1 Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues [期刊论文]
BMC Genomics,2021年
Min Pan, Huajuan Shi, Xiangwei Zhao, Qinyu Ge, Zhiyu Liu, Erteng Jia, Ying Zhou, Ying Wang, Yunfei Bai
LicenseType:CC BY |
2 Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues [期刊论文]
BMC Genomics,2021年
Min Pan, Huajuan Shi, Xiangwei Zhao, Qinyu Ge, Zhiyu Liu, Erteng Jia, Ying Zhou, Ying Wang, Yunfei Bai
LicenseType:CC BY |
BMC Genomics,2017年
Yuan-Xiang Shi, Wei Zhang, Xi Li, Zhao-Qian Liu, Ying Wang, Hong-Hao Zhou, Ji-Ye Yin
LicenseType:Unknown |
4 Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues [期刊论文]
BMC Genomics,2021年
Min Pan, Huajuan Shi, Xiangwei Zhao, Erteng Jia, Qinyu Ge, Ying Wang, Zhiyu Liu, Yunfei Bai, Ying Zhou
LicenseType:Unknown |
BMC Genomics,2016年
Weixing Feng, Jin Li, Wang Cong, Yulin Deng, Lei Wang, Chengzhen Xu, Hong Liang, Yunlong Liu, Ying Wang, Yue Wang, Todd C. Skaar, Xuefeng Dai
LicenseType:CC BY |
BackgroundIn combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks.MethodsIn this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis.Results and DisccussionWe extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways.ConclusionsOur results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.
BMC Genomics,2017年
Ying Wang, Xi Li, Wei Zhang, Hong-Hao Zhou, Ji-Ye Yin, Zhao-Qian Liu, Yuan-Xiang Shi
LicenseType:CC BY |
BackgroundEpigenetic alterations are strongly associated with the development of cancer. The aim of this study was to identify epigenetic pattern in squamous cell lung cancer (LUSC) on a genome-wide scale.ResultsHere we performed DNA methylation profiling on 24 LUSC and paired non-tumor lung (NTL) tissues by Illumina Human Methylation 450 K BeadArrays, and identified 5214 differentially methylated probes. By integrating DNA methylation and mRNA expression data, 449 aberrantly methylated genes accompanied with altered expression were identified. Ingenuity Pathway analysis highlighted these genes which were closely related to the carcinogenesis of LUSC, such as ERK family, NFKB signaling pathway, Hedgehog signaling pathway, providing new clues for understanding the molecular mechanisms of LUSC pathogenesis. To verify the results of high-throughput screening, we used 56 paired independent tissues for clinical validation by pyrosequencing. Subsequently, another 343 tumor tissues from the Cancer Genome Atlas (TCGA) database were utilized for further validation. Then, we identified a panel of DNA methylation biomarkers (CLDN1, TP63, TBX5, TCF21, ADHFE1 and HNF1B) in LUSC. Furthermore, we performed receiver operating characteristics (ROC) analysis to assess the performance of biomarkers individually, suggesting that they could be suitable as potential diagnostic biomarkers for LUSC. Moreover, hierarchical clustering analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation.ConclusionsCollectively, these results suggest that DNA methylation plays critical roles in lung tumorigenesis and may potentially be proposed as a diagnostic biomarker.Trial registrationChiCTR-RCC-12002830 Date of registration: 2012–12-17.