Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer

Qian,Xiaojun; Jin,Xiaolin; He,Jiaying; Zhang,Junjing; Hu,Shan

doi:10.3892/ol.2024.14781

Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer

Authors:
- Xiaojun Qian
- Xiaolin Jin
- Jiaying He
- Junjing Zhang
- Shan Hu
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Affiliations: Department of Breast and Thyroid Surgery, Shaoxing People's Hospital, Shaoxing, Zhejiang 312000, P.R. China, Health Management Center, Shaoxing People's Hospital, Shaoxing, Zhejiang 312000, P.R. China
Published online on: October 29, 2024 https://doi.org/10.3892/ol.2024.14781
Article Number: 34
Copyright: © Qian et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Dysregulated lipid metabolism promotes the progression of various cancer types, including breast cancer. The present study aimed to explore the lipidomic profiles of patients with breast cancer, providing insights into the correlation between lipid compositions and tumor subtypes characterized by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. Briefly, 30 patients with breast cancer were categorized into four groups based on their HR and HER2 status: HR+ no HER2 expression (HER2‑0), HR+ HER2‑low; HR+ HER2‑positive (pos) and HR‑ HER2‑pos. The lipidomic profiles of these patients were analyzed using high‑throughput liquid chromatography‑mass spectrometry. The data were processed through principal component analysis (PCA), partial least squares‑discriminant analysis (PLS‑DA) and random forest (RF) classification to assess the lipidomic variations and significant lipid features among these groups. The profiles of the lipids, particularly triglycerides (TG) such as TG(16:0‑18:1‑18:1)+NH4, were significantly different across the groups. PCA and PLS‑DA identified unique lipid profiles in the HR+ HER2‑pos and HR+ HER2‑0 groups, while RF highlighted phosphatidylinositol‑3,4,5‑trisphosphate(21:2)+NH4 as a crucial lipid feature for accurate patient grouping. Advanced statistical analysis showed significant correlations between lipid carbon chain length and the number of double bonds within the classifications, providing insights into the role of structural lipid properties in tumor biology. Additionally, a clustering heatmap and network analysis indicated significant lipid‑lipid interactions. Pathway enrichment analysis showed critical biological pathways, such as the ‘Assembly of active LPL and LIPC lipase complexes’, which has high enrichment ratio and statistical significance. In conclusion, the present study underscored that lipidomic profiling is crucial in understanding the metabolic alterations associated with different breast cancer subtypes. These findings highlighted specific lipid features and interactions that may serve as potential biomarkers for breast cancer classification and target pathways for therapeutic intervention. Furthermore, advanced lipidomic analyses can be integrated to decipher complex biological data, offering a foundation for further research into the role of lipid metabolism in cancer progression.

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