The pan-cancer analysis uncovers the prognostic and immunotherapeutic significance of CD19 as an immune marker in tumor

doi:10.21203/rs.3.rs-3212266/v1

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The pan-cancer analysis uncovers the prognostic and immunotherapeutic significance of CD19 as an immune marker in tumor

https://doi.org/10.21203/rs.3.rs-3212266/v1

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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. 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), GTEx, and Human Protein Atlas (HPA) for both tumor and normal samples. To evaluate the patients' 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. 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), ESTIMATE score, immunoscore, and stromal 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. The study reveals the potential of CD19 as both a predictive biomarker and a target for different cancer immunotherapies.

CD19

Pan-cancer

Prognosis

Immune infiltration

Biomarker

Immunotherapy has gained popularity as a preferred approach for treating cancer due to the achievements of monoclonal antibody-based immune checkpoint blockade and engineered T cells (Wang et al. 2021). Stimulatory and inhibitory pathways found in immune checkpoints assist in boosting the immune system's reaction to tumors while maintaining self-tolerance. The main observation is the blocking of pathways such as cytotoxic T lymphocyte-associated molecule-4 (CTLA-4), programmed cell death receptor-1 (PD-1), and programmed cell death ligand-1 (PD-L1) (Marin-Acevedo et al. 2021). Studies have shown that PD-L1 in breast cancer cells can facilitate the differentiation of CD19(+) B cells and aid in the evasion of immune cells (Guan et al. 2016).CAR-T cells have shown potential as an alternative immunotherapy method because they can identify antigens associated with tumors on the outer layer of tumor cells and gene products on the outer layer of healthy cells (Ko et al. 2020, Perna et al. 2017). The primary application of these cells has been for the treatment of blood cancers, including acute lymphoblastic leukemia, chronic lymphocytic leukemia, and multiple myeloma. Although the effectiveness of this treatment has been less noticeable in solid tumors, there have been encouraging results observed in neuroblastoma, non-small cell lung cancer, melanoma, breast cancer, and sarcoma (SARC) (Einsele et al. 2020, Zhao et al. 2018). There is a documented association between the level of CD19 expression in tumors and the effectiveness of engineered T cells in causing cell death (Cheadle et al. 2005). The impressive clinical reactions of resistant B-cell cancers to the transfer of anti-CD19 CAR-engineered T cells have emphasized the crucial function of CD19 protein in the treatment of different tumors (Kagoya et al. 2018). Consequently, it is necessary to investigate the different levels of CD19 expression in all types of cancers and their impact on the immune system.

The B-cell immunoglobulin superfamily's signaling receptor, CD19, has a high lineage-specific expression across various stages of B-cell maturation (Schiller et al. 2016). Almost all B-cell lines express this protein, yet its expression is limited in both normal and malignant B-cell lines, indicating CD19 is a desirable target in B-cell malignancies (Kochenderfer et al. 2012). According to prior studies, B cells have been shown to function as antigen-presenting agents, stimulating T cells to produce tumor-specific antibodies and thereby fulfilling an anti-cancer function (Jang et al. 2021). Despite this, B cells can also trigger the immunosuppressive action of macrophages and obstruct the antitumor immunity of T cells. In pancreatic ductal adenocarcinoma, CAR-T cells that specifically target CD19 have demonstrated the ability to eliminate the infiltration of B-cells. A high level of CD19(+) B cells can harm the prognosis of invasive breast carcinoma(Guan et al. 2016). CD19(+) B cells are identified as a significant factor in immune evasion from breast cancer due to their high tumor grade, ER-negative status, and expression of IL-10 and PD-L1. Despite this, CD19-positive B cells have been observed to inhibit the aggressiveness of triple-negative breast cancer (TNBC) and the HER2-positive BC subtype, extend patients' lifespans, and reinforce antitumor immunity (Pousette et al. 2022). Additionally, other studies have demonstrated that CD19(+) B cells prolong the life of those with muscle-invasive bladder cancer (Jiang et al. 2019). The part of CD19(+) tumor-infiltrating B cells in certain solid tumors is still a matter of contention. Insufficient systematic investigations have been carried out on CD19 across various types of cancers, highlighting the need for more comprehensive studies to ascertain its importance as a marker for tumors and a promising target for immunotherapy.

