Evaluation of NFKB1 and MyD88 expression levels in a sample of non-Hodgkin lymphoma patients before and during chemotherapy

ABSTRACT

Triggering of Toll-like receptors (TLRs), expressed mainly on innate immune cells, by their specific ligands (TLRLs) induces NF-κB and MyD88, resulting in stimulation of immune responses. In the tumor microenvironment, however, certain TLRs have been found to act either as pro-tumorigenic or anti-tumorigenic co-factors. This pilot prospective study aimed to explore the impact of non-Hodgkin lymphoma (NHL) on the expression levels of TLRs and if chemotherapy alters this expression. Blood mononuclear cells (PBMCs) were isolated from healthy subjects as well as from NHL patients before, during, and after chemotherapy. We used microarray to analyze the gene profiling of TLR signaling pathway and RTqPCR to confirm the gene expression of TLR9, NF-κB1, and MyD88. Regardless of chemotherapy, TLR9 gene expression was similar in NHL patients as compared to controls. The gene expression of MyD88 and NF-κB1 was higher in the patients as compared to the controls. Interestingly, there was a 11.1-fold decrease and a 3.4-fold increase, respectively, in the gene expression of NF-κB1 and Myd88 in patients after chemotherapy as compared to before chemotherapy. Our pilot study proof the concept that NHL itself induces expression of MyD88 and NF-κB1, while chemotherapy further alters this effect.

KEYWORDS:

• Chemotherapy
• Non-Hodgikin lymphoma
• MyD88
• NF-KB1
• TLRs
• gene expression

Introduction

Toll like receptors (TLRs) act as a bridge between innate and adaptive immunity, fostering the generation of immune responses against microbial infection and cancer [1,2]. TLRs recognize pathogen-associated molecular pattern molecules (PAMPs) and damage-associated molecules (DAMPs) [3]. There are about 13 TLRs in mammals [4]. TLR molecules are encoded by a multi gene family mostly expressed on the surface of innate immune cells such as monocytes, natural killer cell cells, mast cells, antigen presenting cells (dendritic cell and macrophages) and lymphocytes (B cells and T cells) [5] In addition to the ability of TLR signaling pathway to enhance immunity and anti-tumor immunity, several studies have reported that certain TLRs have also the ability to directly inhibit tumor growth [6]. As such, TLR engagement on different stages of cancer might give rise to different results [7]. In contrast, other reports showed that TLR signaling is involved in tumor proliferation, but not in cancer initiation, promoting tumor growth [8]. TLRs signaling pathways are also involved in B-cell maturation, the production of antibodies, differentiation to plasma cells and pathogenesis of B cell lymphoma such as Non Hodgkin lymphoma (NHL) [9].

NHL is a heterogeneous group of malignancies characterized by an abnormal clonal proliferation of T cells, B cells or both. As per Globocan 2020, it is projected that there would be 7305 new cases and 89,042 deaths of NHL [10]. More than 100 distinct lymphoma types have been discovered [11]. Immune system cells, such as B cells, T cells, and dendritic cells are possible source of these lymphomas. The majority of the adults NHL, however, are of B cell origin (about 85%) that arises from clonal expansion and subsequent B-cell invasion of immune organs [12]. T-cell lymphomas are much less common (about 15%) [13,14]. The two most prevalent subtypes of NHL are follicular lymphoma and diffuse large B-cell lymphoma (DLBCL) [15], and NK-cell lymphoma are aggressive subtype [16,17]. Several studies have confirmed that the heterogeneity of lymphoid malignancies are originated from aberrant genetic alterations as well as several dysregulated signaling pathways such as TLRs pathway [18].

Toll-like receptor 9 (TLR9) has been found to be expressed in many lymphomas subtype such as mantle cell lymphoma (MCL), chronic lymphocytic leukemia (CLL), Burkitt lymphoma (BL), and DLBCL, where cancerous B cells have been found to show heterogeneous responses to TLR9 ligands [19]. TLR9 stimulation can induce either apoptosis or proliferation of various types of B cells lymphoma. It can induce the proliferation of several subtypes of lymphoma from patients with progressive disease and non-mutated immunoglobulin V (H) genes, including follicular lymphomas, diffuse large B cell lymphomas, small lymphocytic lymphomas, marginal zone lymphomas, B-CLL cells [20]. On the another hand, TLR9 activation can induce apoptosis of human Burkitt lymphoma [21]. Interestingly, however, triggering of TLR signaling had no effects on mantle cell lymphoma cells [22]. Taken together, it could be suggested that the role of TLR signaling on lymphoma might depend on the subtype and the stage.

