan Y Zhang Zhiming Z Lu Jianlin J

Experimental and therapeutic medicine 20200624 3

The aim of the present study was to develop a circulating microRNA expression signature for early prediction of osteoporotic fractures and to validate the results using Gene Expression Omnibus (GEO) datasets. The GSE70318 dataset was downloaded from GEO and used to build an osteoporotic fracture prediction model based on the receiver operating characteristic curve and support vector machine (SVM) classification index. The GSE74209 dataset was used as a validation dataset. Additionally, <i>in vit ...[more]

PMID: 32765697

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Project description:BACKGROUND: MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g. the miRBase family organization), this paper addresses the problem of automatically and accurately classifying newly found miRNAs to their corresponding families by supervised learning techniques. Concretely, we propose an effective method, miRFam, which uses only primary information of pre-miRNAs or mature miRNAs and a multiclass SVM, to automatically classify miRNA genes. RESULTS: An existing miRNA family system prepared by miRBase was downloaded online. We first employed n-grams to extract features from known precursor sequences, and then trained a multiclass SVM classifier to classify new miRNAs (i.e. their families are unknown). Comparing with miRBase's sequence alignment and manual modification, our study shows that the application of machine learning techniques to miRNA family classification is a general and more effective approach. When the testing dataset contains more than 300 families (each of which holds no less than 5 members), the classification accuracy is around 98%. Even with the entire miRBase15 (1056 families and more than 650 of them hold less than 5 samples), the accuracy surprisingly reaches 90%. CONCLUSIONS: Based on experimental results, we argue that miRFam is suitable for application as an automated method of family classification, and it is an important supplementary tool to the existing alignment-based small non-coding RNA (sncRNA) classification methods, since it only requires primary sequence information. AVAILABILITY: The source code of miRFam, written in C++, is freely and publicly available at: http://admis.fudan.edu.cn/projects/miRFam.htm.

Project description:BackgroundOsteoporotic fractures frequently require inpatient care, and are associated with elevated risks of morbidity, mortality, and re-hospitalization. A comprehensive evaluation of healthcare costs, resource utilization, and outcomes associated with osteoporosis (OP)-related fractures treated in US hospitals was undertaken.MethodsA retrospective analysis using the Premier Perspective Database (2010 - 2013) was conducted. Study population comprised patients aged ≥ 50 years hospitalized with a principal diagnosis of a closed or pathologic fracture commonly associated with OP; the first qualifying hospitalization was designated the "index admission". Patients with evidence of major trauma, malignancy, or other non-OP conditions that may lead to pathologic fracture during the index admission were excluded. Study measures included healthcare costs (in 2013 USD), length of stay (LOS), intensive care unit (ICU) use, and mortality during the index admission, as well as 60-day fracture-related readmission.ResultsA total of 268,477 patients were admitted to hospital (n = 548 hospitals) with a principal diagnosis of an OP-related fracture; mean (SD) age was 78 (11) years, 75% were female, 69% had ≥ 2 comorbidities, and 82% of patients had a diagnostic code for accidental fall. Among all OP-related fracture admissions, mean (95% CI) hospital cost was $12,839 (12,784-12,893) and LOS was 5.1 (5.1-5.1) days; during the admission, ICU use was 7.4% (7.3-7.5) and mortality was 1.5% (1.5-1.6), and during the 60-day post-discharge period, fracture-related readmission was 2.3% (2.2-2.4).ConclusionsHospital costs associated with the acute treatment of OP-related fractures are substantial, especially among patients with fractures of the hip, femur, and spine. Among patients with vertebral fractures-the second most common reason for admission-mortality and ICU use were notably high, and costs and LOS were higher than among those with non-vertebral fractures (excluding hip). Interventions that are effective in reducing fracture risk have the potential to yield substantial cost savings.

Project description:BackgroundOsteoporosis is a bone disease that increases the patient's risk of fracture. We aimed to identify robust marker genes related to osteoporosis based on different bioinformatic methods and multiple datasets.MethodsThree datasets from Gene Expression Omnibus (GEO) were utilized for analysis separately. Significantly differentially expressed genes (DEGs) from comparing high hip and low hip low bone mineral density (BMD) groups in the first dataset were identified for Gene Ontology (GO), Gene set enrichment analysis (GSEA) and Kyoto encyclopedia of genes and genomes (KEGG) to investigate the discrepantly enriched biological processes between high hip and low hip group. Last absolute shrinkage and selection operator (LASSO), SVM model and protein-protein interaction (PPI) regulatory network were performed and generated robust marker genes for downstream TF-target and miRNA-target prediction.ResultsSeveral DEGs between high hip BMD group and low hip BMD group were obtained. And the metabolism-related pathways such as metabolic pathways, carbon metabolism, glyoxylate and dicarboxylate metabolism shown enrichment in these DEGs. Integration with LASSO regression analysis, 8 differential expression genes (SH3BP1, NARF, ANKRD34B, RNF40, ZNF473, AKT1, SHMT1, and VASH1) in GSE62402 were identified as the optimal differential genes combination. Moreover, the SVM validation analysis in GSE56814 and GSE56815 datasets showed that the characteristic gene combinations presented high diagnostic effects, and the model AUC areas for GSE56814 was 0.899 and for GSE56815 was 0.921. Furthermore, the subcellular localization analysis of the 8 genes revealed that 4 proteins were located in the cytoplasm, 3 proteins were located in the nucleus, and 1 protein was located in the mitochondria. Additionally, the related TFs and miRNAs by performing TF-target and miRNA-target prediction for 5 genes (AKT1, SHMT1, ZNF473, RNF40 and VASH1) were investigated from PPI network.ConclusionThe optimal differential genes combination (SH3BP1, NARF, ANKRD34B, RNF40, ZNF473, AKT1, SHMT1, and VASH1) presented high diagnostic effect for osteoporosis risk.

Dataset Information

A validated miRNA signature for the diagnosis of osteoporosis related fractures using SVM algorithm classification.

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A validated miRNA signature for the diagnosis of osteoporosis related fractures using SVM algorithm classification.

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