Establishment and validation of diagnostic model in immunoglobulin A nephropathy based on weighted gene co-expression network analysis

被引:0
|
作者
Liu, Haibo [1 ]
Dai, Lingling [2 ]
Liu, Jie [3 ]
Duan, Kai [1 ]
Yi, Feng [1 ]
Li, Zhuo [4 ]
机构
[1] Yueyang Cent Hosp, Dept Emergency, Yueyang, Hunan, Peoples R China
[2] Shenzhen Nanshan Peoples Hosp, Dept Gynaecol, Shenzhen, Guangdong, Peoples R China
[3] Shenzhen United Family Hosp, Dept Emergency, Shenzhen, Guangdong, Peoples R China
[4] Shenzhen Nanshan Peoples Hosp, Dept Emergency, Shenzhen 518052, Guangdong, Peoples R China
关键词
Diagnostic; IgAN; immunoglobulin A nephropathy; weighted gene correlation network analysis; IGA NEPHROPATHY; IL-17; CELLS;
D O I
10.1097/MD.0000000000039930
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Bioinformatics analysis helps to understand the underlying mechanisms and adjust diagnostic and treatment strategies for immunoglobulin A nephropathy (IgAN) by screening gene expression datasets. We explored the biological function of IgAN, and established and validated a diagnostic model for IgAN using weighted gene co-expression network analysis. Using the GSE93798 and GSE37460 datasets, we performed differential expression analysis, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein-protein network, and identified hub genes. A diagnostic model was built using a receiver operating characteristic curve, calibration plot, and decision curve analysis. Two Gene Expression Omnibus (GEO) datasets were integrated to screen 38 differentially expressed genes between patients with IgAN and normal kidney donors in glomerular samples. KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched in the IL-17 and relaxin signaling pathways. We constructed a protein-protein interaction (PPI) network of differentially expressed genes using the STRING database and cross-compared it with the results of weighted gene correlation network analysis to screen out the top 10 key genes: FOS, EGR2, FOSB, NR4A1, BR4A3, FOSL1, NR4A2, ALB, CD53, C3AR1.We also found that the immune infiltration level was remarkably increased in IgAN tissues. We established a 5-gene panel diagnostic model (ACTA2, ALB, AFM, ALDH1L1, and ALDH6A1). The combined diagnostic ability was high, with the area under the curve (AUC) was 0.964. Based on these 5 genes, we also developed a risk-scoring evaluation system for individuals. The calibration plot indicated that the nomogram-predicted probability of nonadherence was highly correlated with actual diagnosed nonadherence, and the decision curve analysis indicated that patients had a relatively good net benefit. The model and gene expression were also validated using an external dataset. Our study provides directions for exploring the potential molecular mechanisms of IgAN as well as diagnostic and therapeutic strategies.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] The identification and validation of hub genes associated with advanced IPF by weighted gene co-expression network analysis
    Liu, Lingyi
    Yang, Yanru
    Han, Xin
    Hou, Jiwei
    FUNCTIONAL & INTEGRATIVE GENOMICS, 2022, 22 (06) : 1127 - 1138
  • [22] Weighted gene co-expression network analysis of key targets and interventional mechanism of Milkvetch root in diabetic nephropathy
    Zeng, S. -N
    Li, Y.
    Li, Y. -M. -Q.
    Wang, S. -R
    EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES, 2023, 27 (20) : 9614 - 9627
  • [23] Application of weighted gene co-expression network analysis to explore the potential diagnostic biomarkers for colorectal cancer
    Qin, Liping
    Zeng, Jianping
    Shi, Nannan
    Chen, Liu
    Wang, Li
    MOLECULAR MEDICINE REPORTS, 2020, 21 (06) : 2533 - 2543
  • [24] Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis
    Mortezapour, Mahdie
    Tapak, Leili
    Bahreini, Fatemeh
    Najafi, Rezvan
    Afshar, Saeid
    COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 157
  • [25] The identification and validation of hub genes associated with advanced IPF by weighted gene co-expression network analysis
    Lingyi Liu
    Yanru Yang
    Xin Han
    Jiwei Hou
    Functional & Integrative Genomics, 2022, 22 : 1127 - 1138
  • [26] Identification of Hub Genes and Potential ceRNA Networks of Diabetic Nephropathy by Weighted Gene Co-Expression Network Analysis
    Li, Guoqing
    Zhang, Jun
    Liu, Dechen
    Qiong Wei
    Wang, Hui
    Lv, Yingqi
    Ye, Zheng
    Liu, Gaifang
    Li, Ling
    FRONTIERS IN GENETICS, 2021, 12
  • [27] An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network analysis
    Xiong, Yaoyi
    Yuan, Lushun
    Xiong, Jing
    Xu, Huimin
    Luo, Yongwen
    Wang, Gang
    Ju, Lingao
    Xiao, Yu
    Wang, Xinghuan
    JOURNAL OF CELLULAR AND MOLECULAR MEDICINE, 2020, 24 (03) : 2342 - 2355
  • [28] Construction of a Competitive Endogenous RNA Network for Pancreatic Adenocarcinoma Based on Weighted Gene Co-expression Network Analysis and a Prognosis Model
    Wang, Jing
    Xiang, Jinzhu
    Li, Xueling
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2020, 8
  • [29] Identification of Co-Expression Modules of Cotton Plant Height-Related Genes Based on Weighted Gene Co-Expression Network Analysis
    Huang, Qian
    Liu, Li
    Li, Hang
    Wang, Xuwen
    Si, Aijun
    He, Liangrong
    Yu, Yu
    AGRONOMY-BASEL, 2025, 15 (01):
  • [30] Exploration and Validation of Pancreatic Cancer Hub Genes Based on Weighted Gene Co-Expression Network Analysis and Immune Infiltration Score Analysis
    Li, Xiao-Xi
    Li, Hong
    Jin, Li-Quan
    Tan, Yun-Bo
    PHARMACOGENOMICS & PERSONALIZED MEDICINE, 2023, 16 : 467 - 480