A study was carried out to compare the levels of CD19 mRNA and protein expression in tumor and normal samples. This study utilized several databases including TCGA, TIMER, GTEx, and HPA. A comprehensive assessment was conducted to examine the correlation between CD19 expression and prognosis, gene mutation, DNA methylation, and immune infiltration. Coexpression analysis of CD19 and immune-related genes was also utilized to investigate the capacity of the CD19 gene in predicting tumor immunity. To identify possible chemotherapy drugs that specifically target CD19, we performed an analysis of drug sensitivity. Immunohistochemistry was then conducted to confirm the varying expression of CD19. The initial findings suggest that CD19 could serve as a predictive indicator for tumor immunotherapy. Additionally, they offer unique perspectives for future investigations on the tumor microenvironment (TME) and potential mechanisms of immunotherapy.

Data collection

We collected clinical and RNA-sequencing data for 33 types of cancer from TCGA (https://portal.gdc.cancer.gov/) and TIMER 2.0 (http://timer.cistrome.org). To supplement the gene expression and clinical data of the normal and tumor groups with partial cancer deletion, the Genotype-Tissue Expression (GTEx, https //commonfund.nih.gov/GTEx) database was utilized. The HPA (http://www.proteinatlas.org/) was employed to contrast CD19 protein expression between cancer and normal tissues, while the Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/) database was used to compare the expression of CD19 across different pathological stages of samples from various types of cancer. The copy number of DNA and the frequency of gene mutations in CD19 were acquired from the cBioPortal (https://www.cbioportal.org/) database. We employed R 3.6.3 to integrate raw data from the above database.

Prognostic analysis

Using KM analysis, a comparison was made between the survival rates of healthy control patients and cancer patients in the TCGA database to assess their overall survival (OS). Additionally, a study was performed to analyze the disease-specific survival (DSS) and progression-free interval (PFI) of the TCGA cohort. DSS could exclude the influence of nontumor death, and PFI could exclude the impact of other crossover and subsequent treatments. To evaluate the risk ratio of CD19 in predicting OS, DSS, and PFI across various types of cancer, a univariate Cox regression analysis was performed. A statistical significance was indicated when the P value was less than 0.05.

Methylation analysis

We utilized the GSCALite (http://bioinfo.life.hust.edu.cn/GSCA/) system to examine the differences in CD19 promoter methylation between cancerous and healthy tissues. Subsequently, we explored the importance of CD19 methylation in relation to mRNA expression in different types of cancers. By utilizing the UALCAN platform (http://ualcan.path.uab.edu/), it is possible to compare the methylation levels of the CD19 promoter in both cancer and normal groups.

Assessment of tumor mutation burden (TMB) and microsatellite instability (MSI)

To explore the role of CD19 in the immune system of the TME, we investigated the correlation between CD19 expression and two crucial biomarkers of the TME, namely TMB and MSI (Chan et al. 2019, Pietrantonio et al. 2019). TMB denotes the count of base mutations per million bases in every tumor specimen.MSI is the abbreviation for the occurrence of insertions or deletions of repetitive segments of microsatellites in cancerous cells, causing a modification in the length of microsatellites and ultimately resulting in MSI. Using R 3.6.3, we performed an evaluation to examine the correlation between CD19 expression and biomarkers in the TME.

Gene set enrichment analysis (GSEA)

For the analysis of The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO), we utilized the GSEA (https://www.gsea-msigdb.org/gsea/index.jsp) database to explore the function and downstream targets of CD19. Statistical significance was indicated when the P value was less than 0.05. The association between CD19 and immune-associated genes was assessed by generating a coexpression heatmap of CD19 and immune-related genes across various types of cancer. Additionally, a coexpression heatmap was generated to compare the relationship between CD19 expression and the expression of genes associated with immune activation, immunosuppression, chemokines, and chemokine receptors. The publicly accessible multi-omics database LinkedOmics (http://www.linkedomics.org/login.php#dataSource) conducts cancer analysis, explores potential target genes and carries out enrichment analyses (Vasaikar et al. 2018). The Pearson correlation coefficient between CD19 and its associated genes was assessed using LinkedOmics. Subsequently, a coexpression map and volcano plot were generated.

Evaluation of infiltration of immune cells

By utilizing the ESTIMATE score, which combines the matrix and immune elements within the TME, it is possible to assess the quantity of stromal and immune cells present in cancerous tissue. A higher rating indicates a larger quantity of these cells. The overall score represents the total of the matrix and immune scores, indicating the collective proportion of the two elements in the TME. We performed a correlation analysis of gene expression and immune infiltration using the TIMER 2.0 tool (cistrome.shinyapps.io/timer). The database provided is an extensive resource for examining immune infiltration across different forms of cancer (Li et al. 2016, Li et al. 2020). The TIMER algorithm facilitated the calculation of the infiltration prevalence of six immune cell categories: B cells, CD4(+) T cells, CD8(+) T cells, neutrophils, macrophages, and dendritic cells (Li et al. 2017). To illustrate a specific form of cancer, we categorized individuals with tumors into two factions according to their levels of CD19 expression. This allowed us to assess the enrichment scores of immune cells between the two groups.

