Identification of Potential Hub Genes Related to Acute Pancreatitis and Chronic Pancreatitis via Integrated Bioinformatics Analysis and In Vitro Analysis

被引:0
|
作者
Yuan, Lu [1 ]
Liu, Yiyuan [1 ]
Fan, Lingyan [2 ]
Sun, Cai [1 ]
Ran, Sha [1 ]
Huang, Kuilong [1 ]
Shen, Yan [1 ]
机构
[1] Chongqing Univ Technol, Sch Pharm & Bioengn, Chongqing 400054, Peoples R China
[2] Univ Hlth & Rehabil Sci, Qingdao Cent Hosp, Qingdao Cent Med Grp, Qingdao 266042, Peoples R China
关键词
Acute pancreatitis; Chronic pancreatitis; Helper T-cell factor signaling pathway; Hub genes; T-CELLS; RISK; TH17; SMOKING;
D O I
10.1007/s12033-024-01118-5
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Acute pancreatitis (AP) and chronic pancreatitis (CP) are considered to be two separate pancreatic diseases in most studies, but some clinical retrospective analyses in recent years have found some degree of correlation between the two in actual treatment, however, the exact association is not clear. In this study, bioinformatics analysis was utilized to examine microarray sequencing data in mice, with the aim of elucidating the critical signaling pathways and genes involved in the progression from AP to CP. Differential gene expression analyses on murine transcriptomes were conducted using the R programming language and the R/Bioconductor package. Additionally, gene network analysis was performed using the STRING database to predict correlations among genes in the context of pancreatic diseases. Functional enrichment and gene ontology pathways common to both diseases were identified using Metascape. The hub genes were screened in the cytoscape algorithm, and the mRNA levels of the hub genes were verified in mice pancreatic tissues of AP and CP. Then the drugs corresponding to the hub genes were obtained in the drug-gene relationship. A set of hub genes, including Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9, were identified through analysis, demonstrating their pivotal roles in the progression from AP to CP. Notably, these genes were found to be enriched in the Helper T-cell factor (Th17) signaling pathway. Up-regulation of these genes in both AP and CP mouse models was validated through quantitative real-time polymerase chain reaction (qRT-PCR) results. The significance of the Th17 signaling pathway in the transition from AP to CP was underscored by our findings. Specifically, the essential genes driving this progression were identified as Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9. Crucial insights into the molecular mechanisms underlying pancreatitis progression were provided by this research, offering promising avenues for the development of targeted therapeutic interventions.
引用
收藏
页码:1188 / 1200
页数:13
相关论文
共 50 条
  • [41] Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis
    Nguyen, Thong Ba
    Do, Duy Ngoc
    Nguyen-Thanh, Tung
    Tatipamula, Vinay Bharadwaj
    Nguyen, Ha Thi
    BIOLOGY-BASEL, 2021, 10 (10):
  • [42] Identification of key hub genes in knee osteoarthritis through integrated bioinformatics analysis
    Xu, Lilei
    Ma, Jiaqi
    Zhou, Chuanlong
    Shen, Zhe
    Zhu, Kean
    Wu, Xuewen
    Chen, Yang
    Chen, Ting
    Lin, Xianming
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [43] Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis
    Li, Shan
    Li, Ting
    Shi, Yan-Qing
    Xu, Bin-Jie
    Deng, Yu-Yong
    Sun, Xu-Guang
    CANCER BIOMARKERS, 2024, 40 (01) : 27 - 45
  • [44] Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
    Pan, Yangwang
    Wu, Linjing
    He, Shuai
    Wu, Jun
    Wang, Tong
    Zang, Hongrui
    BIOENGINEERED, 2021, 12 (01) : 2928 - 2940
  • [45] Identification of Novel Hub Genes Associated with Psoriasis Using Integrated Bioinformatics Analysis
    Yue, Qi
    Li, Zhaoxiang
    Zhang, Qi
    Jin, Quanxin
    Zhang, Xinyuan
    Jin, Guihua
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2022, 23 (23)
  • [46] Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis
    Ma, Zetao
    Wang, Xizhi
    Lv, Qingbo
    Gong, Yingchao
    Xia, Minghong
    Zhuang, Lenan
    Lu, Xue
    Yang, Ying
    Zhang, Wenbin
    Fu, Guosheng
    Ye, Yang
    Lai, Dongwu
    PHARMACOGENOMICS & PERSONALIZED MEDICINE, 2021, 14 : 823 - 837
  • [47] Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis
    Zhao, Qian
    Zhang, Yan
    Shao, Shichun
    Sun, Yeqing
    Lin, Zhengkui
    PEERJ, 2021, 9
  • [48] Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis
    Chen, Xiaolong
    Xia, Zhixiong
    Wan, Yafeng
    Huang, Ping
    MEDICINE, 2021, 100 (39) : E27117
  • [49] Identification of hub genes in heart failure by integrated bioinformatics analysis and machine learning
    Wang, Tengfei
    Sun, Yongyou
    Zhao, Yingpeng
    Huang, Jinhe
    Huang, Ying
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2024, 10
  • [50] Identification of hub genes associated with diabetic cardiomyopathy using integrated bioinformatics analysis
    Cui, Hailong
    Hu, Die
    Xu, Jing
    Zhao, Shuiying
    Song, Yi
    Qin, Guijun
    Liu, Yanling
    SCIENTIFIC REPORTS, 2024, 14 (01):