Immunotherapy, especially anti-PD-1, is becoming a pillar of modern muscle-invasive bladder cancer (MIBC) treatment. However, the objective response rates (ORR) are relatively low due to the lack of precise biomarkers to select patients. Herein, the molecular subtype, tumor mutation burden (TMB), and CD8+ T cells were calculated by the gene expression and mutation profiles of MIBC patients. MIBC immunotypes were constructed using clustering analysis based on tumor mutation burden, CD8+ T cells, and molecular subtypes. Mutated genes, enriched functional KEGG pathways and GO terms, and co-expressed network-specific hub genes have been identified. We demonstrated that ORR of immunotype A patients identified by molecular subtype, CD8+ T cells, and TMB is about 36% predictable. PIK3CA, RB1, FGFR3, KMT2C, MACF1, RYR2, and EP300 are differentially mutated among three immunotypes. Pathways such as ECM-receptor interaction, PI3K-Akt signaling pathway, and TGF-beta signaling pathway are top-ranked in enrichment analysis. Low expression of ACTA2 was associated with the MIBC survival benefit. The current study constructs a model that could identify suitable MIBC patients for immunotherapy, and it is an important step forward to the personalized treatment of bladder cancers.

T Cell Immune Regulator 1, ATPase H + Transporting V0 Subunit A3 (TCIRG1 gene provides instructions for making one part, the a3 subunit, of a large protein complex known as a vacuolar H + -ATPase (V-ATPase). V-ATPases are a group of similar complexes that act as pumps to move positively charged hydrogen atoms (protons) across membranes. Single amino acid changes in highly conserved areas of the TCIRG1 protein have been linked to autosomal recessive osteopetrosis and severe congenital neutropenia. We used multiple computational approaches to classify disease-prone single nucleotide polymorphisms (SNPs) in TCIRG1. We used molecular dynamics analysis to identify the deleterious nsSNPs, build mutant protein structures, and assess the impact of mutation. Our results show that fifteen nsSNPs (rs199902030, rs200149541, rs372499913, rs267605221, rs374941368, rs375717418, rs80008675, rs149792489, rs116675104, rs121908250, rs121908251, rs121908251, rs149792489 and rs116675104) variants are likely to be highly deleterious mutations as by incorporating them into wild protein they destabilize the wild protein structure and function. They are also located in the V-ATPase I domain, which may destabilize the structure and impair TCIRG1 protein activation, as well as reduce its ATPase effectiveness. These mutants have not yet been identified in patients suffering from CN and osteopetrosis while (G405R, R444L, and D517N) reported in our study are already associated with osteopetrosis. Mutation V52L reported in our study was identified in a patient suspected for CN. Finally, these mutants can help to further understand the broad pool of illness susceptibilities associated with TCIRG1 catalytic kinase domain activation and aid in the development of an effective treatment for associated diseases.

Basal and luminal subtypes of muscle-invasive bladder cancer (MIBC) have distinct molecular profiles and heterogeneous clinical behaviors. The interactions between mRNAs and lncRNAs, which might be regulated by miRNAs, have crucial roles in many cancers. However, the miRNA-dependent crosstalk between lncRNA and mRNA in specific MIBC subtypes still remains unclear. In this study, we first classified MIBC into two conservative subtypes using miRNA, mRNA and lncRNA expression data derived from The Cancer Genome Atlas. Then we investigated subtype-related biological pathways and evaluated the subtype classification performance using Decision Trees, Random Forest and eXtreme Gradient Boosting (XGBoost). At last, we explored potential miRNA-mediated lncRNA-mRNA crosstalks based on co-expression analysis. Our results show that: (1) the luminal subtype is primarily characterized by upregulation of metabolism-related pathways while the basal subtype is predominantly characterized by upregulation of epithelial-mesenchymal transition, metastasis, and immune system process-related pathways; (2) the XGBoost prediction model is consistently robust for classification of the molecular subtypes of MIBC across four datasets (The area under the ROC curve > 0.9); (3) the expression levels of the molecules in the miR-200c and miR141-mediated lncRNA-mRNA crosstalks differ considerably between the two subtypes and have close relationships with the prognosis of MIBC. The miR-200c and miR-141-dependent mRNA-lncRNA crosstalks might be of great significance in tumorigenesis and tumor progression and may serve as the novel prognostic predictors and classification markers of MIBC subtypes.

Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA. Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA. WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models. The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA.

A series of reduced amino pyridine Schiff base platinum(II) complexes were prepared as potential anticancer drugs, and characterized by NMR, IR spectroscopy, elemental analysis, and molar conductivity. UV and CD results showed the binding mode between these compounds and salmon sperm DNA may be intercalation. The cytotoxicity of these complexes was validated against A549, Hela, and MCF-7 cell lines by MTT assay. Some complexes exhibited better cytotoxic activity than cisplatin against Hela and MCF-7 cell lines.

Metabolic reprogramming is a hallmark of cancers, but pan-cancer level roles of lipid metabolism in cancer development are remains poorly understood. We investigated the possible roles of lipid metabolic genes (LMGs) in 14 cancer types. The results indicate that: (1) there is strong evidence for increased lipid metabolism in THCA and KICH. (2) Although the overall levels of lipid metabolic processes are down-regulated in some cancer types, fatty acid synthase activity and fatty acid elongation are moderately up-regulated in more than half of the cancer types. Cholesterol synthesis is up-regulated in five cancers including KICH, BLCA, COAD, BRCA, UCEC, and THCA. (3) The catabolism of cholesterols, triglycerides and fatty acids is repressed in most cancers, but a specific form of lipid degradation, lipophagy, is activated in THCA and KICH. (4) Lipid storage is enhanced in in kidney cancers and thyroid cancer. (5) Similarly to primary tumors, metastatic tumors tend to up-regulate biosynthetic processes of diverse lipids, but down-regulate lipid catabolic processes, except lipophagy. (6) The frequently mutated lipid metabolic genes are not key LMGs. (7) We established a LMG-based model for predicting cancer prognosis. Our results are helpful in expanding our understanding of the role of lipid metabolism in cancer.