Identification of CD28 and PTEN as novel prognostic markers for cervical cancer

Cervical cancer (CC) is the most common malignant tumor with poor clinical outcome among women. Identification of novel biomarkers could be beneficial for the clinical diagnosis and treatment of CC. This study aimed to identify prognostic biomarkers for the prediction of prognostic status of CC patients, and explore the effect of the corresponding methylated genes in the occurrence and development of CC. The methylation microarray data of CC was extracted from The Cancer Genome Atlas (TCGA) dataset. The methylation genes associated with the prognostic status were identified based on the information of the relapse‐free survival (RFS) of the CC patients. The prognostic gene pairs were further identified. Then, the prognostic signature was identified by the forward search algorithm based on the C‐index method. The results were validated by independent dataset. Finally, the functional analysis was performed on the methylation genes. A total of 276 methylation genes and 2508 gene pairs associated with the prognostic status of the CC were identified. A signature composed of eight methylation gene pairs was obtained to predict the prognostic status of cervical patients. A series of genes that played an important role in the occurrence and development of CC were obtained by the functional enrichment analysis. To summary, a prognostic signature consisting of eight methylation gene pairs was obtained. Of note, the CD28 and PTEN gene pair were found to play important roles in the occurrence and development of CC. This study aimed to investigate the molecular mechanisms of Diabetic Kidney Disease (DKD) and to explore new potential therapeutic strategies and biomarkers for DKD. First we analyzed the differentially expressed changes between DKD patients and the control group using the chip data in Gene Expression Omnibus database. Then the gene chip was subjected to be annotated again, so as to screen long non‐coding RNAs (lncRNAs) and study expression differences of these lncRNAs in DKD and controlled samples. At last, the function of the differential lncRNAs was analyzed. A total of 252 lncRNAs were identified, and 14 were differentially expressed. In addition, there were 1629 differentially expressed. Messenger RNAs genes, and PEA15, MIR22, and LINC00472 were significantly differentially expressed in DKD samples. Through functional analysis of the encoding genes coexpressed by the three lncRNAs, we found these genes were mainly enriched in type 1 diabetes and autoimm