ABSTRACT
BACKGROUND: Metabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer. METHODS: The mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction. RESULTS: A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting. CONCLUSION: Our data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.
Subject(s)
Prostatic Neoplasms , Male , Humans , Prostatic Neoplasms/genetics , Disease-Free Survival , Progression-Free Survival , Algorithms , Cell Cycle , Prognosis , Tumor MicroenvironmentABSTRACT
The combustion-supporting effect of steam to coke breeze in sintering has the potential to improve sinter quality and reduce pollutants emissions. The results show that increasing the by-product steam injection concentration (0.32-0.47vol%) and prolonging the injection time (5 min) within a proper range (10-15 min) can improve sinter quality. 2.13kgce/t-sinter of the fuel consumption was decreased by reducing coke breeze usage from 5.60 to 5.45% under the recommended parameters, with 15.16% decrease of CO in sintering waste gas. By comparing experimental data with thermodynamic calculations, although the reaction between CO and steam can reduce CO emission and generate H2, steam tends to react with coke breeze to generate H2 and CO (react at 674â), and OH radical produced by H2 which can reduce the activation energy of CO oxidation reaction is the key to reducing pollutant emissions. The potential economic benefit of steam injection technology was calculated based on a 360m2 sintering machine (the annual sinter output is 3.2million tons), excluding the equipment modification and steam injection cost of $300,000; a profit of $737491.2 per year or 0.23 dollars per ton sinter can be achieved. Therefore, low-carbon and cleaner iron ore sintering production can be realized through applying by-product steam.
Subject(s)
Coke , Iron , SteamABSTRACT
Mol-ecules of the title compound, C(19)H(15)O(3)P, are connected by O-Hâ¯O hydrogen bonds between the carboxylic acid OH group and the phosphinoyl O atom, forming chains running along the crystallographic b axis.
