Identification of Spindle and Kinetochore-Associated Family Genes as Therapeutic Targets and Prognostic Biomarkers in Pancreas Ductal Adenocarcinoma Microenvironment
Authors:
Open Access
- 2 November 2020
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Oncology
- Vol. 10
Abstract
Aim: The role of spindle and kinetochore-associated (SKA) genes in tumorigenesis and cancer progression has been widely studied. However, so far, the oncogenic involvement of SKA family genes in pancreatic cancer and their prognostic potential remain unknown. Methods: Here, we carried out a meta-analysis of the differential expression of SKA genes in normal and tumor tissue. Univariate and multivariate survival analyses were done to evaluate the correlation between SKA family gene expression and pancreas ductal adenocarcinoma (PDAC) prognosis. Joint-effect and stratified survival analysis as well as nomogram analysis were used to estimate the prognostic value of genes. The underlying regulatory and biological mechanisms were identified by Gene set enrichment analysis. Interaction between SKA prognosis-related genes and immune cell infiltration was assessed using the Tumor Immune Estimation Resource tool. Results: We find that SKA1–3 are highly expressed in PDAC tissues relative to non-cancer tissues. Survival analysis revealed that high expression of SKA1 and SKA3 independently indicate poor prognosis but they are not associated with relapse-free survival. The prognostic value of SKA1 and SKA3 was further confirmed by the nomogram, joint-effect, and stratified survival analysis. Analysis of underlying mechanisms reveals that these genes influence cancer-related signaling pathways, kinases, miRNA, and E2F family genes. Notably, prognosis-related genes are inversely correlated with several immune cells infiltrating levels. Conclusion: We find that SKA1 and SKA3 expression correlates with prognosis and immune cell infiltration in PDAC, highlighting their potential as pancreatic cancer prognostic biomarkers.Keywords
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