Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array.

INTRODUCTION: Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. METHODS: Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. RESULTS: Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. CONCLUSION: Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Statistical Evaluation of Total Expiratory Breath Samples Collected throughout a Year: Reproducibility and Applicability toward Olfactory Sensor-Based Breath Diagnostics.

The endogenous volatile organic compounds (VOCs) in exhaled breath can be promising biomarkers for various diseases including cancers. An olfactory sensor has a possibility for extracting a specific feature from collective variations of the related VOCs with a certain health condition. For this approach, it is important to establish a feasible protocol for sampling exhaled breath in practical conditions to provide reproducible signal features. Here we report a robust protocol for the breath analysis, focusing on total expiratory breath measured by a Membrane-type Surface stress Sensor (MSS), which possesses practical characteristics for artificial olfactory systems. To assess its reproducibility, 83 exhaled breath samples were collected from one subject throughout more than a year. It has been confirmed that the reduction of humidity effects on the sensing signals either by controlling the humidity of purging room air or by normalizing the signal intensities leads to reasonable reproducibility verified by statistical analyses. We have also demonstrated the applicability of the protocol for detecting a target material by discriminating exhaled breaths collected from different subjects with pre- and post-alcohol ingestion on different occasions. This simple yet reproducible protocol based on the total expiratory breath measured by the MSS olfactory sensors will contribute to exploring the possibilities of clinical applications of breath diagnostics.

Endogenous reference RNAs for microRNA quantitation in formalin-fixed, paraffin-embedded lymph node tissue.

Lymph node metastasis is one of the most important factors for tumor dissemination. Quantifying microRNA (miRNA) expression using real-time PCR in formalin-fixed, paraffin-embedded (FFPE) lymph node can provide valuable information regarding the biological research for cancer metastasis. However, a universal endogenous reference gene has not been identified in FFPE lymph node. This study aimed to identify suitable endogenous reference genes for miRNA expression analysis in FFPE lymph node. FFPE lymph nodes were obtained from 41 metastatic cancer and from 16 non-cancerous tissues. We selected 10 miRNAs as endogenous reference gene candidates using the global mean method. The stability of candidate genes was assessed by the following four statistical tools: BestKeeper, geNorm, NormFinder, and the comparative ΔCt method. miR-103a was the most stable gene among candidate genes. However, the use of a single miR-103a was not recommended because its stability value exceeded the reference value. Thus, we combined stable genes and investigated the stability and the effect of gene normalization. The combination of miR-24, miR-103a, and let-7a was identified as one of the most stable sets of endogenous reference genes for normalization in FFPE lymph node. This study may provide a basis for miRNA expression analysis in FFPE lymph node tissue.