Abstract
BackgroundPancreatic cancer (PC) is one of the most lethal and aggressive cancer malignancies. The lethality of PC is associated with delayed diagnosis, presence of distant metastasis, and its easy relapse. It is known that clinical treatment decisions are still mainly based on the clinical stage and pathological grade, which are insufficient to determine an appropriate treatment. Considering the significant heterogeneity of PC biological characteristics, the current clinical classificatory pattern relying solely on classical clinicopathological features identification needs to be urgently improved. In this study, we conducted in-depth analyses to establish prognosis-related molecular subgroups based on DNA methylation signature.ResultsDNA methylation, RNA sequencing, somatic mutation, copy number variation, and clinicopathological data of PC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. A total of 178 PC samples were used to develop distinct molecular subgroups based on the 4227 prognosis-related CpG sites. By using consensus clustering analysis, four prognosis-related molecular subgroups were identified based on DNA methylation. The molecular characteristics and clinical features analyses based on the subgroups offered novel insights into the development of PC. Furthermore, we built a risk score model based on the expression data of five CpG sites to predict the prognosis of PC patients by using Lasso regression. Finally, the risk score model and other independent prognostic clinicopathological information were integrative utilised to construct a nomogram model.ConclusionNovel prognosis-related molecular subgroups based on the DNA methylation signature were established. The specific five CpG sites model for PC prognostic prediction and the derived nomogram model are effective and intuitive tools. Moreover, the construction of molecular subgroups based on the DNA methylation data is an innovative complement to the traditional classification of PC and may contribute to precision medicine development, therapeutic efficacy prediction, and clinical decision guidance.
Highlights
Pancreatic cancer (PC) is one of the most lethal and aggressive cancer malignancies
Results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses based on genes corresponding to the CpG sites To further investigate the underlying molecular mechanisms behind the prognosis-related subgroup division, we performed the GO and KEGG analyses based on the genes from the 4227 CpG sites, which were used for the consensus clustering
A total of 2939 genes were identified for further analysis, the heat map constructed based on the expression data of these genes is shown in Fig. 5a, and detailed information is provided in Additional file 1: Table S5
Summary
Pancreatic cancer (PC) is one of the most lethal and aggressive cancer malignancies. The lethality of PC is associated with delayed diagnosis, presence of distant metastasis, and its easy relapse. Identification of specific molecular features in different tumours may contribute to a better elucidation of the underlying aetiology, clinical characteristics, and outcomes of cancers [4, 5]. Collisson et al proposed the classifications of pancreatic ductal adenocarcinoma in three subtypes (classical, quasi-mesenchymal, and exocrine-like) based on the transcriptional profiles of PC samples; these subtypes showed significant differences in crucial aspects such as clinical survival and therapeutic reaction (7). The increase of novel classification methods based on the strength of different omics could contribute to elucidate the underlying mechanisms of oncogenesis and to recognise molecular subtype associated with potential therapeutic targets, enabling the construction of clinically applicable molecular subgroups to complement the current clinical and histopathological criteria
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