Original Article Contemplating the prognostic and therapeutic potential of CD19: a comprehensive analysis across diverse cancer types
Xiuhui Yang1*, Yundong Duan2*, Zongquan Zhao3
1Ministry of Science and Education, Affiliated Hospital of Beihua University, Jilin 132011, Jilin, China; 2Department of Critical Care Medicine, Chengde Central Hospital, The Second Clinical College of Chengde Medical College, Chengde 067000, Hebei, China; 3Department of General Practice, Pingjiang New Town Community Health Service Center, Sujin Street, Gusu District, Suzhou 215000, Jiangsu, China. * Equal contributors.
Received March 15, 2024; Accepted September 23, 2024; Epub November 15, 2024; Published November 30, 2024
Abstract: Background: Cancer represents a highly intricate disease, characterized by the uncontrolled prolifera- tion and invasion of aberrant cells, leading to widespread global morbidity and mortality. This study investigates the influence of CD19, a marker specific to B-cells, within the tumor microenvironment (TME) across a spectrum of cancer types. Methodology: To explore the role of CD19, we employed a wide array of bioinformatics tools and databases, including UALCAN, GEPIA2, univariate Cox regression, KM plotter, HPA, GSCA, cBioPortal, TISIDB, and DAVID. Additionally, we conducted experimental validations using cell culture, Real-time quantitative PCR (RT-qPCR), and western blot analyses. Results: An extensive analysis of CD19 expression was performed using The Cancer Genome Atlas (TCGA) data sourced from TIMER2 and UALCAN, covering 33 different cancer types. We observed a marked variability in CD19 expression, with notable upregulation in Adrenocortical Carcinoma (ACC) and Breast Invasive Carcinoma (BRCA), contrasted by significant downregulation in Cervical Squamous Cell Carcinoma (CESC), Rectum Adenocarcinoma (READ), and Sarcoma (SARC). Prognostic assessments through univariate Cox regression and Kaplan-Meier plots revealed that lower levels of CD19 were linked to a poorer overall survival rate in CESC, READ, and SARC. These findings were reinforced by validation using GEPIA2 and GSCA, where reduced CD19 ex- pression correlated negatively with methylation levels in the affected cancers. Furthermore, immunohistochemi- cal staining data from the Human Protein Atlas (HPA) provided additional confirmation of these results. Mutation analysis through cBioPortal suggested that alterations in CD19 were infrequent and had a minimal impact on tumor mutation burden (TMB) and microsatellite instability (MSI). Correlation studies using TISIDB highlighted significant associations between CD19 expression and immune-related genes, emphasizing its potential role in immune regu- lation. Additionally, GSCA analysis demonstrated that CD19 expression was positively associated with immune cell infiltration, though no significant effect on drug sensitivity was detected. Experimental validation using RT-qPCR in READ cell lines substantiated the down-regulation of CD19. Further functional analysis revealed that reduced CD19 expression significantly influenced the cellular behavior of SW480 cells. Conclusion: These findings underscore the critical role of CD19 within the tumor microenvironment, suggesting its potential as a biomarker and a therapeutic target in specific types of cancer.
Keywords: Cancer, tumor microenvironment (TME), diagnosis, prognosis: CD19, treatment
Introduction
Cancer remains a dominant cause of both ill- ness and death worldwide, with recent statis- tics indicating around 19.3 million new cases and nearly 10 million cancer-related fatalities in 2023 alone [1]. The intricate nature and vari- ability of cancer presents substantial obstacles
in its diagnosis, prognosis, and treatment [2]. However, advancements in genomics and bioin- formatics have revolutionized our understand- ing of cancer at a molecular level, unveiling new biomarkers and potential therapeutic targets [3, 4]. Among the numerous genes implicated in cancer, CD19 has attracted significant interest. This gene encodes a transmembrane glycopro-
CD19: a key immune marker in cancer
tein that is predominantly found on B cells, where it plays a pivotal role in their develop- ment, activation, and differentiation [5-7]. Due to its selective expression in B cells, CD19 has emerged as a promising target for immunother- apy, particularly in B-cell malignancies includ- ing leukemia and lymphoma [8, 9].
The promise of CD19 as a therapeutic target became evident with the development of chi- meric antigen receptor (CAR) T-cell therapy [10]. In this innovative treatment, T-cells are engi- neered to express receptors that specifically recognize CD19, leading to remarkable suc- cess in treating patients with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) and diffuse large B-cell lymphoma (DLBCL) [8, 9, 11]. These therapies have achieved significant clinical success, with high response rates and long-lasting remissions, fundamentally altering the treatment land- scape for these blood cancers [11]. Despite the extensive research on CD19 in hematological cancers, its role in solid tumors remains less explored. Emerging evidence suggests that CD19 may also be present in certain solid tumors, where it could influence tumor progres- sion and help the tumor evade the immune sys- tem [12, 13]. For example, studies have detect- ed CD19 expression in breast cancer and melanoma, opening up the possibility of extend- ing CD19-targeted therapies to these types of cancer [14, 15].
Considering the therapeutic potential of CD19, it is crucial to perform a comprehensive pan- cancer analysis to fully understand its diagnos- tic, prognostic, and therapeutic implications across various cancer types. Pan-cancer stud- ies, which examine large-scale genomic and transcriptomic data across multiple cancer types, are essential for identifying both shared and unique molecular characteristics that can inform clinical strategies. The primary objec- tives of this study are to systematically assess the expression patterns of CD19 across multi- ple cancer types, determine its value as a diag- nostic and prognostic biomarker, and explore the potential for targeting CD19 in solid tumors.
This study utilized extensive data from The Cancer Genome Atlas (TCGA) and other publicly available databases to analyze CD19 expres- sion across a diverse range of cancers. We investigated how CD19 expression correlates with clinical outcomes using sophisticated bio-
informatics approaches. Our goal was to evalu- ate whether CD19 expression can serve as a reliable biomarker for early detection, progno- sis, or response to treatment. Additionally, by drawing on the success of CD19-targeted ther- apies in hematological malignancies, we ex- plored the applicability of these strategies to solid tumors through in vivo models. The study aims to offer a detailed evaluation of the CD19 gene across multiple cancer types by leverag- ing bioinformatics and molecular biology tech- niques. By uncovering the diagnostic, prognos- tic, and therapeutic roles of CD19, we hope to contribute to the development of more precise and effective cancer therapies, ultimately im- proving patient outcomes.
