A retrospective analysis of the clinicopathological features and prognostic value of MAPK12 protein expression in diffuse large B-cell lymphoma

Abstract

Purpose: Mitogen-activated protein kinase 12 (MAPK12), also known as p38γ, is a member of the p38 MAPK family and plays a crucial role in tumor occurrence and invasion. However, there is still uncertainty regarding MAPK12 involvement in diffuse large B-cell lymphoma (DLBCL).

Methods: Our study investigated the expression of MAPK12 mRNA in various types of cancer using bioinformatic analysis. Furthermore, we performed immunohistochemistry (IHC) to detect the expression of MAPK12 in patients with DLBCL and compared clinical indicators and survival rates.

Results: We found that the high expression rate of MAPK12 was 43.1% in DLBCL patients. Several clinical indicators, including IPI scores, Hans classifications, LDH levels, and Ki-67 expression were closely associated with MAPK12 expression. Survival analysis revealed that higher expression of MAPK12 was significantly correlated with shorter progression-free survival (PFS) and overall survival (OS) in DLBCL patients. In addition, both univariate and multivariate analyses revealed IPI score, MAPK12 expression, and rituximab use as the independent OS risk factors (P < 0.05). To explore the functional role of MAPK12 in DLBCL, weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) were used to confirm the involvement of MAPK12 in the regulation of type II interferon production, positive regulation of lymphocyte proliferation, and other related biological processes.

Conclusion: DLBCL patients have poor prognoses when MAPK12 levels are high, which is expected to be a therapeutic target and prognostic factor.

Keywords: Diffuse large B-cell lymphoma; Immunohistochemistry; MAPK12; Prognosis.

Fig. 1

Differential expression of MAPK12 in pan-cancer and DLBCL. a. GEPIA database showing the expression of MAPK12 in tumor and normal tissues. b. In database GEPIA, MAPK12 expression was elevated in DLBCL. c. In GSE56315, the expression of MAPK12 in DLBCL and lymphadenitis was significantly different. d. In GSE32018, the expression of MAPK12 in DLBCL and lymph nodes was significantly different. (*p ≤ 0.05,**p ≤ 0.01,***p ≤ 0.001, ****p ≤ 0.0001)

Fig. 2

Typical IHC pictures of the expression of MAPK12 in DLBCL and adjacent non-tumoral lymphoid tissue. a. Negative, b. slightly positive, c. moderately positive, and d. highly positive MAPK12 expression in DLBCL tissues. e. Negative and f. slightly positive MAPK12 expression in adjacent non-tumoral lymphoid tissue

Fig. 3

A correlation between low survival rates and elevated MAPK12 levels in DLBCL patients. Based on the expression of MAPK12, DLBCL patients were split into two groups in each figure. a-b. PFS and OS survival analyses results for 153 patients. c-d. Analyses of patients survival with age ≤ 60 or > 60. e–f. Analyses of patients survival with ECOG ≤ 1 or > 1. g-h. Analyses of patients survival with normal LDH or LDH > normal. i-j. Analyses of patients survival with extra node < 2 or ≥ 2. k-l. Analyses of patient survival with Ann Arbor stage I–II or III–IV. m–n. Analyses of patient survival with IPI score 0–2 or 3–5. o-p. Analyses of patient survival with non-GCB subtype or GCB subtype

Fig. 4

Association between MAPK12 levels and survival in GSE10846.Based on the expression of MAPK12, DLBCL patients were split into two groups in each figure. a. OS analysis results for 414 patients. b-c. Analyses of patients survival with age ≤ 60 or > 60.d-e. Analyses of patients survival with ECOG ≤ 1 or > 1. f-g. Analyses of patient survival with normal LDH or LDH > normal. h-i. Analyses of patient survival with extra node < 2 or ≥ 2. j-k. Analyses of patient survival with Ann Arbor stage I–II or III–IV. l-m. Analyses of patient survival with IPI score 0–2 or 3–5. n–o. Analyses of patient survival with non-GCB subtype or GCB subtype

Fig. 5

WGCNA and key module identification. a. The scale-free topology requirement in TCGA-DLBCL was used to determine the soft-thresholding power (β) of 9. b. Clustering dendrograms showed genes with similar expression patterns were clustered into co-expression modules in TCGA-DLBCL. The gray module indicates that genes were not assigned to any module. c. Module–trait interactions in TCGA-DLBCL demonstrate connections between each module and MAPK12 expression. d. Module membership (MM) and gene significance (GS) scatter plots for each gene in the TCGA-DLBCL lightcyan module. The correlation between the gene and co-expression module is shown on the horizontal axis, while the correlation between the gene and phenotype is shown on the vertical axis

Fig. 6

MAPK12-related gene enrichment analysis. a. Volcano map of DGEs in GSE10846. b. Volcano map of DGEs in GSE11318. c. The Venn diagram showed the confluence of the GSE10846 DEGs, GSE11318 DEGs, and the TCGA-DLBCL lightcyan module genes. d. Relationship between MAPK12 mRNA expression and the other co-expression genes is presented in the heat map. e. GO analysis of the enriched terms for the 21 genes' biological process (BP), cellular component (CC), and molecular function (MF)