TLR9/ myeloid differentiation response 88(MyD88)/ nuclear factor kappa-light chain-enhancer of activated B cells (NF-κB) pathway has been reported to play an important role in the survival of some types of cancer cells and its proliferation. For instance, a previous study has found that lymphoma can upregulate the TLR9/MyD88/NF-kappa B signaling pathway in B regulatory cells [23]. Furthermore, CpG-NF-κB decoy (double-stranded NF-κB specific DNA sequence with CpG motif) has been reported to reduce NF-κB activity in the tumors and significantly inhibit lymphoma progression [24]. This activation of NF-κB pathway, which is MyD88-dependent, results in secretion of several pro inflammatory cytokines such as IL-8, IL-6, and TNF-α. These cytokines are involved in several immunological response, including proliferation, angiogenesis, cell adhesion, and apoptosis [25]. As such, NF-κB, in response to TLR9 stimulation have both anti-apoptotic and pro-apoptotic effects [26].

Microarray data become an essential technology for the diagnosis and classification of a wide range of malignant tissues and diseases; yet, the difficulties associated with gene expression and classification are effectively addressed by the high gene dimension and the small sample size [27,28]. Using microarray technology, researchers can analyze the activity of 10,000 genes in a single experiment and obtain valuable insights into the functioning of the cell. Numerous disorders, including diabetes diseases and cancer disease [29]such as Breast cancer [30], can be diagnosed using this particular information. We have used this technology to analyze the role of different immune cell types, focusing on myeloid-derived suppressor cells, dendritic cells, and T cells in acute lymphoblastic leukemia [31,32]

Given the results of the above published studies on the role of TLR signaling pathways in the prognosis of lymphoma, it is obvious that the role of TLR signaling in lymphoma depends on the lymphoma subtype and the stage. Therefore, the primary goal of the present study is to explore the impact NHL itself on TLR9 signaling pathway as well as to explore the impact of ant-NHL chemotherapy on this pathway with the goal to use it as a potential prognostic marker for NHL. To the best of our knowledge, this is first study to explore the gene expression profile of TLR9 signaling pathway in NHL before and after chemotherapy.

Subjects and methods

Subjects

This pilot study was performed on adult (age above 18 years) patients diagnosed as non-Hodgkin lymphoma. Initially, 3 patients (1 before, 1 during and 1 after chemotherapy) were recruited for gene profiling by transcriptome analysis. In addition, 15 patients were recruited for confirmation study of the gene expression of TLR9, NF-KB1, and MyD88 by qRT-PCR. Five healthy subjects were recruited as controls. Patients age and sex are shown in (Table 1). The patients were recruited from Tanta Cancer Center, Egypt. This study was approved (Approval code# 3012/01/15) by the Ethical Committee at Faculty of Medicine, Tanta University before the commencement of the study. The inclusion criteria included male and female patients who are diagnosed with non-Hodgkin lymphoma and their age above 18-years old” and who are early diagnosed as well as during and after conventional anti-NHL chemotherapy. The exclusion criteria are adult patients who are not diagnosed with Leukemia, Hodgkin lymphoma and other types of malignancy. The histopathological characteristics have been done during diagnosis by the clinical pathologists as a routine presentation.

Table 1. Individuals parameter of the studied groups.

Parameter		Healthy control	Patient before chemotherapy	Patient during chemotherapy	Patient after chemotherapy
Age	Range	[34] − 72)	[40–59]	[44] − 73)	[33–61,66]
	Mean ± SE	48.4 ± 6.27	50.50 ± 9.50	60.67 ± 4.65	48.60 ± 5.46
Sex	Male	60%	60%	70%	40%
	Female	40%	40%	30%	60%

Patients enrolled in the present study were treated with CHOP protocol, which includes: (1) cyclophosphamide (Endoxan®; 750 mg/m2 (D1), IV infusion over 30 min; (2) doxorubicin (Adramycin®), 50 gm/m2 (D1), IV over 5–10 min; (3) vincristine (Viacristine®), 1.4 mg/m2 (D1), direct IV (Max 0.2 mg) over 5 min; and prednisolone, 60 mg/m2 (D1–5) PO.

Samples collection and isolation of PBMCs

Peripheral blood samples (5 ml) were collected into ethylenediamine tetraacetic acid (EDTA) containing vials. The blood sample were divided into two parts: one part for patients’ routine lab tests (2 mL), and the other part (3 mL) for RNA extraction. Bone marrow aspiration (BMA) was done by a clinical pathologist at Clinical Pathology Department, Faculty of Medicine, Tanta University Hospital. BMA samples were used for diagnosis. Samples were stained with mAbs against the surface markers, including CD20, CD3, CD45, CD23, for diagnosis of lymphoma. For separation of peripheral blood mononuclear cells (PBMCs), peripheral blood samples (3 ml) were slowly layered over the equal volume of Ficoll solution and then centrifuged for 40 min at 400×g, 22°C. PBMCs were separated by centrifugation (100×g for 10 min at 20 °C), washed twice, resuspended in PBS, and stored at −80°C for RNA extraction.

