Machine learning⁃based study on syndrome transformation and metabolic characteristics of spleen deficiency/dampness⁃heat in patients with newly diagnosed esophageal squamous cell carcinoma

GAO Ling; LIU Hong; JI Guang; XU Hanchen; LIU Lei; ZHANG Jie; JIN Xing; DONG Changsheng; ZHENG Miaomiao; QIN Chentai; WANG Siliang; HAO Miaoxiu; ZHANG Ming; SHI Yulin; FENG Li; LIU Dan; YANG Xi; CHEN Wenlian

doi:10.16305/j.1007-1334.2026.z20250825002

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Machine learning⁃based study on syndrome transformation and metabolic characteristics of spleen deficiency/dampness⁃heat in patients with newly diagnosed esophageal squamous cell carcinoma

更新时间：2026-03-17

- Machine learning⁃based study on syndrome transformation and metabolic characteristics of spleen deficiency/dampness⁃heat in patients with newly diagnosed esophageal squamous cell carcinoma
- Shanghai Journal of Traditional Chinese Medicine Vol. 60, Issue 3, Pages: 1-10(2026)
- 作者机构：
  
  1.上海中医药大学（上海 201203）
  2.南通大学附属肿瘤医院（江苏南通 226300）
  3.上海交通大学医学院附属胸科医院（上海 200030）
  4.国家癌症中心（北京 100021）
  5.山西省中医院（山西太原 030012）
- 作者简介：
- 基金信息：
- DOI：10.16305/j.1007-1334.2026.z20250825002
  CLC：
- Received：25 August 2025，
  
  Published：10 March 2026
- 稿件说明：
移动端阅览
高玲,刘红,季光,等.基于机器学习的初诊食管鳞状细胞癌患者脾虚/湿热的证候变化及代谢特征研究[J].上海中医药杂志,2026,60(3):1-10.

GAO Ling,LIU Hong,JI Guang,et al.Machine learning⁃based study on syndrome transformation and metabolic characteristics of spleen deficiency/dampness⁃heat in patients with newly diagnosed esophageal squamous cell carcinoma[J].Shanghai Journal of Traditional Chinese Medicine,2026,60(3):1-10.
高玲,刘红,季光,等.基于机器学习的初诊食管鳞状细胞癌患者脾虚/湿热的证候变化及代谢特征研究[J].上海中医药杂志,2026,60(3):1-10. DOI： 10.16305/j.1007-1334.2026.z20250825002.

GAO Ling,LIU Hong,JI Guang,et al.Machine learning⁃based study on syndrome transformation and metabolic characteristics of spleen deficiency/dampness⁃heat in patients with newly diagnosed esophageal squamous cell carcinoma[J].Shanghai Journal of Traditional Chinese Medicine,2026,60(3):1-10. DOI： 10.16305/j.1007-1334.2026.z20250825002.

摘要

目的

基于机器学习算法探讨初诊食管鳞状细胞癌（ESCC）患者中医脾虚证/湿热证的证候变化及代谢特征。

方法

纳入2个独立ESCC队列，即发现集和验证集，同时招募健康志愿者作为对照。采用《脾虚证/湿热证PRO量表》（PRO量表为患者报告结局评价量表）收集初诊ESCC患者的证候信息并进行评分，采用机器学习K-means聚类分析判定中医证型，将患者分为脾虚证组、湿热证组、兼证组（兼有脾虚、湿热）及非脾虚湿热证组；采集发现集患者术后《脾虚证/湿热证PRO量表》信息，并与术前资料进行对比，分析中医证候征象的变化情况；采集发现集和健康对照受试者的空腹外周静脉血，进行代谢组检测与分析；纳入本课题组前期研究中一个含有生存信息的ESCC队列及其血清代谢组数据，进行相关性分析。

