Kong Yan, Qiao Zhi, Ren Yongyong, Genchev Georgi Z, Ge Maolin, Xiao Hua, Zhao Hongyu, Lu Hui
SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Front Genet. 2020 Aug 28;11:1023. doi: 10.3389/fgene.2020.01023. eCollection 2020.
Lung cancer is one of the most common human cancers both in incidence and mortality, with prognosis particularly poor in metastatic cases. Metastasis in lung cancer is a multifarious process driven by a complex regulatory landscape involving many mechanisms, genes, and proteins. Membrane proteins play a crucial role in the metastatic journey both inside tumor cells and the extra-cellular matrix and are a viable area of research focus with the potential to uncover biomarkers and drug targets. In this work we performed membrane proteome analysis of highly and poorly metastatic lung cells which integrated genomic, proteomic, and transcriptional data. A total of 1,762 membrane proteins were identified, and within this set, there were 163 proteins with significant changes between the two cell lines. We applied the Tied Diffusion through Interacting Events method to integrate the differentially expressed disease-related microRNAs and functionally dys-regulated membrane protein information to further explore the role of key membrane proteins and microRNAs in multi-omics context. was revealed as a key gene involved in the activity of membrane proteins by targeting MET and PXN, affecting membrane proteins through protein-protein interaction mechanism. Furthermore, we found that the membrane proteins CDH2, EGFR, ITGA3, ITGA5, ITGB1, and CALR may have significant effect on cancer prognosis and outcomes, which were further validated . Our study provides multi-omics-based network method of integrating microRNAs and membrane proteome information, and uncovers a differential molecular signatures of highly and poorly metastatic lung cancer cells; these molecules may serve as potential targets for giant-cell lung metastasis treatment and prognosis.
肺癌是发病率和死亡率最高的人类癌症之一，转移性病例的预后尤其差。肺癌转移是一个多方面的过程，由涉及许多机制、基因和蛋白质的复杂调控格局驱动。膜蛋白在肿瘤细胞内部和细胞外基质的转移过程中都起着关键作用，是一个可行的研究重点领域，有可能发现生物标志物和药物靶点。在这项工作中，我们对高转移性和低转移性肺细胞进行了膜蛋白质组分析，整合了基因组、蛋白质组和转录数据。共鉴定出1762种膜蛋白，在这一组中，有163种蛋白在两种细胞系之间存在显著变化。我们应用通过相互作用事件的关联扩散方法整合差异表达的疾病相关微小RNA和功能失调的膜蛋白信息，以进一步探索关键膜蛋白和微小RNA在多组学背景下的作用。通过靶向MET和PXN，被揭示为参与膜蛋白活性的关键基因，通过蛋白质-蛋白质相互作用机制影响膜蛋白。此外，我们发现膜蛋白CDH2、EGFR、ITGA3、ITGA5、ITGB1和CALR可能对癌症预后和结局有显著影响，并得到了进一步验证。我们的研究提供了一种基于多组学的整合微小RNA和膜蛋白质组信息的网络方法，揭示了高转移性和低转移性肺癌细胞的差异分子特征；这些分子可能作为巨细胞肺癌转移治疗和预后的潜在靶点。
