[Abstract unavailable

Core 1 β1,3-galactosyltransferase (C1GALT1) controls the crucial step of GalNAc-type O-glycosylation and is overexpressed in various human malignancies. However, its role in head and neck squamous cell carcinoma (HNSCC) remains unclear. Here we demonstrate that C1GALT1 expression is upregulated in HNSCC tumors and is associated with adverse clinicopathologic features. Moreover, high C1GALT1 expression predicts poor disease-free and overall survivals. C1GALT1 overexpression enhances HNSCC cell viability, migration, and invasion, which can be reversed by erlotinib. Silencing of C1GALT1 suppresses the malignant behavior both in vitro and in vivo. Mass spectrometry and lectin pull-down assays demonstrate that C1GALT1 modifies O-glycans on EGFR. Blocking O-glycan elongation on EGFR by C1GALT1 knockdown decreases EGF-EGFR binding affinity and inhibits EGFR signaling, thereby suppressing malignant phenotypes. Using molecular docking simulations, we identify itraconazole as a C1GALT1 inhibitor that directly binds C1GALT1 and promotes its proteasomal degradation, leading to significant blockade of C1GALT1-mediated effects in HNSCC cells in vitro and in vivo. Collectively, our findings demonstrate a critical role of O-glycosylation in HNSCC progression and highlight the therapeutic potential of targeting C1GALT1 in HNSCC treatment.

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.

Despite of the discovery of protein therapeutic targets and advancement in multimodal therapy, the survival chance of high-risk neuroblastoma (NB) patients is still less than 50%. MYCN amplification is a potent driver of NB, which exerts its oncogenic activity through either activating or inhibiting the transcription of target genes. Recently, long noncoding RNAs (lncRNAs) are reported to be altered in cancers including NB. However, lncRNAs that are altered by MYCN amplification and associated with outcome in high-risk NB patients are limitedly discovered. Herein, we examined the expression profiles of lncRNAs and protein-coding genes between MYCN amplified and MYCN non-amplified NB from microarray (n = 47) and RNA-seq datasets (n = 493). We identified 6 lncRNAs in common that were differentially expressed (adjusted P ≤ 0.05 and fold change ≥ 2) and subsequently validated by RT-qPCR. The co-expression analysis reveals lncRNA, SNHG1 and coding gene, TAF1D highly co-expressed in NB. Kaplan-Meier analysis shows that higher expression of SNHG1 is significantly associated with poor patient survival. Importantly, multivariate analysis confirms high expression of SNHG1 as an independent prognostic marker for event-free survival (EFS) (HR = 1.58, P = 2.36E-02). In conclusion, our study unveils that SNHG1 is up-regulated by MYCN amplification and could be a potential prognostic biomarker for high-risk NB intervention.

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