Background: Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST.Methods: The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction. Results: A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively.Conclusions: This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

No abstract

No abstract

Background: As the incidence of type 2 diabetes increases year by year, the number of individuals diagnosed with Diabetic Nephropathy (DN) has increased steeply. DN is characterized by glomerular sclerosis, tubulointerstitial fi brosis and atrophy. However, most of the previous studies on the pathogenesis of DN were focused on glomeruli, and now more and more evidences show that tubulointerstitial fi brosis plays an important role in the progress of DN. Bioinformatics analysis can be used in discovering disease-causing genes, biomarkers, and therapeutic targets through global analysis. In this study, we used this method to fi nd and verify novel genes in DN. Materials and methods: Microarray expression levels were downloaded from Gene Expression Omnibus datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted after Differentially Expressed Genes (DEGs) were identifi ed. Hub genes were fi ltered on the basis of the result of GO enrichment, and functional analysis was performed by browsing the GeneCards website and the latest literatures. The expression levels of all the hub genes in HK2 cells stimulated by high glucose were verifi ed, and TeNascin C (TNC), which is important and interesting, was verifi ed through animal experiment. Result: The relative expression levels of 54 samples were obtained, and 382 DEGs were identifi ed. 286 GO terms and 100 KEGG pathways were enriched remarkably. Nine of the key genes of interest: MMP7, TNC, LUM, LTF, IGLC1, LYZ, CXCL6, CYP27B1 and FOS were selected and verifi ed in HK2 cells stimulated by high glucose. quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) indicated that the mRNA levels of most of the hub genes were up-regulated and had an accordance rate of approximately 66.7%. In addition, the expression of TNC in renal tubular tissue of STZ-induced diabetic nephropathy rat gradually increased with time. Conclusion: Several novel key genes were discovered and verifi ed in this study, and TNC plays an important role in the renal fi brosis of DN and is expected to be a new therapeutic target. This study provides a theoretical basis and data resources for further research.