ABSTRACT
BACKGROUND: Lipid metabolism is closely related to the occurrence and development of breast cancer. Our purpose was to establish a novel model based on lipid metabolism-related long noncoding RNAs (lncRNAs) and evaluate the potential clinical value in predicting prognosis for patients suffering from breast cancer. METHODS: RNA data and clinical information for breast cancer were obtained from the cancer genome atlas (TCGA) database. Lipid metabolism-related lncRNAs were identified via the criteria of correlation coefficient |R2 | > 0.4 and p < 0.001, and prognostic lncRNAs were identified to establish model through Cox regression analysis. The training set and validation set were established to certify the feasibility, and all samples were separated into high-risk group or low-risk group. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were conducted to evaluate the potential biological functions, and the immune infiltration levels were explored through Cibersortx database. RESULTS: A total of 14 lncRNAs were identified as protective genes (AC022150.4, AC061992.1, AC090948.3, AC092794.1, AC107464.3, AL021707.8, AL451085.2, AL606834.2, FLJ42351, LINC00926, LINC01871, TNFRSF14-AS1, U73166.1 and USP30-AS1) with HRs < 1 while 10 lncRNAs (AC022150.2, AC090948.1, AC243960.1, AL021707.6, ITGB2-AS1, OTUD6B-AS1, SP2-AS1, TOLLIP-AS1, Z68871.1 and ZNF337-AS1) were associated with increased risk with HRs >1. A total of 24 prognostic lncRNAs were selected to construct the model. The patients in low-risk group were associated with better prognosis in both training set (p < 0.001) and validation set (p < 0.001). The univariate and multivariate Cox regression analyses revealed that risk score was an independent prognostic factors in both training set (p < 0.001) and validation set (p < 0.001). GO and GSEA analyses revealed that these lncRNAs were related to metabolism-related signal pathway and immune cells signal pathway. Risk score was negatively correlated with B cells (r = -0.097, p = 0.002), NK cells (r = -0.097, p = 0.002), Plasma cells (r = -0.111, p = 3.329e-04), T-cells CD4 (r = -0.064, p = 0.039) and T-cells CD8 (r = -0.322, p = 2.357e-26) and positively correlated with Dendritic cells (r = 0.077, p = 0.013) and Monocytes (r = 0.228, p = 1.107e-13). CONCLUSION: The prognostic model based on lipid metabolism lncRNAs possessed an important value in survival prediction of breast cancer patients.
Subject(s)
Breast Neoplasms , Lipid Metabolism , RNA, Long Noncoding , Biomarkers, Tumor/genetics , Biomarkers, Tumor/metabolism , Breast Neoplasms/genetics , Breast Neoplasms/metabolism , Female , Gene Expression Regulation, Neoplastic , Humans , Lipid Metabolism/genetics , Mitochondrial Proteins/metabolism , Prognosis , RNA, Long Noncoding/genetics , RNA, Long Noncoding/metabolism , Thiolester Hydrolases/genetics , Thiolester Hydrolases/metabolismABSTRACT
Influence of EOM and NOM on removal of algae and turbidity was investigated. The result showed that EOM had both beneficial and harmful effects on coagulation, it hindered the charge neutrality of the flocculant. Zeta potential of algae decreased from -40.6 mV to -14.7 mV, only when the modified chitosan was added above 0.2 mg x L(-1). But it became a coagulant aid when it combined with flocculant. The experiment indicated that turbidity removal would reach the peak efficiency (96%) with appropriate concentration of EOM (5.18 mg x L(-1)), therefore EOM would enhance the removal efficiency. NOM had the more negative effect on coagulation, the optimum removal efficiency of algae and turbidity decreased by 11% and 18% separately. Besides, the optimum dosage of modified chitosan increased from 0.35 mg x L(-1) and 0.1 mg x L(-1) to 0.7 mg x L(-1) and 0.3 mg x L(-1) respectively. So it is the key point to take advantage of EOM and remove the NOM in practice, as a result the flocculant loading will be decreased, the removal efficiency will be improved.
