Journal of Traditional Chinese Medicine ›› 2026, Vol. 46 ›› Issue (3): 695-705.DOI: 10.19852/j.cnki.jtcm.20251204.001
• Original Articles • Previous Articles Next Articles
Integrated analysis of plasma metabolomics and proteomics reveals the biological characteristics of damp-heat and stasis-toxin syndrome in colorectal cancer
QIAN Jingyang1,2, ZHANG Qinchang1, ZHANG Dong1, SHI Heweng1, GENG Xuechen1, SUN Xiaohe1, SUN Dongdong1(
), CHENG Haibo1()
- 1
Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor ,the First Clinical Medical College, Nanjing University of Chinese Medicine Nanjing 210023, China
2Department of Oncology ,Dafeng People’s Hospital Yancheng 224100, China
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Received:2025-02-11Accepted:2025-08-25Online:2026-06-15Published:2025-12-04 -
Contact:Prof. CHENG Haibo, Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, the First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing 210023, China. hbcheng@njucm.edu.cn;
Prof. SUN Dongdong, Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, the First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing 210023, China. sundd@njucm.edu.cn, Telephone: +86-18861017016 -
Supported by:key project of the National Natural Science Foundation of China: Biological Basis of Core Pathogenesis Syndrome in Colorectal Cancer Liver Metastasis and the Mechanism of Action of Xianlian Jiedu Decoction in Prevention and Treatment(82430122);National Key R&D Program of China: Research on Cancer Toxicity Pathogenesis Theory and Its Guidance for Prevention and Treatment Strategies of Malignant Tumors(2022YFC3500200);National Key R&D Program of China: Research on Cancer Toxicity Pathogenesis Theory and Its Guidance for Prevention and Treatment Strategies of Malignant Tumors(2022YFC3500202);Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine: Inheritance and Innovation Team for Cancer Toxin Pathogenesis Theory in Prevention and Treatment of Gastrointestinal Malignancies(ZYYCXTD-C-202208)
Cite this article
QIAN Jingyang, ZHANG Qinchang, ZHANG Dong, SHI Heweng, GENG Xuechen, SUN Xiaohe, SUN Dongdong, CHENG Haibo. Integrated analysis of plasma metabolomics and proteomics reveals the biological characteristics of damp-heat and stasis-toxin syndrome in colorectal cancer[J]. Journal of Traditional Chinese Medicine, 2026, 46(3): 695-705.
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Table 1 Demographic and clinical characteristics of participants
| Characteristic | SRYD (n = 40) | non-SRYD (n = 40) | Normal (n = 40) | P value | |
|---|---|---|---|---|---|
| Age | 66.000 (57.0, 70.8) | 63.500 (58.0, 70.0) | 30.000 (28.0, 32.8) | 0.722 | |
| BMI | 23.212 (20.9, 25.0) | 23.625 (21.5, 26.1) | 22.059 (19.1, 22.8) | 0.465 | |
| Hypertension | 15 (37.50) | 11 (27.50) | NA | 0.340 | |
| Hyperlipidemia | 2 (5.00) | 3 (7.50) | NA | 0.644 | |
| Diabetes mellitus | 6 (15.00) | 5 (12.50) | NA | 0.745 | |
| Sex | Female | 10 (25.00) | 15 (37.50) | 15 (37.50) | 0.228 |
| Male | 30 (75.00) | 25 (62.50) | 25 (62.50) | ||
| Location | Colon | 25 (62.50) | 22 (55.00) | NA | 0.496 |
| Rectum | 15 (37.50) | 18 (45.00) | NA | ||
| TNM | Ⅰ | 4 (10.00) | 5 (12.50) | NA | 0.224 |
| Ⅱ | 16 (40.00) | 19 (47.50) | NA | ||
| Ⅲ | 16 (40.00) | 16 (40.00) | NA | ||
| Ⅳ | 4 (10.00) | 0 (0.00) | NA | ||
Table 1 Demographic and clinical characteristics of participants
| Characteristic | SRYD (n = 40) | non-SRYD (n = 40) | Normal (n = 40) | P value | |
|---|---|---|---|---|---|
