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 α, 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.

Keywords: Maldi-ToF-MS; proteomics; serum; small cell lung carcinoma; urine.

Figure 1.

Serum peptide profiles of the training group. (A) Lung cancer serum group I and (B) healthy serum group. (C) Clustering analysis of MS-based serum peptide profiles (red, lung cancer serum group I; green, healthy serum group I). MS, mass spectrometry.

Figure 2.

Specific peptide peaks of model profiles (red, lung cancer serum group I; green, healthy serum group I).

Figure 3.

Urine peptide profiles of training group. (A) Lung cancer urine group I and (B) healthy urine group I. (C) Clustering analysis of MS-based urine peptide profiles (red, lung cancer urine group I; green, healthy urine group I). MS, mass spectrometry.

Figure 4.

Specific peptide peaks of model (red, lung cancer urine group I; green, healthy urine group I).