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Identification of novel serum protein biomarkers in the context of 3P medicine for intravenous leiomyomatosis: a data-independent acquisition mass spectrometry-based proteomics study

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Abstract

Background

Intravenous leiomyomatosis (IVL) is a rare endocrine-associated tumor with unique characteristics of intravascular invasion. This study aimed to identify reliable biomarkers to supervise the development or recurrence of IVL in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

A total of 60 cases were recruited to detect differentially expressed proteins (DEPs) in serum samples from IVL patients. These cases included those with recurrent IVL, non-recurrent IVL, uterine myoma, and healthy individuals without uterine myoma, with 15 cases in each category. Then, weighted gene co-expression network analysis (WGCNA), lasso-penalized Cox regression analysis (Lasso), trend clustering, and a generalized linear regression model (GLM) were utilized to screen the hub proteins involved in IVL progression.

Results

First, 93 differentially expressed proteins (DEPs) were determined from 2582 recognizable proteins, with 54 proteins augmented in the IVL group, and the remaining proteins declined. These proteins were enriched in the modulation of the immune environment, mainly by activating the function of B cells. After the integrated analyses mentioned above, a model based on four proteins (A0A5C2FUE5, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3) was developed to efficiently determine the potential of IVL lesions to progress. Among these featured proteins, our results demonstrated that the risk factor A0A5C2FUE5 was associated with IVL progression (OR = 2.64). Conversely, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3 might act in a protective manner and prevent disease development (OR = 0.32, 0.60, 0.53, respectively), which was further supported by the multi-class receiver operator characteristic curve analysis.

Conclusion

Four hub proteins were eventually identified based on the integrated bioinformatics analyses. This study potentiates the promising application of these novel biomarkers to predict the prognosis or progression of IVL by a 3PM approach.

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Data availability

All data generated in this study have been involved in the manuscript or the supplementary files. The raw files of DIA proteomics have been uploaded to the public repository and can be retrieved from the iProX database (https://www.iprox.cn/page/project.html?id=IPX0003343000).

Abbreviations

IVL :

intravenous leiomyomatosis

CO :

control group

CO-no :

control subgroup without uterine myoma

CO-um :

control subgroup with uterine myoma

IVL-no :

IVL subgroup without recurrence

IVL-re :

IVL subgroup with recurrence

CTA :

computerized tomography angiography

LOH :

loss of heterozygosity

DIA :

data-independent acquisition

FASP :

filter-aided sample preparation

DDA :

data dependent acquisition

AGC :

automatic gain control

LC-MS/MS :

liquid chromatography-tandem mass spectrometry

FDR :

false discovery rate

DEPs :

differentially expressed proteins

FC :

fold change

OPLS-DA :

orthogonal partial least-squares discriminant analysis

GO :

gene ontology

KEGG :

Kyoto Encyclopedia of Genes and Genomes

WGCNA :

weighted gene co-expression network analysis

TOM :

topological overlap matrix

PPI :

protein-protein interaction

FCM :

fuzzy c-means

Lasso :

lasso-penalized cox regression

GLM :

generalized linear regression model

ROC :

receiver operator characteristic curve

AUC :

area under the curve

LTBP2 :

latent-transforming growth factor β binding protein 2

OPN :

osteopontin

NK :

natural killer

Tfh :

follicular helper T

Treg :

regulatory T

GC :

germinal center

Cig :

carcinogenic immunoglobulin

PDAC :

pancreatic ductal adenocarcinoma

LMP1 :

latent membrane protein 1

NF-κB :

nuclear factor kappa B

AP-1 :

activating protein-1

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Acknowledgements

The authors would like to thank AJE (https://www.aje.cn/) for English-language editing.

Code availability

The custom codes performed for R analysis could be retrieved from the Bioconductor website (https://www.bioconductor.org/).

Funding

This work was supported by grants from the National High-Level Hospital Clinical Research Funding (2022-PUMCH-B-064, 2022-PUMCH-C-053, and 2022-PUMCH-B-123) and Natural Science Foundation of Beijing (grant number 7234411).

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Author notes

  1. Zhitong Ge, Penghui Feng and Zijuan Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Zhitong Ge & Jianchu Li

  2. Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Penghui Feng & Rong Chen

  3. Department of Pathology, State Key Laboratory of Complex Severe and Rare Disease, Molecular Pathology Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Zijuan Zhang & Zhiyong Liang

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Contributions

Software, data curation, formal analysis, and visualization: ZTG and PHF. Writing-original draft preparation and writing review or editing: ZTG, PHF, and ZJZ. Conceptualization or design, administration, and funding acquisition: JCL, RC, and ZYL.

Corresponding authors

Correspondence to Zhiyong Liang, Rong Chen or Jianchu Li.

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The research procedure was carried out in conformity with the principle of the Helsinki Declaration and reviewed by the Ethics Committee of Peking Union Medical College Hospital.

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Written informed consent for publication was obtained from all participants.

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The authors declare no competing interests.

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Supplementary information

ESM 1

Supplementary Figure 1. Quality control analysis of the project. A. Quantitative fluctuation evaluation of samples. The abscissa represented different samples, and the ordinate represented the amount of protein expression. Different colors were on behalf of different groups. B. Peak capacity evaluation. The abscissa was the sequence of sample loading. The ordinate was the number of peaks; the green line represented the data of all peptides. A red line was displayed to illustrate iRT internal standard data. C. Protein FDR analysis. Cscore was equivalent to the protein reliability score. The black dotted line represented a 1% Q value (equivalent to 1% FDR) standard line. The higher the Csocre at the standard line, the better. (TIF 1960 kb) (PNG 229 kb)

High resolution image (TIF 1960 kb)

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Ge, Z., Feng, P., Zhang, Z. et al. Identification of novel serum protein biomarkers in the context of 3P medicine for intravenous leiomyomatosis: a data-independent acquisition mass spectrometry-based proteomics study. EPMA Journal 14, 613–629 (2023). https://doi.org/10.1007/s13167-023-00338-0

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