Background: Searching for the production mechanism of synovial lesions helps to find precise therapeutic targets and improve prognosis. The previous identification and screening of differential genes in osteoarthritis (OA) pathogenesis were well combined to further build a risk prognosis model of OA, which is beneficial to the diagnosis and treatment of patients with OA. Methods:The synovia-related chip data sets GSE82107, GSE12021, GSE55457, and GSE55235 were downloaded from the public database of Gene Expression Omnibus (GEO), and 40 cases of synovial tissues of OA and 36 cases of normal synovial tissues were included. R software was used to screen differentially expressed genes (DEGs), Gene Ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The STRING online analysis tool and Cytoscape software were used to further screen key genes, and a prognostic model of OA susceptibility risk was constructed. Results:The results showed 1,921 differential genes, including 762 upregulated genes and 1,159 downregulated genes, which were mainly involved cell growth, cell adhesion, skeletal muscle growth, iron ion binding, ubiquitin protein ligase binding, and hormone receptor binding. Co-acquisition based on 10 key target genes of the protein interaction network, containing CTNNB1, GSK3B, STAT1, RHOC, HDAC9, PSEN1, KDM5C, BACE1, JAK3, and CUL1. The area under the concentration-time curve (AUC) was used to evaluate the prognostic model of OA risk, and the curve results showed that the prognostic model had high accuracy and validity (AUC =0.690).Conclusions: Bioinformatics analysis was applied to screen out the DEG profiles of OA. This may provide functional predictions to provide new ideas for treatment of the disease and may be a biological marker for its diagnosis and a potential target for treatment. The construction of the risk and prognosis model is beneficial to the risk assessment of rehabilitation function recovery of patients with OA, the evaluation of the severity of the disease and the subsequent treatment guidance.

HnRNP K is a heterogeneous nuclear ribonucleoprotein and has been identified as an oncogene in most solid tumors via regulating gene expression or alternative splicing of genes by binding both DNA and pre-mRNA. However, how hnRNP K affects tumorigenesis and regulates the gene expression in cervical cancer (CESC) remains to be elucidated. In these data, higher expression of hnRNP K was observed in CESC and was negatively correlated with the patient survival time. We then overexpressed hnRNP K (hnRNP K-OE) and found that its overexpression promoted cell proliferation in HeLa cells ( P = 0.0052). Next, global transcriptome sequencing (RNA-seq) experiments were conducted to explore gene expression and alternative splicing profiles regulated by hnRNP K. It is shown that upregulated genes by hnRNP K-OE were associated with inflammatory response and an apoptotic process of neuron cells, which involves in cancer. In addition, the alternative splicing of those genes regulated by hnRNP K-OE was associated with transcriptional regulation. Analysis of the binding features of dysregulated transcription factors (TFs) in the promoter region of the inflammatory response genes regulated by hnRNP K revealed that hnRNP K may modulate the expression level of genes related to inflammatory response by influencing the alternative splicing of TFs. Among these hnRNP K-TFs-inflammatory gene regulatory networks, quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments and gene silencing were conducted to verify the hnRNP K-IRF1-CCL5 axis. In conclusion, the hnRNP K-TFs-inflammatory gene regulatory axis provides a novel molecular mechanism for hnRNP K in promoting CESC and offers a new therapeutic target.