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已发表论文
通过机器学习鉴定核心 SASP 相关基因 TGFBI 和 ANXA6 用于诊断溃疡性结肠炎相关结直肠癌
Authors Zhang J, Wang P, Yuan S, Zhang Y
Received 7 May 2025
Accepted for publication 1 September 2025
Published 18 September 2025 Volume 2025:18 Pages 12909—12928
DOI https://doi.org/10.2147/JIR.S538960
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Felix Marsh-Wakefield
Jukun Zhang,1 Ping Wang,2 Siqi Yuan,1 Yingjian Zhang1
1Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology, Department of Gastroenterology, the First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan Province, People’s Republic of China; 2School of Basic Medicine and Forens ice Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan Province, People’s Republic of China
Correspondence: Yingjian Zhang, Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology, Department of Gastroenterology, the First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan Province, People’s Republic of China, Email zhangyingjian@haust.edu.cn; zhangjk0409@163.com
Purpose: To explore the role of senescence-associated secretory phenotype (SASP) in ulcerative colitis-related colorectal cancer (UCRCC) precision diagnosis and pathogenesis.
Methods: In this study, we first screened the SASP-related genes (SASPRGs) in the ulcerative colitis (UC) and colorectal cancer (CRC) datasets using differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). Subsequently, the core SASPRGs were further identified using 113 combinations of 10 machine learning algorithms, and evaluating their expression levels, diagnostic efficacy, prognostic value, and relationship with immune cells. Expression patterns of core SASPRGs were analyzed using scRNA-seq, and a molecular regulatory network was constructed. In addition, immunotherapy response and drug sensitivity analysis were conducted to screen therapeutic drugs targeting core SASPRGs. Finally, their expression levels were validated through in vivo experiments.
Results: We identified 12 core SASPRGs, among which TGFBI and ANXA6 exhibited satisfactory diagnostic efficacy and prognostic value for UC and CRC. ANXA6 and TGFBI are highly expressed in monocytes and fibroblasts, and are associated with the infiltration of multiple immune cells. In addition, high TGFBI and ANXA6 groups showed poor responses to immunotherapy. The candidate therapeutic drugs screened by drug sensitivity analysis showed good binding ability with ANXA6 and TGFBI. Finally, the expression levels of TGFBI and ANXA6 were significantly increased in UC and CRC mouse models.
Conclusion: Overall, TGFBI and ANXA6 are crucial in UCRCC, serving as novel diagnostic markers. They exhibit robust predictive capabilities on patient prognosis and immunotherapy response, offering actionable insights to optimize therapeutic decision-making and advance personalized treatment paradigms in UCRCC management.
Keywords: ulcerative colitis, colorectal cancer, ulcerative colitis-related colorectal cancer, senescence-associated secretory phenotype, machine learning
1Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology, Department of Gastroenterology, the First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan Province, People’s Republic of China; 2School of Basic Medicine and Forens ice Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan Province, People’s Republic of China
Correspondence: Yingjian Zhang, Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology, Department of Gastroenterology, the First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan Province, People’s Republic of China, Email zhangyingjian@haust.edu.cn; zhangjk0409@163.com
Purpose: To explore the role of senescence-associated secretory phenotype (SASP) in ulcerative colitis-related colorectal cancer (UCRCC) precision diagnosis and pathogenesis.
Methods: In this study, we first screened the SASP-related genes (SASPRGs) in the ulcerative colitis (UC) and colorectal cancer (CRC) datasets using differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). Subsequently, the core SASPRGs were further identified using 113 combinations of 10 machine learning algorithms, and evaluating their expression levels, diagnostic efficacy, prognostic value, and relationship with immune cells. Expression patterns of core SASPRGs were analyzed using scRNA-seq, and a molecular regulatory network was constructed. In addition, immunotherapy response and drug sensitivity analysis were conducted to screen therapeutic drugs targeting core SASPRGs. Finally, their expression levels were validated through in vivo experiments.
Results: We identified 12 core SASPRGs, among which TGFBI and ANXA6 exhibited satisfactory diagnostic efficacy and prognostic value for UC and CRC. ANXA6 and TGFBI are highly expressed in monocytes and fibroblasts, and are associated with the infiltration of multiple immune cells. In addition, high TGFBI and ANXA6 groups showed poor responses to immunotherapy. The candidate therapeutic drugs screened by drug sensitivity analysis showed good binding ability with ANXA6 and TGFBI. Finally, the expression levels of TGFBI and ANXA6 were significantly increased in UC and CRC mouse models.
Conclusion: Overall, TGFBI and ANXA6 are crucial in UCRCC, serving as novel diagnostic markers. They exhibit robust predictive capabilities on patient prognosis and immunotherapy response, offering actionable insights to optimize therapeutic decision-making and advance personalized treatment paradigms in UCRCC management.
Keywords: ulcerative colitis, colorectal cancer, ulcerative colitis-related colorectal cancer, senescence-associated secretory phenotype, machine learning