Drug sensitivity analysis

By conducting a Spearman correlation analysis, the GSCALite database combined the data on drug sensitivity and gene expression profiles from cancer cell lines in GDSC and CTRP. This allowed for the evaluation of the connection between the expression of individual genes in the gene set and the IC₅₀ of small molecules/drugs. Drug sensitivity analysis based on CD19 expression was conducted using the GSCALite platform. A positive correlation implies that tumor cells expressing high levels of CD19 are more prone to developing drug resistance, whereas a negative correlation suggests that tumor cells with high CD19 expression are more susceptible to drug effects.

Immunohistochemistry

Immunohistochemical staining was performed on three instances of stomach adenocarcinoma (STAD), three instances of kidney renal clear cell carcinoma (KIRC), and their corresponding adjacent samples at the Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. The clinical samples used in this study adhered to the principles outlined in the Declaration of Helsinki. The research was approved by the Ethics Committee of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Anti-CD19 antibodies were procured from Abcam's website. After the manufacturer's declaration, immunohistochemical staining was conducted, and then the images were taken with various fields randomly selected under a light microscope.

Statistical analysis

The expression of the CD19 gene in both the tumor and its corresponding normal tissues was analyzed using Wilcox's test. Using the R package 'ggpubr', box plots were generated to display the variances in CD19 expression among 33 different types of cancer. Additionally, the 'limma' package was employed to evaluate the correlation between CD19 expression and clinical advancement across diverse cancer types. The R packages 'survminer', 'limma' and 'survival' were used to determine OS, DSS, and PFI. The R package 'survival' was utilized to perform Cox analysis on OS, DSS, and PFI, whereas 'fmsb' was employed to create TMB and MSI correlation radar charts. Gene coexpression heatmaps were created using the package 'reshape2', and enrichment analysis was performed using the packages 'limma', 'org.hs.eg.db', 'clusterprofiler' and 'enrichplot'. Statistical analyses of R versions 3.6.3, and 4.2.1 were conducted with P < 0.05 being deemed statistically significant.

The expression of CD19 varies between tumor tissues and pan-cancer tissues

Examining CD19 mRNA levels in 33 cancer types from the TCGA database, it was discovered that BRCA, diffuse large B-cell lymphoma (DLBC), glioblastoma multiforme (GBM), head and neck squamous carcinoma (HNSC), KIRC, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and uterus corpus endometrial cancer (UCEC) exhibited higher CD19 expression compared to normal tissues. In contrast, the levels of CD19 were reduced in colon adenocarcinoma (COAD), kidney chromophobe (KICH), rectum adenocarcinoma (READ), prostate adenocarcinoma (PRAD), and thyroid carcinoma (THCA) (Fig. 1A). According to the TIMER database, the expression of CD19 in different types of cancers was found, with DLBC showing a notable level of expression, consistent with the findings of the TCGA database (Fig. 1B).

Due to the limited number of normal samples in the TCGA database (Fig. 1C), it was necessary to compare normal samples from GTEx with tumor tissues from the TCGA database. CD19 expression was higher in adrenocortical carcinoma (ACC), BRCA, DLBC, esophageal carcinoma (ESCA), GBM, HNSC, LUAD, LUSC, skin cutaneous melanoma (SKCM), STAD, and UCEC when compared to normal samples. CD19 exhibited a noticeable difference, appearing significantly lower compared to the healthy tissues of COAD, KICH, kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), ovarian serous cystadenocarcinoma (OV), PRAD, READ, testicular germ cell tumor (TGCT), THCA, and thymoma (THYM). CD19 expressions in bladder carcinoma (BLCA), cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), KIRC, liver hepatocellular carcinoma (LIHC), pheochromocytomas and paragangliomas (PCPG), and uterine carcinosarcoma (UCS) did not show any significant differences between tumor and normal tissues. The expression of CD19 in 5 healthy samples was noticeably reduced than tumor samples paired with various types of cancer (Fig. 1D).

To further investigate the differential expression of CD19, we utilized the HPA database to compare the protein levels of CD19 in tumor and normal tissues. In comparison to normal colon and rectal tissues (Supplementary Fig. 1), COAD and READ exhibited a decline in CD19 expression. Conversely, CD19 expression was elevated in SKCM and PRAD compared to normal skin and prostate tissues. The staining results of COAD, SKCM and READ are in good agreement with those of the TCGA database combined with the GTEx database.

