Kexian Li

BACKGROUND: Although a substantial increase in the survival of patients with other cancers has been observed in recent decades, pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest diseases. No effective screening approach exists. METHODS: Differential exosomal long noncoding RNAs (lncRNAs) isolated from the serum of patients with PDAC and healthy individuals were profiled to screen for potential markers in liquid biopsies. The functions of LINC00623 in PDAC cell proliferation, migration and invasion were confirmed through in vivo and in vitro assays. RNA pulldown, RNA immunoprecipitation (RIP) and coimmunoprecipitation (Co-IP) assays and rescue experiments were performed to explore the molecular mechanisms of the LINC00623/NAT10 signaling axis in PDAC progression. RESULTS: A novel lncRNA, LINC00623, was identified, and its diagnostic value was confirmed, as it could discriminate patients with PDAC from patients with benign pancreatic neoplasms and healthy individuals. Moreover, LINC00623 was shown to promote the tumorigenicity and migratory capacity of PDAC cells in vitro and in vivo. Mechanistically, LINC00623 bound to N-acetyltransferase 10 (NAT10) and blocked its ubiquitination-dependent degradation by recruiting the deubiquitinase USP39. As a key regulator of N4-acetylcytidine (ac4C) modification of mRNA, NAT10 was demonstrated to maintain the stability of oncogenic mRNAs and promote their translation efficiency through ac4C modification. CONCLUSIONS: Our data revealed the role of LINC00623/NAT10 signaling axis in PDAC progression, showing that it is a potential biomarker and therapeutic target for PDAC.

BACKGROUND: Cancer stem cells (CSCs) are crucial to the malignant behaviour and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). In recent years, CSC biology has been widely studied, but practical prognostic signatures based on CSC-related genes have not been established or reported in PDAC. METHODS: A signature was developed and validated in seven independent PDAC datasets. The MTAB-6134 cohort was used as the training set, while one local Chinese cohort and five other public cohorts were used for external validation. CSC-related genes with credible prognostic roles were selected to form the signature, and their predictive performance was evaluated by Kaplan-Meier survival, receiver operating characteristic (ROC), and calibration curves. Correlation analysis was employed to clarify the potential biological characteristics of the gene signature. RESULTS: A robust signature comprising DCBLD2, GSDMD, PMAIP1, and PLOD2 was developed. It classified patients into high-risk and low-risk groups. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) than low-risk patients. Calibration curves and Cox regression analysis demonstrated powerful predictive performance. ROC curves showed the better survival prediction by this model than other models. Functional analysis revealed a positive association between risk score and CSC markers. These results had cross-dataset compatibility. Impact This signature could help further improve the current TNM staging system and provide data for the development of novel personalized therapeutic strategies in the future.

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported. Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan–Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature. Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways. Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.