Materials and methods
Expression landscape of CD19 in pan-cancer
TIMER2 (http://timer.cistrome.org/) [16] and UALCAN (https://ualcan.path.uab.edu/) [17] are powerful bioinformatics platforms designed to analyze gene expression and its clinical sig- nificance across a wide range of cancers. TIMER2 facilitates an in-depth examination of immune cell infiltration and gene expression patterns within multiple cancer types, utilizing data from The Cancer Genome Atlas (TCGA) project to shed light on the intricate interac- tions between tumors and the immune system. UALCAN, on the other hand, provides an intui- tive interface for accessing TCGA data, with a focus on analyzing gene expression, survival outcomes, and epigenetic alterations. These tools are indispensable for researchers aiming to uncover gene functions, pinpoint potential biomarkers, and decipher the molecular mech- anisms driving cancer progression. In our study, both TIMER2 and UALCAN were employed to map the expression profile of CD19 across a spectrum of cancers, offering valuable insights into its role within the pan-cancer landscape.
Prognostic significance of CD19 in pan-cancer
To assess the prognostic impact of CD19 on overall survival (OS) across different cancer types, we conducted a univariate Cox regres- sion analysis. Additionally, the KM Plotter tool (https://kmplot.com/analysis/) [18] was uti- lized to generate Kaplan-Meier (KM) survival curves for CD19 in various cancer cohorts. KM Plotter is an invaluable resource for survival analysis, enabling researchers to link gene
CD19: a key immune marker in cancer
expression levels with patient outcomes across a multitude of cancers, thereby aiding in the identification of prognostic biomarkers. Thr- ough this approach, we sought to determine the potential of CD19 as a predictor of patient survival in a pan-cancer context.
Validation of CD19 expression and promoter methylation analysis
GEPIA2 (http://gepia2.cancer-pku.cn/#index) [19] and the Human Protein Atlas (HPA) (https:// www.proteinatlas.org/) [20] are indispensable tools in cancer research. GEPIA2 offers cus- tomizable and interactive analysis of RNA sequencing data derived from TCGA and GTEx, allowing researchers to explore gene expres- sion trends, survival outcomes, and differential expression between tumor and normal tissues. Complementing this, the HPA database pro- vides extensive protein expression data via immunohistochemistry, enabling the visualiza- tion of protein distribution across various tis- sues and cancer types. By combining the insights from these databases, researchers can gain a comprehensive understanding of gene and protein expression, aiding in the iden- tification of novel biomarkers and therapeutic targets in cancer research. In our study, we employed GEPIA2 and HPA to validate CD19 expression at both the mRNA and protein lev- els, utilizing additional patient cohorts to ensure robust findings.
The GSCA (https://guolab.wchscu.cn/GSCA/) database is another powerful resource that supports extensive cancer genomics research [21]. By integrating multi-omics data-including gene expression, mutations, methylation pat- terns, and copy number variations-across nu- merous cancer types, GSCA facilitates in-depth analysis of gene sets and their relationships with clinical outcomes, immune infiltration, and drug response. In our research, we used GSCA to examine the correlation between CD19 expression and its promoter methylation levels across specific cancer types, providing deeper insights into the epigenetic regulation of CD19.
Mutational Landscape of CD19
The cBioPortal (https://www.cbioportal.org/) serves as a comprehensive, open-access plat- form for visualizing, analyzing, and downloading large-scale cancer genomics datasets [22].
Developed by the Memorial Sloan Kettering Cancer Center, it aggregates data from multiple sources, including TCGA, and offers detailed information on mutations, copy number altera- tions, mRNA expression, DNA methylation, and protein levels. In this study, we utilized cBioPor- tal to investigate the mutational landscape of CD19 across selected cancer types, enabling a deeper understanding of how genetic altera- tions in CD19 may influence cancer develop- ment and progression.
To further explore CD19’s involvement in the tumor microenvironment (TME) and its interac- tion with the immune system, we analyzed its correlation with two critical TME biomarkers: Tumor Mutational Burden (TMB) and Mi- crosatellite Instability (MSI). TMB measures the number of mutations per million bases in tumor DNA, while MSI represents changes in the length of repetitive DNA sequences within tumor cells due to insertions or deletions. We conducted an analysis using R 3.6.3 to investi- gate the relationship between CD19 expres- sion and these biomarkers, offering insights into how CD19 might influence immune res- ponses within the TME.
Associations of CD19 gene expression with immune-related genes and immune subtypes across various cancers
TISIDB (http://cis.hku.hk/TISIDB/) is a pivotal resource for examining the interplay between tumors and the immune system. This platform amalgamates data from extensive high-thr- oughput experiments and diverse public reposi- tories, offering a rich compendium of informa- tion on tumor-immune interactions [23]. TISIDB provides insights into gene expression, immune cell infiltration, and the role of immunomodula- tors across a broad spectrum of cancer types. In our investigation, we employed TISIDB to analyze the correlations between CD19 gene expression.
Gene enrichment analysis
The STRING database (https://string-db.org/) is an essential tool for exploring protein-protein interactions (PPIs) and functional relationships [24]. It combines experimental data, computa- tional predictions, and curated knowledge to construct comprehensive networks depicting protein interactions. In this study, we utilized
CD19: a key immune marker in cancer
STRING to develop a network of proteins asso- ciated with CD19, revealing the broader func- tional context of CD19-enriched genes.
DAVID (https://david.ncifcrf.gov/) is a promi- nent bioinformatics platform for functional annotation and enrichment analysis of gene lists. It provides sophisticated tools to eluci- date the biological significance of extensive gene lists generated from high-throughput studies [25]. DAVID integrates a range of bio- logical databases and analytical resources to perform functional annotation, gene ontology (GO) enrichment, and pathway analysis. By leveraging DAVID, researchers can uncover the biological processes, molecular functions, and cellular components related to their gene lists, thereby facilitating hypothesis generation and biological interpretation. In our study, DAVID was employed to conduct an enrichment analy- sis of genes associated with CD19.
Associations of CD19 with immune infiltrates and drug sensitivity
To examine the relationship between CD19 expression and immune infiltrates as well as drug sensitivity across various cancers, we uti- lized the GSCA database (https://guolab.wchs- cu.cn/GSCA/) [21]. This analysis aimed to eluci- date how CD19 expression correlates with immune cell presence and response to thera- peutic agents, contributing to a more nuanced understanding of CD19’s role in cancer treat- ment and immune modulation.
Cell lines and cell culture
We utilized a range of cell lines for our experi- ments, including five normal rectal epithe- lial cell lines-FHC, CCD 841 CoN, NCM460, HCoEpiC, and NCM356-and ten colorectal cancer cell lines-HCT-15, HT-29, Caco-2, SW- 480, SW620, DLD-1, LS174T, Colo205, LoVo, and RKO. These cell lines were sourced from the American Type Culture Collection (ATCC) in the USA. The cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) from Gibco, supplemented with 10% fetal bovine serum (FBS), also from Gibco. Cultures were main- tained in a controlled environment at 37℃ with 5% CO2 to ensure optimal growth conditions.