Advanced stages of cancer show an inverse correlation with CD19

CD19 expression in different pathological stages of cancer was assessed using the stage plot feature of the GEPIA database. Figure 2 showed a notable decrease in CD19 expression in advanced THCA, UCS, UCEC, pancreatic adenocarcinoma (PAAD), SARC, OV, LIHC, brain lower grade glioma (LGG), and TGCT.

Prognostic importance of elevated CD19 levels in various types of cancer

Survival association analyses, including OS, DSS, and PFI, were used to investigate the correlation between CD19 expression and prognosis in different tumor types. Initially, data from the TCGA database was used to employ Univariate Cox regression analysis to further investigate the connection between CD19 expression and OS in cancer. The findings indicated that CD19 posed a risk element for KIRC (P = 0.005), LGG (P < 0.001), and KIRP (P = 0.006). Nonetheless, it acted as a safeguarding element for HNSC (P < 0.001), SKCM (P < 0.001), LUAD (P = 0.002), CESC (P = 0.007), UCEC (P = 0.01), BRCA (P = 0.041), and ESAD (P = 0.05) (Fig. 3A). According to a KM survival analysis, CD19 was found to have a beneficial effect on various types of cancer, including BRCA (P = 0.046), GBM (P = 0.040), HNSC (P < 0.001), LUAD (P = 0.010), SKCM (P = 0.001), and UCEC (P = 0.032) (Fig. 3B-G). Nevertheless, a negative outlook in LGG individuals (P = 0.031) was linked to elevated CD19 levels (Fig. 3H). Then, the association between CD19 expression and DSS was examined using Cox regression analysis. The results obtained from the DSS were similar to those obtained from the OS. Cox regression analysis revealed that CD19 was identified as a risk factor for KIRP (P = 0.002), KIRC (P < 0.001), and LGG (P < 0.001), while playing a protective role in HNSC (P < 0.001), SKCM (P = 0.001), LUAD (P = 0.006), CESC (P = 0.007), UCEC (P = 0.007), and OSCC (P = 0.045) (Supplementary Fig. 2A). The KM analysis demonstrated a significant improvement in the prognosis of cancer patients with high expression of CD19 (Supplementary Fig. 2B-J). The Cox regression analysis of PFI revealed that CD19 was associated with increased risk in KIRC (P = 0.001), STAD (P = 0.023), and LGG (P < 0.001), while it acted as a protective factor in CESC (P = 0.006), BRCA (P = 0.007), CHOL (P = 0.041), and HNSC (P < 0.001) (Supplementary Fig. 3A). According to KM analysis, it was found that LGG patients who had higher levels of CD19 expression experienced a poorer PFI compared to those with lower CD19 expression (Supplementary Fig. 3B-G). The results were similar to those of OS.

The potential reduction of unfavorable prognosis in most tumors by the high expression of CD19 makes it a promising marker for improving the outlook of various types of cancer.

Correlation between CD19 expression and DNA methylation in human cancers

The GSCALite platform was utilized to evaluate the correlation between DNA methylation and mRNA expression of CD19 and immune-related genes (CD28, CD48, CD276, CD80) as a potential indicator for early cancer identification, which is closely associated with the development and appearance of tumors (Agathanggelou et al. 1995). In all tumors studied (Fig. 4A), a negative association was found between the expression of CD19 mRNA and DNA methylation, as well as between DNA methylation and other immunity-related genes. Additionally, a comparison was made between cancerous and healthy tissues to evaluate the differences in CD19 and immune-related genes (CD28, CD48, CD276, CD80). Significantly reduced CD19 methylation was revealed by the results in HNSC, LIHC, BRCA, LUSC and LUAD (Fig. 4B).

We also verified the results using the online tool UALCAN. The UALCAN platform allows for a comparison of the DNA methylation level of CD19 between cancer patients and normal controls, using βvalues that range from 0 (unmethylated) to 1 (fully methylated). The β values ranging from 0.7 to 0.5 and 0.3 to 0.25 respectively indicated the presence of both hypermethylation and hypomethylation. In HNSC, LIHC, BRCA, LUSC, and LUAD, the CD19 promoter methylation level was notably decreased compared to the normal group (Fig. 5).