结果

①共纳入初诊ESCC患者252例，其中发现集147例、验证集105例，另招募健康对照者75名。所有患者均有完整的术前《脾虚证/湿热证PRO量表》数据，共计252份；在发现集队列中，共收集到75例患者治疗前的血清样本，追踪获取到其中73例患者根治术后第10天的《脾虚证/湿热证PRO量表》数据。②K-means聚类分析可以有效识别初诊ESCC患者的中医证型，发现集、验证集脾虚/湿热相关证型占比分别为40.8%、41.9%。③手术会扰动患者的脾虚与湿热证候，并加剧“苔黄厚腻”表现。④代谢组分析显示，湿热证组与兼证组代谢谱显著偏离非脾虚湿热证组。鉴定出各证型特征代谢物，脾虚证如吲哚基硫酸盐、假尿苷等，湿热证如黄柏酮、苏氨酸，兼证如冉乌头碱、水杨酸。⑤生存分析提示，吲哚基硫酸盐、假尿苷和冉乌头碱高表达预示不良的总生存期，而黄柏酮高表达则提示良好预后。

结论

机器学习辅助的PRO量表数据分析可准确识别初诊ESCC患者的中医证型及其动态变化。各证型对应特异代谢谱与特征代谢物，部分代谢物对预后具有独立预测价值。

Abstract

Objective

To explore the syndrome transformation and metabolic characteristics of spleen deficiency/dampness-heat syndromes of traditional Chinese medicine （TCM） in patients with newly diagnosed esophageal squamous cell carcinoma （ESCC） based on machine learning algorithms.

Methods

Two independent ESCC cohorts were enrolled， namely the discovery cohort and validation cohort， and healthy volunteers were recruited as controls. The

Spleen Deficiency/Dampness-Heat Syndrome Patient-Reported Outcome Scale

（hereinafter referred to as the

PRO Scale

） was used to collect and score syndrome information of newly diagnosed ESCC patients. Machine learning K‑means clustering analysis was applied to determine TCM syndromes， and patients were classified into the spleen deficiency syndrome （SDS） group， dampness-heat syndrome （DHS） group， combined syndrome （CS） group （with both spleen deficiency and dampness-heat syndromes）， and non-spleen deficiency/dampness-heat syndrome （NSD/DHS） group. Postoperative

PRO Scale

data were collected from the disc

overy cohort and compared with preoperative data to analyze the syndrome transformation and dynamic changes in TCM syndrome manifestations. Fasting peripheral venous blood samples were collected from subjects in the discovery cohort and healthy controls for metabolomic detection and analysis. Additionally， an ESCC cohort with survival information and the corresponding serum metabolomic data from the preliminary study of our research group were included for correlation analysis.

Results

①A total of 252 newly diagnosed ESCC patients were enrolled， including 147 cases in the discovery cohort and 105 cases in the validation cohort， with 75 healthy controls recruited separately. Complete preoperative

PRO Scale

data were obtained from all 252 patients. For the discovery cohort， serum samples were collected from 75 patients before treatment， and

PRO Scale

data on the 10th day after radical surgery were followed up and acquired from 73 of these patients. ②K-means clustering analysis effectively identified TCM syndromes in newly diagnosed ESCC patients. The proportions of spleen deficiency/dampness-heat related syndromes were 40.8% in the discovery cohort and 41.9% in the validation cohort. ③Surgery disturbed spleen deficiency and dampness-heat syndromes in patients and exacerbated the manifestation of yellow， thick and greasy tongue coating.④Metabolomic analysis showed that the metabolic profiles of the DHS group and CS group were significantly deviated from those of the NSD/DHS group. Syndrome-specific metabolites were identified： indoxyl sulfate and pseudouridine for spleen deficiency syndrome； obacunone and threonine for dampness-heat syndrome； ranaconitine and salicylic acid for combined syndrome.⑤Survival analysis indicated that high expressions of indoxyl sulfate， pseudouridine and ranaconitine predicted poor overall survival， whereas high expression of obacunone indicated favorable prognosis.

Conclusions

Machine learnin

g-assisted analysis of

PRO Scale

data can accurately identify TCM syndromes and their dynamic changes in newly diagnosed ESCC patients. Each syndrome corresponds to specific metabolic profiles and characteristic metabolites， and some of these metabolites have independent predictive value for prognosis.

关键词

Keywords

references

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