Abstract
Pancreatic cancer (PC) is a highly malignant cancer with poor prognosis and high mortality. Aberrant DNA methylation plays a critical role in the occurrence, progression and prognosis of malignant tumors. In this study, we employed multiple datasets from APGI, TCGA and GEO to perform Multi-Omics analysis, including DNA methylation and expression profiling analysis. Three differentially expressed genes (SULT1E1, IGF2BP3, MAP4K4) with altered status of DNA methylation were identified and then enrolled into prognostic risk score model using LASSO regression. Univariate cox regression analysis indicated that high risk score was significantly associated with poor prognosis. Multivariate cox regression analysis proved the risk score was an independent prognostic factor for PC. In addition, time-dependent ROC curves indicated good performance of our model in predicting the 1-, 3- and 5-year survival of PC patients. Besides, stratified survival analysis revealed that the risk score model had greater prognostic value for patients of late stage with T3/T4 and N+. Pathway enrichment analysis suggested that these three genes might promote tumor progression by affecting signaling by Rho GTPases and chromosome segregation. In summary, three hypomethylated gene signature were significantly associated with patients’ overall survival, which might serve as potential prognostic biomarkers for PC patients.
Highlights
Pancreatic cancer (PC) is a highly malignant cancer with poor prognosis and high mortality which almost parallels to its disease incidence
By using the combination of methylation and expression profiling data, we aimed to identify the prognostic significance of differentially expressed genes (DEGs) with altered DNA methylation status in PC and to set up a reliable prognostic model for PC patients
Our results revealed that all three genes (SULT1E1, IGF2BP3 and MAP4K4) were all significantly overexpressed in PC tissues, suggesting that they might be potential biomarkers for PC patients (Figure 2)
Summary
Pancreatic cancer (PC) is a highly malignant cancer with poor prognosis and high mortality which almost parallels to its disease incidence. It is reported that PC has become the fourth leading cause of cancer-related death in the USA and are predicted to be the second leading cause of cancer death in the USA by 2030 [1]. It is quite often that PC patients have reached late stage when they are diagnosed, leading to the poor prognosis [2]. Aside from the progress in surgical resection and other adjuvant therapies, exploring efficient methods of early diagnosis is another important way to improve the clinical prognosis of PC patients. Compared to CT scanning and MRI, the more specific and sensitive biomarkers present greater value in early diagnosis, prognosis prediction and even therapeutic treatment
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