Real-time quantitative PCR (RT-qPCR)
Total RNA was extracted from the cells using the Simply P Total RNA Extraction Kit from
BIOER, following the manufacturer’s instruc- tions. cDNA was then synthesized using the ReverTra AceTM qPCR RT Kit from TOYOBO. RT-qPCR was conducted using the SYBR® Green Realtime PCR Master Mix from TOYOBO, allowing for precise quantification of gene expression levels. The GAPDH gene was used as an internal control and expression was cal- culated using 2^-AACT method. Following primer sequences were used; GAPDH-F 5’-ACCCAC- TCCTCCACCTTTGAC-3’, GAPDH-R 5’-CTGTT- GCTGTAGCCAAATTCG-3’;CD19-F:5’-GGCTATGA- GGAACCTGACAGTG-3’,CD19-R:5’-TCATCCTCAG- GGTTCTCGTAGC-3’.
Induction of CD19 overexpression in SW480 cells
To achieve CD19 overexpression in SW480 cells, we utilized a plasmid engineered to express the CD19 gene under a potent promot- er. The SW480 cells were maintained in Dul- becco’s Modified Eagle Medium (DMEM) sup- plemented with 10% fetal bovine serum (FBS) and incubated in a controlled environment at 37°℃ with 5% CO2. For the transfection pro- cess, we employed Lipofectamine™ 3000 Transfection Reagent (Thermo Fisher Scientific, Cat. No. L3000008) combined with Opti-MEM™ I Reduced Serum Medium (Thermo Fisher Sci- entific, Cat. No. 31985062) to enhance trans- fection efficiency. The cells were plated in 6-well plates at a density of 2 x 10^5 cells per well and incubated overnight to achieve 70-90% confluency. The transfection complex was pre- pared by diluting 2.5 µg of the CD19 expression vector in 125 uL of Opti-MEM™ and mixing it with 5 uL of P3000™ Reagent. In a separate tube, 7.5 uL of Lipofectamine™ 3000 was dilut- ed in 125 uL of Opti-MEM™. After a 15-minute incubation at room temperature, the diluted DNA and Lipofectamine™ solutions were com- bined and then added dropwise to each well containing SW480 cells and 1.5 mL of fresh DMEM. The cells were incubated at 37℃ in a CO2 incubator for 48 hours to facilitate CD19 overexpression. Following transfection, the ce- lls were harvested for subsequent analyses to verify CD19 overexpression.
RT-qPCR and western blot analyses
Post-transfection, we confirmed CD19 overex- pression in SW480 cells through RT-qPCR and Western blot analyses. For RT-qPCR, total RNA
CD19: a key immune marker in cancer
was extracted using the Simply P Total RNA Extraction Kit from BIOER, and cDNA synthesis was carried out with the ReverTra AceTM qPCR RT Kit from TOYOBO. The quantification of CD19 expression was performed using SYBR® Green Realtime PCR Master Mix (TOYOBO). For Wes- tern blot analysis, cells were lysed with RIPA buffer supplemented with protease inhibitors. Protein concentrations were determined using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific, Cat. No. 23225). Equal protein amounts (20-30 µg) were separated by SDS- PAGE and transferred to a PVDF membrane. The membrane was blocked with 5% non-fat dry milk in TBST (TBS + 0.1% Tween 20) for 1 hour at room temperature and then incubated overnight at 4℃ with primary antibodies against CD19 (Thermo Fisher Scientific, Cat. No. MA5-13141) and GAPDH (Thermo Fisher Scientific, Cat. No. MA5-15738) as a loading control. After washing, the membrane was probed with HRP-conjugated secondary an- tibodies (Thermo Fisher Scientific, Cat. No. 31460 for anti-mouse and 31430 for anti-rab- bit) for 1 hour at room temperature. Protein bands were visualized using SuperSignal™ We- st Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific, Cat. No. 34580) and imaged with a chemiluminescence detection system.
Colony formation assay
To evaluate the clonogenic potential of CD19- overexpressing SW480 cells, we performed a colony formation assay. Transfected cells were seeded in 6-well plates at a density of 500 cells per well and cultured for 10-14 days in DMEM with 10% FBS, with media changes every 3 days. After the incubation period, colonies were fixed with 4% paraformaldehyde for 15 minutes and stained with 0.5% crystal violet for 30 min- utes. The plates were washed with PBS to remove excess dye, and colonies were counted under a microscope.
Cell proliferation assay
Cell proliferation was assessed using the Cell- Titer 96® AQueous One Solution Cell Pro- liferation Assay (MTS) (Promega, Cat. No. G3580). Transfected SW480 cells were plated in a 96-well plate at a density of 2,000 cells per
well in 100 uL of DMEM. At various time points (24, 48, and 72 hours), 20 uL of the MTS reagent was added to each well and incubated for 1-4 hours at 37℃. Absorbance was mea- sured at 490 nm using a microplate reader to evaluate cell proliferation relative to control cells.
Wound healing assay
To investigate the migratory ability of CD19- overexpressing SW480 cells, a wound healing assay was performed. Transfected cells were seeded in 6-well plates and grown to 90% con- fluency. A straight scratch (wound) was made across the cell monolayer with a sterile 200 uL pipette tip. After washing with PBS to remove detached cells, the cells were incubated in DMEM with 1% FBS to minimize proliferation. Images of the wound were captured at 0 and 24 hours using a phase-contrast microscope. The wound area was analyzed using ImageJ software, and the percentage of wound closure was calculated by comparing the initial wound area to the area after 24 hours.
Statistical analysis
Statistical analysis was performed using Gra- phPad Prism 7.0 software. An independent sample t-test was employed to compare means between two groups, with significance set at P < 0.05. Pearson correlation analysis was used to assess relationships between variables. Additionally, Receiver Operating Characteristic (ROC) curve analysis was conducted to evalu- ate the diagnostic efficacy of CD19 expression in distinguishing between control and treated groups. The area under the ROC curve (AUC) was calculated to determine the accuracy, sen- sitivity, and specificity of CD19 as a potential biomarker. Statistical significance was defined as P < 0.05.
Results
Variations in CD19 expression across tumor and pan-cancer tissues
Initially, we explored CD19 expression across 33 cancer types from the TCGA using the TIMER2 platform. This analysis revealed a marked elevation of CD19 expression in tumor
CD19: a key immune marker in cancer
D
CD19 Expression Level (log2 TPM)
**
*
*
**
*
7.5
C
3
5.0
..
2.5
9
-
-
L
-
ACC.Tumor (n=79)
BLCA.Tumor (n=408)
BLCA.Normal (n=19)
BRCA.Tumor (n=1093)
BRCA.Normal (n=112)
BRCA-Basal.Tumor (n=190)
BRCA-Her2.Tumor (n=82)
BRCA-LumA. Tumor (n=564).
