Mn/carbon aerogels derived from the water-induced self-assembly of UIO-66 (Mn) for the thermal decomposition of ammonium perchlorate.

In solid propellants, combustion catalysts play a crucial role. Here, we introduce a convenient method for the self-assembly of UIO-66 (Mn) in the presence of water, leading to the preparation of Mn/C aerogels. The aerogels were successfully utilized in the thermocatalytic decomposition of ammonium perchlorate (AP). The results indicate that the incorporation of 2% mass fraction of Mn/C aerogels enhances the peak temperature of AP decomposition by approximately 87.5°C. Mn/C aerogels demonstrate excellent catalytic performance. In combination with kinetics, we propose a thermal catalytic mechanism.

Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis.

The importance of anti-androgen therapy for prostate cancer (PC) has been well recognized. However, the mechanisms underlying prostate cancer resistance to anti-androgens are not completely understood. Therefore, identifying pharmacological targets in driving the development of castration-resistant PC is necessary. In the present study, we sought to identify core genes in regulating steroid hormone pathways and associating them with the disease progression of PC. The selection of steroid hormone-associated genes was identified from functional databases, including gene ontology, KEGG, and Reactome. The gene expression profiles and relevant clinical information of patients with PC were obtained from TCGA and used to examine the genes associated with steroid hormone. The machine-learning algorithm was performed for key feature selection and signature construction. With the integrative bioinformatics analysis, an eight-gene signature, including CA2, CYP2E1, HSD17B, SSTR3, SULT1E1, TUBB3, UCN, and UGT2B7 was established. Patients with higher expression of this gene signature had worse progression-free interval in both univariate and multivariate cox models adjusted for clinical variables. The expression of the gene signatures also showed the aggressiveness consistently in two external cohorts, PCS and PAM50. Our findings demonstrated a validated eight-gene signature could successfully predict PC prognosis and regulate the steroid hormone pathway.

Identification of DNA Damage Repair-Associated Prognostic Biomarkers for Prostate Cancer Using Transcriptomic Data Analysis.

In the recent decade, the importance of DNA damage repair (DDR) and its clinical application have been firmly recognized in prostate cancer (PC). For example, olaparib was just approved in May 2020 to treat metastatic castration-resistant PC with homologous recombination repair-mutated genes; however, not all patients can benefit from olaparib, and the treatment response depends on patient-specific mutations. This highlights the need to understand the detailed DDR biology further and develop DDR-based biomarkers. In this study, we establish a four-gene panel of which the expression is significantly associated with overall survival (OS) and progression-free survival (PFS) in PC patients from the TCGA-PRAD database. This panel includes DNTT, EXO1, NEIL3, and EME2 genes. Patients with higher expression of the four identified genes have significantly worse OS and PFS. This significance also exists in a multivariate Cox regression model adjusting for age, PSA, TNM stages, and Gleason scores. Moreover, the expression of the four-gene panel is highly correlated with aggressiveness based on well-known PAM50 and PCS subtyping classifiers. Using publicly available databases, we successfully validate the four-gene panel as having the potential to serve as a prognostic and predictive biomarker for PC specifically based on DDR biology.

Comparative analysis of gene expression profiles of gastric cardia adenocarcinoma and gastric non-cardia adenocarcinoma.

In the present study, gene expression profiles were analyzed to identify the molecular mechanisms underlying gastric cardia adenocarcinoma (GCA) and gastric non-cardia adenocarcinoma (GNCA). A gene expression dataset (accession number GSE29272) was downloaded from Gene Expression Omnibus, and consisted of 62 GCA samples and 62 normal controls, as well as 72 GNCA samples and 72 normal controls. The two groups of differentially-expressed genes (DEGs) were compared to obtain common and unique DEGs. A differential analysis was performed using the Linear Models for Microarray Data package in R. Functional enrichment analysis was conducted for the DEGs using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) networks were constructed for the DEGs with information from the Search Tool for the Retrieval of Interacting Genes. Subnetworks were extracted from the whole network with Cytoscape. Compared with the control, 284 and 268 genes were differentially-expressed in GCA and GNCA, respectively, of which 194 DEGs were common between GCA and GNCA. Common DEGs [e.g., claudin (CLDN)7, CLDN4 and CLDN3] were associated with cell adhesion and digestion. GCA-unique DEGs [e.g., MAD1 mitotic arrest deficient like 1, cyclin (CCN)B1, CCNB2 and CCNE1] were associated with the cell cycle and the regulation of cell proliferation, while GNCA-unique DEGs (e.g., GATA binding protein 6 and hyaluronoglucosaminidase 1) were implicated in cell death. A PPI network with 141 nodes and 446 edges were obtained, from which two subnetworks were extracted. Genes [e.g., fibronectin 1, collagen type I α2 chain (COL1A2) and COL1A1] from the two subnetworks were implicated in extracellular matrix organization. These common DEGs could advance our understanding of the etiology of gastric cancer, while the unique DEGs in GCA and GNCA could better define the properties of specific cancers and provide potential biomarkers for diagnosis, prognosis or therapy.