Cerumenogram: a new frontier in cancer diagnosis in humans.

Cancer is the deadliest human disease and the development of new diagnosis methods is important to increase the chances of a cure. In this work it was developed a new method, named here for the first time as cerumenogram, using cerumen (earwax) as a new biomatrix for diagnosis. Earwax samples collected from cancer patients (cancer group) and cancer-free patients (control group) were analyzed by Headspace/Gas Chromatography-Mass Spectrometry (HS/GC-MS), following with multivariate analysis steps to process the raw data generated. In total, 158 volatile organic metabolites (VOMs) were identified in the cerumen samples. The 27 selected as potential VOMs biomarkers for cancer provided 100% discrimination between the cancer and control groups. This new test can thus be routinely employed for cancer diagnoses that is non-invasive, fast, cheap, and highly accurate.

Earwax metabolomics: An innovative pilot metabolic profiling study for assessing metabolic changes in ewes during periparturition period.

Important metabolic changes occur during transition period of late pregnancy and early lactation to meet increasing energy demands of the growing fetus and for milk production. The aim of this investigation is to present an innovative and non-invasive tool using ewe earwax sample analysis to assess the metabolic profile in ewes during late pregnancy and early lactation. In this work, earwax samples were collected from 28 healthy Brazilian Santa Inês ewes divided into 3 sub-groups: 9 non-pregnant ewes, 6 pregnant ewes in the last 30 days of gestation, and 13 lactating ewes ≤ 30 days postpartum. Then, a range of metabolites including volatile organic compounds (VOC), amino acids (AA), and minerals were profiled and quantified in the samples by applying headspace gas chromatography/mass spectrometry, high performance liquid chromatography/tandem mass spectrometry, and inductively coupled plasma-optical emission spectrometry, respectively. As evident in our results, significant changes were observed in the metabolite profile of earwax between the studied groups where a remarkable elevation was detected in the levels of non-esterified fatty acids, alcohols, ketones, and hydroxy urea in the VOC profile of samples obtained from pregnant and lactating ewes. Meanwhile, a significant decrease was detected in the levels of 9 minerals and 14 AA including essential AA (leucine, phenyl alanine, lysine, isoleucine, threonine, valine), conditionally essential AA (arginine, glycine, tyrosine, proline, serine), and a non-essential AA (alanine). Multivariate analysis using robust principal component analysis and hierarchical cluster analysis was successfully applied to discriminate the three study groups using the variations of metabolites in the two stress states (pregnancy and lactation) from the healthy non-stress condition. The innovative developed method was successful in evaluating pre- and post-parturient metabolic changes using earwax and can in the future be applied to recognize markers for diagnosis, prevention, and intervention of pregnancy complications in ewes.

Earwax: A neglected body secretion or a step ahead in clinical diagnosis? A pilot study.

This work combines the advantages of volatile metabolites profiling as a young growing research field with a non-invasive sampling technique using earwax "a neglected body secretion" for detection and monitoring of biomarkers for diabetes mellitus (types 1 and 2). Earwax samples were collected from 26 diabetic patients of both types, analyzed by headspace gas chromatography mass spectrometry and confronted to the volatile earwax composition of 33 healthy individuals. Data mining analysis was conducted using different models to discriminate the healthy individuals from the diabetic patients and to discriminate between both types of diabetes as well. The model with the best discriminating ability was found to be partial least squares discriminant analysis (PLS-DA) after variable selection. The 6 most important biomarkers were ethanol, acetone, methoxyacetone, hydroxyurea, isobutyraldehyde, and acetic acid. The multivariate model constructed was validated using a test data set and was able to correctly predict all the samples. The receiver operating characteristic (ROC) curves were built for the 6 variables for diabetes types 1 and 2 diagnoses. Among the 6 variables selected, methoxyacetone was the only biomarker able solely to perfectly discriminate between diabetes types 1 and 2. The method is simple, non-invasive, accurate, and highly accepted by patients. SIGNIFICANCE: Our method involves a volatolomic approach by headspace gas chromatography coupled with mass spectrometry as a single analytical technique combined with multivariate data analysis to detect biomarkers of diabetes in earwax samples. Our method was able to discriminate with high accuracy between 33 healthy controls and 26 diabetic patients as well as its types (1 and 2). Our method employing earwax, a "neglected biological matrix" not only has the advantage of non-invasive sampling but also overcomes the limitations of the applied procedures in other biological samples, involving no or minimum sample pretreatment, no external contamination and utilizing a simple sample collection technique.