BRCA-LumB.Tumor (n=217)
CESC.Tumor (n=304)
CESC.Normal (n=3)
CHOL.Tumor (n=36)
CHOL.Normal (n=9)
COAD.Tumor (n=457)
COAD.Normal (n=41)
DLBC.Tumor (n=48)
ESCA.Tumor (n=184)
ESCA.Normal (n=11)
GBM.Tumor (n=153)
GBM.Normal (n=5)
HNSC.Tumor (n=520)
HNSC.Normal (n=44)
HNSC-HPV+.Tumor (n=97)
HNSC-HPV -. Tumor (n=421) -
KICH.Tumor (n=66)
KICH.Normal (n=25)
KIRC.Tumor (n=533)
KIRC.Normal (n=72)
KIRP.Tumor (n=290)
KIRP.Normal (n=32)
LAML.Tumor (n=173)
LGG.Tumor (n=516)
LIHC.Tumor (n=371)-
LIHC. Normal (n=50)
LUAD. Tumor (n=515)
LUAD.Normal (n=59)
LUSC.Tumor (n=501)
LUSC.Normal (n=51)-
MESO.Tumor (n=87)-
OV.Tumor (n=303)
PAAD.Tumor (n=178)
PAAD.Normal (n=4)
PCPG.Tumor (n=179)
PCPG.Normal (n=3)
PRAD.Tumor (n=497)
PRAD.Normal (n=52)
READ.Tumor (n=166)
READ.Normal (n=10)
SARC.Tumor (n=259)
SKCM.Tumor (n=103)
SKCM.Metastasis (n=368)
STAD.Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n=150)
THCA.Tumor (n=501)
THCA.Normal (n=59)
THYM. Tumor (n=120)
UCEC.Tumor (n=545)
UCEC.Normal (n=35)
UCS. Tumor (n=57)
UVM. Tumor (n=80)-
B
Expression of CD19 across TCGA cancers (with tumor and normal samples)
8
9
log2 (TPM+1)
1
A
-
N
1
0
F
¥
L
1
1
1
-2
BLCA
BRCA
CESC
CHOL
COAD
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LIHC
LUAD
TCGA samples
LUSC
PAAD
PRAD
PCPG
READ
SARC
SKCM
THCA
THYM
STAD
UCEC
tissues relative to normal tissues in several cancers, including Adrenocortical Carcinoma (ACC), Breast Invasive Carcinoma (BRCA), Chol- angiocarcinoma (CHOL), Glioblastoma Multi- forme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Renal Clear Cell Carcinoma (KIRC), Liver Hepatocellular Car- cinoma (LIHC), and Pancreatic Adenocarcinoma (PAAD) (Figure 1A). Conversely, CD19 expres- sion was notably reduced in Cervical Squamous Cell Carcinoma (CESC), Rectum Adenocarci- noma (READ), and Sarcoma (SARC) (Figure 1A). Consistent with TIMER2 data, the UALCAN database (Figure 1B) also highlighted similar patterns, where many cancers exhibited re- duced CD19 expression in tumor tissues com- pared to normal samples.
Prognostic relevance of CD19 expression
Figure 2A illustrates the outcomes of a univari- ate Cox regression analysis, which assessed
the impact of CD19 expression on overall sur- vival (OS) across various cancers. A significant increase in hazard ratios was observed with lower CD19 expression, correlating with a poor- er prognosis in cancers such as CESC, READ, and SARC. Figure 2B-D depicts KM survival curves for these cancers, showing a clear asso- ciation between diminished CD19 expression and reduced OS. Collectively, these analyses suggest that CD19 may serve as a prognostic indicator in CESC, READ, and SARC.
Validation of CD19 expression and methylation analysis
Figure 3A presents box plots from the GEPIA2 database, illustrating CD19 expression levels across CESC, READ, and SARC. In CESC, CD19 expression is significantly lower in tumor tis- sues compared to normal tissues (Figure 3A). A similar pattern is seen in READ, where tumor tissues exhibit decreased CD19 expression
CD19: a key immune marker in cancer
A
Forest Plot of Hazard Ratios
B
(CESC)
LUAD
526
0.822 (0.726 - 0.930)
0.002
KIRC
539
1.320 (1.090 - 1.597)
0.006
KIRP
288
1.831 (1.521 - 2.205)
0.009
1.0
HR = 0.33 (0.17 - 0.67)
CESC
307
1.658 (0.485 - 0.893)
0.002
UCEC
319
0.710 (0.553 - 0.911)
0.001
logrank P = 0.0012
BRCA
813
1.010 (0.885 - 1.152)
0.001
ESAD
OSCC
184
1.149 (0.882 - 1.497)
0.282
106
0.844 (0.708 - 1.007)
0.8
1.789 (0.607 - 1.027)
0.106
READ
377
0.081
SARC
260
1.877 (0.703 - 1.095)
0.311
UVM
80
0.181 (0.081 - 0.404)
0.001
ESCA
COAD
184
1.021 (0.832 - 1.253)
PRAD
447
1.107 (0.890 - 1.377)
0.883
Probability
0.6
0.38
499
1.337 (1.114 - 1.605)
0.003
GBM
1.415 (1.057 - 1.705)
LAML
143
173
0.001
CHOL
0.982 (0.824 - 1.170)
0.875
36
0.892 (0.598 - 1.331)
0.634
0.4
PAAD
178
0.735 (0.507 - 1.065)
0.124
PCPG
178
1.386 (1.003 - 1.915)
LIHC
371
1.192 (1.057 - 1.344)
0.065
0.004
THYM
1.429 (1.133 - 1.804)
0.049
LUSC
118
501
0.984 (0.874 - 1.107)
0.815
0.2
KICH
65
0.803 (0.497 - 1.133)
0.815
Expression
MESO
BLCA
87
1.157 (0.914 - 1.464)
0.281
406
STAD
1.124 (0.911 - 1.388)
415
1.257 (1.076 - 1.469)
0.281
0.001
low
ACC
79
1.110 (0.851 - 1.448)
0.815 (0.694 - 0.958)
0.003
0.0
high
THCA
DLBC
503
48
1.159 (0.872 - 1.541)
0.009
0.131
UCS
56
0.846 (0.768 - 0.932)
0.875
0
50
100
150
200
HNSC
513
0.968 (0.814 - 1.153)
LGG
0.006
529
0.846 (0.768 - 0.932)
0.003
0.846 (0.768 - 0.932)
Time (months)
SKCM
456
0.001
Number at risk
0
0.5
1
1.5
low
225
39
14
3
1
Hazard Ratio (95% CI)
2
high
79
22
6
4
1
C
(READ)
D
(SARC)
1.0
HR = 0.45 (0.19 - 1.07)
1.0
HR = 0.53 (0.34 - 0.83)
logrank P = 0.033
logrank P = 0.0045
0.8
0.8
Probability
0.6
Probability
0.6
0.4
0.4
0.2
Expression
0.2
Expression
low
low
0.0
high
0.0
high
0
20
40
60
80
100
120
0
50
100
150
Time (months)
Time (months)
Number at risk
Number at risk
low
95
49
14
4
2
2
2
low
156
43
8
3
high
70
41
15
3
2
1
0
high
103
30
8
2
(Figure 3A). However, SARC shows consider- able variability in CD19 expression levels be- tween tumor and normal samples (Figure 3A). Figure 3B explores the relationship between CD19 mRNA expression and promoter methyla- tion levels using the GSCA database. The analy- sis reveals a strong negative correlation between CD19 methylation and expression in CESC, READ, and SARC (Figure 3B). Figure 3C evaluates survival outcomes based on high ver-
