Evidence map›Paper›PMID 40443546›Full record

ArticleOpen life sciences2025

Alterations of metabolites related to microbiota-gut-brain axis in plasma of colon cancer, esophageal cancer, stomach cancer, and lung cancer patients.

Yuqin Zhao, Kangwei Shen, Qing Lu, Wei Huang, Xuejun Kang, Li Xie

Abstract read
In one paragraph

Article in Open life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yuqin ZhaoGynecology Department, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China.
Kangwei ShenKey Laboratory of Child Development and Learning Science (Ministry of Education), School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, PR China.
Qing LuKey Laboratory of Child Development and Learning Science (Ministry of Education), School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, PR China.
Wei HuangDepartment of Radiotherapy, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China.
Xuejun KangKey Laboratory of Child Development and Learning Science (Ministry of Education), School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, PR China.ORCID https://orcid.org/0000-0002-8581-4446
Li XieCollege of Animal Science and Technology, Jinling Institute of Technology, Nanjing, 210038, PR China.ORCID https://orcid.org/0000-0002-7135-6835

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Co-occurring symptoms such as depression, anxiety, fatigue, and sleep disorders are frequently comorbid with cancer. The causes of these cancer-related symptom clusters are hypothesized sharing a common biological mechanism. This study explored pattern differences of some gut metabolites (glucocorticoids, short-chain fatty acids, gut microbial metabolites from tryptophan) in plasma samples from patients with four types of cancer. Metabolomics analysis was performed to indicate the differences of metabolites. Discrimination model and diagnostic model were constructed using orthogonal partial least squares discriminant analysis, and differential metabolites were screened, then receiver-operating characteristic curve analysis was performed to evaluate the performance of these models. Melatonin (MLT), indole propionic, and skatole were screened as the common differential metabolites shared by four types of cancer, indicating that the intestinal microbial metabolic pathway of tryptophan plays a key role in the occurrence and development of malignant tumors. The area under the curve values for the potential candidate biomarker predictors in univariate analysis ranged from 0.771 to 0.989, and in multivariate analysis ranged from 0.985 to 1.00. The sensitivity and specificity of the multivariable model were 94.7-100 and 96.4-100%, respectively. These biomarkers also had good performance in discriminating different pairs of cancer. The analysis of gut microbiota metabolites allows us to characterize the common metabolic characteristics of patients with various cancers. The intestinal microbial metabolic pathway of tryptophan plays a key role in the occurrence and development of malignant tumors.

Indexed as

cancerchromatographydiscrimination and diagnostic modelgut microbiota metabolitesmass spectrometrymetabolomics

Identifiers

PMID40443546
PMCPMC12120410

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.