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129 results on '"Cai, Wanxia"'

17. Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus

Author

Li, Yixi, primary, Ma, Chiyu, additional, Liao, Shengyou, additional, Qi, Suwen, additional, Meng, Shuhui, additional, Cai, Wanxia, additional, Dai, Weier, additional, Cao, Rui, additional, Dong, Xiangnan, additional, Krämer, Bernhard K., additional, Yun, Chen, additional, Hocher, Berthold, additional, Hong, Xiaoping, additional, Liu, Dongzhou, additional, Tang, Donge, additional, He, Jingquan, additional, Yin, Lianghong, additional, and Dai, Yong, additional

Published
2022
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20. A comprehensive investigation discovered the novel methyltransferase METTL24 as one presumably prognostic gene for kidney renal clear cell carcinoma potentially modulating tumor immune microenvironment

Author

Jiang, Zhongji, primary, Zhang, Wei, additional, Zeng, Zhipeng, additional, Tang, Donge, additional, Li, Chujiao, additional, Cai, Wanxia, additional, Chen, Yumei, additional, Li, Ya, additional, Jin, Qiu, additional, Zhang, Xinzhou, additional, Yin, Lianghong, additional, Liu, Xueyan, additional, Xu, Yong, additional, and Dai, Yong, additional

Published
2022
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21. Integrated computer analysis and a self-built Chinese cohort study identified GSTM2 as one survival-relevant gene in human colon cancer potentially regulating immune microenvironment

Author

Zhang, Wei, primary, Shi, Yutong, additional, Niu, Shumeng, additional, Li, Lintai, additional, Lin, Liewen, additional, Gao, Xucan, additional, Cai, Wanxia, additional, Chen, Yumei, additional, Zhong, Yafang, additional, Tang, Donge, additional, Tang, Min, additional, and Dai, Yong, additional

Published
2022
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23. Screening Biomarkers for Systemic Lupus Erythematosus Based on Machine Learning and Exploring Their Expression Correlations With the Ratios of Various Immune Cells

Author

Zhong, Yafang, primary, Zhang, Wei, additional, Hong, Xiaoping, additional, Zeng, Zhipeng, additional, Chen, Yumei, additional, Liao, Shengyou, additional, Cai, Wanxia, additional, Xu, Yong, additional, Wang, Gang, additional, Liu, Dongzhou, additional, Tang, Donge, additional, and Dai, Yong, additional

Published
2022
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24. Identification of lysine acetylome of oral squamous cell carcinoma by label-free quantitative proteomics

Author

Dong, Jingjing, primary, He, Jingquan, additional, Zhang, Zeyu, additional, Zhang, Wei, additional, Li, Yixi, additional, Li, Dandan, additional, Xie, Hongliang, additional, Zuo, Wenxin, additional, Tang, Jianming, additional, Zeng, Zhipeng, additional, Cai, Wanxia, additional, Lai, Liusheng, additional, Yun, Manhua, additional, Shen, Lingjun, additional, Yin, Lianghong, additional, Tang, Donge, additional, and Dai, Yong, additional

Published
2022
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28. Systematic Analysis of Neurotransmitter Receptors in Human Breast Cancer Reveals a Strong Association With Outcome and Uncovers HTR6 as a Survival-Associated Gene Potentially Regulating the Immune Microenvironment

Author

Zhang, Wei, primary, Li, Lintai, additional, Li, Jianxuan, additional, Yu, Haiyan, additional, Zheng, Fengping, additional, Yan, Bin, additional, Cai, Wanxia, additional, Chen, Yumei, additional, Yin, Lianghong, additional, Tang, Donge, additional, Xu, Yong, additional, and Dai, Yong, additional

Published
2022
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31. Additional file 1 of Spatial metabolomics on liver cirrhosis to hepatocellular carcinoma progression

Author

He, Michelle Junyi, Pu, Wenjun, Wang, Xi, Zhong, Xiaoni, Zhao, Dong, Zeng, Zhipeng, Cai, Wanxia, Liu, Jiayi, Huang, Jianrong, Tang, Donge, and Dai, Yong