sus low CD19 methylation levels across four metrics: Disease-Free Interval (DFI), Disease- Specific Survival (DSS), OS, and Progression- Free Survival (PFS). Notably, in READ, lower CD19 methylation is associated with poorer DSS outcomes (Figure 3C). However, no signifi- cant survival differences are observed for CESC and SARC. Lastly, Figure 3D provides immuno- histochemical staining images of CD19 in CESC and READ tissues from the HPA database,
A
B
Correlation between methylation and mRNA expression
D
CESC
CESC
6
5
FDR
o ⇐ 0.05
Expression -log_[TPM +1]
4
-Log10(FDR)
10
Symbol
15 20
3
CD19
Spearman cor. 0
2
-
-1
0
CESC
READ
SARC
CESC
READ
SARC
(num(T)=306; num(N)=3) (num(T)=92; num(N)=10) (num(T)=262; num(N)=2)
Cancer type
Staining: Low
Staining: Low
C
Survival difference between high and low methylation in each cancer
READ
READ
DFI
DSS
OS
PFS
Hazard ratio
0.0
1.0
1.5
3.0
Symbol
CD19
Cox P value
⇐ 0.05 >0.05
-Log10(FDR)
0.5
1.0
1.5
CESC
READ
SARC
CESC
READ
SARC
CESC
READ
SARC
CESC
READ
SARC
Staining: Low
Staining: Low
Cancer type
CD19: a key immune marker in cancer
A
Altered in 2 (0.69%) of 289 samples.
D
CHOL
CESC BRCA
OV
BRCA
1872
BLCA
CESC
COAD
0.3
ACC
CHOL
DLBC
UVM
COAD
0:15
DLBC
0
1
0
L
ESCA
UCS
ESCA
0
GBM
GBM
UCEC
HNSC
15
КІCH
HNSC
THYM
KIRC
0
KIRP
CD19
0%
LAML
KICH
THCA
LGG
LIHC
KIRC
TGCT
LUAD
LUSC
MESO
= Missense_Mutation
KIRP
STAD
PAAD
PCPG
B
Altered in 2 (2.17%) of 92 samples.
LAML
SKCM
PRAD
1602
READ
LGG
SARC
SARC
SKCM
LIHC
READ
STAD
0
1
LUAD
PRAD
TGCT
0
LUSC
MESO PAAD
PCPG
THCA
THYM
E
CHOL
CESC BRCA
OV
BRCA
BLCA
CESC
CD19
1%
COAD
0.5
ACC
CHOL
DLBC
COAD
0.25
UVM
DLBC
ESCA
UCS
ESCA
0
GBM
GBM
UCEC
HNSC
KICH
· Missense_Mutation
0
KIRC
C
Altered in 1 (1.09%) of 92 samples.
HNSC
05
THYM
KIRP
LAML
848
KICH
THCA
LGG
LIHC
KIRC
TGCT
LUAD
LUSC
0
1
MESO
0
L
KIRP
STAD
PAAD
PCPG
LAML
SKCM
PRAD
READ
SARC
CD19
1%
LGG
SARC
SKCM
LIHC
READ
STAD
LUAD
PRAD
TGCT
LUSC
MESO PAAD
PCPG
THCA
· THYM
· Missense_Mutation
showing minimal protein expression. This indi- cates that CD19 protein levels are relatively low in these cancer types.
Mutational characteristics of CD19
Figure 4A-C displays the mutation analysis of CD19 derived from cBioPortal, which reveals that CD19 mutations occur in a small fraction of cancer samples: 0.69% in 289 Cervical Squamous Cell Carcinoma (CESC) samples, 2.17% in 92 Rectum Adenocarcinoma (READ) samples, and 1.09% in 92 Sarcoma (SARC) samples. These mutations are predominantly missense alterations. This indicates that CD19 mutations are relatively infrequent in these cancers. Figure 4D shows Tumor Mutational Burden (TMB) analysis, which aligns with the mutation data, revealing that CESC, READ, and
SARC have low rates of CD19 mutations. Specifically, CESC shows minimal CD19 altera- tions, READ shows a modest increase, and SARC maintains a low mutation rate. This sug- gests that CD19 mutations have a limited impact on the overall mutational burden in these cancers. Figure 4E presents Microsa- tellite Instability (MSI) results, indicating that CD19 mutations are present in a minor propor- tion of CESC, READ, and SARC samples. In CESC, MSI is not significantly altered, READ shows a slight increase in MSI, and SARC exhib- its minimal CD19-related MSI. Overall, these findings suggest that CD19 mutations are rare and have minimal influence on tumor mutation- al burden and microsatellite instability in these cancers, indicating that CD19 may not signifi- cantly contribute to genomic instability in these contexts.
CD19: a key immune marker in cancer
Correlations of CD19 with immune-related genes and subtypes
Figure 5A features heatmaps illustrating the correlation between CD19 expression and immune inhibitor genes using the TISIDB data- base. Figure 5B details correlations with immune stimulator genes, while Figure 5C shows correlations with MHC genes. In CESC, READ, and SARC, CD19 expression demon- strates significant associations with various immune inhibitors such as PDCD1 (PD-1), CTLA4, LAG3, TIGIT, and CD274 (PD-L1), all cru- cial for immune checkpoint regulation and T cell activity modulation. Notable correlations with immune stimulators include CD80, CD86, TNFRSF9 (4-1BB), and ICOS, which are vital for T cell activation and longevity. Additionally, CD19 expression is linked with MHC genes like HLA-A, HLA-B, HLA-C, HLA-DRA, and HLA- DRB1, important for antigen presentation to T cells. The Kruskal-Wallis test for CD19 expres- sion across immune subtypes in CESC (Figure 5D) yields a p-value of 3.91e-06, highlighting notable differences among subtypes. Violin plots indicate significantly higher CD19 expres- sion in subtype C2 compared to others, sug- gesting a unique immunological profile. For READ, Figure 5E shows variable CD19 expres- sion across subtypes, with subtype C3 exhibit- ing slightly elevated levels. In SARC, the Kruskal-Wallis test results in a p-value of 3.17e- 06, indicating substantial differences in CD19 expression across immune subtypes. Violin plots reveal that subtype C4 has the highest CD19 expression, reflecting a distinctive im- mune environment with elevated CD19 levels (Figure 5F). Overall, these results illustrate significant correlations between CD19 expres- sion and various immune-related genes across CESC, READ, and SARC, with distinct expres- sion patterns observed among different immu- ne subtypes within each cancer type.