Mutational characteristics of CD19 across cancers

The cBioPortal database facilitates the examination of genetic mutations, structural variants, and copy numbers in cancers. Exploring the genomic alterations in CD19 among patients with various cancers, we employed the cBioPortal database. In 1.8% of cancer patients, the CD19 gene was discovered to have been altered (Fig. 6A). Out of all the individuals, those with UCES exhibited the most frequent alterations in the CD19 gene, with a remarkable occurrence of 6%. CD19 gene changes are mainly reflected in copy number amplification and gene mutation (Fig. 6B). A total of 112 mutations were found in CD19's tumor samples from the TCGA database, including 94 missense mutations, 13 truncating mutations, and 5 splice mutations. The protein encoded by CD19 had the most mutations in residues 99–103, with 27 mutations (Fig. 6C). It is conjectured that CD19 could be implicated in tumorigenesis and cancer growth, due to the varying expression and genetic alterations that take place during cancer development.

The effectiveness of tumor immunotherapy can be predicted by TMB and MSI in the TME, which is associated with antitumor immune response (Hu et al. 2021). To examine if the CD19 status can serve as a predictor of tumor immunity, we conducted a comparison of the gene expression profiles in tumors exhibiting high and low levels of CD19 expression. In UVM, THCA, TGCT, STAD, PRAD, PAAD, MESO, LUSC, and LIRC (Fig. 6D), TMB showed a negative correlation with CD19. Positive correlation between MSI and CD19 expression was observed in THCA, LUAD, and DLBC tumors, while a negative correlation was found in TGCT, STAD, SKCM, and LUSC (Fig. 6E). These findings suggest that CD19 may have a potential impact on immunotherapy, potentially influencing the composition and mechanism of the TME.

Enrichment analysis of CD19-related genes

In the assessment of 33 tumor types, we employed GSEA to analyze the pathways associated with CD19. In CESC, PAAD, PCPG, UCEC, and UVM, the correlation between the signaling pathway of the B-cell receptor and CD19 suggests that CD19 might impact the TME by participating in this pathway (Fig. 7A-E). Further analysis using GO revealed that the immune response was the main contributor to the enrichment of biological processes (Fig. 7F-I). Next, we used the LinkedOmics database and OV as an example to verify the genes related to CD19 expression. In the OV dataset, there were 1034 genes (represented by red dots) that exhibited a significant positive correlation with CD19, whereas 166 genes (represented by green dots) showed a significant negative correlation with CD19 (FDR < 0.01) (P < 0.05) (Fig. 7L). Heatmaps illustrate a positive and negative correlation between genes and CD19 expression (Fig. 7J-K). CD19 exhibited a positive correlation with the majority of immune genes in OV among the aforementioned factors.

Evaluation of genes associated with the immune system

In order to explore the molecular mechanism underlying the regulation of immunity during tumor formation by CD19, we performed a screening of 47 genes related to the immune system and examined their association with CD19 in different types of cancer. In various types of cancer, such as BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, and UVM, our analysis discovered a positive relationship between CD19 and the expression of genes related to the immune system (Fig. 8A). These findings indicate that CD19 may impact the operation of immune-related genes in multiple signaling pathways, implying that CD19 could serve as a valuable target for immunotherapeutic intervention in instances where CD19 expression is high. Nonetheless, in DLBC, there exists a negative correlation between CD19 and immune-associated genes, indicating that the elevated CD19 expression in DLBC may not be an optimal candidate for tumor immunotherapy. Hence, CD19, a novel target for anticancer immunotherapy, is anticipated to be utilized in conjunction with immune-related genes to enhance targeted immunotherapy. The extensive occurrence of T-cell exhaustion in different types of tumors is a natural reaction to continuous antigenic stimulation, serving to prevent tissue harm resulting from the excessive immune response. Tumors can evade the immune system by depleting T-cell populations. To gain further insight into the potential involvement of CD19 in immune evasion, we investigated the correlation between CD19 and immune-stimulating genes (Fig. 8B), immune-inhibiting genes (Fig. 8C), genes encoding chemokines (Fig. 8D), and genes encoding chemokine receptors (Fig. 8E) in T-cells that had undergone depletion. The analysis showed a strong connection between the expression of CD19 and the majority of genes that activate or suppress the immune system, including TNFRSF13B, CD27, and TIGIT. An observed positive correlation was found between CD19 and chemokines such as CCL19 and CXCL13, along with chemokine receptors like CXCR5 and CCR7.

TME analysis

The TME scores, which are crucial for the survival of cancer cells and play a significant role in tumor growth and metastasis, were determined using the ESTIMATE algorithm (Liu et al. 2022). The ESTIMATE scores may be perceived as a sign of its purity, while immune and stromal scores can be interpreted as a representation of its immune and stromal components. In most cancers, there was a significant link between CD19 and the infiltration of tumor immune cells as well as the makeup of immune elements in the TME. This association was found to be positively correlated with the ESTIMATE score, immune score, and stromal score. However, DLBC exhibited a negative correlation with the ESTIMATE score, as shown in Fig. 9 and Supplementary Fig. 4.

