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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. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to evaluate the clinical efficacy of APC gene promoter methylation in serum as a biomarker for breast cancer (BC) diagnosis. Methods: Two reviewers systematically searched online resources to identify the publications relevant to APC gene promoter methylation and BC. The data of true positive, false positive, false negative, and true negative were extracted from each included study and pooled for diagnostic sensitivity, specificity, and summary receiver operating characteristic curve. Results: Twelve studies finally fulfilled the inclusion criteria and were included in this meta‐analysis. The diagnostic sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and area under the receiver operating characteristic curve were 0.20 (95% confidence interval [CI] 0.17–0.23), 0.96 (95% CI 0.93–0.97), 3.69 (95% CI 1.60–8.50), 0.83 (95% CI 0.75–0.92), 4.58 (95% CI 1.85–11.37) and 0.80, respectively. A Deeks’ funnel plot and Egger's line regression test (t = 1.43, P = 0.18) indicated no publication bias was present. Conclusion: Because of low sensitivity, APC gene promoter methylation in serum was not suitable for BC screening. However, as specificity was very high, detection of serum APC gene promoter methylation could be used as tool to confirm BC. [ABSTRACT FROM AUTHOR]