Gene enrichment analysis
Figure 6A depicts the Protein-Protein Interac- tion (PPI) network involving CD19 from STRING, highlighting its interactions with proteins such as FCGR3A, FCGR3B, CD22, CD81, CD79A, CD79B, CR2, IFITM1, LYN, and VAV1. This net- work underscores the intricate connections among these proteins. Figure 6B presents cel-
lular component (CC) enrichment analysis, identifying significant enrichment in com- plexes such as the B cell receptor complex, plasma membrane signaling receptor complex, and integrin alpha2-beta1 complex. Figure 6C illustrates molecular function (MF) enrichment, emphasizing significant associations with phosphorylation-dependent protein binding, IgM binding, and MHC class II protein binding. Figure 6D highlights biological process (BP) enrichment, revealing strong associations with B cell receptor signaling regulation, B cell prolif- eration, and antigen receptor-mediated signal- ing pathways. Lastly, Figure 6E outlines path- way enrichment analysis, identifying crucial pathways like the B cell receptor signaling path- way, hematopoietic cell lineage, Fc epsilon RI signaling pathway, and Epstein-Barr virus infec- tion. These findings collectively emphasize the critical roles of CD19 and its associated pro- teins in various cellular components, molecular functions, biological processes, and pathways integral to B cell function and immune response.
Correlation of CD19 with immune infiltrates and drug sensitivity
The relationship between CD19 expression and immune cell infiltration was assessed using the GSCA database. Figure 7A highlights that in CESC, CD19 expression shows a significant positive correlation with several immune cell types, including B cells, CD8+ T cells, cytotoxic T cells, macrophages, and dendritic cells, with the strongest associations noted with B cells and CD8+ T cells. Similarly, Figure 7B reveals that in READ, CD19 levels are positively corre- lated with the infiltration of B cells, CD8+ T cells, and cytotoxic T cells. For SARC, Figure 7C confirms that CD19 expression correlates posi- tively with B cells, CD8+ T cells, cytotoxic T cells, and macrophages. These findings collec- tively suggest that CD19 is intricately linked to the immune cell landscape in CESC, READ, and SARC, indicating its potential role in the immune microenvironment of these cancers. However, Figure 7D illustrates that CD19 expression does not show significant correlations with drug sensitivity across various treatments, as observed in the GDSC database. This suggests that while CD19 may influence immune cell infiltration, it does not notably affect the sensi- tivity of cancer cells to therapeutic drugs.
A
B
C
ADORA2A
C10orf54”
B2M
BTLA
CD27-
CD270
HLA-A
CD160
CD28
CD244
CD40LG ]
HLA-B
CD48”
CD274
ČD70-
CD80-
HLA-C
CD96
cxcL13-
HLA-DMA
CSF1R
HLA-DMB
CTLA4
HHLA2
ICOS”
HLA-DOA
HAVCR2
1
ICOSLG”
IL2RA-
HLA-DOB
IDO1
KLRC1 ]
HLA-DPA1
IL10
IL10RB
KLRK1-
LTA ]
HLA-DPB1
KDR
NTSE-
HLA-DQA1
KIR2DL1
HLA-DQA2
KIR2DL3
TMEM173
TMIGD2
HLA-DQB1
LAG3
-1
TNFRSF13B
HLA-DRA
LGALS9
TNFRSF14
HLA-DRB1
PDCD1
INFRSF17 ]
INFRSF18
TNERSF25-
HLA-E
PDCD 1LG2
TNFRSF4-
PVRL2
TNFRSF8”
HLA-F
TGFB1
TNFSETER-
HLA-G
TGFBR1
TNECEIA INFSF14
TNFSF15 7
TAP1
TIGIT
INFSF4
TAP2
VTCN1
TNFSF97
ULBP1
TAPBP
D
CESC :: CD19_exp
READ :: CD19_exp
SARC :: CD19_exp
Kruskal-Wallis Test: Pv=3.91e-06
E
Kruskal-Wallis Test: Pv=4.19e-02
F
Kruskal-Wallis Test: Pv=3.17e-06 n=C1 64,C2 38,C3 42,C4 59,C6 20
n=C1 77,C2 217,C4 6
n=C1 127,C2 18,C3 9,C4 1,C6 1
Expression (log2CPM)
Expression (log2CPM)
Expression (log2CPM)
5
4
5
0
0
0
-5
-4
-5
-10
-8
-10
C1
C2
C4
C1
C2
C3
C4
C6
C1
1 C2 C3 C4 C6
Subtype
Subtype
Subtype
A
FCGR3A
B
N. of Genes
C
VAV1
B cell receptor complex
Phosphorylation-dependent protein binding
LYN
Integrin alpha2-beta1 complex
· 1
IgM binding
N. of Genes
FCGR3B
Immunoglobulin complex
Plasma membrane signaling receptor complex
MHC class Il protein binding
2
CD4 receptor binding
1.0
Receptor complex
3
Glycosphingolipid binding
1.5
External side of plasma membrane
4
Complement receptor activity
2.0
Membrane raft
Transferrin receptor binding
CD79B
Membrane microdomain
5
Platelet-derived growth factor receptor binding
2.5
=
CD22
Side of membrane
6
Sialic acid binding
3.0
Plasma membrane protein complex
7
Sphingolipid binding
CD19
Cell surface
Integral component of plasma membrane
Glycolipid binding
Ephrin receptor binding
-log10(FDR)
F
Extracellular exosome
Complement binding
CD79A
Extracellular vesicle
-log10(FDR)
Gamma-tubulin binding
1.5
2
CD81
Extracellular organelle
Extracellular membrane-bounded organelle
2.5
Phosphoprotein binding
2.0
Virus receptor activity
IFITM1
5
Intrinsic component of plasma membrane
Exogenous protein binding
2.5
CR2
Extracellular space
3.0
3.5
Integrin binding
3.0
=
Vesicle
Extracellular region
Modification-dependent protein binding
4.0
Protein-containing complex binding
3.5
0
250
500
750
1000
0
250
500
750
1000
Fold Enrichment
Fold Enrichment
D
Reg. of B cell receptor signaling pathway
N. of Genes
E
B cell receptor signaling pathway
B cell receptor signaling pathway
N. of Genes
B cell proliferation
4
Antigen receptor-mediated signaling pathway
5
Hematopoietic cell lineage
· 1
Immune response-activating cell surface receptor signaling pathway
Immune response-activating signal transduction
6
· 2
B cell activation
7
Fc epsilon RI signaling pathway
3
Activation of immune response
8
· 4
Lymphocyte proliferation
Primary immunodeficiency
5
Mononuclear cell proliferation
Immune response-regulating signaling pathway
-log10(FDR)
Fc gamma R-mediated phagocytosis
6
Leukocyte proliferation
7
Positive reg. of immune response
8
Epstein-Barr virus infection
Leukocyte differentiation
9
-log10(FDR)
Lymphocyte activation
10
Chemokine signaling pathway
Positive reg. of immune system proc.
