Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy. CD8⁺ T cells, cancer stem cells (CSCs), and tumor budding (TB) have been significantly correlated with the outcome of patients with PDAC, but the correlations have been independently reported. In addition, no integrated immune-CSC-TB profile for predicting survival in patients with PDAC has been established.

Multiplexed immunofluorescence and artificial intelligence (AI)-based comprehensive analyses were used for quantification and spatial distribution analysis of CD8⁺ T cells, CD133⁺ CSCs, and TB. In vivo humanized patient-derived xenograft (PDX) models were established. Nomogram analysis, calibration curve, time-dependent receiver operating characteristic curve, and decision curve analyses were performed using R software.

The established ‘anti-/pro-tumor’ models showed that the CD8⁺ T cell/TB, CD8⁺ T cell/CD133⁺ CSC, TB-adjacent CD8⁺ T cell, and CD133⁺ CSC-adjacent CD8⁺ T cell indices were positively associated with survival of patients with PDAC. These findings were validated using PDX-transplanted humanized mouse models. An integrated nomogram-based immune-CSC-TB profile that included the CD8⁺ T cell/TB and CD8⁺ T cell/CD133⁺ CSC indices was established and shown to be superior to the tumor-node-metastasis stage model in predicting survival of patients with PDAC.

‘Anti-/pro-tumor’ models and the spatial relationship among CD8⁺ T cells, CSCs, and TB within the tumor microenvironment were investigated. Novel strategies to predict the prognosis of patients with PDAC were established using AI-based comprehensive analysis and machine learning workflow. The nomogram-based immune-CSC-TB profile can provide accurate prognosis prediction for patients with PDAC.

Hypoxia is an important feature of pancreatic ductal adenocarcinoma (PDAC). Previously, we found that hypoxia promotes ENO1 expression and PDAC invasion. However, the underlying molecular mechanism was remains unclear.

The relationship between ENO1 expression and clinicopathological characteristics was analyzed in 84 patients with PADC. The effects of CoCl₂-induced hypoxia and ENO1 downregulation on the apoptosis, invasion, and proliferation of PDAC cells were evaluated in vitro and in vivo. Hypoxia- and ENO1-induced gene expression was analyzed by transcriptomic sequencing.

The prognosis of PDAC with high ENO1 expression was poor (P < 0.05). High ENO1 expression was closely associated with histological differentiation and tumor invasion in 84 PDAC cases (P < 0.05). Hypoxia increased ENO1 expression in PDAC and promoted its migration and invasion. Apoptotic cells and the apoptosis marker caspase-3 in the CoCl₂-treated ENO1-sh group were significantly elevated (P < 0.05). Transcriptomic sequencing indicated that CoCl₂-induced PDAC cells initiated MAPK signaling. Under hypoxic conditions, PDAC cells upregulated ENO1 expression, thereby accelerating ERK phosphorylation and inhibiting apoptosis (P < 0.05). Consistent results were also observed in a PDAC-bearing mouse hindlimb ischemia model.

Hypoxia-induced ENO1 expression promotes ERK phosphorylation and inhibits apoptosis, thus leading to PDAC survival and invasion. These results suggest that ENO1 is a potential therapeutic target for PDAC.

Accurate preoperative identification of benign or malignant pancreatic cystic neoplasms (PCN) may help clinicians make better intervention choices and will be essential for individualized treatment.

Preoperative ultrasound and laboratory examination findings, and demographic characteristics were collected from patients. Multiple logistic regression was used to identify independent risk factors associated with malignant PCN, which were then included in the nomogram and validated with an external cohort. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were calculated to evaluate the improvement in the predictive power of the new model with respect to that of a combined imaging and tumor marker prediction model.

Malignant PCN were found in 83 (40.7%) and 33 (38.7%) of the model and validation cohorts, respectively. Multivariate analysis identified age, tumor location, imaging of tumor boundary, blood type, mean hemoglobin concentration, neutrophil-to-lymphocyte ratio, carbohydrate antigen 19-9, and carcinoembryonic antigen as independent risk factors for malignant PCN. The calibration curve indicated that the predictions based on the nomogram were in excellent agreement with the actual observations. A nomogram score cutoff of 192.5 classified patients as having low vs. high risk of malignant PCN. The model achieved good C-statistics of 0.929 (95% CI 0.890–0.968, P < 0.05) and 0.951 (95% CI 0.903–0.998, P < 0.05) in predicting malignancy in the development and validation cohorts, respectively. NRI = 0.268; IDI = 0.271 (P < 0.001 for improvement). The DCA curve indicated that our model yielded greater clinical benefits than the comparator model.

The nomogram showed excellent performance in predicting malignant PCN and may help surgeons select patients for detailed examination and surgery. The nomogram is freely available at https://wangjunjinnomogram.shinyapps.io/DynNomapp/.

Hypoxia is a significant feature of solid tumors, including pancreatic ductal adenocarcinoma (PDAC). It is associated with tumor invasion, metastasis, and drug resistance. However, the spatial distribution of hypoxia-related heterogeneity in PDAC remains unclear.

Spatial transcriptomics (STs), a new technique, was used to investigate the ST features of engrafted human PDAC in the ischemic hind limbs of nude mice. Transcriptomes from ST spots in the hypoxic tumor and the control were clustered using differentially-expressed genes. These data were compared to determine the spatial organization of hypoxia-induced heterogeneity in PDAC. Clinical relevance was validated using the Tumor Cancer Genome Atlas and KM-plotter databases. The CMAP website was used to identify molecules that may serve as therapeutic targets for PDAC.

ST showed that the tumor cell subgroups decreased to 7 subgroups in the hypoxia group, compared to 9 subgroups in the control group. Different subgroups showed positional characteristics and different gene signatures. Subgroup 6 located at the invasive front showed a higher proliferative ability under hypoxia. Subgroup 6 had active functions including cell proliferation, invasion, and response to stress. Expressions of hypoxia-related genes, LDHA, TPI1, and ENO1, induced changes. CMAP analysis indicated that ADZ-6482, a PI3K inhibitor, was targeted by the invasive subgroup in hypoxic tumors.

This study is the first to describe hypoxic microenvironment-induced spatial transcriptome changes in PDAC, and to identify potential treatment targets for PDAC. These data will provide the basis for further investigations of the prognoses and treatments of hypoxic tumors.