Acute aortic dissection (AAD) is a life-threatening disease. Despite the higher risk of mortality, currently there are no effective therapies that can ameliorate AAD development or progression. Identification of meaningful clusters of co-expressed genes or representative biomarkers for AAD may help to identify new pathomechanisms and foster development of new therapies. To this end, we performed a weighted gene co-expression network analysis (WGCNA) and calculated module-trait correlations based on a public microarray dataset (GSE 52093) and discovered 9 modules were found to be related to AAD. The module which has the strongest positive correlation with AAD was further analyzed and the top 10 hub genes SLC20A1, GINS2, CNN1, FAM198B, MAD2L2, UBE2T, FKBP11, SLMAP, CCDC34, and GALK1 were identified. Furthermore, we validated the data by qRT-PCR in an independent sample set originated from our study center. Overall, the qRT-PCR results were consistent with the results of the microarray analysis. Intriguingly, the highest change was found for FKBP11, a protein belongs to the FKBP family of peptidyl-prolyl cis/trans isomerases, which catalyze the folding of proline-containing polypeptides. In congruent with the gene expression analysis, FKBP11 expression was induced in cultured endothelial cells by angiotensin II treatment and endothelium of the dissected aorta. More importantly we show that FKBP11 provokes inflammation in endothelial cells by interacting with NF-kB p65 subunit, resulting in pro-inflammatory cytokines production. Accordingly, siRNA mediated knockdown of FKBP11 in cultured endothelial cells suppressed angiotensin II induced monocyte transmigration through the endothelial monolayer. Based on these data, we hypothesize that pro-inflammatory cytokines elicited by FKBP11 overexpression in the endothelium under AAD condition could facilitate transendothelial migration of the circulating monocytes into the aorta, where they differentiate into active macrophages and secrete MMPs and other extracellular matrix (ECM) degrading proteins, contributing to sustained inflammation and AAD. Taken together, our data identify important role of FKBP11 which can serve as biomarker and/or therapeutic target for AAD.

Background Ovarian cancer has greatly endangered and deteriorated female health conditions worldwide. Refinement of predictive biomarkers could enable patient stratification and help optimize disease management. Methods RAD51 expression profile, target-disease associations, and fitness scores of RAD51 were analyzed in ovarian cancer using bioinformatic analysis. To further identify its role, gene enrichment analysis was performed, and a regulatory network was constructed. Survival analysis and drug sensitivity assay were performed to evaluate the effect of RAD51 expression on ovarian cancer prognosis. The predictive value of RAD51 was then confirmed in a validation cohort immunohistochemically. Results Ovarian cancer expressed more RAD51 than normal ovary. RAD51 conferred ovarian cancer dependency and was associated with ovarian cancer. RAD51 had extensive target-disease associations with various diseases, including ovarian cancer. Genes that correlate with and interact with RAD51 were involved in DNA damage repair and drug responsiveness. High RAD51 expression indicated unfavorable survival outcomes and resistance to platinum, taxane, and PARP inhibitors in ovarian cancer. In the validation cohort (126 patients), high RAD51 expression indicated platinum resistance, and platinum-resistant patients expressed more RAD51. Patients with high RAD51 expression had shorter OS (HR = 2.968, P < 0.0001) and poorer PFS (HR = 2.838, P < 0.0001). RAD51 expression level was negatively correlated with patients’ survival length. Conclusions Ovarian cancer had pronounced RAD51 expression and RAD51 conferred ovarian cancer dependency. High RAD51 expression indicated poor survival and decreased drug sensitivity. RAD51 has predictive value in ovarian cancer and can be exploited as a predictive biomarker.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer worldwide, and appropriate cancer biomarkers facilitate early diagnosis, treatment, and prognosis prediction in cancer management. However, an accurate biomarker for ccRCC is lacking. This study identified 356 differentially expressed genes in ccRCC tissues compared with normal kidney tissues by integrative analysis of eight ccRCC datasets. Enrichment analysis of the differentially expressed genes unveiled improved adaptation to hypoxia and metabolic reprogramming of the tumor cells. Aldehyde oxidase 1 (AOX1) gene was identified as a biomarker for ccRCC among all the differentially expressed genes. ccRCC tissues expressed significantly lower AOX1 than normal kidney tissues, which was further validated by immunohistochemistry at the protein level and The Cancer Genome Atlas (TCGA) data mining at the mRNA level. Higher AOX1 expression predicted better overall survival in ccRCC patients. Furthermore, AOX1 DNA copy number deletion and hypermethylation were negatively correlated with AOX1 expression, which might be the potential mechanism for its dysregulation in ccRCC. Finally, we illustrated that the effect of AOX1 as a tumor suppressor gene is not restricted to ccRCC but universally exists in many other cancer types. Hence, AOX1 may act as a potential prognostic biomarker and therapeutic target for ccRCC.