11
5
Leukocyte activation
Cell activation
12
Lipid and atherosclerosis
10
0 100 200300 400 500
50
100
150
200
250
Fold Enrichment
Fold Enrichment
A
Correlation between expression and immune infiltrates in CESC
Symbol
CD19
CD19: a key immune marker in cancer
InfiltrationScore
C
Bcell CD4_T
CD8_T
Central_memory
Cytotoxic
Exhausted
Correlation between expression and immune infiltrates in SARC
Gamma_delta
ITreg Macrophage
Correlation -0.6
B
NK
Tfh
Th1
0.0
Tr1
Symbol
Correlation between expression and immune infiltrates in READ
DC
-Log10(FDR)
0.7
MAIT
Cell type
NKT
☐
CD19
Th2
☐
2.5
CD4_naive
☐
5.0
CD8_naive
7.5
Symbol
Effector_memory
Monocyte
FDR
10.0
Neutrophil
CD19
nTreg Th17
> ⇐ 0.05
>0.05
InfiltrationScore
CD8_T
Central_memory
and sarcoma (SARC). A. Correlation between CD19 expression and immune infiltrates in CESC. B. Correlation between CD19 expression and immune infiltrates in READ. C. Correlation between CD19 expression and immune infiltrates in SARC. D. Correlation between CD19 mRNA expression and drug sensitivity. P-value < 0.05.
Figure 7. Correlations of CD19 expression with immune infiltrates and drug sensitivity in cervical squamous cell carcinoma (CESC), rectum adenocarcinoma (READ),
Cytotoxic Exhausted
Gamma_delta
InfiltrationScore
iTreg
D
Bcell CD4 T
Macrophage
MAIT
Correlation
CD8_T
NK
-0.6
Central_memory
fh
Cytotoxic
Th1
MAIT
Bcell
0.0
Correlation between GDSC drug sensitivity and mRNA expression
NK
6377
CD4_T
Tfh
Correlation
DC
0.6
Th2
-0.5
Cell type
Effector_memory
-Log10(FDR)
CD4_naive DC Effector_memory
NKT
0.0
nTreg Th2
☐
5.0
2.5
Exhausted
Tr1
7.5
Symbol
Gamma_delta
-Log10(FDR)
0.7
CD4_naive
iTreg
☐
CD8_naive
Monocyte Neutrophil
FDR
10.0
CD19
Macrophage
☐
o ⇐ 0.05
Cell type
NKT
☐
Th1
☐ 7.5
2.5
5.0
Th17
>0.05
Th17
Tr1
CD8_naive
FDR
10.0
Monocyte
Neutrophil
⇐ 0.05
nTreg
>0.05
AR-42 AT-7519
BHG712 CAL-101
CAY10603
CUDC-101 GSK2126458
GSK690693 I-BET-762
IPA-3
JW-7-24-1
KIN001-102
KIN001-236
FDR
KIN001-244
Methotrexate NG-25
o ⇐ 0.05
NPK76-11-72-1
OSI-027
Correlation
☐ -Log10(FDR) 10
Drug
PHA-793887
PI-103
PIK-93
QL-X-138
QL-XI-92 SNX-2112
-0.5
TL-1-85
-0.3
TPCA-1
Tubastatin A
XMD13-2
0.0
YM201636
ZSTK474
A CD19 Gene Expression in READ and Control Cell Lines
B
ROC Curve for CD19 Gene
1.0
6.5
CD19 Expression (RT-qPCR)
0.8
6.0
Sensitivity
0.6
0.4
5.5
0.2
5.0
0.0
AUC = 0.92
Control
Cell Line Type
READ
1.0
0.8
0.6
Specificity
0.4
0.2
0.0
Expression validation of CD19 in READ cell lines
To validate CD19 expression levels, RT-qPCR was performed on 10 READ cell lines and 5 control lines. Figure 8A presents a box plot demonstrating a significant reduction in CD19 expression in READ cell lines compared to con- trols. Additionally, Figure 8B features an ROC curve for CD19, with an area under the curve (AUC) of 0.92. This high AUC indicates strong sensitivity and specificity in distinguishing READ samples from normal controls based on CD19 expression levels.
Evaluation of CD19 overexpression on SW480 cell functionality
The effects of CD19 overexpression on SW480 cell functionality were evaluated through vari- ous assays. Figure 9A confirms the successful overexpression of CD19 in SW480 cells (OE- CD19-SW480) compared to control cells (Ctrl- SW480) using RT-qPCR. Figure 9B and 9C show Western blot analyses confirming increased CD19 protein levels in OE-CD19-SW480 cells. Figure 9D presents the results of a cell prolif- eration assay, which reveals a significant decrease in proliferation in OE-CD19-SW480 cells compared to Ctrl-SW480 cells, indicating that CD19 overexpression impairs cell growth. Figure 9E and 9F illustrate the colony formation
assay results, showing a reduction in the num- ber of colonies formed by OE-CD19-SW480 cells, suggesting decreased clonogenic poten- tial. Figure 9G and 9H depict the wound healing assay, where OE-CD19-SW480 cells demon- strate a significantly higher wound closure per- centage compared to Ctrl-SW480 cells, reflect- ing enhanced migratory capability due to CD19 overexpression. These results indicate that CD19 overexpression in SW480 cells leads to reduced cell proliferation and colony formation but enhances wound healing, emphasizing its multifaceted role in cellular dynamics.