Correlation between CD19 expression and immune cell infiltration

To investigate the correlation between CD19 expression and the infiltration of immune cells, which is closely connected to tumor growth and immunity, an analysis was performed on the TIMER2.0 database (Görgün et al. 2013). The results (Fig. 10) demonstrated a favorable association between CD19 expression and the infiltration of CD8(+) T cells, Treg cells, B cells, DCs, macrophages, NK cells, and follicular helper T cells. Conversely, no significant correlation was observed between CD19 expression and the infiltration of other immune cells, including CD4(+) T cells, monocytes, and Th2 cells. By taking BRCA as a case study, we examined how CD19 expression impacts the score of immune cell enrichment. We observed a strong association between the expression of CD19 and the presence of diverse immune cells, including CD8(+) T cells, Treg cells, B cells, DCs, macrophages, NK cells, and follicular helper T cells (Supplementary Fig. 5). The provided data could potentially elucidate the reasons behind tumor progression inhibition by CD19, indicating a correlation between CD19 and the response to immunotherapy. This discovery offers a fresh perspective on CD19 as a potential biomarker for assessing immunotherapy response.

CD19 and drug reactivity

To illustrate the impact of CD19 on tumor immunity, researchers conducted analyses on different cancers, examining the diverse manifestation of CD19. These analyses included prognostic evaluations, gene mutation assessments, investigations on the TME, and studies on immune cell infiltration. The GSCALite platform was utilized to conduct a correlation analysis between CD19 and drug sensitivity in order to identify potential drugs associated with CD19. The findings indicated a negative correlation between CD19 expression and the antitumor drugs/molecules mithramycin, momelotinib, depsipeptide, TAK-659(isomer 1), actinomycin D, A-1210477, PF-03758309, A-911, AT-9283, doxorubicin, AT-7519, defactinib, BMS-387032, PF-562271, KW-2449, and MG-132, as illustrated in Fig. 11.

Validation of some tumor expression in pan-cancer by immunohistochemistry

The use of immunohistochemistry revealed the presence of CD19 in gastric cancer, renal cancer, and the surrounding tissues adjacent to these cancers. In Fig. 12, it was observed that the expression level of CD19 in STAD and KIRC exceeded that in neighboring tissues.

Cancer cells can stimulate inhibitory immune checkpoints to form a TME with immunosuppressive characteristics. The use of CAR-engineered T cells that target CD19 has initiated a fresh era of cancer immunotherapy known as 'living drugs' (Liu et al. 2019). Despite the observation of cytotoxicity in various refractory tumors, such as primary double-hit lymphoma cells (Mihara et al. 2017), multiple myeloma (Xiang et al. 2020), and relapsed or refractory B-lineage acute lymphoblastic leukemia (Schneider et al. 2021), the exact role of CD19 in numerous cancers remains uncertain. This study extensively explored the diverse expression of the CD19 gene in different cancer tissues and its association with pathological progression, cancer prognosis, mutation and DNA methylation, TMB and MSI, immune infiltration, and drug susceptibility. Our research revealed that elevated levels of CD19 expression have the potential to enhance the prognosis of individuals diagnosed with BRCA, GBM, HNSC, LUAD, SKCM, and UCEC. Additionally, it can also elevate the extent of immune infiltration in the TME, particularly in cases of BRCA. CD19 gene mutations occur in a variety of tumors. The highest frequency of CD19 gene changes was found in UCES patients. The CD19 gene expression was hindered by the methylation of DNA in CD19, particularly in HNSC, LIHC, BRCA, LUSC, and LUAD, where there was a significant decrease in CD19 methylation. In addition, CD19 was strikingly associated with TMB, MSI, ESTIMATE score, and immune and stromal scores in a variety of tumors.CD19 is expressed alongside several immune-related genes, such as TNFRSF13B, CD27, and TIGIT, which serve as indicators of the response to immunotherapy. Tumors with high CD19 expression were sensitive to a variety of chemotherapeutic drugs and molecules, including mithramycin, momelotinib, depsipeptide, TAK-659 (isomer 1), actinomycin D, A-1210477, PF-03758309, A-911, AT-9283, doxorubicin, AT-7519, defactinib, BMS-387032, PF-562271, KW-2449 and MG-132.

