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.

Background: Pancreatic cancer (PC) remains a highly aggressive disease with a poor postoperative 5-year survival of around 25%, attributable to its immunosuppressive and fibrotic tumor microenvironment. Prognostic models that combine immune checkpoint markers with fibrotic features are still needed.Methods: We analyzed qualifying surgically resected PC specimens. Immunohistochemistry was used to evaluate PD-L1, CTLA-4, and α-SMA expression. Extracellular matrix volume (ECV) at the tumor center (ECVC) and peritumoral region (ECVP) was measured by three radiologists using single-energy CT. Collagen fraction (CF) was assessed via Masson’s trichrome staining. Multivariate Cox regression identified independent predictors of overall survival (OS); a prognostic nomogram was then developed.Results: Among 268 enrolled patients, divided into training (n=215) and validation (n=53) sets via Five-fold cross-validation, PD-L1 expression correlated positively with α-SMA, T stage, and N stage. Multivariate analysis identified α-SMA H-score, Masson-CF, ECVC, ECVP, T stage, N stage, CA19-9, neutrophil-to-lymphocyte ratio (NLR), vascular invasion, and chemotherapy as independent OS predictors. The nomogram integrating these factors outperformed TNM staging in predicting OS.Conclusion: High PD-L1 expression is associated with enhanced fibrosis, greater tumor burden, and nodal metastasis in PC. Patients exhibiting elevated PD-L1 levels, significant fibrotic burden, advanced T or N stage, or increased NLR demonstrate reduced OS. The developed nomogram enhances individualized prediction of OS. These findings support the hypothesis that combining immune checkpoint blockade, TGF-β inhibition, and chemotherapy may represent a promising therapeutic strategy for PC patients with high PD-L1 expression and pronounced fibrosis.

Background: The expression of Programmed Death-Ligand 1 (PD-L1) on tumor-infiltrating immune cells (TIICs), plays a crucial role in tumor progression and immune evasion, impacting both the natural immune response and immune-targeted therapeutic strategies. The neutrophil-to-lymphocyte ratio (NLR) has also gained attention as a potential predictive biomarker for immunotherapy efficacy, as it may correlate with treatment outcomes. Objective: To examine the expression of PD-L1 on TIICs and assess the influence of PD-L1 and NLR on immunotherapy outcomes following biliary tract cancers (BTC) recurrence. Methods: From January 1, 2017, to January 1, 2020, this study enrolled 239 patients from the Department of Pancreaticobiliary Surgery at Sun Yat-sen University Cancer Center. Immunohistochemical analysis of PD-L1 on TIICs was conducted on pathological tissue sections from these patients. Clinical data, including overall survival (OS), disease-free survival (DFS), and pathological findings, were collected during follow-up. Statistical analyses were performed to assess outcomes related to the study objectives. Furthermore, data from The Cancer Genome Atlas (TCGA) were utilized to examine PD-L1 expression profiles and related information. Results: Tumor stage did not differ significantly (P = 0.173), while metastasis stage approached significance (P = 0.093), with a higher proportion of M0 cases in the PD-L1 low group. Univariate analysis revealed vascular tumor thrombus, tumor differentiation, node stage, and preoperative CA199 levels as factors associated with DFS. Notably, vascular tumor thrombus (HR = 1.791, P = 0.002), moderate tumor differentiation (HR = 0.537, P = 0.002), and elevated preoperative CA199 levels (>35, HR = 1.624, P = 0.009) emerged as significant risk factors. Elevated NLR demonstrated a significant association with reduced DFS (HR = 1.54, p = 0.017 one week prior; HR = 1.70, p = 0.007 one month after) and diminished OS (HR = 2.30, p < 0.001 one week prior; HR = 1.94, p = 0.005 one month after). Exploratory analysis in a limited immunotherapy subgroup (n=35) suggested patients exhibiting high PD-L1 levels on TIICs may be associated with worse OS following immunotherapy after recurrence (HR = 3.03, p = 0.036). High NLR, both one month before recurrence (HR = 2.23, p = 0.015) and one month after recurrence (HR = 2.10, p = 0.027), correlated with decreased OS. Conclusion: PD-L1 expression on TIICs and dynamic NLR may be indicative of prognosis in BTC and could provide insights into immune status and response to immunotherapy after recurrence. These findings highlight the potential value of integrating local immune contexture with systemic inflammatory markers, but further validation in larger and prospective cohorts is warranted.