Hub genes and their key effects on prognosis of Burkitt lymphoma

Abstract

Background: Burkitt lymphoma (BL) is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells. Patients with BL often present with rapid tumor growth and require high-intensity multi-drug therapy combined with adequate intrathecal chemotherapy prophylaxis, however, a standard treatment program for BL has not yet been established. It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.

Aim: To identify hub genes and perform gene ontology (GO) and survival analysis in BL.

Methods: Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression modules, and the cytoHubba tool was used to find the hub genes. Then, the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis. Additionally, a Protein-Protein Interaction network and a Genetic Interaction network were constructed. Prognostic candidate genes were identified through overall survival analysis. Finally, a nomogram was established to assess the predictive value of hub genes, and drug-gene interactions were also constructed.

Results: In this study, we obtained 8 modules through WGCNA analysis, and there was a significant correlation between the yellow module and age. Then we identified 10 hub genes (SRC, TLR4, CD40, STAT3, SELL, CXCL10, IL2RA, IL10RA, CCR7 and FCGR2B) by cytoHubba tool. Within these hubs, two genes were found to be associated with OS (CXCL10, P = 0.029 and IL2RA, P = 0.0066) by survival analysis. Additionally, we combined these two hub genes and age to build a nomogram. Moreover, the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL.

Conclusion: From WGCNA and survival analysis, we identified CXCL10 and IL2RA that might be prognostic markers for BL.

Keywords: Burkitt lymphoma; Functional enrichment analysis; Microarray data; Prognosis; Therapeutic target; Weighted gene co-expression network analysis.

Figure 1

Clustering tree of 36 samples of Burkitt lymphoma extracted from GSE4475. Red indicated more gene expression, white less, and grey indicated deletion. CCS: Chromosomal Complexity Score.

Figure 2

Sample clustering to detect outliers and construction of co-expression modules. A: The constructed co-expression modules of Burkitt lymphoma genes by weighted gene co-expression network analysis; B: Interaction analysis between gene co-expression modules. The heatmap showed the Topological Overlap Matrix among genes in the analysis. Different colors on the x-axis and y-axis represented different modules. The intensity of inter-module connections was visually represented by the yellow brightness in the central region, gradually transitioning into deeper shades of orange.

Figure 3

Module-trait association. Correlation thermography between modular feature genes and clinical features of Burkitt lymphoma. Each row corresponded to a module feature, and the column corresponded to a clinical feature. Each cell contained the correlation and the corresponding P value. CCS: Chromosomal Complexity Score; ME: Module membership.

Figure 4

The scatter plot of the correlation for an age-related gene between module membership and gene significance in the yellow module.

Figure 5

Genetic and Protein-Protein interaction network of hub genes. A: GeneMANIA was used to construct a genetic interaction network. The black nodes with a slash represent the query gene, while the other nodes represent the predicted genes. The purple edges indicate co-expression, whereas the blue edges signify co-localization; B: A physically and functionally connected Protein-Protein Interaction network implemented common goals through Search Tool for the Retrieval of Interacting Genes/Proteins, where nodes represented proteins and edges represented pairs of interactions between proteins. Node size and color indicated richness, while edge size and color reflected combined scores.

Figure 6

Functional enrichment analysis results of hub genes. A: The top 10 gene ontology terms of hub genes; B: The top 10 Kyoto Encyclopedia of Genes and Genomes pathways of hub genes. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Figure 7

Kaplan–Meier survival curve. A to J: Kaplan–Meier survival curve of identified hub genes in GSE69051.

Figure 8

Nomogram and calibration plot for GSE69051 cohort. A: The nomogram was constructed to predicting1, 3-year survival rate of Burkitt lymphoma patients; B: The calibration curves for predicting patient survival at 1 and 3 years in the cohort. OS: Overall survival.

Figure 9

Drugs related to IL2RA and CXCL10.