Journal of Traditional Chinese Medicine ›› 2025, Vol. 45 ›› Issue (4): 896-908.DOI: 10.19852/j.cnki.jtcm.2025.04.020
• Original Articles • Previous Articles Next Articles
WANG Siliang1, MA Yushui1,2, LIU Lei3, WANG Pei4, WU Jia1,2, JIN Xing1,2, JIN Qiang1,2, WANG Congcong1,2, QIN Chentai1,2, ZHENG Miaomiao1,2, YANG Xi5, PAN Jun6, XU Hanchen2,7, DONG Changsheng1,2(
), CHEN Wenlian1,2(
)
Received:2024-04-12
Accepted:2024-11-18
Online:2025-07-25
Published:2025-07-25
Contact:
DONG Changsheng,CHEN Wenlian
About author:CHEN Wenlian, Cancer Institute, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China and Shanghai Frontiers Science Center of Disease and Syndrome Biology of Inflammatory Cancer Transformation, Shanghai 200032, China. chenwl8412@shutcm.edu.cn,Telephone: +86-17756177005Supported by:WANG Siliang, MA Yushui, LIU Lei, WANG Pei, WU Jia, JIN Xing, JIN Qiang, WANG Congcong, QIN Chentai, ZHENG Miaomiao, YANG Xi, PAN Jun, XU Hanchen, DONG Changsheng, CHEN Wenlian. Reversal of amino acid metabolism patterns between circulating blood and tumors as a new biomarker for the Zhengxu Xieshi syndrome in patients with esophageal squamous cell carcinoma[J]. Journal of Traditional Chinese Medicine, 2025, 45(4): 896-908.
| Variable | Study 1 | Study 2 | ||||
|---|---|---|---|---|---|---|
| HC (n = 34) | ESCC (n = 34) | P value | ESCC (n = 24) | |||
| Age (years) | Median | 67 | 67 | 0.745 | 68 | |
| Range | 56-78 | 54-77 | 50-76 | |||
| Gender [n (%)] | Male | 21 (61.76) | 26 (76.47) | 0.291 | 15 (62.50) | |
| Female | 13 (38.24) | 8 (23.53) | 9 (37.50) | |||
| BMI | Median | 22.18 | 22.01 | |||
| Range | 18.37-30.37 | 18.37-28.31 | ||||
| Hypertension [n (%)] | Yes | 6 (17.65) | 4 (16.67) | |||
| No | 28 (82.35) | 19 (79.17) | ||||
| Not available | 0 | 1 (4.17) | ||||
| Diabetes [n (%)] | Yes | 0 | 2 (8.33) | |||
| No | 34 (100) | 21 (87.50) | ||||
| Not available | 0 | 1 (4.17) | ||||
| Alcohol use [n (%)] | Never a drinker | 29 (85.29) | 1 (4.17) | |||
| Drinker | 5 (14.71) | 22 (91.67) | ||||
| Not available | 0 | 1 (4.17) | ||||
| Cigarette use [n (%)] | Never a smoker | 20 (58.82) | 4 (16.67) | |||
| Smoker | 14 (41.18) | 19 (79.17) | ||||
| Not available | 0 | 1 (4.17) | ||||
| WBC (109/L) | Median | 5.8 | 5.4 | |||
| Range | 3.4-11.6 | 2.2-8.4 | ||||
| RBC (1012/L) | Median | 4.63 | 4.39 | |||
| Range | 3.59-5.59 | 3.41-5.32 | ||||
| TNM stage [n (%)] | Ⅰ | 9 (26.47) | 1 (4.17) | |||
| Ⅱ | 18 (52.94) | 14 (58.33) | ||||
| III | 6 (17.65) | 7 (29.17) | ||||
| Ⅳ | 1 (2.94) | 2 (8.33) | ||||
| Metastasis [n (%)] | Yes | 12 (35.29) | 8 (33.33) | |||
| No | 22 (64.71) | 16 (66.67) | ||||
Table 1 Basic characteristics of enrolled human subjects
| Variable | Study 1 | Study 2 | ||||
|---|---|---|---|---|---|---|
| HC (n = 34) | ESCC (n = 34) | P value | ESCC (n = 24) | |||
| Age (years) | Median | 67 | 67 | 0.745 | 68 | |
| Range | 56-78 | 54-77 | 50-76 | |||
| Gender [n (%)] | Male | 21 (61.76) | 26 (76.47) | 0.291 | 15 (62.50) | |
| Female | 13 (38.24) | 8 (23.53) | 9 (37.50) | |||
| BMI | Median | 22.18 | 22.01 | |||
| Range | 18.37-30.37 | 18.37-28.31 | ||||
| Hypertension [n (%)] | Yes | 6 (17.65) | 4 (16.67) | |||
| No | 28 (82.35) | 19 (79.17) | ||||
| Not available | 0 | 1 (4.17) | ||||
| Diabetes [n (%)] | Yes | 0 | 2 (8.33) | |||
| No | 34 (100) | 21 (87.50) | ||||
| Not available | 0 | 1 (4.17) | ||||
| Alcohol use [n (%)] | Never a drinker | 29 (85.29) | 1 (4.17) | |||
| Drinker | 5 (14.71) | 22 (91.67) | ||||
| Not available | 0 | 1 (4.17) | ||||
| Cigarette use [n (%)] | Never a smoker | 20 (58.82) | 4 (16.67) | |||
| Smoker | 14 (41.18) | 19 (79.17) | ||||
| Not available | 0 | 1 (4.17) | ||||