Laboratory tests

Serum (400 µl) from NHL patients and healthy controls were used to estimate the concentration of liver function (alanine aminotransferase (ALT), aspartate aminotransferase (AST) and total bilirubin) using Human kit, kidney function (blood urea, serum creatinine and serum uric acid) and lactic acid dehydrogenase (LDH) using SPINREACT, S.A.U. The assays were performed using a STAT FAX 3300 (USA). Peripheral blood samples (1 ml) were collected into EDTA was used to analyze complete blood count (CBC) by (Abacus Junior 30 hematology analyzer, Diatron medical, Hungary).

RNA extraction

RNA used for the microarrays experiments and RT– qPCR was purified with the RNeasy Kit (Qiagen, Valencia, CA, USA # 217004) according to the manufacturer protocol. RNA quality and concentration were determined by using the Nano Drop spectrophotometer (Nano Drop Technologies). The RNA 6000 Nano Lab Chip Series II Assay used to assess the RNA Integrity on a BioAnalyzer Agilent 2100. The assay generated an RNA integrity number (RIN) for each sample based on the ratio of 28S:18S ribosomal RNA [33].

Microarray analysis

RNA was reverse transcribed using the WT PLUS Reagent Kit (Affymetrix, Santa Clara, CA) according to the manufacturer protocol. Synthesis of biotinylated complementary RNA was performed using a GeneChip® IVT Labeling kit (Affymetrix). After fragmentation, 10 μg of complementary RNA was hybridized for 16 h at 45°C on a Gene- Chip® Human Genome U133 Plus 2.0 Array®. Gene Chips were washed and stained in a Gene Chip®Fluidics Station 450. Fluorescence intensities for chips were examined on a Gene Chip Scanner 3000 7 G. Expression Console software was used to normalize data and generate probe set intensity values and transcriptome analysis Console (TAC) software was used to statistically analyze data.

Confirmation with q RT PCR

TKR9, MyD88 and NF-ΚB1 were selected from the array data analysis for the confirmation of the PCR array findings. Total RNA was reverse transcribed to cDNA using QuantiTect Reverse Transcription kit (Qiagen, Germany). QRT-PCR was performed with 2X Maxima SYBR Green/ROX qPCR Master Mix following the manufacturer protocol (Thermo scientific, USA, # K0221) in a Step One Plus real time thermal cycler (Applied Biosystems, Life technology, USA) according to the manufacturer’s instructions. The relative quantification of the target transcripts normalized to the endogenous control (GABDH) was determined by the comparative cycle threshold (CT) method. Relative changes in gene expression between samples were analyzed using the 2^−ΔΔ (Ct) method [33]. Gene levels were normalized using GAPDH as an internal control. The following primer sequences of GAPDH gene was used:

F: GGATTTDDTCGTATTGGG;R:GGAAGATGGTGATGGGATT

HS_MYD88_1_SG Quantitect primer Assay (cat # QT00203490)

HS_ NFKB1_1_SG Quantitect primer Assay (cat # QT00063791)

HS_TLR9_1_SG Quantitect primer Assay (cat # QT00015183)

Statistical analysis

The clinical data was collected along the study and analyzed for each patient; each value was calculated as the mean ± SE. The differences between groups were analyzed by one-way analysis of variance (ANOVA). The p values ≤ 0.05 were considered statistically significant. For normally distributed quantitative variables, to compare between more than two groups, and (Bonferroni) for pairwise comparisons. For RT PCR statistical analysis, the statistical significance was evaluated by ANOVA using SPSS, 18.0 sftware, 2011 and the individual comparisons were obtained by Duncan’s multiple range test (DMRT). Values were considered statistically significant when p < 0.05.

Results

The clinical characteristics of both healthy control and NHL patients are shown in Tables 2 and 3. Hemoglobin (Hb%) levels in patients during treatment showed significant decrease as compared to healthy control (p value = 0.01). Significant decrease in mean corpuscular volume (MCV) (FL) in patients during chemotherapy as compared to healthy control (p value = 0.01). Significant increase in the numbers of neutrophil was observed in patients before chemotherapy as compared to the healthy control, while it significantly decreased after chemotherapy as compared to before chemotherapy (p value = 0.0009). In regard to lymphocytes, there was a significant decrease in patients (before and during chemotherapy) as compared to healthy control, where the decrease was more pronounced after chemotherapy as compared to before chemotherapy (p value = 0.001). Monocyte showed significant decrease during chemotherapy when compared to healthy control (p value = 0.015).

