Keqian Xu

Keqian Xu


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2017 in Science of The Total Environment
Verified
Journal Article
Closed access
CTChaochao Tan
Chaochao Tan
SLShijie Lu
Shijie Lu
YWYupeng Wang
Yupeng Wang
YZYan Zhu
Yan Zhu
TSTing Shi
Ting Shi
KXKeqian Xu
Keqian Xu

1 February 2020 in Biochemical and Biophysical Research Communications
Verified
Journal Article
Closed access
HWHai Wang
Hai Wang
CLCan Liu
Can Liu
YZYongxin Zhao
Yongxin Zhao
WZWenling Zhang
Wenling Zhang
KXKeqian Xu
Keqian Xu
GGGe Gao
Ge Gao

1 March 2018 in Environmental Toxicology and Pharmacology
Verified
Journal Article
Closed access
CTChaochao Tan
Chaochao Tan
YWYupeng Wang
Yupeng Wang
MLMingyue Lin
Mingyue Lin
ZWZhu Wang
Zhu Wang
LHLi He
Li He
KXKeqian Xu
Keqian Xu

17 October 2019 in PeerJ
Verified
Journal Article
Open access
PDF file
LGLihuang Guo
Lihuang Guo
MLMingyue Lin
Mingyue Lin
ZCZhenbo Cheng
Zhenbo Cheng
YCYi Chen
Yi Chen
YHYue Huang
Yue Huang
KXKeqian Xu
Keqian Xu
Background: Cancer metastasis is well known as the most adverse outcome and the major cause of mortality in cancer patients, including prostate cancer (PCa). There are no credible predictors, to this day, that can reflect the metastatic ability of localized PCa. In the present study, we firstly identified the differentially expressed genes (DEGs) and molecular pathways involved in the metastaic process of PCa by comparing gene expressions of metastaic PCa with localized PCa directly, with the purpose of identifying potential markers or therapeutic targets. Methods: The gene expression profiles (GSE6919 and GSE32269) were downloaded from the Gene Expression Omnibus database, which contained 141 tissue samples, including 87 primary localized PCa samples and 54 metastaic PCa samples. After data processing, DEGs were identified by R language using the Student’s t-test adjusted via the Beniamini–Hochberg method. Subsequently, the gene ontology functional and pathway enrichment analyses of DEGs were performed and the protein–protein interaction network was constructed. Hub genes were identified using the plug-in cytoHubba in Cytoscape software by MCC and degree. Furthermore, validation and prognostic significance analysis of the hub genes were performed by UALCAN and gene expression profiling interactive analysis (GEPIA). Results: A total of 90 DEGs were identified between localized and metastaic PCa, which consisted of 47 upregulated and 43 downregulated genes. The enriched functions and pathways of the DEGs include catabolic process, cell cycle, response to steroid hormone, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. A total of 10 genes were identified as hub genes and biological process analysis of hub genes showed that cell cycle phase, cell division, and mitotic cell cycle process were mainly enriched. The expression of hub genes were confirmed in metastaic PCa when compared with localized PCa tissues by The Cancer Genome Atlas database. Moreover, the disease-free survival analysis of hub genes revealed that these genes may play an important role in invasion, progression or recurrence. Therefore, these hub genes might be the key genes contributed to tumor progression or metastasis in PCa and provide candidate therapeutic targets for PCa. Conclusions: The present study identified some DEGs between localized and metastaic PCa tissue samples. These key genes might be potential therapeutic targets and biomarkers for the metastaic process of PCa.

1 June 2020 in Science of The Total Environment
Verified
Journal Article
Closed access
ZDZhonghua Deng
Zhonghua Deng
CTChaochao Tan
Chaochao Tan
YXYangen Xiang
Yangen Xiang
JPJianhua Pan
Jianhua Pan
GSGuomin Shi
Guomin Shi
KXKeqian Xu
Keqian Xu

19 November 2020 in PeerJ
Verified
Journal Article
Open access
PDF file
HDHanying Dai
Hanying Dai
LGLihuang Guo
Lihuang Guo
MLMingyue Lin
Mingyue Lin
ZCZhenbo Cheng
Zhenbo Cheng
JLJiancheng Li
Jiancheng Li
KXKeqian Xu
Keqian Xu
Background: Melanoma is a malignant tumor of melanocytes, and the incidence has increased faster than any other cancer over the past half century. Most primary melanoma can be cured by local excision, but metastatic melanoma has a poor prognosis. Cutaneous melanoma (CM) is prone to metastasis, so the research on the mechanism of melanoma occurrence and metastasis will be beneficial to diagnose early, improve treatment, and prolong life survival. In this study, we compared the gene expression of normal skin (N), primary cutaneous melanoma (PM) and metastatic cutaneous melanoma (MM) in the Gene Expression Omnibus (GEO) database. Then we identified the key genes and molecular pathways that may be involved in the development and metastasis of cutaneous melanoma, thus to discover potential markers or therapeutic targets. Methods: Three gene expression profiles (GSE7553, GSE15605 and GSE46517) were downloaded from the GEO database, which contained 225 tissue samples. R software identified the differentially expressed genes (DEGs) between pairs of N, PM and MM samples in the three sets of data. Subsequently, we analyzed the gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the DEGs, and constructed a protein-protein interaction (PPI) network. MCODE was used to seek the most important modules in PPI network, and then the GO function and KEGG pathway of them were analyzed. Finally, the hub genes were calculated by the cytoHubba in Cytoscape software. The Cancer Genome Atlas (TCGA) data were analyzed using UALCAN and GEPIA to validate the hub genes and analyze the prognosis of patients. Results: A total of 134, 317 and 147 DEGs were identified between N, PM and MM in pair. GO functions and KEGG pathways analysis results showed that the upregulated DEGs mainly concentrated in cell division, spindle microtubule, protein kinase activity and the pathway of transcriptional misregulation in cancer. The downregulated DEGs occurred in epidermis development, extracellular exosome, structural molecule activity, metabolic pathways and p53 signaling pathway. The PPI network obtained the most important module, whose GO function and KEGG pathway were enriched in oxidoreductase activity, cell division, cell exosomes, protein binding, structural molecule activity, and metabolic pathways. 14, 18 and 18 DEGs were identified respectively as the hub genes between N, PM and MM, and TCGA data confirmed the expression differences of hub genes. In addition, the overall survival curve of hub genes showed that the differences in these genes may lead to a significant decrease in overall survival of melanoma patients. Conclusions: In this study, several hub genes were found from normal skin, primary melanoma and metastatic melanoma samples. These hub genes may play an important role in the production, invasion, recurrence or death of CM, and may provide new ideas and potential targets for its diagnosis or treatment.