LAUSR

Nowadays, there are more and more people who have been diagnosed osteoarthritis (OA). However, due to the complex changes of OA, the treatment outcome is not very well. In order to improve this situation, I decided to aggregate a series of data and use complex bioinformatical methods to analyze them, hoping to explore new therapeutic targets. After downloading and processing the data from Gene Expression Omnibus (GEO) database, I analyzed the relationship between genes and OA formation by the weighted correlation network analysis (WGCNA)and selected the turquoise module which owned the closest relationship with clinical traits. Then, via online database and CIBERSORT algorithm method, I analyzed the function of this key module and the situation of immune infiltration in OA tissues. With the help of WGCNA and functional enrichment analysis, I found out that most of genes in the turquoise module took part in the inflammation development, immune responses, and cell proliferation, especially the hub gene PRKACB. At the same time, my results of immune infiltration and expression level analysis also showed that PRKACB has a close relationship with immune cells, especially M2 macrophage. In a word, my results suggested that PRKACB played an essential role in osteoarthritis development. Key Points • Used the “sva” R package to combine the data of 59 samples from four studies to do the bioinformatical analysis. • Identifying the hub gene PRKACB as potential marker for OA and using validation sets to confirm it. • Detecting the situation of immune infiltration in synovium by CIBERSORT algorithm method. Key Points • Used the “sva” R package to combine the data of 59 samples from four studies to do the bioinformatical analysis. • Identifying the hub gene PRKACB as potential marker for OA and using validation sets to confirm it. • Detecting the situation of immune infiltration in synovium by CIBERSORT algorithm method.