MALDI-TOF MS-based total serum protein fingerprinting for liver cancer diagnosis. Issue 7 (8th March 2019) - Park, Han-Gyu; Jang, Kyoung-Soon; Park, Hae-Min; Song, Won-Suk; Jeong, Yoon-Yi; Ahn, Da-Hee; Kim, Seong-Min; Yang, Yung-Hun; Kim, Yun-Gon
Abstract : Serum is one of the most commonly used samples in many studies to identify protein biomarkers to diagnose cancer. Abstract : Serum is one of the most commonly used samples in many studies to identify protein biomarkers to diagnose cancer. Although conventional enzyme-linked immunosorbent assay (ELISA) or liquid chromatography-mass spectrometry (LC-MS)-based methods have been applied as clinical tools for diagnosing cancer, there have been troublesome problems, such as inferior multiplexing capabilities, high development costs and long turnaround times, which are inappropriate for high-throughput analytical platforms. Here, we developed a simple and robust cancer diagnostic method using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based total serum protein fingerprinting. First, serum samples were simply diluted with distilled water and subsequently spotted onto a MALDI plate without prior chromatographic purification or separation. The sample preparation method was enough to collect reproducible total serum protein fingerprints and would be highly advantageous for high-throughput assay. Each of the integrated main spectrum profiles (MSPs), which are representative of liver cancer patients ( n = 40) or healthy controls ( n = 80), was automatically generated by the MALDI Biotyper 3 software. The reliability of the integrated MSPs was successfully evaluated in comparison with a blind test set ( n = 31), which consisted of 13 liver cancer patients and 18 healthy controls. Additionally, our partial least squares discriminant analysis (PLS-DA) demonstrated a statistically significant difference in MALDI-TOF MS-based total serum protein fingerprints between liver cancer patients and healthy controls. Taken together, this work suggests that this method may be an effective high-throughput platform technology for various cancer diagnoses and disease evaluations.