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Steroid-induced osteonecrosis of femoral head (ONFH) is a serious complication of glucocorticoid (GC) use. We investigated the differential expression of miRs in the mesenchymal stem cells (MSCs) of patients with ONFH, and aimed to explain the relationship between GC use and the development of MSC dysfunction in ONFH. Cells were collected from bone marrow of patients with ONFH. Samples were assigned to either GCs Group or Control Group at 1:1 matched with control. We then used miRNA microarray analysis and real-time PCR to identify the differentially expressed miRs. We also induced normal MSCs with GCs to verify the differential expression above. Subsequently, we selected some of the miRs for further studies, including miRNA target and pathway prediction, and functional analysis. We discovered that miR-708 was upregulated in ONFH patients and GC-treated MSCs. SMAD3 was identified as a direct target gene of miR-708, and functional analysis demonstrated that miR-708 could markedly suppress osteogenic differentiation and adipogenesis differentiation of MSCs. Inhibition of miR-708 rescued the suppressive effect of GC on osteonecrosis. Therefore, we determined that GC use resulted in overexpression of miR-708 in MSCs, and thus, targeting miR-708 may serve as a novel therapeutic biomarker for the prevention and treatment of ONFH.

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms.