Chemical Research in Chinese Universities ›› 2024, Vol. 40 ›› Issue (1): 136-144.doi: 10.1007/s40242-024-3246-y

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A Bird's Eye View of Quantitative Proteome of Tumor Tissues from Lung Cancer Patients by a High Precision Mass Spectrometry Method

DU Xiaohui2,3, LI Encheng3, WANG Qi1,3, YOU Xin1   

  1. 1. Lung Cancer Transitional Medicine Center, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, P. R. China;
    2. Department of Scientific Research Center, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, P. R. China;
    3. Department of Respiratory Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, P. R. China
  • Received:2023-11-06 Online:2024-02-01 Published:2024-01-24
  • Contact: WANG Qi, YOU Xin E-mail:wqdlmu@163.com;alchemist_riot@126.com
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (Nos. 82003318, 81903185, 81972916), the “1+X” Program for Clinical Competency Enhancement-Interdisciplinary Innovation Project of the Second Hospital of Dalian Medical University and the Talent Innovation Support Plan of Dalian, China (No. 2021RQ008). The mass spectrometry analysis was partially assisted by Genechem Company (Shanghai, China).

Abstract: Lung cancer produces a high incidence of malignant tumors. There have been many studies on lung cancer using mass spectrometry (MS) technologies. However, most studies have focused on humoral samples. In this work, 26 pairs of tissue samples (tumor vs. para-tumor) from patients with lung cancer were analyzed using liquid chromatography-tandem MS (LC-MS/MS) with data-independent acquisition mode. In total, 3152 proteins were quantified from tissue samples with high confidence, including 189 up-regulated and 522 down-regulated proteins (tumor vs. para-tumor). In addition, 79 and 690 proteins were identified only in para-cancerous samples and cancerous samples, respectively. The results from bio-informatics tools indicated that altered proteins like PEBP1, HRG and LYZ could be ideal reservoirs for screening the potential biomarkers for lung cancer. It is believed these tissue-specific proteomics results will assist in the studies of lung cancer.

Key words: Lung cancer, Proteome, Data-independent aquisition