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Neuroblastoma is a neural crest-derived embryonal tumor and accounts for about 15% of all cancer deaths in children. MYCN amplification is associated with aggressive and advanced stage of high-risk neuroblastoma, which remains difficult to treat and exhibits poor survival under current multimodality treatment. Here, we analyzed the transcriptomic profiles of neuroblastoma patients and showed that aurora kinases lead to poor survival and had positive correlation with MYCN amplification and high-risk disease. Further, pan-aurora kinase inhibitor (tozasertib) treatment not only induces cell-cycle arrest and suppresses cell proliferation, migration, and invasion ability in MYCN-amplified (MNA) neuroblastoma cell lines, but also inhibits tumor growth and prolongs animal survival in Th-MYCN transgenic mice. Moreover, we performed quantitative proteomics and identified 150 differentially expressed proteins after tozasertib treatment in the Th-MYCN mouse model. The functional and network-based enrichment revealed that tozasertib alters metabolic processes and identified a mitochondrial flavoenzyme in fatty acid β-oxidation, ACADM, which is correlated with aurora kinases and neuroblastoma patient survival. Our findings indicate that the aurora kinase inhibitor could cause metabolic imbalance, possibly by disturbing carbohydrate and fatty acid metabolic pathways, and ACADM may be a potential target in MNA neuroblastoma.

DNA replication control is vital for maintaining genome stability and the cell cycle, perhaps most notably during cell division. Malignancies often exhibit defective minichromosome maintenance protein 2 (MCM2), a cancer proliferation biomarker that serves as a licensing factor in the initiation of DNA replication. MCM2 is also known to be one of the ATPase active sites that facilitates conformational changes and drives DNA unwinding at the origin of DNA replication. However, the biological networks of MCM2 in lung cancer cells via protein phosphorylation remain unmapped. The RNA-seq datasets from The Cancer Genome Atlas (TCGA) revealed that MCM2 overexpression is correlated with poor survival rate in lung cancer patients. To uncover MCM2-regulated functional networks in lung cancer, we performed multi-dimensional proteomic approach by integrating analysis of the phosphoproteome and proteome, and identified a total of 2361 phosphorylation sites on 753 phosphoproteins, and 4672 proteins. We found that the deregulation of MCM2 is involved in lung cancer cell proliferation, the cell cycle, and migration. Furthermore, HMGA1 S99 phosphorylation was found to be differentially expressed under MCM2 perturbation in opposite directions, and plays an important role in regulating lung cancer cell proliferation. This study therefore enhances our capacity to therapeutically target cancer-specific phosphoproteins.

Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.