Bioinformatics Analysis of ZBTB16 as a Prognostic Marker for Ewing's Sarcoma

Integrative analyses of bulk microarray data to discover genes, pathways, and immune infiltration characteristics associated with targeting of Ewing sarcoma

PURPOSE

To explore transcriptome and immunological features of patients with Ewing sarcoma (ES) using all publicly available microarray data.

METHODS

Data of 479 ES tissues were integrated and normalized. Gene expression, immune infiltration, and cancer-specific pathways were analyzed. Genes of interest were knocked down, followed by cell proliferation and colony formation assays.

RESULTS

Consistent with the previous reports of differential expressed genes (DEGs) in ES, our analysis identified CCND1, HMCN1, and NKX2-2 were among the most highly expressed, while TWNC1, MYBPC1, and CKM were among the lowest expressed genes. GO, KEGG, and GSEA enrichment analysis identified that the DEGs related to bone and muscle functioning, those that contributed to crucial cellular, and metabolism pathways such as actin binding, apoptosis, TCA cycle, and cell cycle were also significantly enriched. Immune infiltration analysis discovered that many T cell subsets including CD4T, CD8 T, and Gamma delta T cells were highly infiltrated, while monocytes and B cells were less infiltrated in tumors. A total of 138 genes were both significantly up-regulated in tumors and associated with decreased survival, while 38 significantly down-regulated genes were associated with increased survival, many of which were previously reported as oncogenes and tumor suppressors in ES and other cancers. Silencing of four newly identified top ranked up-regulated genes with decreased survivals in ES inhibited proliferation and colony formation of ES cells.

CONCLUSION

This study may provide a clear representative transcriptome profile of ES, providing diagnostic biomarkers, pathways, and immune infiltrative characteristics targets for ES.

COL8A1 Predicts the Clinical Prognosis of Gastric Cancer and Is Related to Epithelial-Mesenchymal Transition

Background

Gastric cancer (GC) is the fifth most common malignant tumor and the third leading cause of cancer-related deaths. Because GC has the characteristics of high heterogeneity, unclear mechanism, limited treatment methods, and low five-year survival rate, it is necessary to find the prognostic biomarkers of GC and explore the mechanism of GC.

Methods

We first identified differentially expressed genes (DEGs) between gastric cancer and normal gastric cells through expression analysis. A protein-protein interaction (PPI) network was constructed to find tightly connected modules. We performed survival analysis on the DEGs in the modules to identify genes with prognostic significance. Gene set enrichment analysis (GSEA) was used to identify gene enrichment pathways. Finally, we used our own collected clinical samples of 119 gastric adenocarcinoma (STAD) tissues and 40 normal gastric tissues to perform immunohistochemical (IHC) staining to verify the differential expression of COL8A1 in STAD tissues and normal gastric tissues and its correlation with epithelial-mesenchymal transition- (EMT-) related factors.

Results

We identified 356 DEGs through differential expression analysis. Through PPI analysis and survival analysis, we determined that the collagen type VII alpha-1 chain (COL8A1) gene has prognostic significance. GSEA analysis showed that COL8A1 was significantly enriched in the EMT. IHC results showed that COL8A1 was upregulated in STAD tissues and could be used as an independent prognostic factor and was related to EMT.

Conclusion

This study shows that COL8A1 is related to the prognosis of GC patients and might affect the progress of GC through the EMT pathway. Therefore, COL8A1 may be a biomarker for predicting the prognosis of GC.