Evidence map›Paper›PMID 40000747›Full record

ArticleScientific reports2025

Multilayer analysis of ethnically diverse blood and urine biomarkers for breast cancer risk and prognosis.

Jia Feng, Xing Qi, Chen Chen, Baolin Li, Min Wang, Xuelong Xie, Kailan Yang, Xuan Liu, Rui Min Chen, Tongtong Guo and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Jia Feng *Department of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Xing Qi *Department of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Chen ChenDepartment of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Baolin LiDepartment of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Min WangDepartment of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Xuelong XieClinical Laboratory of Yibin Second People's Hospital, Yibin, 644000, Sichuan, China.
Kailan YangClinical Laboratory of Zigong First People's Hospital, Zigong, 643000, Sichuan, China.
Xuan LiuDepartment of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Rui Min ChenDepartment of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Tongtong GuoDepartment of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. guotongtong0822@163.com.
Jinbo LiuDepartment of Laboratory Medicine, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. liujb7203@swmu.edu.cn.

Funding

2024 Open Research Project of the Sichuan Provincial Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases 24GCZXZD03Luzhou City Science and Technology Plan Project Program 2024JYJ014Luzhou City Science and Technology Plan Project Program 2024JYJ030Scientific Research Program of Southwest Medical University 2023QN027Sichuan Provincial Department of Science and Technology 2022YFS0152Sichuan Provincial Department of Science and Technology 22ZYZF0007the Jiucheng Talent·Science and Technology Innovation Team Luzutong [2021] No. 162the Open fund project of Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou FZZD2023-01the Student innovation and entrepreneurship project of Southwest Medical University 2023
6 · The paper itself

Abstract

Breast cancer (BC) is one of the most common malignancies among women globally, characterized by complex pathogenesis involving various biomarkers present in blood and urine. To enhance understanding of the genetic associations between biomarkers and BC via multidimensional, cross ethnic investigations. Based on GWAS data of 35 blood and urine biomarkers from European populations, we adopted multiple analysis strategies including univariable Mendelian randomization (MR) analysis, reverse MR analysis, sensitivity analysis and multivariate MR to identify potential biomarkers associated with BC risk and survival. Our initial analysis included 122,977 BC and 105,974 controls of European ancestry. Building upon these findings, we conducted cross ethnic validation by applying the same analyses to East Asian populations using data from the IEU GWAS database, which included 5,552 BC and 89,731 controls. This step allowed us to investigate the universality and heterogeneity of our identified biomarkers across different ancestries. Subsequently, utilizing clinical laboratory detection data from multiple regions in China, we performed differential analyses and survival assessments on these potential biomarkers to evaluate their clinical relevance and utility. Notably, we leveraged Luzhou's clinical data to integrate HDL-C with conventional tumor markers (CEA, CA125, CA153) into a machine learning model, comparing its diagnostic efficacy against tumor marker combination. Our study validated associations of ALP, HDL-C, TG, SHBG, and IGF-1 with BC risk, reinforcing the reliability of these findings. Moreover, notable interethnic disparities emerged in the association between HDL-C and BC risk, where in HDL-C demonstrates a contrasting role: acting as a genetic protective agent against BC and suggesting promise as an auxiliary diagnostic marker in East Asian populations, yet inversely, it serves as a genetic dangerous predictor in European populations. Analyzing BC subtypes, we identified associations of HDL-C, TG, SHBG, and CRP with ER

Indexed as

Biomarkers, TumorBreast NeoplasmsChinaEast Asian PeopleFemaleGenome-Wide Association StudyHumansMendelian Randomization AnalysisMiddle AgedPrognosisRisk FactorsBiomarkers, TumorBlood and urine biomarkersBreast cancerMachine learningMendelian randomization

Identifiers

PMID40000747
PMCPMC11861975

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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.