| Age | 66.000 (57.0, 70.8) | 63.500 (58.0, 70.0) | 30.000 (28.0, 32.8) | 0.722 | |
| BMI | 23.212 (20.9, 25.0) | 23.625 (21.5, 26.1) | 22.059 (19.1, 22.8) | 0.465 | |
| Hypertension | 15 (37.50) | 11 (27.50) | NA | 0.340 | |
| Hyperlipidemia | 2 (5.00) | 3 (7.50) | NA | 0.644 | |
| Diabetes mellitus | 6 (15.00) | 5 (12.50) | NA | 0.745 | |
| Sex | Female | 10 (25.00) | 15 (37.50) | 15 (37.50) | 0.228 |
| Male | 30 (75.00) | 25 (62.50) | 25 (62.50) | ||
| Location | Colon | 25 (62.50) | 22 (55.00) | NA | 0.496 |
| Rectum | 15 (37.50) | 18 (45.00) | NA | ||
| TNM | Ⅰ | 4 (10.00) | 5 (12.50) | NA | 0.224 |
| Ⅱ | 16 (40.00) | 19 (47.50) | NA | ||
| Ⅲ | 16 (40.00) | 16 (40.00) | NA | ||
| Ⅳ | 4 (10.00) | 0 (0.00) | NA | ||
Figure 1 Analysis of differential proteins through bioinformatics A: GO enrichment analysis: the x-axis represents enrichment scores, and the y-axis displays the top five items for BP, CC, and MF; B: KEGG enrichment analysis: the x-axis indicates enrichment scores, and the y-axis lists the top 20 pathways; C: hub proteins identified within the PPI network. D: venn diagram showing DPs overlap among SRYD_Normal, non-SRYD_Normal, and SRYD_non-SRYD groups. GO: gene ontology; BP: biological process; CC: cellular component; MF: molecular function; KEGG: kyoto encyclopedia of genes and genomes; PPI: protein-protein interaction; DPs: differential proteins; SRYD: patients with damp-heat and stasis-toxin syndrome (n = 20); non-SRYD: patients without damp-heat and stasis-toxin syndrome (n = 20); Normal: volunteers without underlying diseases or SRYD-related symptoms (n = 20).
Figure 1 Analysis of differential proteins through bioinformatics A: GO enrichment analysis: the x-axis represents enrichment scores, and the y-axis displays the top five items for BP, CC, and MF; B: KEGG enrichment analysis: the x-axis indicates enrichment scores, and the y-axis lists the top 20 pathways; C: hub proteins identified within the PPI network. D: venn diagram showing DPs overlap among SRYD_Normal, non-SRYD_Normal, and SRYD_non-SRYD groups. GO: gene ontology; BP: biological process; CC: cellular component; MF: molecular function; KEGG: kyoto encyclopedia of genes and genomes; PPI: protein-protein interaction; DPs: differential proteins; SRYD: patients with damp-heat and stasis-toxin syndrome (n = 20); non-SRYD: patients without damp-heat and stasis-toxin syndrome (n = 20); Normal: volunteers without underlying diseases or SRYD-related symptoms (n = 20).
Figure 2 Bioinformatics-based analysis of differential metabolites A: KEGG pathway enrichment analysis of DMs in SRYD-Normal group; B: venn diagram of DMs overlap among the SRYD_Normal, non-SRYD_Normal, and SRYD_non-SRYD groups. KEGG: kyoto encyclopedia of genes and genomes; DMs: differential metabolites; SRYD: patients with damp-heat and stasis-toxin syndrome (n = 40); non-SRYD: patients without damp-heat and stasis-toxin syndrome (n = 40); Normal: volunteers without underlying diseases or SRYD-related symptoms (n = 40).
Figure 2 Bioinformatics-based analysis of differential metabolites A: KEGG pathway enrichment analysis of DMs in SRYD-Normal group; B: venn diagram of DMs overlap among the SRYD_Normal, non-SRYD_Normal, and SRYD_non-SRYD groups. KEGG: kyoto encyclopedia of genes and genomes; DMs: differential metabolites; SRYD: patients with damp-heat and stasis-toxin syndrome (n = 40); non-SRYD: patients without damp-heat and stasis-toxin syndrome (n = 40); Normal: volunteers without underlying diseases or SRYD-related symptoms (n = 40).
