Evidence map›Paper›PMID 41189333›Full record

ArticleJournal of clinical laboratory analysis2025

Integrated Transcriptomic and Metabolomic Analysis Revealed That Tryptophan Metabolism-Related Metabolites Are Biomarkers of Trastuzumab Resistance.

Xinxin Si, Hanzhe Wu, Yiwei Cao, Bangbang Wei, Juan Wei, Zhongcheng Wang, Changfei Mao

Abstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

7 authors.

Xinxin SiJiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, China.
Hanzhe WuJiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, China.
Yiwei CaoJiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, China.
Bangbang WeiJiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, China.
Juan WeiDepartment of Oncology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China.
Zhongcheng WangDepartment of Pharmacy, The First People's Hospital of Lin'an District and Lin'an People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, China.ORCID https://orcid.org/0009-0005-2399-8891
Changfei MaoDepartment of General Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.

Funding

National Natural Science Foundation of China 81703557National Natural Science Foundation of China 82204524Natural Science Foundation Project of Nanjing University of Chinese Medicine XZR2023068The Second Hospital of Nanjing Talent Support Project RCMS23003
6 · The paper itself

Abstract

backgroundTrastuzumab resistance strongly affects the prognosis of HER2-positive breast cancer patients, and tryptophan metabolism plays a key role in cancer progression and cancer prognosis. However, the relationship between trastuzumab resistance and tryptophan metabolism in HER2-positive breast cancer remains unclear.

methodsFour HER2-positive breast cancer cell lines (TS: BT-474, SK-BR-3; TR: HCC1954, JIMT-1) were cultured in specific media. Metabolites from cells, tissues, and serum were extracted via pretreatment, centrifugation, drying, and redissolution. UPLC-MS/MS was used for metabolite detection, while tryptophan metabolism-related gene expression was measured using qRT-PCR. T-test was used to calculate statistical differences. Data visualization, machine learning models construction (RF, LASSO logistic regression), and nomogram were achieved through R language.

resultsPLS-DA showed distinct separation between TS and TR breast cancer cell lines in tryptophan metabolism-related gene expression (four differentially expressed genes: HAAO, KYNU, KMO, AFMID) and metabolite abundance (five differentially abundant metabolites). Integration analysis identified 3-hydroxyanthranilic acid, cinnabarinic acid, kynurenine, and kynurenic acid as potential metabolic biomarkers. These four metabolites differed significantly in serum of HCs, TS and TR patients. Four predictors were selected by LASSO, and a nomogram was constructed based on logistic regression. Finally, biomarkers and related genes were validated in tissues.

conclusionOur explored the changes induced by trastuzumab resistance in terms of metabolite abundance and gene transcription levels associated with tryptophan metabolism. Furthermore, this work provided new insights into novel diagnostic biomarkers and prediction models of trastuzumab resistance in HER2-positive breast cancer patients.

Indexed as

Biomarkers, TumorBreast NeoplasmsDrug Resistance, NeoplasmMetabolomeTranscriptomeTrastuzumabTryptophanCell Line, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMetabolomicsBiomarkers, TumorTrastuzumabTryptophanHER2‐positive breast cancermetabolic biomarkersprediction modeltrastuzumab resistancetryptophan metabolism

Identifiers

PMID41189333
PMCPMC12699193

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