Identification of specific biomarkers and pathways in the synovial tissues of patients with osteoarthritis in comparison to rheumatoid arthritis

Osteoarthritis and rheumatoid arthritis are two common arthritis with different pathogenesis. Here, we explore the difference of genes and biological pathways in human synovial fibroblasts by using a bioinformatics method to clarify their potential pathogenesis. The GSE7669 dataset was originally produced by using an Affymetrix Human Genome U95 platform. We used the KEGG and GO analysis to identify the functional categories and pathways. Our results suggested that biological adhesion and cell adhesion are the main signaling pathways in osteoarthritis in comparison to rheumatoid arthritis. Furthermore, Albumin, MAPK3, PTPRC, COL1A1, and CXCL12 may be key genes in osteoarthritis. Therefore, our study provides potential targets for the specific and accurate therapy of osteoarthritis.

Prediction of early breast cancer patient survival using ensembles of hypoxia signatures

BackgroundBiomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.ResultsWe risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization. The 24 binary risk profiles determined for each hypoxia signature were combined using a random forest to evaluate the efficacy of a preprocessing ensemble classifier. We demonstrate that the best way of merging preprocessing methods varies from signature to signature, and that there is likely no ‘best’ preprocessing pipeline that is universal across datasets, highlighting the need to evaluate ensembles of preprocessing algorithms. Further, we developed novel signatures for each preprocessing method and the risk classifications from each were incorporated in a meta-random forest model. Interestingly, the classification of these biomarkers and its ensemble show striking consistency, demonstrating that similar intrinsic biological information are being faithfully represented. As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.ConclusionsPerformance of different prognostic signatures varies with pre-processing method. A simple classifier by unanimous voting of classifications is a reliable way of improving on single preprocessing methods. Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.AbbreviationsAUCarea under the receiver operating characteristic curveGCRMAGeneChip Robust Multi-array AverageHG-U133AAffymetrix Human Genome U133AHG-U133 Plus 2.0Affymetrix Human Genome Plus 2.0HRhazard ratioMAS5MicroArray Suite 5.0MBEIModel-base Expression IndexNSCLCNon-small cell lung cancerRFRandom forestROCreceiver operator characteristicRMARobust Multi-array Average

Establishment of TRAIL-Resistance Kasumi-1 Cell Line and the Analysis of It different mRNA Expression Profile with the Original Kasumi-1 Cell Line

Introduction Tumor necrosis factor related apoptosis inducing ligand (TRAIL) can induce the apoptosis of many human leukemia cells while sparing of normal cells, but its resistance is also universal. Our previous study on apoptosis of t(8;21) positive acute myeloid leukemia cell line Kasumi-1 induced by rhTRAIL showed that the survival rate no longer decreased significantly when rsTRAIL reached a certain concentration which implied Kasumi-1 cells might have a resistant tendency to TRAIL. Then, we established a TRAIL-resistant Kasumi-1 cell line (Kasumi-1 TR) by intermittently escalating rsTRAIL concentration in culture media, and compared the mRNA expression profile with the original Kasumi-1 cell line by using Affymetrix Human Genome U133 Plus 2.0 Array. Methods Kasumi-1 TR cell line was established by intermittently treated Kasumi-1 cells with progressively escalating rsTRAIL concentration. Proliferation of leukemia cells were measured by CCK-8 assay, and rsTRAIL IC50 of cells and resistance index were calculated according to proliferation of cells treated with rsTRAIL at different concentrations. TRAIL and TRAIL receptors 1-4 on cells surface were detected by flow cytometry. Expression profiles of Kasumi-1 cells and Kasumi-1 TR cells were analyzed by Affymetrix Human Genome U133 Plus 2.0 Array to identify differentially expressed genes, and the search of genes possibly related with TRAIL-resistance were using by GO functional analysis and pathway enrichment analysis. Results 1) Kasumi-1 TR cells proliferation was faster than that of Kasumi-1 cells(Fig 1A); 2) IC50 of 24 hours for Kasumi-1 cells was 756.833ng/ml (logIC50 2.879 ± 0.148), IC50 of 24 hours for Kasumi-1 TR cells was 1634646.005ng/ ml (logIC50 6.213 ± 0.637), the RI of 24h was 2159 (Fig 1B); IC50 of 48 hours for Kasumi-1 cells was 345.390ng/ml (logIC50 2.538 ± 0.153), IC50 of 48 hours for Kasumi-1 TR cells was 33642.641ng/ml (logIC50 is 4.257 ± 0.317), the RI for 48h was 97 (Fig 1C); 3) Cell surface expression of TRAIL and its receptors 1-4 had no difference between two cell lines(Fig 1D). 4) There were 1537 genes up regulated by more than 2 times while 487 genes down regulated by more than 2 times in Kasumi-1 TR cells compared with the original Kasumi-1 cells (Fig 1E). Of which BCL-2 family antiapoptotic gene BCL2 is increased by 3.153 times and BCL2A1 increased by 18.23 times, IFNAR1 involved in JAK/STAT pathway increased by 12.841 times and TRAIL death receptor TNFRSF10A down regulated by 3.256 times(Fig 1F). Conclusions: The Kasumi-1 cell line with rsTRAIL resistance (Kasumi-1 TR) is established, and its resistance may be associated with the up expression of BCL2, BCL2A1, IFNAR1 and down regulated expression of DR4. Acknowledgment This work was supported by grants from NSFC (30672415) and STCSM (054119528). Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.