Identification of PSMD14 as a potential novel prognosis biomarker and therapeutic target for osteosarcoma

Abstract

Background: Osteosarcoma is the most common primary bone tumor. The survival rate of osteosarcoma patients has not significantly increased in the past decades. Uncovering the mechanisms of malignancy, progression, and metastasis will shed light on the development of new therapeutic targets and treatment for osteosarcoma.

Aim: The aim of this study is to identify potential osteosarcoma biomarker and/or therapeutic targets by using integrated bioinformatics analysis.

Methods and results: We utilized existing gene expression datasets to identify differential expressed genes (DEGs) that could serve as osteosarcoma biomarkers or even as therapeutic targets. We found 48 DEGs were overlapped in three datasets. Among these 48 DEGs, PSMD14 was on the top of the up-regulated gene list. We further found that higher PSMD14 expression was correlated with higher risk group (younger age group, ≤20.83 years of age), metastasis within 5 years and higher grade of tumor. Higher PSMD14 expression in osteosarcoma had positive correlation with higher infiltration of CD8+ T cells, neutrophils and myeloid dendritic cells. Kaplan-Myer survival data further revealed that higher expression of PSMD14 predicted significantly worse prognosis (p = .013). Gene set enrichment analysis was further performed for the DEGs related to PSMD14 in osteosarcoma. We found that lower PSMD14 expression group had more immune responses such as interferon γ, α responses, inflammation response etc. However, the higher PSMD14 expression group had more cell proliferation-related biological processes, such as G2M checkpoints and Myc targets. Through establishing protein-protein interaction networks using PSMD14 related DEGs, we identified 10 hub genes that were all ribosomal proteins. These hub genes may play roles in osteosarcoma tumorigenesis, progression and/or metastasis.

Conclusion: We identified PSMD14 gene as a possible osteosarcoma biomarker, and/or a possible therapeutic target.

Keywords: PSMD14; biomarker; differential expressed genes; gene set enrichment analysis; metastasis; osteosarcoma; prognosis.

FIGURE 1

Identification of differential expressed genes (DEGs) in osteosarcoma and analysis of their function. (A) Vann diagram showed numbers of significant DEGs derived from GSE14359, GSE42352, and GSE39262 3 datasets. (B) Gene ontology analysis of DEGs showed over represented biological process categories. (C) Gene ontology analysis of DEGs showed over represented molecular function categories. (D) Gene ontology analysis of DEGs showed over represented cellular component categories. (E) Protein–protein‐interaction network of DEGs. (F) PPI network constructed from proteins coded by DEGs and the proteins they interact with

FIGURE 2

PSMD14 expression was up regulated in osteosarcoma compare with normal tissue. (A) PSMD14 expression was up regulated in osteosarcoma compare with normal tissue in GSE14359 dataset. (B) PSMD14 expression was up regulated in osteosarcoma compare with normal tissue in GSE42352 dataset. (C) PSMD14 expression was up regulated in osteosarcoma compare with normal tissue in GSE39262 dataset. (D) A receiver operating characteristic (ROC) curve plotted with data from GSE42352 dataset showed that PSMD14 expression could positively predict whether the tissue was osteosarcoma

FIGURE 3

PSMD14 expression was correlated to osteosarcoma clinical features. (A) Higher PSMD14 expression was positively correlated to higher risk age group (≤20.83 years old), p = .0136. (B) PSMD14 expression was higher in patients had metastatic osteosarcoma in 5 years compare with those who did not have metastasis. (C) PSMD14 expression level comparison between the different osteosarcoma Huvos grades. Grade 4 osteosarcoma exhibited higher PSMD14 expression compared with grade 2 (p = .0397) and grade 3 osteosarcoma (p = .0421). (D) Correlation of PSMD14 expression to the clinical characters of osteosarcoma patients

FIGURE 4

Higher PSMD14 expression was correlated to immune cells infiltration and worse prognosis. (A) Positive correlation of PSMD14 expression and CD8+ T cells infiltration in osteosarcoma. (B) Positive correlation of PSMD14 expression and neutrophil infiltration in osteosarcoma. (C) Positive correlation of PSMD14 expression and myeloid dendritic cells in osteosarcoma. (D) Correlation of PSMD14 expression and patient survival. Data analyzed from osteosarcoma patients in TARGET dataset. (E) Kaplan–Meier curve of PSMD14 high and low expression groups (p = .013)

FIGURE 5

Identification of significant differential expressed genes (DEGs) in osteosarcoma between PSMD14 high and low expression groups and analysis of their function. (A) Volcano plot of significant DEGs between PSMD14 high and low expression groups to identify up and down regulated genes. In PSMD14 high expression group, 619 genes were found up‐regulated and 134 genes were found down‐regulated. (B) Heat map showed the relative expression of DEGs in each patients. (C) DEGs Gene Ontology analysis showed categories enriched in Biological Processes. (D) DEGs Gene Ontology analysis showed categories enriched in Molecular Function. (E) DEGs Gene Ontology analysis showed categories enriched in Cellular Component

FIGURE 6

Gene Set Enrichment Analysis (GSEA) of significant differential expressed genes. (A–N) Enriched biological states or processes identified by GSEA in PSMD14 low expression group. (O and P) Enriched biological states or processes identified by GSEA in PSMD14 high expression group

FIGURE 7

Visualization of protein–protein‐interaction networks of significant differential expressed genes (DEGs) based on the expression of PSMD14. (A–E) Five subnetworks identified by the MCODE algorism. (F) Ten hub genes of significant DEGs identified by PPI networks