Integrative Analysis of Membrane Proteome and MicroRNA Reveals Novel Lung Cancer Metastasis Biomarkers

Abstract

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. Has-miR-137 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 in vitro. 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.

Keywords: lung cancer metastasis; membrane proteome; microRNA; multi-omics analysis; prognostic.

FIGURE 1

Overall workflow of multi-omics integrative analysis.

FIGURE 2

Characterization of the TieDIE network. (A) The distribution of background scores and real score. The distribution of background scores is shown as blue bars, while the green line represents the real score. (B) Compact subnetwork after manually deleting linker proteins whose degree was one from the raw subnetwork constructed by the TieDIE program. The rhombus nodes represent for microRNAs, the circle nodes represent for linker proteins, while the square nodes represent differentially expressed membrane proteins. The node color changes according to the fold change values. (C) PPI subnetwork of 32 differentially expressed membrane proteins. (D) Wheel plot of cancer hallmark enrichment of the TieDIE subnetwork.

FIGURE 3

Kaplan-Meier curves of overall survival for CHE2, EGFR, ITGA3, ITGA5, ITGB1, and CALR.

FIGURE 4

Western blot verification results. (A) Western blot assays of the protein level of CDH2, EGFR, ITGA3, TIGB1, TIGA5, and CALR. (B) The gray levels of Western blotting are shown by bar graph.