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作者

Guanting Pang, Zhao Wang, Zijian Sun, Xiaojuan Lv, Hui Ye, Liting Shi, Jiahui Ma, Yaohan Li, Zhen Zhang, Jingkui Tian, Hanmei Lou, Wei Zhu, Yue Feng

作者单位

Affiliations ¹ Department of Gynecological Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China. ² Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China. ³ College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310022, China. ⁴ Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macau SAR 999078, China. ⁵ School of Basic Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250000, China. ⁶ College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250000, China.

刊名

Journal of proteome research

年份

2025

ISSN

1535-3907

关键词

biomarker cervical cancer metabolomics pelvic lymph node metastasis proteomics.

摘要

Pelvic lymph node metastasis significantly affects the prognosis of cervical cancer . However, current imaging examinations and serum squamous cell carcinoma antigen testing are inadequate for assessing the pelvic lymph node status in CC. To identify accurate noninvasive biomarkers for diagnosing PLNM and minimizing unnecessary postoperative lymphadenectomy and its associated complications, we performed a comprehensive proteomic and metabolomic analysis of plasma from 124 patients with CC, alo...更多

Pelvic lymph node metastasis significantly affects the prognosis of cervical cancer . However, current imaging examinations and serum squamous cell carcinoma antigen testing are inadequate for assessing the pelvic lymph node status in CC. To identify accurate noninvasive biomarkers for diagnosing PLNM and minimizing unnecessary postoperative lymphadenectomy and its associated complications, we performed a comprehensive proteomic and metabolomic analysis of plasma from 124 patients with CC, along with a proteomic analysis of 60 paired tissue samples. Through machine learning methods, we identified potential plasma biomarkers and constructed a diagnostic model. In the validation cohort, the diagnostic model combined with SCCA exhibited a higher sensitivity than SCCA and imaging examination . The plasma protein biomarkers were consistently validated in paired tissue samples. Additionally, immune infiltration analysis demonstrated that CD4 and CD8 T cells were highly infiltrated in the PLNM group, suggesting a potentially enhanced response to immunotherapy. Here, we established a biomarker panel for PLNM and highlighted the altered immune characteristics associated with PLNM, offering valuable insights for the development of immunotherapy strategies for patients with PLNM.收起

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