Liang Liu

Quantitative Detection of Serum Protein-Specific Glycosylation in Ovarian Cancer Based on a Signal-Convertible Mass-Tagged Probe Set

The aberrant expression of sialic acid (Sia) on the surface of serum CA125 protein (CA125-Sia) is closely related to the occurrence of ovarian cancer and may have potential utility for early detection of ovrian cancer. However, the accurate determination of protein-specific glycosylation profiles poses significant analytical challenges, primarily due to the need for simultaneous identification of the target protein and quantification of specific glycosylation as well as their low levels in the early stages of certain diseases. Herein, we report a signal-convertible mass-tagged probe set system for the mass spectrometric detection of serum CA125-Sia. This probe set consists of three functional probes: a capture probe (CP), a labeling probe (LP), and a mass-tagged probe (MP). The serum CA125 protein was first captured by CP, and the terminal Sia was labeled by LP with the help of a heterobifunctional cross-linker. Then, the MP can hybridize with the LP attached to the Sia. Once the hybridization was formed, the MP in the hybridization was hydrolyzed into small fragments in the presence of exonuclease III (Exo III), while the LP reverted to a single-stranded state and could continuously perform the cycle process of hybridization and hydrolysis, thus realizing signal amplification. This strategy has been successfully used to quantify CA125-Sia in serum. It provides a promising platform for the quantification of protein-specific glycoforms in serum samples. Our findings suggest that CA125-Sia may be a novel potential diagnostic marker for the early detection of ovarian cancer.

Multilevel Transcriptomic Association Analysis Reveals Key Genes and Potential Mechanisms in Endometrial, Ovarian, and Cervical Cancers

Abstract Objective: This study aims to investigate the genetic associations of endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC), identify potential key genes using multiple genomic analysis approaches, and analyze their roles in cancer development. Methods: We integrated large-scale genome-wide association study (GWAS) data from Jiang L et al. and Zhou W et al., combined with blood eQTL data. We employed S-PrediXcan, SMR, and mBAT-combo to assess gene associations with EC, OC, and CC. Additionally, RNA sequencing data were used to analyze the expression levels of key genes across different tissues, followed by functional enrichment analysis to explore their potential biological functions. Results: Through S-PrediXcan, SMR, and mBAT-combo analyses, we identified 690, 620, and 624 genes associated with OC, CC, and EC, respectively. Among them, 79, 59, and 61 genes were consistently significant across all three methods, suggesting their crucial roles in cancer development. Furthermore, we identified multiple comorbidity-related genes, including SPX, which exhibited a negative association with OC, CC, and EC. Transcriptomic analysis revealed that 26 key genes displayed significant expression differences between tumor and normal tissues. Functional enrichment analysis further identified the molecular pathways potentially involved. Conclusion: This study identified a set of key genes associated with EC, OC, and CC and suggested that SPX may play a protective role in cancer development. The integration of multilevel genetic and transcriptomic analyses provides new insights into the molecular mechanisms underlying gynecological malignancies and offers potential biomarkers for future precision medicine research.