Initially, we assessed the CD19 expression, pathological stage, and prognostic importance across various cancers by utilizing TCGA, TIMER, GTEx, HPA, and GEPIA datasets. Initially, the presence of CD19 was detected in various types of cancers and their corresponding healthy tissues, revealing significant differences in gene expression between 23 cancer types and control tissues. High levels of CD19 were detected in 12 types of cancer, namely ACC, BRCA, DLBC, ESCA, GBM, HNSC, KIRC, LUAD, LUSC, SKCM, STAD, and UCEC. On the other hand, COAD, KICH, KIRP, LAML, OV, PRAD, READ, PRAD, TGCT, THCA, and THYM showed low levels of CD19 expression. The HPA's immunohistochemical findings supported our results, aligning with the CD19 mRNA expression results obtained from the TCGA and GTEx databases. Studies conducted prior have only indicated a correlation between CD19 expression and breast cancer (Guan et al. 2016), bladder cancer (Jiang et al. 2019), acute lymphoblastic leukemia (Schneider et al. 2021), lymphoma (Mihara et al. 2017), and myeloma cells (Xiang et al. 2020). Despite this, there is a lack of comprehensive research on CD19 in many solid tumors.

Improved survival can be achieved by examining the differential expression of CD19 in different pathological stages of cancer, which can serve as early indicators of diseases when cancer is diagnosed early. At later stages, the analysis revealed a reduction in CD19 expression. To gain a deeper understanding of the significance of CD19 in clinical risk stratification, we conducted additional evaluations to examine the association between CD19 and prognosis in various types of cancer. An analysis of KM OS indicated that CD19 acted as a safeguarding element for BRCA, GBM, HNSC, LUAD, SKCM, and UCEC while posing a threat to LGG. To evaluate the association between CD19 and DSS and PFI in cancer patients, a univariate Cox regression analysis was performed. This analysis was conducted because OS, which includes non-cancer-related deaths, does not provide an accurate reflection of the impact of treatment on tumor growth response, migration, and invasion (Zhou et al. 2021). The DSS findings indicated that CD19 provided a safeguarding impact on patients with CESC, HNSC, UCEC, BRCA, LUSC, LUAD, and SKCM while posing a potential threat to patients with KIRP, COAD, KIRC, and LGG. The PFI findings indicated that CD19 conferred protection to patients with HNSC, CESC, and BRCA while posing a risk to patients with LGG and KIRC. Therefore, we can infer that CD19 benefits most tumors.

Epigenetic carcinogenesis is fundamentally characterized by aberrant DNA methylation (Zhang et al. 2021). This study suggests a correlation between the extent of DNA methylation and the suppressive impact of CD19 on tumors. It provides evidence that CD19, along with other genes associated with the immune system, can hinder DNA methylation in different types of tumors. To obtain a thorough understanding of the molecular characteristics of the CD19 gene, which is associated with drug resistance in most tumor cells (Orlando et al. 2018), the cBioPortal database was utilized. For instance, a research study found that many individuals who had received CART-19 immunotherapy for B-cell acute lymphoblastic leukemia (B-ALL) experienced a relapse because they lacked the cognate CD19 epitope (Cortés-López et al. 2022). UCES exhibited the most elevated rate of CD19 gene alteration when compared to other tumors. Rarely was splice mutation seen, whereas amplification was the most frequent alteration. A foundation for further genetic exploration of genes related to CD19 is established by this discovery. Furthermore, we discovered a significant inverse association between CD19 and TMB in the majority of tumors, whereas both positive and negative associations were observed with MSI. These results indicate that CD19 has differential immunomodulatory effects in different cancers. A powerful correlation between CD19 and MSI and TMB implies a powerful bond between CD19 and the TME.

The GSEA uncovered a significant correlation between CD19 and immune-related pathways, specifically highlighting the B-cell receptor signaling pathway. According to reports, CD19 plays a significant role in the development and activation of B-cells. It has been identified as the docking and recruitment site for various kinases and signaling components in the B-cell signaling pathway, along with its involvement in B-cell receptor (BCR) signaling (Susa et al. 2021). CD19 activation enhances signaling pathways induced by the B-cell antigen receptor, which are crucial for the proliferation of the B-cell population (Chung et al. 2012). Furthermore, we acquired 47 genes associated with the immune system, which encompassed the lymphocyte activation molecule known as CD70.CD70 plays a crucial role in the immune response of T cells, significantly contributing to their successful proliferation. Widely employed in the surveillance of B cells, CD70 is anticipated to become a target for immunotherapy in B-cell malignancies (Agathanggelou et al. 1995, Izawa et al. 2017). We discovered that CD19 and its related genes, when examined through a coexpression network, could collaborate to control the immune reaction and antigen presentation in tumor immunotherapy. The potential for cancer-targeted immunotherapy is immense due to the amalgamation of CD19 and genes associated with immunity.

