Abstract

Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive form of pancreatic cancer that typically manifests itself at an advanced stage and does not respond to most treatment modalities. The survival rate of a PDAC patient is less than 5%, with a median survival of just a couple of months. A better understanding of the molecular pathology of PDAC is needed to guide research for the development of better clinical treatment modalities for PDAC patients. Gene expression studies performed to date have identified different subtypes of PDAC with prognostic and clinical relevance. Subtypes identified to date are highly heterogeneous since pancreatic cancer is heterogeneous cancer. Tumor microenvironment and stroma constitute a major chunk of PDAC and contribute to the heterogeneity. Better subtyping methods are need of the hour for better prognosis and classification of PDAC for future personalized treatment. In this work, we have performed an integrated analysis of DNA methylation and gene expression datasets to provide better mechanistic and molecular insights into Pancreatic cancers, especially PDAC. The use of varied and diverse datasets has provided valuable insights into different cancer types and can play an integral role in revealing the complex nature of underlying biological mechanisms. We performed subtyping of TCGA-PAAD gene expression and methylation datasets into different subtypes using state-of-the-art normalization methods and unsupervised clustering methods that reveal latent hidden factors, leading to additional insights for subtyping. Differential expression and differential methylation were performed for each of the subtypes obtained from clustering. Our analysis gave a consensus of five cluster solution with relevant pathways like MAPK, MET. The five subtypes corresponded to the tumor and stromal subtypes. This analysis helps in distinguishing and identifying different subtypes based on enriched putative genes. These results help propose novel experimentally-verifiable PDAC subtyping and demonstrate that using varied data sets and integrated methods can contribute to disease prognostication and precision medicine in PDAC treatment.

Highlights

  • Studies have shown that epigenetic processes are often changed during different stages of cancer, including the initial stage and progression of tumor stages

  • The standard The Cancer Genome Atlas (TCGA) dataset for pancreatic cancer TCGA-PAAD was downloaded from TCGAbiolink, including 183 cancer and four normal samples

  • After looking for seven missing samples for DNA methylation data, 146 matched Pancreatic Ductal Adenocarcinoma (PDAC) samples were selected [49]. These 146 PDAC samples consisted of expression profile for DNA methylation as well as gene expression

Read more

Summary

Introduction

Studies have shown that epigenetic processes are often changed during different stages of cancer, including the initial stage and progression of tumor stages. The changes include a global change in the DNA methylation profiles concerning normal DNA methylation patterns [1]. This change in DNA methylation is characterized by overall genome-wide hypomethylation and DNA hyper-methylation of CpG island promoters [2, 3]. PDAC accounts for most exocrine pancreatic cancer cases, with variants being less common and, apart from differences in prognosis, being uninformative for management decisions [6]. Adenosquamous carcinoma is an uncommon variant of PDAC and shares the features of adenocarcinoma and squamous cell carcinoma, showing a mixture of glandular and squamous differentiation [7]. Five-year survival of PC is less than 5%, with survival just a couple of months [8]

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call