Discussion
Cancer remains one of the foremost causes of death globally, characterized by its intricate interplay of genetic, epigenetic, and environ- mental elements, which complicates both its understanding and treatment [26-29]. Recent strides in molecular biology and advanced high-throughput techniques have opened ave- nues to investigate various biomarkers that could potentially enhance cancer diagnosis, prognosis, and therapy [30-33]. Among these biomarkers is CD19, a transmembrane protein predominantly found on B cells, plays a pivotal role in B cell maturation and functionality [34, 35]. Although CD19 has gained prominence in hematological malignancies as a target for chi- meric antigen receptor (CAR) T cell therapies
A
Expression Levels
B
C Relative Band Densities of CD19 to GAPDH
GAPDH and CD19
6
0.3
GAPDH
CD19
Expression
Relative Intensity
A
0.2
Ctrl-SW480
OE-CD19-SW480
Ctrl-SW480
OE-CD19-SW480
2
0.1
0
0.0
Ctrl-SW480
OE-CD19-SW480
Ctrl-SW480
OE-CD19-SW480
Sample
Sample
D
Proliferation (% Control)
E
OE-CD19-SW480
F
100
Colony Numbers
200
Ctrl-SW480
150
75
Proliferation (%)
Number of Colonies
100
50
50
25
0
0
Ctrl-SW480 OE-CD19-SW480 Sample
Ctrl-SW480
OE-CD19-SW480
Sample
G
Ctrl-SW480
0 h
OE-CD19-SW480
H
Wound Closure Percentage
60
Wound Closure (%)
40
24 h
20
0
Ctrl-SW480
OE-CD19-SW480
Sample
[36, 37], its role in solid tumors remains under- explored, presenting a unique opportunity to evaluate its expression and implications across different cancer types.
CD19’s involvement in hematological malignan- cies, such as B-cell acute lymphoblastic leuke-
mia (B-ALL) and B-cell non-Hodgkin lymphoma (B-NHL), is well-established [38, 39]. The- rapeutic strategies targeting CD19, particularly through CAR-T cell therapies, have shown con- siderable success, transforming treatment approaches for patients with refractory or relapsed B-cell malignancies [13]. However, the
CD19: a key immune marker in cancer
extent of CD19’s role in solid tumors has not been thoroughly examined [40]. Emerging evi- dence suggests that CD19 might influence immune cell infiltration and modulate the tumor microenvironment in solid tumors, hinting at a broader functional role than previously under- stood [41, 42].
In our investigation, we examined CD19 expres- sion across 33 cancer types using data from the TCGA database, analyzed through TIMER2 and UALCAN platforms. Our analysis revealed notably elevated CD19 levels in tumor tissues of several cancers, including Adrenocortical Carcinoma (ACC), Breast Invasive Carcinoma (BRCA), Cholangiocarcinoma (CHOL), Gliob- lastoma Multiforme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Renal Clear Cell Carcinoma (KIRC), Liver Hepatocellular Carcinoma (LIHC), and Pan- creatic Adenocarcinoma (PAAD) compared to normal tissues. Conversely, we observed a sig- nificant down-regulation of CD19 in Cervical Squamous Cell Carcinoma (CESC), Rectum Adenocarcinoma (READ), and Sarcoma (SARC). These observations align with existing litera- ture indicating heterogeneous CD19 expres- sion patterns across different cancer types [43, 44]. Our prognostic analysis demonstrated that diminished CD19 expression correlates with poorer outcomes in CESC, READ, and SARC. Both univariate Cox regression analysis and Kaplan-Meier survival curves substantiat- ed that reduced CD19 levels are associated with adverse overall survival in these cancers, corroborating previous findings that CD19 expression could serve as a prognostic indica- tor in specific malignancies [45, 46].
Additionally, our methylation studies revealed a significant inverse relationship between CD19 gene methylation and mRNA expression in CESC, READ, and SARC. This epigenetic modifi- cation suggests that DNA methylation may play a crucial role in regulating CD19 expression in these cancers, supporting earlier research that highlights DNA methylation’s impact on gene expression in cancer [47, 48]. We further inves- tigated the mutational landscape of CD19, dis- covering that mutations in this gene are rela- tively rare in CESC, READ, and SARC, with negligible effects on tumor mutational burden and microsatellite instability. This indicates that CD19 mutations do not substantially con- tribute to the genomic instability of these can- cers, consistent with previous studies [49, 50].
Our analysis also explored the correlations between CD19 expression and immune-related genes, revealing significant associations with various immune inhibitors, stimulators, and MHC genes. These findings underscore CD19’s potential involvement in the tumor immune microenvironment, suggesting that it might influence immune cell infiltration and interac- tions within the tumor. Finally, we validated CD19 expression in READ cell lines, confirming significantly lower CD19 levels at both the gene and protein levels in tumor cells com- pared to normal controls. This down-regulation was corroborated by Western blot analysis, with an ROC curve demonstrating high sensi- tivity and specificity for CD19 as a distinguish- ing marker between READ tumors and normal tissues.
Conclusion
This investigation offers an in-depth explora- tion of CD19 expression across a diverse array of cancer types, uncovering notable variations and their potential prognostic significance. Our analysis revealed elevated CD19 levels in sev- eral cancers, including Adrenocortical Car- cinoma (ACC), Breast Invasive Carcinoma (BRCA), Cholangiocarcinoma (CHOL), Gliob- lastoma Multiforme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Renal Clear Cell Carcinoma (KIRC), Liver Hepatocellular Carcinoma (LIHC), and Pan- creatic Adenocarcinoma (PAAD). Conversely, significant down-regulation of CD19 was ob- served in Cervical Squamous Cell Carcinoma (CESC), Rectum Adenocarcinoma (READ), and Sarcoma (SARC). The association of reduced CD19 expression with poor overall survival in CESC, READ, and SARC underscores its prog- nostic relevance. Our methylation studies re- vealed an inverse relationship between CD19 methylation and its gene expression, highlight- ing the role of epigenetic regulation in modulat- ing CD19 levels. Furthermore, the significant correlations between CD19 expression and various immune-related genes and infiltrates point to its involvement in shaping the tumor immune microenvironment. Although the study predominantly relies on public databases, and further protein-level and functional validations are necessary, these findings provide crucial insights into CD19’s role beyond hematological malignancies. The results emphasize CD19’s
CD19: a key immune marker in cancer
potential as a biomarker for diagnostic, prog- nostic, and therapeutic purposes in solid tumors. Future investigations should prioritize experimental validation and clinical trials to substantiate these observations and elucidate the mechanisms by which CD19 influences cancer progression and immune modulation. Such research could pave the way for innova- tive therapeutic strategies targeting CD19.
Disclosure of conflict of interest
None.
Address correspondence to: Zongquan Zhao, De- partment of General Practice, Pingjiang New Town Community Health Service Center, Sujin Street, Gusu District, Suzhou 215000, Jiangsu, China. E-mail: zhaozongquan7863@outlook.com
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