The primary immune cells that function as antitumor responders are CD8 cytotoxic T cells, which are activated when their T-cell receptor (TCR) recognizes tumor antigenic peptides on tumor cells (Kumar et al. 2021, Li and Yang et al. 2021). Nevertheless, tumor-associated macrophages and Treg cells are responsible for the formation of an immunosuppressive TME, thus enabling tumor immune escape. It is thought that Treg cells, which sustain immune balance, are the primary impediment to antitumor immunity (Yan et al. 2022). During our investigation, we discovered a strong connection between the presence of CD19 and the infiltration of CD8(+) T cells, Treg cells, B cells, DCs, macrophages, NK cells, and follicular helper T cells that assist in the development of follicles. This suggests that CD19 can have various effects on tumors by influencing the extent of immune cell infiltration into the tumor. By investigating the correlation between the expression level of CD19 and the sensitivity to drugs, we put forward potential therapeutic medications that specifically aim at CD19, and confirmed its significance as a promising target for cancer treatment.

Limitations

Despite offering novel perspectives on the expression level of the CD19 gene across various cancer types, our investigation does possess certain constraints. Initially, certain tumor samples in the TCGA database did not have matching normal control samples. As a result, we opted to include mRNA expression data from the GTEx database as an additional resource. While examining genetic alterations in CD19 across different types of cancers, we unintentionally overlooked studying the potential relationship between these alterations and the clinical outcomes of individuals affected by cancer. Furthermore, we focused exclusively on the correlation between CD19 and immune cells in the TME, overlooking the differences in CD19 gene expression among tumor cells and various stromal cells like fibroblasts and inflammatory cells. This study, at last, preliminarily connected CD19 to the growth and advancement of various malignancies; yet, more molecular tests are still necessary to authenticate the exact purpose of CD19 in tumors.

To summarize, we performed an extensive analysis of the CD19 gene across various types of cancer, elucidated its prognostic significance in pan-cancer samples, and unveiled its strong correlation with tumor immunity. The importance of CD19 as a predictive indicator of tumor response to immunotherapy is emphasized by our research. Further exploration is needed to ascertain the biological purpose of CD19, and the cellular operations of various mechanisms still need to be uncovered. To summarize, our findings offer fresh theoretical backing for the development of anti-cancer medications that focus on CD19 in the TME.

Acknowledgements

Not applicable.

Author contributions

LW collated the data and drafted the manuscript. JM, DX, QY, YZ and LX provided arrangement of Fig.s and statistical verification. JC and YH performed the experimental design and revised the manuscript. All the authors read and approved the submitted manuscript.

Funding

This study was funded by the National Natural Science Foundation of China, grant number 81773949 and 82003987; the Natural Science Foundation of Shanghai, grant number 20ZR1442400; the Health Industry Special Project of Shanghai Pudong New Area Health Commission, grant number PW2021E-03.

Data availability

The datasets generated and/or analyzed during the current study are publicly available in the TCGA (https://portal.gdc.cancer.gov/),TIMER 2.0 (http://timer.cistrome.org), GTEx (https://commonfund.nih.gov/GTEx), HPA (http://www.proteinatlas.org/), GEPIA (http://gepia.cancer-pku.cn/) and cBioPortal (https://www.cbioportal.org/) database. Further inquiries can be directed to the corresponding authors.

Conflict of interest

The authors declare no competing interests.

Ethical approval

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Informed consent was obtained from all subjects involved in the study.

Consent for publication

Not applicable.

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No competing interests reported.

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The pan-cancer analysis uncovers the prognostic and immunotherapeutic significance of CD19 as an immune marker in tumor

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Abstract

Figures

Introduction

Materials and methods

Data collection

Prognostic analysis

Methylation analysis

Assessment of tumor mutation burden (TMB) and microsatellite instability (MSI)

Gene set enrichment analysis (GSEA)

Evaluation of infiltration of immune cells

Drug sensitivity analysis

Immunohistochemistry

Statistical analysis

Results

The expression of CD19 varies between tumor tissues and pan-cancer tissues

Advanced stages of cancer show an inverse correlation with CD19

Prognostic importance of elevated CD19 levels in various types of cancer

Correlation between CD19 expression and DNA methylation in human cancers

Mutational characteristics of CD19 across cancers

Enrichment analysis of CD19-related genes

Evaluation of genes associated with the immune system

TME analysis

Correlation between CD19 expression and immune cell infiltration

CD19 and drug reactivity

Validation of some tumor expression in pan-cancer by immunohistochemistry

Discussion

Limitations

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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