Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes.

Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies.

Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

简介：肺癌是全球癌症死亡的主要原因。蛋白质组学是一种整合基因组学，转录组学和蛋白质组学的方法，已经成为理解癌症肿瘤发生中分子原因的一种方法。这种理解将有助于确定提高个体患者预后所迫切需要的治疗目标。

涵盖的领域：为了探索肺癌亚型的潜在分子机制，已经采取了一些蛋白质组学方法，这些方法结合了下一代测序（NGS）和基于质谱（MS）的技术。

专家意见：基于MS的大规模蛋白质组学分析，以及基于NGS的基因组数据和临床病理信息，将有助于建立广泛的肺癌亚型数据库，可用于进一步的蛋白质组学分析。蛋白质组学策略将进一步理解主要驱动基因突变如何影响下游分子网络，从而导致肺癌进展和恶性肿瘤，以及耐药性癌症的分子结构如何。这些策略需要基于网络系统动态理论而不是统计数据的高级生物信息学，以准确识别突变蛋白及其受影响的关键网络。