Evidence map›Paper›PMID 40448029›Full record

ArticleBMC microbiology2025

Integrated microbiome and metabolome analysis reveals a novel interplay between gut microbiota and metabolites in differentiated thyroid carcinoma.

Xue Jiang, Qian Liu, Dongkun Xu, Hua Pang, Yuhong Shi

Abstract read
In one paragraph

Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Review
  2. The gut-joint axis in osteoarthritis.Nature reviews. Rheumatology · 2026
    Review
  3. Review
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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

5 authors.

Xue Jiang *Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
Qian Liu *Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China.
Dongkun XuDepartment of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China.
Hua PangDepartment of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China. phua1973@163.com.
Yuhong ShiDepartment of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China. shiyuhong89@126.com.

Funding

China Baoyuan Research Fund Project CBYI202105Sichuan Provincial Medical Youth Innovation Research Project Q21076Sichuan Science and Technology Innovation and Entrepreneurship Seedling Project 2021JDRC0170Young Talent Program of China National Nuclear Corporation CNNC2021137
6 · The paper itself

Abstract

backgroundDifferentiated Thyroid carcinoma (DTC) is the most prevalent endocrine malignancy. The identification of novel biomarkers for thyroid carcinoma is essential for enhancing our understanding of the molecular mechanisms underlying DTC development. Notably, gut microorganisms and their metabolites play a role in the development of DTC, although their influence is modulated by the host's genetic background and environmental factors. Our study aimed to identify and classify gut microbiota and metabolites associated with DTC.

methods90 patients with a confirmed diagnosis of DTC and 33 healthy volunteers donated stool samples for our analysis. To examine the gut microbiota, we utilized 16 S rRNA gene sequencing, a technique that allows for the identification and classification of microorganisms. Additionally, we employed liquid chromatography-mass spectrometry (LC-MS) to investigate the alterations in metabolites present in thyroid carcinoma patients compared to healthy individuals.

resultsThe Venn diagram visualized the distribution of bacterial species, with 926 species shared by both groups and 12,225 species unique to DTC patients. Notably, the gut microbiota of DTC patients exhibited higher species richness and diversity compared to healthy individuals. LDA Effect Size (LEfSe) analysis identified Faecalibacterium and Prevotella_9 as more abundant in healthy individuals, while Oscillospiraceae, Subdoligranulum, and Actinobacteriota were significantly more prevalent in DTC patients. We successfully characterized 3255 metabolites in both groups, which were primarily associated with biosynthesis of plant secondary metabolites, neomycin, kanamycin, and gentamicin biosynthesis, bile secretion, and steroid hormone biosynthesis. Among these metabolites, 550 were differentially expressed, with 402 metabolites being highly expressed in DTC patients. Six metabolites exhibiting an area under the curve (AUC) value exceeding 0.87 were identified as potential clinical diagnostic markers for DTC. Furthermore, Spearman's rank correlations were utilized to explore the potential functional relationships between the 10 distinctive microbial species and the top 10 differential metabolites.

conclusionsThe gut microbiota and its associated metabolites may play a crucial role in the development of DTC. The identification of altered metabolites and microbiota in DTC patients suggests their potential as diagnostic markers and therapeutic targets. This offers new insights into the molecular pathogenesis of DTC, providing opportunities for early diagnosis and improved treatment strategies. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

BacteriaGastrointestinal MicrobiomeMetabolomeThyroid NeoplasmsAdultAgedChromatography, LiquidFecesFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16SRNA, Ribosomal, 16SDifferentiated thyroid carcinomaMetaboliteMetabolome analysisMicrobiome analysisMicrobiota

Identifiers

PMID40448029
PMCPMC12125819

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LicenceCC BY-NC-ND
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Registered trials

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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.