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- 저자 : He Yancheng (검색결과 2 건)
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Cometabolism degradation of lignin in sequencing batch biofilm reactors
Faguo Kuang,Yancheng Li,Lei He,Yongqiu Xia,Shubai Li,Jian Zhou 대한환경공학회 2018 Environmental Engineering Research Vol.23 No.3
Cometabolism technology was employed to degrade lignin wastewater in Sequencing Batch Biofilm Reactor. Cometabolic system (with glucose and lignin in inflow) and the control group (only lignin in inflow) were established to do a comparative study. In contrast with the control group, the average removal rates of lignin increased by 14.7% and total oarganic carbon increased by 32% in the cometabolic system with glucose as growth substrate, under the condition of 5 mg/L DO, 0.2 kgCOD/(m³·d) lignin and glucose 1.0 kgCOD/(mm³·d). Functional groups of lignin are degraded effectively in cometabolic system proved by fourier transform infrared spectroscopy and Gas Chromatography-Mass Spectrometer, and the degradation products were amides (mainly including acetamide, N-ethylacetamide and N, N-diethylacetamide), alcohols (mainly including glycerol and ethylene glycol) and acids. Meanwhile, results of Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis showed great differences in microbial population richness between cometabolic system and the control group. The Margalef’s richness index and Shannon-Wiener’s diversity index of microorganism in cometabolic system were 3.075 and 2.61, respectively. The results showed that extra addition of glucose, with a concentration of 943 mg/L, was beneficial to lignin biodegradation in cometabolic system.
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A novel immune-related LncRNA prognostic signature for cutaneous melanoma
Hu Nan,Huang Cancan,He Yancheng,Li Shuyang,Yuan Jingyi,Zhong Guishu,Chen Yan 대한독성 유전단백체 학회 2024 Molecular & cellular toxicology Vol.20 No.2
Backgrounds Among tumor microenvironment, the immune components in it have an important influence on gene expression and clinical efficacy. We aim to find out the role of those in skin cutaneous melanoma (SKCM). Objectives Gene expression profile and homologous clinical information of SKCM patients were obtained by TCGA (The Cancer Genome Atlas) and UCSC Toil. SsGSEA method was used to evaluate the immune cell infiltration of 468 TCGA-SKCM samples divided into high immune cell infiltration group (HICI) and low immune cell infiltration group (LICI). We used the Edger packet to conduct difference analysis on normal samples (GTEx) and cancer samples (TCGA), and combined it with the difference of the HICI group and LICI group, to find out the common differential expression of lncRNA in both groups. The prognostic value of immune-related lncRNAs was studied by univariate Cox, Lasso-Cox and multivariate Cox regression analysis, and a prognostic model was established. C index and calibration diagram were used to judge the accuracy of the model, and DCA was used to judge the net benefit. Results Six prognostic markers of immune-related lncRNA genes were established, which could be used as independent prognostic factors. The net benefit and prediction accuracy are significantly higher than TNM Stage. Conclusion The prognostic model identified in this study is a reliable biomarker for SKCM. The Nomogram survival prediction model based on it is a reliable way to predict the median survival time of patients, which may lay the foundation for future treatment of this disease. Backgrounds Among tumor microenvironment, the immune components in it have an important influence on gene expression and clinical efficacy. We aim to find out the role of those in skin cutaneous melanoma (SKCM). Objectives Gene expression profile and homologous clinical information of SKCM patients were obtained by TCGA (The Cancer Genome Atlas) and UCSC Toil. SsGSEA method was used to evaluate the immune cell infiltration of 468 TCGA-SKCM samples divided into high immune cell infiltration group (HICI) and low immune cell infiltration group (LICI). We used the Edger packet to conduct difference analysis on normal samples (GTEx) and cancer samples (TCGA), and combined it with the difference of the HICI group and LICI group, to find out the common differential expression of lncRNA in both groups. The prognostic value of immune-related lncRNAs was studied by univariate Cox, Lasso-Cox and multivariate Cox regression analysis, and a prognostic model was established. C index and calibration diagram were used to judge the accuracy of the model, and DCA was used to judge the net benefit. Results Six prognostic markers of immune-related lncRNA genes were established, which could be used as independent prognostic factors. The net benefit and prediction accuracy are significantly higher than TNM Stage. Conclusion The prognostic model identified in this study is a reliable biomarker for SKCM. The Nomogram survival prediction model based on it is a reliable way to predict the median survival time of patients, which may lay the foundation for future treatment of this disease.
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