Bladder cancer was a malignant disease in patients, our research aimed at discovering the possible biomarkers for the diseases. The gene chip GSE31684, including 93samples, was downloaded from the GEO datasets and co-expression network was constructed by the data. Molecular complex detection(MCODE) was used to identify hub genes. The most significant cluster including 16 genes: CDH11, COL3A1, COL6A3, COL5A1, AEBP1, COL1A2, NTM, COL11A1, THBS2, COL8A1, COL1A1, BGN, MMP2, PXDN, THY1, and TGFB1I1 was identified. After annotated by BiNGO, they were suggested associated with collagen fibril organization and blood vessel development. In addition, the Kaplan Meier curves were obtained by UALCAN. The high expression of THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1, and TGFB1I1 indicated poor prognosis of the patients(P < 0.05). Finally, we examined genes’ expression between low and high tumor stage by the Wilcoxon test(P < 0.05), TGFB1I1 was excluded. THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1 associated with the tumor stage as well as tumor patients’ prognosis. COL5A1, COL8A1(P < 0.01) may serve as therapeutic targets for the disease.

Background N6-Methyladenosine (m6A) are significant in directing different genes’ expression and their phenotype in different types of cancers. On the other hand, the function of m6A in seminomatous germ cell tumors (SGCT) is still unexplored. Our research attempts to demonstrate the disease-free prognosis value of m6A RNA methylation regulators in SGCT. Methods The gene expression and accompanying clinical data of 65 SGCT tissues and 85 NSGCT tissues retrieved from the TCGA database were analyzed. A series of R packages were applied for our study. Results The landscape of m6A RNA methylation regulators in SGCT was displayed. All SGCT patients were grouped into two clusters by consensus clustering of m6A RNA methylation regulators, and the clusters were connected to the disease-free prognosis and clinicopathological characteristics of SGCT(p = 0.015). Furthermore, the risk score(1.52YTHDC1 + 2.22HNRNPC-2.735WTAP) model was obtained and constructed by the univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) Cox regression. The risk score was interrelated with stage status and laterality status of SGCT. Under the same conditions, the right side may have a higher risk score than the left one. More significantly, the risk score can serve as an self-supporting disease-free prognostic marker for SGCT patients. Conclusions m6A RNA methylation regulators played an important role of SGCT in its disease-free survival prognosis.

N6-Methyladenosine (m6A) are significant in directing different genes’ expression and their phenotype in different types of cancers. On the other hand, the function of m6A in seminomatous germ cell tumors (SGCT) is still unexplored. Our research attempts to demonstrate the disease-free prognosis value of m6A RNA methylation regulators in SGCT. The gene expression and accompanying clinical data of 65 SGCT tissues and 85 NSGCT tissues retrieved from the TCGA database were analyzed. The landscape of m6A RNA methylation regulators in SGCT was displayed. All SGCT patients were grouped into two clusters by consensus clustering of m6A RNA methylation regulators, and the clusters were connected to the disease-free prognosis and clinicopathological characteristics of SGCT(p = 0.015). Furthermore, the risk score(1.52YTHDC1 + 2.22HNRNPC-2.735WTAP) model was obtained and constructed by the univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) Cox regression. The risk score was interrelated with stage status and laterality status of SGCT. Under the same conditions, the right side may have a higher risk score than the left one. More significantly, the risk score can serve as an self-supporting disease-free prognostic marker for SGCT patients. Therefore, m6A RNA methylation regulators also played an important role of SGCT in its disease-free survival prognosis.

Object: Understanding hub genes associated with gastric adenocarcinoma (GC) development could lead to effective advances to diagnose and treat the diseases. In order to discover possible signal pathways and hub genes for the disease, we utilized the bioinformatics tools to analyze its mechanism.Method: The gene chip of GSE7997was download from the GEO Datasets. Expression data of gastric cancer and its adjacent normal tissues were compared and the DEGs were acquired. The clusterProfiler and KEGG.db R packages were used for the analysis of its gene ontology process and KEGG pathways. What’s more, the PPI network was constructed by the STRING website. The hub genes were acquired by the plugin of the Cytohubba in Cytoscape. Finally, these genes were examined by the TCGA datasets and potential drugs were explored by Connectivity map.Results: The up regulated DEGs was mainly associated with the process of an extracellular matrix organization, an extracellular matrix organization, Collagen catabolic process, and multicellular organismal catabolic process. The down regulated DEGs have mainly associated with the process of digestion， cellular response to zinc ion， digestive system process, and organic hydroxy compound metabolic process. The up regulated DEGs was mainly located on PI3K-AKTsignal pathways, human papillomavirus infection，Cytokine-cytokine receptor interaction, and focal adhesion process. The down regulated genes were mainly associated with protein digestion and absorption, mineral absorption, and the pancreatic secretion. Cytohubba had found hub genes of COL1A2, COL5A1, COL4A1, COL5A2, COL6A3, COL11A1, SERPINH1, FN1, and down-regulated COL2A1.These genes were associated with the process of transforming growth factor, extracellular matrix, cell adhesion, wound healing and so on. As examined by the TCGA datasets, these altered genes were associated with overall survivaland no disease progress time (P<0.05). Finally, we got the small molecule drugs of fenofibrate, benzbromarone, semustine, chloroquine, ondansetron, hydroxyachillin, megestrol, ciclopirox and monastrol for gastric cancer by Connectivity map (P<0.05).Conclusion: As mentioned above, we got 9 hub genes and tested by the TCGA datasets of gastric adenocarcinoma. They were associated with overall survival and disease free progressive time in gastric cancer patients. The bioinformatical analysis of the disease may enhance the understanding of the mechanism of disorders.