Abstract

Additional file 1: Figure S1. Total metabolite distributions in the HC, LC and HCC tissue samples. a HE and MSI diagrams of HC, LC and HCC whole samples under the negative ionization mode. b PLS-DA comparison of AFADESI-MSI data under the negative ionization mode. c, d Alterations of metabolites detected by AFADESI-MSI method in the HC, LC and HCC samples based on positive (c) and negative (d) ionization modes. e, f K-means diagrams of the HC, LC and HCC tissue samples based on positive (e) and negative (f) ionization modes. Figure S2. The alterations of metabolites’ spatial distributions in HCC sub-regions under negative ionization mode. a HE and MSI images of different sub-regions of HCC. b PLS-DA analysis of different HCC sub-regions. c Number of metabolites detected from different HCC sub-regions. d Heatmap of significantly differentiated metabolites based on VIP > 1. e KEGG analysis of key altered metabolic pathways in the sub-regions of HCC. Figure S3. Metabolites’ spatial distributions in the non-cancerous regions (CV) in the HC, LC and HCC samples under negative ionization mode. a HE and MSI images of the CV regions in the HC, LC and HCC samples. b PLS-DA analysis for the CV regions. c Heatmap of significantly differentiated metabolites under positive ionization mode in the CV regions based on VIP > 1. d KEGG analysis of key altered metabolic pathways in the CV regions. e Heatmap of significantly differentiated metabolites based on VIP > 1 under negative ionization mode. f Sample time series analysis of the key metabolite expressions in the CV regions. Figure S4. Metabolites’ spatial distributions of the cancer-related regions in the HC, LC and HCC samples under negative ionization mode. a HE and MSI images (negative mode) of the cancer-related regions in the HC, LC and HCC samples. b PLS-DA analysis of the cancer-related regions. c Heatmap of significantly differentiated metabolites in the cancer-related regions based on variable VIP > 1 under positive ionization mode. d Heatmap of significantly differentiated metabolites in the cancer-related regions based on variable VIP > 1 under negative ionization mode. e KEGG analysis of key altered metabolic pathways in cancer-related regions. Figure S5. Metabolic changes in different tissues and its predictability of disease progression. a Sample time series analysis of key metabolite expressions (negative mode) in the cancer-related regions. b Examples of key metabolites’ spatial expressions in cancer-related regions under negative ion mode. c ROC analyses of key metabolites at distinguishing between diseased tissues and between different subregions (anabasine was detected under positive ionization mode, docosahexaenoic acid was detected under negative ionization mode).

Published
2022
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36. Robust Glycogene-Based Prognostic Signature for Proficient Mismatch Repair Colorectal Adenocarcinoma

Author

Li, Yixi, primary, Li, Dehua, additional, Chen, Yang, additional, Lu, Yongping, additional, Zhou, Fangbin, additional, Li, Chunhong, additional, Zeng, Zhipeng, additional, Cai, Wanxia, additional, Lin, Liewen, additional, Li, Qiang, additional, Ye, Mingjun, additional, Dong, Jingjing, additional, Yin, Lianghong, additional, Tang, Donge, additional, Zhang, Gong, additional, and Dai, Yong, additional

Published
2021
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38. Identification of lysine acetylome of oral squamous cell carcinoma by label-free quantitative proteomics

Author

Dong, Jingjing, primary, He, Jingquan, additional, Zhang, Zeyu, additional, Zhang, Wei, additional, Li, Yixi, additional, Xie, Hongliang, additional, Zuo, Wenxin, additional, Tang, Jianming, additional, Zeng, Zhipeng, additional, Cai, Wanxia, additional, Lai, Liusheng, additional, Yun, Manhua, additional, Shen, Lingjun, additional, Yin, Lianghong, additional, Tang, Donge, additional, and Dai, Yong, additional

Published
2021
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40. Integrative multiplatform-based molecular profiling of human colorectal cancer reveals proteogenomic alterations underlying mitochondrial inactivation

Author

Zhang, Wei, Tang, Donge, Lin, Liewen, Fan, Tingting, Xia, Ligang, Cai, Wanxia, Dai, Weier, Zou, Chang, Yin, Lianghong, Xu, Yong, and Dai, Yong

Subjects
Original Article, neoplasms, digestive system diseases
Abstract

Mitochondria play leading roles in initiation and progression of colorectal cancer (CRC). Proteogenomic analyses of mitochondria of CRC tumor cells would likely enhance our understanding of CRC pathogenesis and reveal new independent prognostic factors and treatment targets. However, comprehensive investigations focused on mitochondria of CRC patients are lacking. Here, we investigated global profiles of structural variants, DNA methylation, chromatin accessibility, transcriptome, proteome, and phosphoproteome on human CRC. Proteomic investigations uncovered greatly diminished mitochondrial proteome size in CRC relative to that found in adjacent healthy tissues. Integrated with analysis of RNA-Seq datasets obtained from the public database containing mRNA data of 538 CRC patients, the proteomic analysis indicated that proteins encoded by 45.5% of identified prognostic CRC genes were located within mitochondria, highlighting the association between altered mitochondrial function and CRC. Subsequently, we compared structural variants, DNA methylation, and chromatin accessibility of differentially expressed genes and found that chromatin accessibility was an important factor underlying mitochondrial gene expression. Furthermore, phosphoproteomic profiling demonstrated decreased phosphorylation of most mitochondria-related kinases within CRC versus adjacent healthy tissues, while also highlighting MKK3/p38 as an essential mitochondrial regulatory pathway. Meanwhile, systems-based analyses revealed identities of key kinases, transcriptional factors, and their interconnections. This research uncovered a close relationship between mitochondrial dysfunction and poor CRC prognosis, improve our understanding of molecular mechanism underlying mitochondrial linked to human CRC, and facilitate identifies of clinically relevant CRC prognostic factors and drug targets.

