Serum Metabolomic Profiles for Breast Cancer Diagnosis, Grading and Staging by Gas Chromatography-Mass Spectrometry

• Published in: Scientific reports Vol. 7; no. 1; pp. 1715 - 11
• Main Authors: Hadi, Naila Irum, Jamal, Qamar, Iqbal, Ayesha, Shaikh, Fouzia, Somroo, Saleem, Musharraf, Syed Ghulam
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.05.2017; Nature Publishing Group; Nature Portfolio
• Subjects: 631/45/320; 692/4028/67/1347; 82/58; Adult; Aged; Breast cancer; Breast Neoplasms - blood; Breast Neoplasms - diagnosis; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Case-Control Studies; Chromatography; Diagnosis; Discriminant Analysis; Female; Gas chromatography; Gas Chromatography-Mass Spectrometry - methods; Glycolysis; Humanities and Social Sciences; Humans; Least-Squares Analysis; Lipogenesis; Mammography; Mass spectrometry; Mass spectroscopy; Metabolism; Metabolites; Metabolome; Metabolomics; Middle Aged; multidisciplinary; Neoadjuvant Therapy; Neoplasm Grading; Neoplasm Staging; Science; Science (multidisciplinary); Scientific imaging; Signal Transduction; Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-01924-9

Detection of metabolic signature for breast cancer (BC) has the potential to improve patient prognosis. This study identified potentially significant metabolites differentiating between breast cancer patients and healthy controls to help in diagnosis, grading, staging and determination of neoadjuvant status. Serum was collected from 152 pre-operative breast cancer (BC) patients and 155 healthy controls in this case-controlled study. Gas chromatography-mass spectrometry (GC-MS) was used to obtain metabolic profiles followed by chemometric analysis with the identification of significantly differentiated metabolites including 7 for diagnosis, 18 for grading, 23 for staging, 15 for stage III subcategory and 10 for neoadjuvant status ( p -value < 0.05). Partial Least Square Discriminant Analysis (PLS-DA) model revealed a distinct separation between healthy controls and BC patients with a sensitivity of 96% and specificity of 100% on external validation. Models for grading, staging and neoadjuvant status were built with Decision Tree Algorithm with predictive accuracy of 71.5%, 71.3% and 79.8% respectively. Pathway analysis revealed increased glycolysis, lipogenesis, and production of volatile organic metabolites indicating the metabolic alterations in breast cancer.