| WBC (109/L) | Median | 5.8 | 5.4 | |||
| Range | 3.4-11.6 | 2.2-8.4 | ||||
| RBC (1012/L) | Median | 4.63 | 4.39 | |||
| Range | 3.59-5.59 | 3.41-5.32 | ||||
| TNM stage [n (%)] | Ⅰ | 9 (26.47) | 1 (4.17) | |||
| Ⅱ | 18 (52.94) | 14 (58.33) | ||||
| III | 6 (17.65) | 7 (29.17) | ||||
| Ⅳ | 1 (2.94) | 2 (8.33) | ||||
| Metastasis [n (%)] | Yes | 12 (35.29) | 8 (33.33) | |||
| No | 22 (64.71) | 16 (66.67) | ||||
Figure 1 Repressed amino acid metabolism in patient sera A: comparative analysis of amino acid metabolic features in the sera between ESCC and HCs; B: heatmap showing the differentially expressed amino acids in the sera of patients with ESCC compared to those of HCs. (1): essential amino acids; (2): non-essential amino acids; (3): non-canonical amino acids. C: KEGG pathway-based differential abundance analysis revealing differences in amino acid metabolic pathways in the sera of patients relative to those of HCs. HC: healthy control; ESCC: esophageal squamous cell carcinoma; FC: foldchange; KEGG: kyoto encyclopedia of genes and genomes.
Figure 2 Heightened amino acid metabolism in clinical ESCC tissues A: comparative assessment of amino acid metabolic features of clinical ESCC tissues and matched NATs from patients; B: heatmap presenting differentially expressed amino acids in clinical ESCC tissues and matched NATs from patients. (1): essential amino acids; (2): non-essential amino acids; (3): non-canonical amino acids; C: KEGG pathway-based differential abundance analysis depicting alterations in amino acid metabolic pathways in clinical ESCC tissues compared to those in matched NATs. NATs: paired normal adjacent tissues; ESCC: esophageal squamous cell carcinoma; KEGG: kyoto encyclopedia of genes and genomes dataset.
Figure 3 Amino acid metabolic pathways and biomarkers with opposite alterations between patient sera and clinical ESCC tissues A: depiction of seven amino acid metabolic pathways exhibiting opposite changes in patient sera and tumor tissues; B: heatmap illustrating expression shifts in 40 metabolic enzymes involved in the amino acid pathways shown in A, in clinical ESCC tissues and paired NATs in patients; C: comprehensive analysis of metabolic enzyme and amino acid alterations in the top three pathways from A: glutathione metabolism, pantothenate and CoA biosynthesis, and glycine/serine/threonine metabolism; D: identification of four amino acid metabolites with converse alterations in tumor tissues and sera of patients with ESCC. NATs: paired normal adjacent tissues; ESCC: esophageal squamous cell carcinoma; CoA: coenzyme A.
Figure 4 Interpreting ZXXS syndrome via amino acid metabolic reprogramming The interpretation of ZXXS syndrome in patients diagnosed with ESCC is underpinned by the profound perturbations observed across seven amino acid metabolic pathways. These pathways, which are implicated in immune modulation, generation of reducing agents, energy currency supply, and building block synthesis, are substantially dysregulated in both circulating blood and regional neoplastic tissues in patients with ESCC. Within patients, attenuated activity of these pathways in sera signifies compromised vital Qi, whereas enhanced activity in ESCC tissues indicates locally heightened evil Qi. Thus, this study delineates opposing amino acid metabolic changes between the systemic circulation and localized tumor tissues, offering insights into the biological hallmarks of ZXXS syndrome in patients with ESCC. NATs: paired normal adjacent tissues; ESCC: esophageal squamous cell carcinoma; ZXXS: Zhengxu Xieshi.
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