Proteomic biomarkers for lung cancer progression

Author:

Ren Yanjiao1,Zhao Shishun2,Jiang Dandan2,Feng Xin1,Zhang Yexian1,Wei Zhipeng1,Wang Zhongyu1,Zhang Wenniu1,Zhou Qing F3,Li Yong4,Hou Hanxu3,Xu Ying56,Zhou Fengfeng1

Affiliation:

1. College of Computer Science & Technology, & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, PR China

2. Center for Applied Statistical Research, College of Mathematics, Jilin University, Changchun, Jilin 130012, PR China

3. School of Electrical Engineering & Intelligentization, Dongguan University of Technology, Dongguan 523000, PR China

4. Department of Electronic Engineering, Tsinghua University, Beijing 100084, PR China

5. Computational Systems Biology Lab, Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602, USA

6. College of Computer Science & Technology, & College of Public Health, Jilin University, Changchun, Jilin 130012, PR China

Abstract

Aim: Lung adenocarcinoma (LUAD) and lung squamous-cell carcinoma (LUSC) are two major subtypes of lung cancer and constitute about 70% of all the lung cancer cases. The patient's lifespan and living quality will be significantly improved if they are diagnosed at an early stage and adequately treated. Methods & results: This study comprehensively screened the proteomic dataset of both LUAD and LUSC, and proposed classification models for the progression stages of LUAD and LUSC with accuracies 86.51 and 89.47%, respectively. Discussion & conclusion: A comparative analysis was also carried out on related transcriptomic datasets, which indicates that the proposed biomarkers provide discerning power for accurate stage prediction, and will be improved when larger-scale proteomic quantitative technologies become available.

Publisher

Future Medicine Ltd

Subject

Biochemistry, medical,Clinical Biochemistry,Drug Discovery

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