Construction and Validation of an Immune-Based Prognostic Model for Pancreatic Adenocarcinoma Based on Public Databases

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.

METHODS

Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.

RESULTS

A total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.

CONCLUSIONS

We constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell-cell communication in the tumor microenvironment.

Focus on Therapeutic Options for Surgically Resectable Pancreatic Adenocarcinoma Based on Novel Biomarkers

Pancreatic ductal adenocarcinoma remains associated with a poor prognosis, even when diagnosed at an early stage. Consequently, it is imperative to carefully consider the available therapeutic options and tailor them based on clinically relevant biomarkers. In our comprehensive review, we specifically concentrated on the identification of novel predictive and prognostic markers that have the potential to be integrated into multiparametric scoring systems. These scoring systems aim to accurately predict the efficacy of neoadjuvant chemotherapy in surgically resectable pancreatic cancer cases. By identifying robust predictive markers, we can enhance our ability to select patients who are most likely to benefit from neoadjuvant chemotherapy. Furthermore, the identification of prognostic markers can provide valuable insights into the overall disease trajectory and inform treatment decisions. The development of multiparametric scoring systems that incorporate these markers holds great promise for optimizing the selection of patients for neoadjuvant chemotherapy, leading to improved outcomes in resectable pancreatic neoplasia. Continued research efforts are needed to validate and refine these markers and scoring systems, ultimately advancing the field of personalized medicine in pancreatic adenocarcinoma management.

A Novel Immunogenomic Signature to Predict Prognosis and Reveal Immune Infiltration Characteristics in Pancreatic Ductal Adenocarcinoma

Background

The immune response in the tumor microenvironment (TME) plays a crucial role in cancer progression and recurrence. We aimed to develop an immune-related gene (IRG) signature to improve prognostic predictive power and reveal the immune infiltration characteristics of pancreatic ductal adenocarcinoma (PDAC).

Methods

The Cancer Genome Atlas (TCGA) PDAC was used to construct a prognostic model as a training cohort. The International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO) databases were set as validation datasets. Prognostic genes were screened by using univariate Cox regression. Then, a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating the relative subsets of RNA transcripts (CIBERSORT) and estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithms were used to characterize tumor immune infiltrating patterns. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict immunotherapy responsiveness.

Results

A prognostic signature based on five IRGs (MET, ERAP2, IL20RB, EREG, and SHC2) was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts. Multivariate Cox regression analysis demonstrated that this signature had an independent prognostic value. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve at 1, 3, and 5 years of survival were 0.724, 0.702, and 0.776, respectively. We further demonstrated that our signature has better prognostic performance than recently published ones and is superior to traditional clinical factors such as grade and tumor node metastasis classification (TNM) stage in predicting survival. Moreover, we found higher abundance of CD8+ T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC, and predicted a higher proportion of immunotherapeutic responders in the low-risk group.

Conclusions

We constructed an optimal prognostic model which had independent prognostic value and was comprehensively validated in external PDAC databases. Additionally, this five-genes signature could predict immune infiltration characteristics. Moreover, the signature helped stratify PDAC patients who might be more responsive to immunotherapy.

An Inflammatory Response Related Gene Signature Associated with Survival Outcome and Gemcitabine Response in Patients with Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive tumors with an extremely low 5-year survival rate. Accumulating evidence has unveiled that inflammatory response promotes tumor progression, enhances angiogenesis, and causes local immunosuppression. Herein, we aim to develop an inflammatory related prognostic signature, and found it could be used to predict gemcitabine response in PDAC.

Methods: PDAC cohorts with mRNA expression profiles and clinical information were systematically collected from the four public databases. An inflammatory response related genes (IRRGs) prognostic signature was constructed by LASSO regression analysis. Kaplan–Meier survival analysis, receiver operating characteristic analysis, principal component analysis, and univariate and multivariate Cox analyses were carried out to evaluate effectiveness, and reliability of the signature. The correlation between gemcitabine response and risk score was evaluated in the TCGA-PAAD cohort. The GDSC database, pRRophetic algorithm, and connectivity map analysis were used to predict gemcitabine sensitivity and identify potential drugs for the treatment of PDAC. Finally, we analyzed differences in frequencies of gene mutations, infiltration of immune cells, as well as biological functions between different subgroups divided by the prognostic signature.

Results: We established a seven IRRGs (ADM, DCBLD2, EREG, ITGA5, MIF, TREM1, and BTG2) signature which divided the PDAC patients into low- and high-risk groups. Prognostic value of the signature was validated in 11 PDAC cohorts consisting of 1337 PDAC patients from 6 countries. A nomogram that integrated the IRRGs signature and clinicopathologic factors of PDAC patients was constructed. The risk score showed positive correlation with gemcitabine resistance. Two drugs (BMS-536924 and dasatinib) might have potential therapeutic implications in high-risk PDAC patients. We found that the high-risk group had higher frequencies of KRAS, TP53, and CDKN2A mutations, increased infiltration of macrophages M0, neutrophils, and macrophages M2 cells, as well as upregulated hypoxia and glycolysis pathways, while the low-risk group had increased infiltration of CD8⁺ T, naïve B, and plasma and macrophages M1 cells.

Conclusion: We constructed and validated an IRRGs signature that could be used to predict the prognosis and gemcitabine response of patients with PDAC, as well as two drugs (BMS-536924 and dasatinib) may contribute to PDAC treatment.