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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

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Project description:BackgroundHigh-grade bladder cancer (HGBC) has a higher malignant potential, recurrence and progression rate compared to low-grade phenotype. Its early symptoms are often vague, making non-invasive diagnosis using urinary biomarkers a promising approach.MethodsThe gene expression data from urine samples of patients with HGBC was extracted from the GSE68020 dataset. The clinical information and gene expression data in tumor tissues of HGBC patients were obtained from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis was used to predict the optimal risk model. The protein-protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. Overall survival (OS) was evaluated in the Gene Expression Profiling Interactive Analysis (GEPIA) online platform. Competing endogenous RNA (ceRNA) network was also visualized using Cytoscape. The expression levels of specific genes were assessed through quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Moreover, co-expressed genes and potential biological functions related to specific genes were explored based on the Cancer Cell Line Encyclopedia (CCLE) database.ResultsA total of 560 differentially expressed genes (DEGs) were identified when comparing the urine sediment samples from HGBC patients with the benign ones. Using these urinary DEGs and the clinical information of HGBC patients, we developed an optimal risk model consisting of eight genes to predict the patient outcome. By integrating the node degree values in the PPI network with the expression changes in both urine and tissue samples, eighteen hub genes were selected out. Among them, DKC1 and SNRPG had the most prominent comprehensive values, and EFTUD2, LOR and EBNA1BP2 were relevant to a worse OS in bladder cancer patients. The ceRNA network of hub genes indicated that DKC1 may be directly regulated by miR-150 in HGBC. The upregulation of both SNRPG and DKC1 were detected in HGBC cells, which were also observed in various tumor tissues and malignant cell lines, displaying high correlations with other hub genes.ConclusionsOur study may provide theoretical basis for the development of effective non-invasive detection and treatment strategies, and further research is necessary to explore the clinical applications of these findings.

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SRPX2 and RAB31 are effective prognostic biomarkers in pancreatic cancer.

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