Figure 3 Combined analysis of differential proteins and metabolites A: correlation analysis between the top 20 DPs and DMs ranked by VIP in the SRYD_Normal group: each row represents a different metabolite, and each column represents the corresponding protein. Red denotes a positive correlation, blue a negative correlation, with darker colors indicating stronger correlations. Asterisks indicate significance levels; B: correlation analysis between the top 20 DPs and DMs ranked by VIP in the SRYD_non-SRYD groups (***P < 0.001, **P between 0.01 and 0.001, * P between 0.05 and 0.01); C: bubble plot of shared pathways between DPs and DMs in the SRYD_Normal groups; D: bubble plot of shared pathways between DPs and DMs in the SRYD_non-SRYD groups; E: IPA-based molecular network of CRC-SRYD-specific differential proteins and metabolites; E1: centered on key molecules; E2: primarily centered on EGFR, ERK1/2, and lactic acid. DPs: differential proteins; DMs: differential metabolites; VIP: variable importance in projection; SRYD: patients with damp-heat and stasis-toxin syndrome; non-SRYD: patients without damp-heat and stasis-toxin syndrome; Normal: volunteers without underlying diseases or SRYD-related symptoms; IPA: ingenuity pathway analysis; CRC-SRYD syndrome: colorectal cancer with damp-heat and stasis-toxin syndrome; EGFR: epidermal growth factor receptor; ERK1/2: extracellular signal-regulated kinases.
Figure 3 Combined analysis of differential proteins and metabolites A: correlation analysis between the top 20 DPs and DMs ranked by VIP in the SRYD_Normal group: each row represents a different metabolite, and each column represents the corresponding protein. Red denotes a positive correlation, blue a negative correlation, with darker colors indicating stronger correlations. Asterisks indicate significance levels; B: correlation analysis between the top 20 DPs and DMs ranked by VIP in the SRYD_non-SRYD groups (***P < 0.001, **P between 0.01 and 0.001, * P between 0.05 and 0.01); C: bubble plot of shared pathways between DPs and DMs in the SRYD_Normal groups; D: bubble plot of shared pathways between DPs and DMs in the SRYD_non-SRYD groups; E: IPA-based molecular network of CRC-SRYD-specific differential proteins and metabolites; E1: centered on key molecules; E2: primarily centered on EGFR, ERK1/2, and lactic acid. DPs: differential proteins; DMs: differential metabolites; VIP: variable importance in projection; SRYD: patients with damp-heat and stasis-toxin syndrome; non-SRYD: patients without damp-heat and stasis-toxin syndrome; Normal: volunteers without underlying diseases or SRYD-related symptoms; IPA: ingenuity pathway analysis; CRC-SRYD syndrome: colorectal cancer with damp-heat and stasis-toxin syndrome; EGFR: epidermal growth factor receptor; ERK1/2: extracellular signal-regulated kinases.
Figure 4 Diagnostic biomarkers for CRC-SRYD syndrome based on specific DPs and DMs A: ReliefF feature selection cumulative score plot: The x-axis represents the number of top-ranked features selected, while the y-axis indicates the cumulative importance scores calculated by the ReliefF algorithm. Higher cumulative scores reflect stronger discriminative power of the feature subset for CRC-SRYD syndrome classification; B: ROC curves of five models based on the training set; C: ROC curves of five models based on the validation set; D: calibration curves of five models on the validation set; E: forest plot of AUC scores for the five models: Each point represents the mean AUC value of the corresponding model, with error bars indicating the confidence intervals; CRC-SRYD syndrome: colorectal cancer with damp-heat and stasis-toxin syndrome; DPs: differential proteins; DMs: differential metabolites; ROC: receiver operating characteristic; AUC: area under curve; XGBoost: eXtreme Gradient Boosting; SVM: support vector machine; KNN: K-Nearest Neighbors.
Figure 4 Diagnostic biomarkers for CRC-SRYD syndrome based on specific DPs and DMs A: ReliefF feature selection cumulative score plot: The x-axis represents the number of top-ranked features selected, while the y-axis indicates the cumulative importance scores calculated by the ReliefF algorithm. Higher cumulative scores reflect stronger discriminative power of the feature subset for CRC-SRYD syndrome classification; B: ROC curves of five models based on the training set; C: ROC curves of five models based on the validation set; D: calibration curves of five models on the validation set; E: forest plot of AUC scores for the five models: Each point represents the mean AUC value of the corresponding model, with error bars indicating the confidence intervals; CRC-SRYD syndrome: colorectal cancer with damp-heat and stasis-toxin syndrome; DPs: differential proteins; DMs: differential metabolites; ROC: receiver operating characteristic; AUC: area under curve; XGBoost: eXtreme Gradient Boosting; SVM: support vector machine; KNN: K-Nearest Neighbors.
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