Differences in the clinicopathological features of pancreatic head carcinoma in dorsal and ventral pancreas: A single institution retrospective review

Pancreatic head carcinoma derived from the dorsal pancreas is more likely to metastasize early than from the ventral pancreas through microvascular invasion

The development of the pancreatic head originates from the fusion of the ventral and dorsal pancreatic primordia during embryonic development. Theoretically, the origin of pancreatic head cancer also exists from the ventral pancreas and the dorsal pancreas. Among 49 patients with pancreatic head cancer, pancreatic head cancer was divided into pancreatic head cancer originating from the ventral (PHCv) or dorsal pancreas (PHCd) through imaging and pathological classification. The clinical data was collected and compared between the PHCv group and the PHCd group. The results showed that the patients from the PHCd group had worse long-term survival than those from the PHCv group (10 months vs 14.5 months). Similarly, the progression-free survival (PFS) results also indicate that patients from the PHCd group had a shorter time than those from the PHCv group (5 months vs 9.5 months). Further stratified analysis of potentially related factors showed that microvascular invasion is related to poor prognosis, and patients with pancreatic head cancer derived from the dorsal pancreas are more likely to develop microvascular invasion.

Splenic Hilar Involvement and Sinistral Portal Hypertension in Unresectable Pancreatic Tail Cancer

BACKGROUND

Pancreatic tail cancer (PTC) frequently displays splenic hilar involvement (SHI), but its impact on clinical outcomes remains unclear. We investigated the clinical impact of SHI in patients with unresectable PTC.

METHODS

We retrospectively reviewed all patients with unresectable PTC who received first-line therapy at our institution from 2016 to 2020.

RESULTS

Of the 111 included patients, 48 had SHI at diagnosis. SHI was significantly associated with younger age, liver metastasis, peritoneal dissemination, larger tumor size, modified Glasgow prognostic score of 1 or more, splenic artery involvement, gastric varices, and splenomegaly. Shorter median overall survival (OS; 9.3 vs. 11.6 months, p = 0.003) and progression-free survival (PFS; 4.3 vs. 6.3 months, p = 0.013) were observed in SHI patients. Poor performance status of 1 or 2, tumor size > 50 mm, hepatic metastasis, mGPS of 1 or 2, and SHI (hazard ratio: 1.65, 95% confidence interval: 1.08-2.52, p = 0.020) were independent predictors of shorter OS. Splenic artery pseudoaneurysm rupture and variceal rupture were rare and only observed in cases with SHI.

CONCLUSIONS

Splenic hilar involvement is associated with worse outcomes in pancreatic tail cancer.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

Background

Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.

Methods

In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.

Results

Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.

Conclusion

Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.