Dataset Information

The Role of PDGFRA in Predicting Oncological and Immune Characteristics in Pancreatic Ductal Adenocarcinoma.

ABSTRACT:

Objective

Pancreatic ductal adenocarcinoma (PDAC) is a lethal solid gastrointestinal malignancy with poor immune infiltration and a limited response to immunotherapy. The aim of our study was to explore the predictive value of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs), which are widely expressed in various tumor cells.

Methods

Transcriptomic data with follow-up information were obtained from the GEO, TCGA and ArrayExpress. The Kaplan-Meier (KM) method and univariate Cox (UniCox) proportional hazard regression were used to show the survival outcomes of the groups. Immune infiltration was analyzed using the online databases TISCH, TISIDB, TIMER2.0, and TIDE as well as the R packages "estimate" and "GSVA." Mutation and functional enrichment analyses were conducted using the R packages "maftools," "clusterProfiler," and online repository HOME for Researchers. Finally, the results were validated in 79 samples from our cancer center.

Results

Survival analysis using public databases and the FUSCC cohort indicated PDGFRA to be associated with prolonged overall survival (OS) (both p < 0.05). PDGFRA expression was highest in cancer-associated fibroblasts (CAFs) of PDAC, as validated in public databases and cell lines from our cancer center. The high expression of PDGFRA was associated with increased immune infiltration and potent T cell cytotoxicity in PDAC.

Conclusion

In summary, high PDGFRA expression is associated with increased immune infiltration and prolonged OS. This finding might provide a new strategy for regulating immune cell infiltration in PDAC and improving the efficacy of immunotherapy.

SUBMITTER: Wu Z

PROVIDER: S-EPMC8976608 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Role of PDGFRA in Predicting Oncological and Immune Characteristics in Pancreatic Ductal Adenocarcinoma.

Wu Zijian Z Xu Jin J Tang Rong R Wang Wei W Zhang Bo B Yu Xianjun X Liu Jiang J Shi Si S

Journal of oncology 20220326

<h4>Objective</h4>Pancreatic ductal adenocarcinoma (PDAC) is a lethal solid gastrointestinal malignancy with poor immune infiltration and a limited response to immunotherapy. The aim of our study was to explore the predictive value of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs), which are widely expressed in various tumor cells.<h4>Methods</h4>Transcriptomic data with follow-up information were obtained from the GEO, TCGA and ArrayExpress. The Kaplan-Meier (KM) method an ...[more]

PMID: 35378770

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Project description:PurposePancreatic ductal adenocarcinoma (PDAC) is one of the highest fatality rate cancers with poor survival rates. The tumor microenvironment (TME) is vital for tumor immune responses, leading to resistance to chemotherapy and poor prognosis of PDAC patients. This study aimed to provide a comprehensive evaluation of the immune genes and microenvironment in PDAC that might help in predicting prognosis and guiding clinical treatments.MethodsWe developed a prognosis-associated immune signature (i.e., PAIS) based on immune-associated genes to predict the overall survival of patients with PDAC. The clinical significance and immune landscapes of the signature were comprehensively analyzed.ResultsOwing to gene expression profiles from TCGA database, functional enrichment analysis revealed a significant difference in the immune response between PDAC and normal pancreas. Using transcriptome data analysis of a training set, we identified an immune signature represented by 5 genes (ESR2, IDO1, IL20RB, PPP3CA, and PLAU) related to the overall survival of patients with PDAC, significantly. This training set was well-validated in a test set. Our results indicated a clear association between a high-risk score and a very poor prognosis. Stratification analysis and multivariate Cox regression analysis revealed that PAIS was an important prognostic factor. We also found that the risk score was positively correlated with the inflammatory response, antigen-presenting process, and expression level of some immunosuppressive checkpoint molecules (e.g., CD73, PD-L1, CD80, and B7-H3). These results suggested that high-risk patients had a suppressed immune response. However, they could respond better to chemotherapy. In addition, PAIS was positively correlated with the infiltration of M2 macrophages in PDAC.ConclusionsThis study highlighted the relationship between the immune response and prognosis in PDAC and developed a clinically feasible signature that might serve as a powerful prognostic tool and help further optimize the cancer therapy paradigm.

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Project description:IntroductionPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant therapy prior to surgical resection for an optimal treatment outcome. Computerized tomography (CT) scan is the most common imaging modality obtained prior to surgery. However, the ability of CT scans to assess the nodal status and resectability remains suboptimal and depends heavily on physician experience. Improved preoperative radiographic tumor staging with the prediction of postoperative margin and the lymph node status could have important implications in treatment sequencing. This paper proposes a novel machine learning predictive model, utilizing a three-dimensional convoluted neural network (3D-CNN), to reliably predict the presence of lymph node metastasis and the postoperative positive margin status based on preoperative CT scans.MethodsA total of 881 CT scans were obtained from 110 patients with PDAC. Patients and images were separated into training and validation groups for both lymph node and margin prediction studies. Per-scan analysis and per-patient analysis (utilizing majority voting method) were performed.ResultsFor a lymph node prediction 3D-CNN model, accuracy was 90% for per-patient analysis and 75% for per-scan analysis. For a postoperative margin prediction 3D-CNN model, accuracy was 81% for per-patient analysis and 76% for per-scan analysis.DiscussionThis paper provides a proof of concept that utilizing radiomics and the 3D-CNN deep learning framework may be used preoperatively to improve the prediction of positive resection margins as well as the presence of lymph node metastatic disease. Further investigations should be performed with larger cohorts to increase the generalizability of this model; however, there is a great promise in the use of convoluted neural networks to assist clinicians with treatment selection for patients with PDAC.

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Dataset Information

The Role of PDGFRA in Predicting Oncological and Immune Characteristics in Pancreatic Ductal Adenocarcinoma.

Objective

Methods

Results

Conclusion

Publications

The Role of PDGFRA in Predicting Oncological and Immune Characteristics in Pancreatic Ductal Adenocarcinoma.

Similar Datasets

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