Li TT, Li JN, Yang HW, Dou XY, Jiang L, Lai LX, Yu Q, Chen XY, Wang Y, Zhang XC, Ma HF, Song XB. Single-cell and bulk transcriptomics with machine learning decode B cell hub genes and diagnostic biomarkers in anal fistula. World J Gastroenterol 2026; 32(38): 121425 [DOI: 10.3748/wjg.121425]
Corresponding Author of This Article
Jia-Nan Li, Department of Proctology, China-Japan Friendship Hospital, No. 2 Yingyuan Garden East Street, Chaoyang District, Beijinjavascript:;g 100029, China. wuqu3@163.com
Research Domain of This Article
Gastroenterology & Hepatology
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research-article
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Li TT, Li JN, Yang HW, Dou XY, Jiang L, Lai LX, Yu Q, Chen XY, Wang Y, Zhang XC, Ma HF, Song XB. Single-cell and bulk transcriptomics with machine learning decode B cell hub genes and diagnostic biomarkers in anal fistula. World J Gastroenterol 2026; 32(38): 121425 [DOI: 10.3748/wjg.121425]
Ting-Ting Li, Jia-Nan Li, Han-Wen Yang, Li-Xia Lai, Qiang Yu, Xiao-Yu Chen, Yue Wang, Xue-Cheng Zhang, Huang-Fu Ma, Xin-Bo Song, Department of Proctology, China-Japan Friendship Hospital, Beijing 100029, China
Xin-Yu Dou, Department of Pain Medicine, China-Japan Friendship Hospital, Beijing 100029, China
Li Jiang, Integrated Chinese and Western Medicine Department of Diabetes, China-Japan Friendship Hospital, Beijing 100029, China
Co-first authors: Ting-Ting Li and Jia-Nan Li.
Author contributions: All authors contributed significantly to the research and approved the submitted manuscript; the study was conceived and designed by Li JN and Li TT; Li TT drafted the manuscript, which was revised and refined by Yang HW, Song XB and Dou XY; Jiang L, Lai LX, Yu Q, and Ma HF analyzed, interpreted, and visualized the data; Wang Y, Zhang XC, and Chen XY accessed and validated the data presented in the manuscript; and Li TT and Li JN contributed equally to this work as co-first authors.
AI contribution statement: AI tools (specifically ChatGPT) were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors. In addition, an AI assistant (ChatGPT) was used during the preparation of the response letter to the reviewers for language polishing purposes. All AI-generated outputs in both the manuscript and the response letter were critically reviewed and revised by the authors. No AI tool is listed as an author of this work.
Supported by the Elite Medical Professionals Initiative of the China-Japan Friendship Hospital, No. ZRJY2025-QM11; and National High Level Hospital Clinical Research Funding, No. 2023-NHLHCRF-YYPPLC-ZR-03 and No. 2025-NHLHCRF-DLYJ-PY-08.
Institutional review board statement: This study was reviewed and authorized by the Ethics Committee of the China-Japan Friendship Hospital (Approval No. 2023-KY-363).
Conflict-of-interest statement: The authors declare no conflicting financial or personal interests.
Data sharing statement: The raw single-cell and bulk RNA sequencing data will be deposited in the Gene Expression Omnibus database upon acceptance and will be available at https://www.ncbi.nlm.nih.gov/geo/.
Corresponding author: Jia-Nan Li, Department of Proctology, China-Japan Friendship Hospital, No. 2 Yingyuan Garden East Street, Chaoyang District, Beijinjavascript:;g 100029, China. wuqu3@163.com
Received: March 25, 2026 Revised: May 6, 2026 Accepted: June 4, 2026 Published online: October 14, 2026 Processing time: 165 Days and 14.5 Hours
Abstract
BACKGROUND
Anal fistula (AF) is a perianal inflammatory disorder with a complex etiology and high recurrence rates, causing substantial functional impairment. The immune microenvironment of AF is heterogeneous, but its potential mechanisms of action remain unclear.
AIM
To establish a multimodal framework for AF by integrating single-cell and bulk RNA sequencing datasets.
METHODS
High-dimensional weighted gene co-expression network analysis combined with five machine learning (ML) algorithms were used to identify B-cell-associated hub genes. Immune infiltration profiling, functional enrichment, pseudotime trajectory, and intercellular communication analyses were performed. A gene-based predictive model was constructed and validated using receiver operating characteristic curves and a nomogram to evaluate diagnostic performance.
RESULTS
About 29 cellular clusters with enriched B cells were identified in the AF tissues. Among 129 B-cell-related candidate genes, core genes such as long intragenic noncoding RNA p53-induced transcript, lipopolysaccharide-responsive and beige-like anchor, and spleen tyrosine kinase, were consistently selected for all ML algorithms. These genes showed strong correlations with multiple immune cell types and inflammatory pathways. B cells associated hub genes enrichment was detected in the pathway related to transforming growth factor beta/mitogen-activated protein kinase signaling, primary immunodeficiency, and natural killer cell-mediated cytotoxicity, and these B cells acted as central signaling nodes promoting inflammatory amplification, angiogenesis, and fibrosis. The predictive model demonstrated robust diagnostic accuracy, with an area under the curve of 0.858.
CONCLUSION
This study is the first to integrate single-cell and bulk transcriptomics with ML to systematically decode the B-cell associated immune network in AF. The identified hub genes may serve as crucial diagnostic biomarkers and therapeutic targets.
Core Tip: This study is the first to combine single-cell RNA sequencing (RNA-seq) with bulk RNA-seq and machine learning to systematically characterize B-cell-associated immune networks in anal fistula (AF). Three hub genes, LINC-PINT, LRBA, and SYK, were identified as central signaling nodes linking inflammatory amplification, angiogenesis, and fibrosis within the AF microenvironment. A predictive model incorporating these genes achieved robust diagnostic accuracy, offering promising biomarker candidates for precision diagnosis and targeted therapeutic intervention in AF.
