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Exploratory study on application of MALDI?TOF?MS to detect serum and urine peptides related to small cell lung carcinoma.

ABSTRACT: Matrix?assisted laser desorption/ionization time?of?flight mass spectrometry (MALDI?TOF?MS) was employed to analyze differential serum and urine peptides in patients with small cell lung cancer (SCLC) and healthy individuals, and SCLC diagnostic classification models were constructed. Serum and urine samples from 72 patients with SCLC, age? and gender?matched with 72 healthy individuals, were divided into training and testing sets in a 3:1 ratio. Serum and urine peptides were extracted using copper ion?chelating nanomagnetic beads, and mass spectra were obtained using MALDI?TOF?MS. Peptide spectra for the training set were analyzed, and the classification model was constructed using ClinProTools (CPT). The testing set was used for blinded model validation. For training?set sera, 122 differential peptide signal peaks with a mass of 0.8?10 kDa were observed, and 19 peptides showed significantly different expression [P<0.0005; area under curve (AUC) ?0.80]. CPT screened 5 peptide peaks (0.8114, 0.83425, 1.86655, 4.11133 and 5.81192 kDa) to construct the classification model. The testing set was used for the blinded validation, which had 95.0% sensitivity and 90.0% specificity. For the training?set urine, 132 differential peptide signal peaks with m/z ratios of 0.8?10 kDa were observed, and 8 peptides had significantly different expression (P<0.0005; AUC ?0.80). Then, 5 peaks (1.0724, 2.37692, 2.7554, 4.75475 and 4.7949 kDa) were used for classification model construction. The testing set was used for 36 blinded validation, which had 85.0% sensitivity and 80.0% specificity. Among the differential peptides, 3 had the same significant peaks at 2.3764, 0.8778 and 0.8616 kDa, identified as fibrinogen ?, glucose?6?phosphate isomerase and cyclin?dependent kinase?1, respectively. The present study highlighted the differences that exist in serum and urine peptides between patients with SCLC and healthy individuals. Serum and urine peptide diagnostic classification models could be constructed using MALDI?TOF?MS, and showed high sensitivity and specificity.

SUBMITTER: Lv P

PROVIDER: S-EPMC6896340 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Exploratory study on application of MALDI‑TOF‑MS to detect serum and urine peptides related to small cell lung carcinoma.

Lv Panpan P Liu Zeyuan Z Xu Bin B Tang Chuanhao C Li Xiaoyan X Qin Haifeng H Yang Shaoxing S Gao Hongjun H He Kun K Liu Xiaoqing X

Molecular medicine reports 20191105 1

Matrix‑assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF‑MS) was employed to analyze differential serum and urine peptides in patients with small cell lung cancer (SCLC) and healthy individuals, and SCLC diagnostic classification models were constructed. Serum and urine samples from 72 patients with SCLC, age‑ and gender‑matched with 72 healthy individuals, were divided into training and testing sets in a 3:1 ratio. Serum and urine peptides were extracted using cop ...[more]

PMID: 31746355

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Project description:SARS-CoV-2 has caused a large outbreak since its emergence in December 2019. COVID-19 diagnosis became a priority so as to isolate and treat infected individuals in order to break the contamination chain. Currently, the reference test for COVID-19 diagnosis is the molecular detection (RT-qPCR) of the virus from nasopharyngeal swab (NPS) samples. Although this sensitive and specific test remains the gold standard, it has several limitations, such as the invasive collection method, the relative high cost and the duration of the test. Moreover, the material shortage to perform tests due to the discrepancy between the high demand for tests and the production capacities puts additional constraints on RT-qPCR. Here, we propose a PCR-free method for diagnosing SARS-CoV-2 based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling and machine learning (ML) models from salivary samples. Kinetic saliva samples were collected at enrollment and ten and thirty days later (D0, D10 and D30), to assess the classification performance of the ML models compared to the molecular tests performed on NPS specimens. Spectra were generated using an optimized protocol of saliva collection and successive quality control steps were developed to ensure the reliability of spectra. A total of 360 averaged spectra were included in the study. At D0, the comparison of MS spectra from SARS-CoV-2 positive patients (n = 105) with healthy healthcare controls (n = 51) revealed nine peaks that significantly distinguished the two groups. Among the five ML models tested, support vector machine with linear kernel (SVM-LK) provided the best performance on the training dataset (accuracy = 85.2%, sensitivity = 85.1%, specificity = 85.3%, F1-Score = 85.1%). The application of the SVM-LK model on independent datasets confirmed its performances with 88.9% and 80.8% of correct classification for samples collected at D0 and D30, respectively. Conversely, at D10, the proportion of correct classification had fallen to 64.3%. The analysis of saliva samples by MALDI-TOF MS and ML appears as an interesting supplementary tool for COVID-19 diagnosis, despite the mitigated results obtained for convalescent patients (D10).

Dataset Information

Exploratory study on application of MALDI?TOF?MS to detect serum and urine peptides related to small cell lung carcinoma.

Publications

Exploratory study on application of MALDI‑TOF‑MS to detect serum and urine peptides related to small cell lung carcinoma.

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