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Updated: Jan 29, 2026

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
Published on: April 23, 2021
Quantitative label-free mass spectrometry using contralateral and adjacent breast tissues reveal differentially
Talita Helen Bombardelli Gomig1, Iglenir João Cavalli1, Ricardo Lehtonen Rodrigues de Souza1
1Genetics Department, Federal University of Parana, Curitiba, Brazil.
Abstract:
Proteins play an essential role in the biological processes associated with cancer. Their altered expression levels can deregulate critical cellular pathways and interactive networks. In this study, the mass spectrometry-based label-free quantification followed by functional annotation was performed to investigate the most significant deregulated proteins among tissues of primary breast tumor (PT) and axillary metastatic lymph node (LN) and corresponding non-tumor tissues contralateral (NCT) and adjacent (ANT) from patients diagnosed with invasive ductal carcinoma. A total of 462 proteins was observed as differentially expressed (DEPs) among the groups analyzed. A high level of similarity was observed in the proteome profile of both non-tumor breast tissues and DEPs (n = 12) were mainly predicted in the RNA metabolism. The DEPs among the malignant and non-tumor breast tissues [n = 396 (PTxNCT) and n = 410 (LNxNCT)] were related to pathways of the LXR/RXR, NO, eNOS, eIF2 and sirtuins, tumor-related functions, fatty acid metabolism and oxidative stress. Remarkable similarity was observed between both malignant tissues, which the DEPs were related to metastatic capabilities. Altogether, our findings revealed differential proteomic profiles that affected cancer associated and interconnected signaling processes. Validation studies are recommended to demonstrate the potential of individual proteins and/or pathways as biological markers in breast cancer. SIGNIFICANCE: The proteomic analysis of this study revealed high similarity in the proteomic profile of the contralateral and adjacent non-tumor breast tissues. Significant differences were identified among the proteome of the malignant and non-tumor tissue groups of the same patients, providing relevant insights into the hallmarks, signaling pathways, biological functions, and interactive protein networks that act during tumorigenesis and breast cancer progression. These proteins are suggested as targets of relevant interest to be explored as potential biological markers related to tumor development and metastatic progression in the breast cancer disease.
Insights
This study identified 462 differentially expressed proteins in breast cancer tissues, revealing distinct proteomic profiles in tumors versus non-tumor tissues. These findings offer insights into cancer progression and potential biomarkers for invasive ductal carcinoma.
Area of Science:
- Proteomics
- Cancer Biology
- Molecular Oncology
Background:
- Altered protein expression is critical in cancer development and progression.
- Understanding proteomic differences in tumor and non-tumor tissues is key to identifying cancer hallmarks.
Purpose of the Study:
- To investigate differentially expressed proteins in primary breast tumors (PT), metastatic lymph nodes (LN), and adjacent non-tumor tissues (NCT, ANT).
- To identify protein pathways and functions associated with breast cancer tumorigenesis and metastasis.
Main Methods:
- Mass spectrometry-based label-free quantification.
- Functional annotation and pathway analysis of differentially expressed proteins (DEPs).
Main Results:
- 462 DEPs identified across analyzed tissue groups.
- Non-tumor tissues (NCT, ANT) showed similar proteomic profiles.
- Malignant tissues (PT, LN) exhibited DEPs related to RNA metabolism, LXR/RXR, NO, eIF2, sirtuins, fatty acid metabolism, oxidative stress, and metastatic capabilities.
Conclusions:
- Differential proteomic profiles distinguish breast tumors from non-tumor tissues.
- Identified proteins and pathways are relevant to cancer progression and metastasis.
- Proteins involved in RNA metabolism, signaling, and metabolism are potential breast cancer biomarkers.
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