Dataset Information

SRPX2 and RAB31 are effective prognostic biomarkers in pancreatic cancer.

ABSTRACT: Introduction: SRPX2 and RAB31 play important roles in tumorigenesis and metastasis; however, their prognostic value in pancreatic cancer remains unclear. This study aimed to investigate the potential interactions and effects of SRPX2 and RAB31 on the diagnosis and prognosis of pancreatic cancer. Methods: The expression of SRPX2 and RAB31 in pancreatic tumor tissues and cells was evaluated through database mining of the Oncomine, Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and validated the results through immunohistochemistry (IHC) and Western blot in our clinical database. Protein-protein interactions were explored by immunofluorescence and Co-immunoprecipitation (Co-IP). Two hundred tissue microarray specimens from patients (79 training and 121 validation), who underwent curative pancreatectomy for pancreatic ductal adenocarcinoma (PDAC) were used. Additionally, the association between the SRPX2 and RAB31 and prognosis of PDAC patients after surgery was analyzed. Results: The expression of SRPX2 and RAB31 was highly increased in pancreatic cancer, and there was a significant positive correlation between these two proteins. Co-IP showed the direct interaction between SRPX2 and RAB31. Kaplan-Meier analysis showed that positive expression of SRPX2 and RAB31 was associated with reduced disease-free survival (DFS) and overall survival (OS) of PDAC patients in the training set and the validation sets. Furthermore, multivariate analysis indicated that the 8th edition TNM stage and combination of SRPX2 and RAB31 were independent prognostic factors that associated with OS and DFS in the training, and the validation sets, respectively. Conclusions: The combination of SRPX2 and RAB31 can be important markers for the prognosis of pancreatic cancer.

SUBMITTER: Li H

PROVIDER: S-EPMC6584922 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.MethodsWe obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.ResultsEight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.ConclusionsAn eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

Dataset Information

SRPX2 and RAB31 are effective prognostic biomarkers in pancreatic cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets