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INTRODUCTION: Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients' serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection. MATERIALS AND METHODS: Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites. RESULTS: The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination. CONCLUSION: Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.
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Aminoácidos/análisis , Neoplasias de la Mama/diagnóstico , Neoplasias de la Mama/metabolismo , Detección Precoz del Cáncer/métodos , Ácido Linoleico/análisis , Biomarcadores de Tumor/metabolismo , Cromatografía Líquida de Alta Presión , Cromatografía Liquida/métodos , Femenino , HumanosRESUMEN
PURPOSE: Endometrial cancer (EC) is the most common gynecological malignancy with high disease burden especially in advanced stages of the disease. Our study investigated the metabolomic profile of EC patient's serum with the aim of identifying novel diagnostic biomarkers that could be used especially in early disease detection. MATERIAL AND METHODS: Using targeted metabolomic serum profiling based on HPLC-TQ/MS, women with EC (n â= â15) and controls (n â= â21) were examined for 232 endogenous metabolites. RESULTS: Top performing biomarkers included ceramides, acylcarnitines and 1-methyl adenosine. Top 4 biomarkers combined achieved 94% sensitivity with 75% specificity with AUC 92.5% (CI 90.5-94.5%). Individual markers also provided significant predictive values: C16-ceramide achieved sensitivity 73%, specificity 81%, AUC 0.83, C22-ceramide sensitivity 67%, specificity 81%, AUC 0.77, hydroxyhexadecenoylcarnitine sensitivity 60%, specificity 96%, AUC 0.76 and 1-methyladenosine sensitivity 67%, specificity 81%, AUC 0.75. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination. CONCLUSIONS: Using mass spectrometry targeted metabolomic profiling, ceramides, acylcarnitines and 1-methyladenosine were identified as potential diagnostic biomarkers for EC. Additionally, these identified metabolites may provide additional insight into cancer cell metabolism.
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Biomarcadores de Tumor/sangre , Neoplasias Endometriales/diagnóstico , Espectrometría de Masas/métodos , Metaboloma , Estudios de Casos y Controles , Cromatografía Líquida de Alta Presión , Neoplasias Endometriales/sangre , Femenino , Estudios de Seguimiento , Humanos , Persona de Mediana Edad , Pronóstico , Estudios Prospectivos , Curva ROCRESUMEN
The data presented here are related to the research paper entitled "Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer." (Kozar et al., 2018) [1]. Metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign gynecological conditions. HPLC-TQ/MS was performed on all samples. PLS-DA was used for the first line classification of epithelial ovarian cancer and healthy control group based on metabolomic profiles. Random forest algorithm was used for building a prediction model based over most significant markers. Univariate analysis was performed on individual markers to determine their distinctive roles. Furthermore, markers were also evaluated for their biological significance in cancer progression.
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INTRODUCTION: Epithelial ovarian cancer (EOC) is a disease with a poor survival rate mostly due to its discovery in late stages. The aim of this study was to investigate the metabolomic profile of ovarian cancer with the intention of identifying and describing novel biomarkers with diagnostic potential. MATERIAL AND METHODS: Targeted serum metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign ovarian conditions, using HPLC-TQ/MS. RESULTS: Panel of 49 top performing biomarkers shows separation between EOC and controls with 87% specificity and 87% sensitivity with AUC of 93% (CI 90%-95%). Using only 5 top biomarkers, specificity of 80% and sensitivity of 83% was achieved on extended sample set. Most significant biomarkers belong to sphingolipid classes, especially long chain ceramides and sphingomyelins. Different concentrations of various fatty acid chain lengths ceramides and sphingomyelins are also implying their respective roles in cell proliferation and growth inhibition. CONCLUSION: Long chain ceramides and sphingomyelins may serve as a novel biomarker for epithelial ovarian cancer detection and may also offer insight into the role of sphingolipid metabolism in cell proliferation.