Identification and functional characterization of key biomarkers in diffuse large B-cell lymphoma: emphasis on STYX as a prognostic marker and therapeutic target

Abstract

Diffuse large B-cell lymphoma (DLBC) is the most common subtype of non-Hodgkin lymphoma, characterized by its aggressive nature and poor prognosis in advanced stages. Despite advances in treatment, the molecular mechanisms driving DLBC progression remain incompletely understood, necessitating the identification of novel biomarkers for diagnosis and prognosis. In this study, we analyzed two publicly available datasets (GSE32018 and GSE56315) from the Gene Expression Omnibus database (GEO) to identify overlapping differentially expressed genes (DEGs). Later on, a comprehensive in silico and in vitro methodology was adopted to decipher the role of identify DEGs in DLBC. DEGs analysis of GSE32018 and GSE56315 datasets identified five overlapping gene: SP3, CSNK1A1, STYX, SIRT5, and MGEA5. Expression validation using the GEPIA2 database confirmed the upregulation of SP3, CSNK1A1, STYX, and SIRT5, and the downregulation of MGEA5 in DLBC tissues compared to normal controls. Furthermore, mutational analysis revealed that CSNK1A1 was the only gene among these DEGs to exhibit mutations, with a 2.7% mutation frequency in DLBC patients. Methylation analysis highlighted a negative correlation between DEGs methylation levels and mRNA expression, while survival analysis identified high STYX expression as significantly associated with poorer overall survival in DLBC patients. Functional assays demonstrated that STYX knockdown in U2932 cells led to reduced cell proliferation, colony formation, and enhanced wound healing, indicating STYX's pivotal role in DLBC cell survival and migration. Additionally, gene enrichment analysis revealed the involvement of these DEGs in key biological processes, including intracellular trafficking and myeloid progenitor cell differentiation. These findings emphasize the potential of SP3, CSNK1A1, STYX, SIRT5, and MGEA5 as biomarkers and therapeutic targets in DLBC, particularly highlighting STYX as a promising prognostic marker and potential target for therapeutic intervention.

Keywords: DLBC; Diagnosis; Prognosis: Treatment; Therapeutic target.

Fig. 1

Identification of differentially expressed genes (DEGs) across two gene expression datasets and their common signatures. (A & B) Volcano plots of differentially expressed genes (DEGs) in two datasets, GSE32018 and GSE56315. In both plots, the x-axis represents the log2 fold change, and the y-axis represents the -log10 p-value. Red dots indicate upregulated genes, blue dots indicate downregulated genes, and gray dots represent genes that are not DEGs. (C) Venn diagram showing the overlap DEGs between GSE32018 and GSE56315. (D) Heatmap representing the expression levels of the 5 common DEGs identified in both datasets. P-value < 0.05

Fig. 2

Expression and clinical significance of common differentially expressed genes (DEGs) in diffuse large B-cell lymphoma (DLBC) using GEPIA2 database. (A) Box plots showing the expression levels of five common DEGs (SP3, CSNK1A1, STYX, SIRT5, and MGEA5) in DLBC tissue compared to normal tissue. (B) Violin plots illustrating the expression of the same five DEGs across different stages (I-IV) of DLBC. P-value < 0.05

Fig. 3

Mutation analysis and survival impact of common differentially expressed genes (DEGs) in cancer in diffuse large B-cell lymphoma (DLBC) using cBioPortal database. (A) Bar graph showing the mutation frequency of the five common DEGs (CSNK1A1, SP3, SIRT5, STYX, MGEA5) across 37 cancer samples. (B) Detail of the observed mutation analysis of CSNK1A1. (C) Kaplan-Meier survival curve showing overall survival probability for patients with (red) and without (blue) mutations in the five common DEGs. (D) Kaplan-Meier survival curve showing progression-free survival probability for patients with (red) and without (blue) mutations in the five common DEGs. P-value < 0.05

Fig. 4

Correlation between DNA methylation, mRNA expression, and survival analysis of differentially expressed Genes (DEGs) in diffuse large B-cell lymphoma (DLBC). (A) Correlation between methylation and mRNA expression for DEGs in DLBC using GSCA database. (B) Survival analysis of DEGs based on methylation levels. The plot presents the survival difference (measured as Disease-Free Interval [DFI], Disease-Specific Survival [DSS], Overall Survival [OS], and Progression-Free Survival [PFS]) between high and low methylation groups across different cancer types. (C) GEPIA2-based Kaplan-Meier survival curves of DEGSs for DLBC patients stratified by expression levels. P-value < 0.05

Fig. 5

Gene expression levels and diagnostic performance differentially expressed genes (DEGs) in diffuse large B-cell lymphoma (DLBC) and normal control cell lines using RT-qPCR. (A) Box plots comparing the expression levels of five genes (SP3, CSNK1A1, STYX, SIRT5, and MGEA5) between DLBC and normal cell lines. (B) Receiver Operating Characteristic (ROC) curves assessing the diagnostic performance of the five genes (SP3, CSNK1A1, STYX, SIRT5, MGEA5) in distinguishing DLBC from normal individuals. P-value** < 0.01

Fig. 6

Correlation of Differentially expressed genes (DEGs) expression with immune infiltrates and drug sensitivity in diffuse large B-Cell lymphoma (DLBC). (A) Correlation between DEGs expression and various immune cells infiltrates in DLBC. (B) Correlation between DEGs expression and drug sensitivity based on the Genomics of Drug Sensitivity in Cancer (GDSC) database. P-value < 0.05

Fig. 7

Differential expression analysis of miRNAs in diffuse large B-cell lymphoma (DLBC) versus normal control cell lines using RT-qPCR. (A) The Sankey diagram illustrates the interactions between selected miRNAs and their predicted target genes. (B) Boxplots showing the expression levels of miRNAs in DLBC cell lines compared to normal control cell lines. P-value < 0.05

Fig. 8

Gene ontology and pathway enrichment analysis of differentially expressed genes (DEGs). (A) Cellular component enrichment analysis. (B) Molecular function enrichment analysis. (C) Biological process enrichment analysis. (D) Pathway enrichment analysis. P-value < 0.05

Fig. 9

Effect of STYX knockdown on U2932 cell proliferation, colony formation, and wound healing. (A) Quantitative RT-PCR analysis showing the expression level of STYX mRNA in U2932 cells transfected with control siRNA (Ctrl-U2932) and STYX-specific siRNA (si-STYX-U2932). (B) Western blot analysis of STYX protein expression in U2932 cells after transfection with control siRNA (Ctrl-U2932) and STYX-specific siRNA (si-STYX-U2932). GAPDH is used as a loading control. (C) Quantification of the Western blot results showing normalized STYX protein expression levels in Ctrl-U2932 and si-STYX-U2932 cells. (D) Cell proliferation assay comparing the proliferation rate of U2932 cells after transfection with control siRNA and STYX-specific siRNA. (E) Representative images from the colony formation assay in Ctrl-U2932 and si-STYX-U2932 cells. (F) Quantification of the colony formation assay, showing a significant reduction in the number of colonies formed by si-STYX-U2932 cells compared to control cells. (G) Wound healing assay images at 0 h and 24 h post-scratch in Ctrl-U2932 and si-STYX-U2932 cells. (H) Quantification of the wound healing assay, showing increased wound closure in si-STYX-U2932 cells compared to Ctrl-U2932 cells at 24 h. (I) Time-course analysis of wound closure in Ctrl-U2932 and si-STYX-U2932 cells, indicating a faster wound healing rate in the si-STYX-U2932 group over time. P**-value < 0.01