Published
2021

42. Co-occurrence of Protein Crotonylation and 2-Hydroxyisobutyrylation in the Proteome of End-Stage Renal Disease

Author

Dong, Jingjing, primary, Li, Yixi, additional, Zheng, Fengping, additional, Chen, Wenbiao, additional, Huang, Shaoying, additional, Zhou, Xianqing, additional, Wang, Kang, additional, Cai, Wanxia, additional, Liu, HaiPing, additional, Yin, Lianghong, additional, Li, Qiang, additional, Tang, Donge, additional, and Dai, Yong, additional

Published
2021
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43. Additional file 2 of Multi-omics analyses of human colorectal cancer revealed three mitochondrial genes potentially associated with poor outcomes of patients

Author

Zhang, Wei, Lin, Liewen, Xia, Ligang, Cai, Wanxia, Dai, Weier, Zou, Chang, Yin, Lianghong, Tang, Donge, Xu, Yong, and Dai, Yong

Subjects
digestive system diseases
Abstract

Additional file 2. The cox analyses of HIGD1A, SUCLG2 and SLC25A24; The overall survival rates of HIGD1A, SUCLG2 and SLC25A24 in colon and rectum cancer.

Published
2021
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44. Additional file 1 of Single-cell chromatin accessibility landscape of human umbilical cord blood in trisomy 18 syndrome

Author

Qiu, Xiaofen, Yu, Haiyan, Wu, Hongwei, Hu, Zhiyang, Zhou, Jun, Lin, Hua, Xue, Wen, Cai, Wanxia, Chen, Jiejing, Yan, Qiang, Dai, Weier, Yang, Ming, Tang, Donge, and Dai, Yong

Abstract

Additional file 1: Supplementary Figure 1. (A) UMAP plots for the disease and control groups. (B) UMAP cluster to perform dimensionality reduction and clustering on the ES and NC data. (C) Expression of genes in the ES and NC groups in cluster 0 of megakaryocyte erythroid cells that participate in the human T cell leukemia virus 1 infection KEGG pathway. (D) Expression of genes in the ES and NC groups in cluster 1 of megakaryocyte erythroid cells that participate in the apoptosis KEGG pathway.

Published
2021
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46. Signalling networks constructed by integrative analysis of multi-omics in peripheral blood mononuclear cells in systemic lupus erythematous patients

Author

Song, Wencong, primary, Zheng, Fengping, additional, Huang, Shaoying, additional, Cai, Wanxia, additional, Yu, Haiyan, additional, Hong, Xiaoping, additional, He, Jingquan, additional, Dai, Weier, additional, Yin, Lianghong, additional, Tang, Donge, additional, Liu, Dongzhou, additional, and Dai, Yong, additional

Published
2021
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49. Quantitative Proteomic Analyses To Reveal the Key Features of Proteins in New Onset Ankylosing Spondylitis Patients

Author

Lu, Yong-Ping, primary, Zhang, Xiao-Li, additional, Zheng, Fengping, additional, Yun, Chen, additional, Zhu, Chengxin, additional, Cai, Wanxia, additional, Liu, Dongzhou, additional, Hong, Xiaoping, additional, Li, Qiang, additional, Hu, Bo, additional, Tang, Donge, additional, Yin, Liang-Hong, additional, and Dai, Yong, additional

Published
2020
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50. Gene regulatory network study of rheumatoid arthritis in single-cell chromatin landscapes of peripheral blood mononuclear cells.

Author

Zhang C, Hong X, Yu H, Xu H, Qiu X, Cai W, Hocher B, Dai W, Tang D, Liu D, and Dai Y

Subjects
Humans, Gene Regulatory Networks, Chromatin, Case-Control Studies, Transcription Factors, Adult, Middle Aged, Aged, Male, Female, Arthritis, Rheumatoid genetics, Arthritis, Rheumatoid pathology, Leukocytes, Mononuclear, Chromatin Immunoprecipitation Sequencing
Abstract

Objectives: Assays for transposase-accessible chromatin with single-cell sequencing (scATAC-seq) contribute to the progress in epigenetic studies. The purpose of our project was to discover the transcription factors (TFs) that were involved in the pathogenesis of rheumatoid arthritis (RA) at a single-cell resolution using epigenetic technology., Methods: Peripheral blood mononuclear cells of seven RA patients and seven natural controls were extracted nuclei suspensions for library construction. Subsequently, scATAC-seq was performed to generate a high-resolution map of active regulatory DNA for bioinformatics analysis., Results: We obtained 22 accessible chromatin patterns. Then, 10 key TFs were involved in RA pathogenesis by regulating the activity of mitogen-activated protein kinase. Consequently, two genes (PTPRC and SPAG9) regulated by 10 key TFs were found, which may be associated with RA disease pathogenesis, and these TFs were obviously enriched in RA patients (P < .05, fold change value > 1.2). With further quantitative polymerase chain reaction validation on PTPRC and SPAG9 in monocytes, we found differential expression of these two genes, which were regulated by eight TFs [ZNF384, HNF1B, DMRTA2, MEF2A, NFE2L1, CREB3L4 (var. 2), FOSL2::JUNB (var. 2), and MEF2B], showing highly accessible binding sites in RA patients., Conclusions: These findings demonstrate the value of using scATAC-seq to reveal transcriptional regulatory variation in RA-derived peripheral blood mononuclear cells, providing insights into therapy from an epigenetic perspective., (© Japan College of Rheumatology 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Published
2023
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