Prognostic effect of CD73 in pancreatic ductal adenocarcinoma for disease-free survival after radical surgery.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with a high potency of metastasis or recurrence after radical resection. Effective predictors for metastasis and recurrence postoperatively were dominant for the development of systemic adjuvant treatment regimens. The ATP hydrolase correlated gene CD73 was described as a promoter in tumor growth and immune escape of PDAC. However, there lacked research focused on the role of CD73 in PDAC metastasis. This study aimed to investigate the expression of CD73 in PDAC patients with different outcomes as well as the prognostic effect of CD73 for disease-free survival (DFS). METHODS: The expression level of CD73 in cancerous samples from 301 PDAC patients was evaluated by immunohistochemistry (IHC) and translated into a histochemistry score (H-score) by the HALO analysis system. Then, the CD73 H-score was involved in multivariate Cox regression along with other clinicopathological characteristics to find independent prognostic factors for DFS. Finally, a nomogram was constructed based on those independent prognostic factors for DFS prediction. RESULTS: Higher CD73 expression was found in PDAC patients with tumor metastasis postoperatively. Meanwhile, higher CD73 expressions were also investigated in PDAC patients diagnosed with advanced N stage and T stage. Furthermore, CD73 H-score along with tumor margin status, CA19-9, 8th N stage, and adjuvant chemotherapy was indicated as independent prognostic factors for DFS in PDAC patients. The nomogram based on these factors predicted DFS in a good manner. CONCLUSION: CD73 was associated with PDAC metastasis and served as an effective prognostic factor for DFS in PDAC patients after radical surgery.

Serum Amyloid a Predicts Prognosis and Chemotherapy Efficacy in Patients with Advanced Pancreatic Cancer.

Purpose: There is an urgent need to discover a predictive biomarker to help patients with advanced pancreatic cancer (APC) choose appropriate chemotherapy regimens. This study aimed to determine whether baseline serum amyloid A (SAA) levels were associated with overall survival (OS), progression-free survival (PFS), and treatment response in patients with APC received chemotherapy. Patients and Methods: This retrospective study included 268 patients with APC who received first-line chemotherapy at the Sun Yat-Sen University Cancer Center between January 2017 and December 2021. We examined the effect of baseline SAA on OS, PFS and chemotherapy response. The X-Tile program was used to determine the critical value for optimizing the significance of segmentation between Kaplan-Meier survival curves. The Kaplan-Meier curves and Cox regression analyses were used to analyze OS and PFS. Results: The best cut-off value of baseline SAA levels for OS stratification was 8.2 mg/L. Multivariate analyses showed that SAA was an independent predictor of OS (Hazard Ratio (HR) = 1.694, 95% Confidence Interval (CI) = 1.247-2.301, p = 0.001) and PFS (HR = 1.555, 95% CI = 1.152-2.098, p = 0.004). Low SAA was associated with longer OS (median, 15.7 months vs 10.0 months, p < 0.001) and PFS (median, 7.6 months vs 4.8 months, p < 0.001). The patients with a low SAA who received mFOLFIRINOX had longer OS (median, 28.5 months vs 15.1 months, p = 0.019) and PFS (median, 12.0 months vs 7.4 months, p = 0.035) than those who received nab-paclitaxel plus gemcitabine (AG) or SOXIRI, whereas there was no significant difference among the three chemotherapy regimens in patients with a high SAA. Conclusion: Owing to the rapid and simple analysis of peripheral blood, baseline SAA might be a useful clinical biomarker, not only as a prognostic biomarker for patients with APC, but also as a guide for the selection of chemotherapy regimens.

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes.

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.

Type 1 T Helper Cell-Based Molecular Subtypes and Signature Are Associated with Clinical Outcome in Pancreatic Ductal Adenocarcinoma.

Lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC) is shown to be related with poor prognosis. To construct an immune-related gene prognostic risk model for PDAC and clarify the molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in prognostic risk model-defined subgroups of PDAC, we analyze the association between the density of immune cell infiltration and lymph node metastatic status and further study the potential role of immune cells, immune cell-related genes, and immunotherapy outcomes in PDAC patients using bioinformatics models and machine learning methods. Based on The Cancer Genome Atlas (TCGA), PACA-AU and PACA-CA data sets, 62 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA). Four genes were selected to construct a molecular subtype identification based on the type 1 T helper cell-related hub genes by using the Cox regression method. We found that lower type 1 T helper cell abundance was correlated with prolonged survival in PDAC patients. Further, prognostic risk score model constructed with the type 1 T helper cell-related signature showed high accuracy in predicting overall survival and response to immunotherapy. This study might improve the understanding of the role of type 1 T helper cells in PDAC patients and aid in the development of immunotherapy and personalized treatments for these patients.