Abstract
Background Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment. Methods Gene-expression datasets of pancreatic cancer tissues and normal pancreatic tissues were obtained from the GEO database, and differentially expressed genes analysis and WGCNA analysis were performed after merging and normalizing the datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. The performance of the risk signature was subsequently validated by Kaplan–Meier curves, receiver operating characteristic (ROC), and univariate and multivariate Cox analyses. Result A three-gene risk signature containing CDKN2A, BRCA1, and UBL3 was established. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival. Conclusion This study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis.
Highlights
Pancreatic cancer is a highly malignant solid tumor [1], and its incidence and mortality rates continue to increase [2].e most common symptoms in patients with pancreatic cancer are abdominal pain, anorexia, fatigue, and weight loss [3]; pancreatic cancer lacks specific biomarkers [4], and the main serum markers commonly used today are carcinoembryonic antigen and carbohydrate antigen 19-9; their sensitivity is not ideal [3]
Weighted gene co-expression networks of GSE15471, GSE16515, GSE28735, and GSE57495 were constructed by the “Weighted gene co-expression network analysis (WGCNA)” package in R. e samples were clustered, and the sample clustering tree was drawn after removing the outliers (Figure 2(a))
Erefore, there is an urgent need to find biomarkers that affect the prognosis of pancreatic cancer in clinical treatment, which will facilitate the assessment of patient prognosis and will help to improve the prognosis by tailoring the treatment to the individual patient
Summary
Pancreatic cancer is a highly malignant solid tumor [1], and its incidence and mortality rates continue to increase [2].e most common symptoms in patients with pancreatic cancer are abdominal pain, anorexia, fatigue, and weight loss [3]; pancreatic cancer lacks specific biomarkers [4], and the main serum markers commonly used today are carcinoembryonic antigen and carbohydrate antigen 19-9; their sensitivity is not ideal [3]. Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival. Is study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis Conclusion. is study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis
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