The Pan-Cancer Analysis Uncovers the Prognostic and Immunotherapeutic Significance of CD19 as an Immune Marker in Tumor

Abstract

Background: The specific cytotoxic effects of anti-CD19 chimeric antigen receptor (CAR) T-cell therapy have led to impressive outcomes in individuals previously treated for B-cell malignancies. However, the specific biological role of CD19(+) target cells, which exert antitumor immunity against some solid tumors, remains to be elucidated.

Methods: We collected information regarding the level of CD19 mRNA and protein expression from various databases including The Cancer Genome Atlas (TCGA), Tumor Immune Estimation Resource (TIMER), Genotype-Tissue Expression (GTEx), and Human Protein Atlas (HPA) for both tumor and normal samples. To evaluate the patient's prognosis according to CD19 expression, a Kaplan-Meier (KM) analysis and univariate Cox regression were performed. Furthermore, using the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using the Expression Data (ESTIMATE) algorithm, we estimated the ratio of immune cells infiltrating malignant tumor tissues. Afterward, the GSCALite repository was employed to evaluate the vulnerability of tumors expressing CD19 to drugs used in chemotherapy. To validate the results in clinical samples of certain cancer types, immunohistochemistry was then performed.

Results: Most tumor types exhibited CD19 expression differently, apart from colon adenocarcinoma (COAD). The early diagnostic value of CD19 has been demonstrated in 9 different tumor types, and the overexpression of CD19 has the potential to extend the survival duration of patients. Multiple tumors showed a positive correlation between CD19 expression and tumor mutation burden (TMB), microsatellite instability (MSI), and ESTIMATE score. Furthermore, a direct association was discovered between the expression of CD19 and the infiltration of immune cells, particularly in cases of breast invasive carcinoma (BRCA). Moreover, CD19 is highly sensitive to a variety of chemotherapy drugs.

Conclusion: The study reveals the potential of CD19 as both a predictive biomarker and a target for different cancer immunotherapies.

Keywords: CD19; biomarker; immune infiltration; pan-cancer; prognosis.

Figure 1

The mRNA expression levels of CD19 in both healthy tissues and cancerous tissues. (A) Comparison of CD19 expression differences among 24 types of cancer and adjacent tissues using data from the TCGA database. (B) Expression differences in CD19 between tumor and normal tissues in 21 cancers were obtained from the TIMER database. (C) The differential expression of CD19 between tumor and normal tissues in 31 cancers was compared in the TCGA and GTEx databases. (D) Differential expression of CD19 in tumor and paired normal samples. *p < 0.05, **p < 0.01, ***p < 0.001. ns, not statistically significant.

Figure 2

Correlation between CD19 expression and pathological staging in different cancers. (A) THCA, (B) UCS, (C) UCEC, (D) PAAD, (E) SARC, (F) OV, (G) LIHC, (H) LGG, (I) TGCT.

Figure 3

The correlation between the expression of CD19 and the overall survival (OS) of individuals. (A) Forest plot displaying the outcomes of the univariate Cox regression analysis of CD19 in pan-cancer samples obtained from the TCGA database. (B–H) Kaplan‒Meier curve demonstrates the correlation between CD19 expression and OS in samples from various types of cancer.

Figure 4

Methylation levels of CD19 in human cancers. (A) Relationship between CD19 mRNA expression and DNA methylation across 33 different types of cancer. (B) Evaluation of CD19 DNA methylation differences between tumor and normal tissues in 13 different types of cancers using the GSCALite platform.

Figure 5

The methylation level of the CD19 promoter was compared between the tumor group and the normal group.

Figure 6

Mutational profiles of CD19 in human cancers. (A) Changes in the CD19 gene in multiple tumors according to the cBioPortal tool. (B) The mutation sites in CD19 in multiple tumors according to the cBioPortal tool. (C) Relationship between CD19 expression and TMB in human cancers. (D) Relationship between CD19 expression and MSI in human cancers.

Figure 7

GSEA of CD19. (A–E) KEGG pathways enriched for CD19 across cancers. (F) Volcano plot of immune-related genes that were significantly associated with CD19 expression. (G) Coexpression heatmap of immune-related genes that were positively correlated with CD19 expression. (H) Coexpression heatmap of immune-related genes that were negatively correlated with CD19 expression.

Figure 8

Coexpression analysis of CD19 and immune-related genes. Coexpression of CD19 and (A) immune-related genes, (B) immune-activating genes, (C) immunosuppressor genes, (D) chemokine genes, and (E) chemokine receptor genes. *p < 0.05, **p < 0.01, ***p < 0.001. ns, not statistically significant.

Figure 9

Relationship between CD19 expression in certain cancers and the ESTIMATE score of the TME. (A) PRAD, (B) STAD, (C) GBM, (D) ESAD, (E) UVM, (F) LUSC, (G) OSCC, (H) MESO, (I) DLBC, (J) LGG, (K) TGCT, (L) READ.

Figure 10

Relationship between CD19 expression and immune cell infiltration in the pan-cancer TME. (A) B cells, (B) CD8(+) T cells, (C) DCs, (D) macrophages, (E) NK cells, (F) follicular helper T cells, and (G) Treg cells.

Figure 11

The relationship between CD19 expression and drug response was predicted. (A) Mithramycin, (B) momelotinib, (C)depsipeptide, (D) TAK-659 (isomer 1), (E) actinomycin D, (F) A-1210477, (G) PF-03758309, (H) A-911, (I) AT-9283, (J) doxorubicin, (K) AT-7519, (L) defactinib, (M) BMS-387032, (N) PF-562271, (O) KW-2449, and (P) MG-132.

Figure 12

Immunohistochemistry of CD19 in STAD, KIRC, and normal tissues. (A) CD19 expression was higher in STAD than that in normal gastric tissues. (B) CD19 expression in KIRC was higher than that in normal renal tissue.