Identification of Molecular Markers to Classify IDC and DCIS/LCIS in Breast Cancer Using Spatial Transcriptomics

Jinyi Zhou

doi:10.54254/2755-2721/2025.22397

Research Article

Open access

Published on 24 April 2025

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Zhou,J. (2025). Identification of Molecular Markers to Classify IDC and DCIS/LCIS in Breast Cancer Using Spatial Transcriptomics. Applied and Computational Engineering,150,1-8.

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Identification of Molecular Markers to Classify IDC and DCIS/LCIS in Breast Cancer Using Spatial Transcriptomics

Jinyi Zhou *^,1,

¹ School of Biomedical Engineering and Information Technology, Nanjing Medical University, No. 101, Longmian Avenue, Jiangning District, Nanjing, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.22397

Abstract

Breast cancer is one of the most common malignant tumors among women globally, posing significant threats to women's health and lives. Identifying specific molecular markers is crucial for early diagnosis, precision treatment, and accurate prognostic assessment of breast cancer. In this study, spatial transcriptomics technology combined with machine learning methods successfully identified molecular markers capable of effectively distinguishing invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS). By analyzing the gene expression profiles of 36,601 genes across 3,798 cells, significant differentially expressed genes (DEGs) were screened using the DEsingle method. Functional enrichment analysis indicated that these genes are significantly associated with breast cancer-related pathways, breast cell-specific expression, and the regulation of core transcription factors, such as TP53, SP1, and NFKB1. Further classification analysis employing machine learning models including random forest, decision tree, support vector machine, and logistic regression revealed that the random forest model demonstrated the highest performance, achieving an accuracy rate of 95.78%. Ultimately, ten key molecular markers were identified: MGP, ALB, S100G, KRT37, SERPINA3, AC087379.2, ZNF350-AS1, IGHG3, IGHG4, and IGKC. These markers exhibited robust discrimination between IDC and DCIS/LCIS, suggesting their potential roles in tumor invasion and metastasis. This study provides novel molecular evidence for early diagnosis, individualized treatment, and prognostic evaluation of breast cancer, contributing new research insights and theoretical support for precision medicine approaches in breast cancer.

Keywords

breast cancer, spatial transcriptomics, Molecular Markers, biomarker

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References

[1]. S. Libson and M. Lippman, “A review of clinical aspects of breast cancer,” International Review of Psychiatry, vol. 26, no. 1, pp. 4–15, Feb. 2014, doi: 10.3109/09540261.2013.852971.

[2]. J. Wang et al., “Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance,” Sig Transduct Target Ther, vol. 9, no. 1, pp. 1–28, Apr. 2024, doi: 10.1038/s41392-024-01779-3.

[3]. L. Moses and L. Pachter, “Museum of spatial transcriptomics,” Nat Methods, vol. 19, no. 5, pp. 534–546, May 2022, doi: 10.1038/s41592-022-01409-2.

[4]. P. Androvic et al., “Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury,” Nat Commun, vol. 14, no. 1, p. 4115, Jul. 2023, doi: 10.1038/s41467-023-39447-9.

[5]. D. Dar, N. Dar, L. Cai, and D. K. Newman, “Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution,” Science, vol. 373, no. 6556, p. eabi4882, Aug. 2021, doi: 10.1126/science.abi4882.

[6]. X. Sun et al., “SpaGRA: Graph augmentation facilitates domain identification for spatially resolved transcriptomics,” Journal of Genetics and Genomics, Oct. 2024, doi: 10.1016/j.jgg.2024.09.015.

[7]. Z. Miao, K. Deng, X. Wang, and X. Zhang, “DEsingle for detecting three types of differential expression in single-cell RNA-seq data,” Bioinformatics, vol. 34, no. 18, pp. 3223–3224, Sep. 2018, doi: 10.1093/bioinformatics/bty332.

[8]. J. Piñero et al., “DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants,” Nucleic Acids Research, vol. 45, no. D1, pp. D833–D839, Jan. 2017, doi: 10.1093/nar/gkw943.

[9]. J.-B. Pan et al., “PaGenBase: A Pattern Gene Database for the Global and Dynamic Understanding of Gene Function,” PLOS ONE, vol. 8, no. 12, p. e80747, Dec. 2013, doi: 10.1371/journal.pone.0080747.

[10]. H. Han et al., “TRRUST: a reference database of human transcriptional regulatory interactions,” Sci Rep, vol. 5, no. 1, p. 11432, Jun. 2015, doi: 10.1038/srep11432.

Cite this article

Zhou,J. (2025). Identification of Molecular Markers to Classify IDC and DCIS/LCIS in Breast Cancer Using Spatial Transcriptomics. Applied and Computational Engineering,150,1-8.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning

Conference website: https://www.confseml.org/

ISBN：978-1-80590-063-4(Print) / 978-1-80590-064-1(Online)

Conference date: 2 July 2025

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.150

ISSN：2755-2721(Print) / 2755-273X(Online)

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