Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning

被引:0
|
作者
Zhou, Tao [1 ]
Zhu, Can [1 ]
Zhang, Wei [1 ]
Wu, Qiongfang [2 ]
Deng, Mingqiang [2 ]
Jiang, Zhiwei [1 ]
Peng, Longfei [1 ]
Geng, Hao [1 ]
Tuo, Zhouting [1 ,3 ]
Zou, Ci [1 ]
机构
[1] Anhui Med Univ, Affiliated Hosp 2, Dept Urol, Hefei, Peoples R China
[2] Chinese Acad Sci Guangzhou, Guangzhou Inst Biomed & Hlth, Ctr Cell Lineage & Dev, Guangzhou, Peoples R China
[3] Army Med Univ, Daping Hosp, Army Med Ctr PLA, Dept Urol Surg, Chongqing, Peoples R China
来源
FRONTIERS IN IMMUNOLOGY | 2025年 / 16卷
关键词
IC/BPS; bioinformatics; machine-learning; PLAC8; immune cell landscape; GENE-EXPRESSION; PROTEINS; 8; CONTRIBUTE; PACKAGE; MARKERS;
D O I
10.3389/fimmu.2025.1511529
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Background The etiology of interstitial cystitis/painful bladder syndrome (IC/BPS) remains elusive, presenting significant challenges in both diagnosis and treatment. To address these challenges, we employed a comprehensive approach aimed at identifying diagnostic biomarkers that could facilitate the assessment of immune status in individuals with IC/BPS.Methods Transcriptome data from IC/BPS patients were sourced from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) crucial for gene set enrichment analysis. Key genes within the module were revealed using weighted gene co-expression network analysis (WGCNA). Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. The expression and biological functions of key genes in IC/BPS were further validated through in vitro experiments.Results A total of 87 DEGs were identified, comprising 43 up-regulated and 44 down-regulated genes. The integration of predictions from the three machine-learning algorithms highlighted three pivotal genes: PLAC8 (AUC: 0.887), S100A8 (AUC: 0.818), and PPBP (AUC: 0.871). Analysis of IC/BPS tissue samples confirmed elevated PLAC8 expression and the presence of immune cell markers in the validation cohorts. Moreover, PLAC8 overexpression was found to promote the proliferation of urothelial cells without affecting their migratory ability by inhibiting the Akt/mTOR/PI3K signaling pathway.Conclusions Our study identifies potential diagnostic candidate genes and reveals the complex immune landscape associated with IC/BPS. Among them, PLAC8 is a promising diagnostic biomarker that modulates the immune response in patients with IC/BPS, which provides new insights into the future diagnosis of IC/BPS.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Identification of Diagnostic Biomarkers Associated with Stromal and Immune Cell Infiltration in Fatty Infiltration After Rotator Cuff Tear by Integrating Bioinformatic Analysis and Machine-Learning
    Wang, Si
    Ying, Jin-He
    Xu, Huan
    INTERNATIONAL JOURNAL OF GENERAL MEDICINE, 2022, 15 : 1805 - 1819
  • [32] Combination of urinary biomarkers and machine-learning models provided a higher predictive accuracy to predict long-term treatment outcomes of patients with interstitial cystitis/bladder pain syndrome
    Jhang, Jia-Fong
    Yu, Wan-Ru
    Huang, Wan-Ting
    Kuo, Hann-Chorng
    WORLD JOURNAL OF UROLOGY, 2024, 42 (01)
  • [33] Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning
    Liu, Jinya
    Liu, Leping
    Antwi, Paul Akwasi
    Luo, Yanwei
    Liang, Fang
    FRONTIERS IN GENETICS, 2022, 13
  • [34] Bioinformatics Approach for Identifying Novel Biomarkers and Their Signaling Pathways Involved in Interstitial Cystitis/Bladder Pain Syndrome with Hunner Lesion
    Saha, Subbroto Kumar
    Jeon, Tak-Il
    Jang, Soo Bin
    Kim, Se Jong
    Lim, Kyung Min
    Choi, Yu Jin
    Kim, Hyeong Gon
    Kim, Aram
    Cho, Ssang-Goo
    JOURNAL OF CLINICAL MEDICINE, 2020, 9 (06) : 1 - 17
  • [35] Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer
    Vaziri-Moghadam, Ayoub
    Foroughmand-Araabi, Mohammad-Hadi
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [36] Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome Using Machine Learning Based Predictions
    Lamb, Laura E.
    Janicki, Joseph J.
    Bartolone, Sarah N.
    Ward, Elijah P.
    Abraham, Nitya
    Laudano, Melissa
    Smith, Christopher P.
    Peters, Kenneth M.
    Zwaans, Bernadette M. M.
    Chancellor, Michael B.
    UROLOGY, 2024, 189
  • [37] Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies
    Yu, Rongguo
    Zhang, Jiayu
    Zhuo, Youguang
    Hong, Xu
    Ye, Jie
    Tang, Susu
    Zhang, Yiyuan
    FRONTIERS IN IMMUNOLOGY, 2021, 12
  • [38] Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning
    Shi, Jinbao
    Xu, Aliang
    Huang, Liuying
    Liu, Shaojie
    Wu, Binxuan
    Zhang, Zuhong
    PHARMACOGENOMICS & PERSONALIZED MEDICINE, 2024, 17 : 497 - 510
  • [39] Identification of diagnostic signatures associated with immune infiltration in Alzheimer's disease by integrating bioinformatic analysis and machine-learning strategies
    Tian, Yu
    Lu, Yaoheng
    Cao, Yuze
    Dang, Chun
    Wang, Na
    Tian, Kuo
    Luo, Qiqi
    Guo, Erliang
    Luo, Shanshun
    Wang, Lihua
    Li, Qian
    FRONTIERS IN AGING NEUROSCIENCE, 2022, 14
  • [40] Identification of Diagnostic Biomarkers in Systemic Lupus Erythematosus Based on Bioinformatics Analysis and Machine Learning
    Jiang, Zhihang
    Shao, Mengting
    Dai, Xinzhu
    Pan, Zhixin
    Liu, Dongmei
    FRONTIERS IN GENETICS, 2022, 13