Table 2. The clinical characteristics of healthy control and NHL patients groups.

Parameters	Healthy control (n = 5)	Patients before chemotherapy (n = 5)	Patients during chemotherapy (n = 5)	Patients after chemotherapy (n = 5)	F	P
AST (U/L)	21.8 ± 0.86	36.66 ± 6.4	26.66 ± 4.27	27.83 ± 4.8	1.84	0.155
ALT(U/L)	19.9 ± 0.99	27.04 ± 2.9	17.48 ± 3.78	18.96 ± 3.19	2.133	0.11
Total bilirubin (mg/dl)	0.75 ± 0.02	0.80 ± 018	0.59. ± 0.07	0.47 ± 0.05	2.27	0.09
Blood urea (mg/dl)	27.07 ± 1.13	31.54 ± 2.01	30.55 ± 1.65	25.70 ± 2.19	2.13	0.113
Serum creatinine (mg/dl)	0.87 ± 0.05	0.74 ± 0.01	0.77 ± 0.0.8	0.69 ± 0.03	2.32	0.09
Serum uric acid(mg/dl)	5.28 ± 0.43	4.30 ± 1.30	3.03 ± 0.69	4.56 ± 1.30	1.539	0.252
LDH(U/I)	317.2 ± 12.46	341.6 ± 22.41	391.25 ± 36.69	400 ± 37.39	1.86	0.17

Table 3. The complete blood picture count in both healthy control and NHL patients.

CBC	Healthy control	Patient before chemotherapy	Patient during chemotherapy	Patient after chemotherapy	F	p
HB (g/dl)	13.5 ± 0.42	12.7 ± 0.58	10.72 ^a+ ±0.49	11.9 ± 0.63	4.73	0.01
PLT x 10^3	282.8 ± 12.46	171.25 ± 48.57	243.0 ± 38.51	206.7 ± 21.33	2.04	0.14
WBCs x10^3	7.81 ± 0.61	8.38 ± 1.27	7.77 ± 0.25	5.47 ± 0.62	0.67	0.57
RBCs (mill/cu.mm)	4.65 ± 0.15	4.52 ± 0.67	4.03 ± 0.10	4.27 ± 0.32	1.50	0.23
HCT (%)	40.92 ± 1.3	37.5 ± 1.2	32.57 ± 1.7	36.45 ± 1.89	1.01	0.39
MCV (FL)	91.6 ± 1.07	82.77 ± 1.6	80.45 ^a+ ±2.25	86.52 ± 3.37	4.711	0.01
MCH (pg)	28.78 ± 0.21	27.7 ± 0.76	26.5 ± 0.55	28.92 ± 1.12	2.60	0.08
MCHC (g/dl)	32.38 ± 0.5	32.37 ± 1.12	33.12 ± 0.35	32.58 ± 0.64	0.24	0.86
Neutrophil (%)	56.01 ± 1.96	76.3 ^a + ±3.18	68.07 ± 3.7	59.92 ^b+ ± 2.81	9.14	0.0009
Lymphocyte (%)	35.07 ± 1.73	11.42 ^a+ ± 1.74	20.27 ^a+ ± 3.59	26.51 ^b+ ± 2.65	15.38	<0.001
Monocyte (%)	5.69 ± 0.46	9.72 ^a+ ± 0.98	9.85^a+ ± 1.16	9.41^a+ ± 0.92	4.70	0.015
Eosinophil (%)	2.79 ± 0.34	1.9 ± 0.8	1.6 ± 0.37	2.7 ± 0.56	1.14	0.35
Basophil (%)	0.47 ± 0.07	0.65 ± 0.13	0.85 ± 0.16	0.67 ± 0.04	1.77	0.19

Data was expressed by using mean ± SE. *Statistically significant at p ≤ 0.05.

^a+Statistically significant with Control

^b+Significant when compared with lymphoma patients before chemotherapy

^FF for ANOVA test p: p value for comparing between the studied groups.

Gene expression levels using Affymetrix

To identify the overall differentially expressed genes in the PBMCs of lymphoma patients, we used microarray technology (Affymetrix® to analyze the transcriptome, including the TLR pathways in patients before chemotherapy as compared to healthy controls. We found that 1106 genes are dysregulated (n = 1, linear fold change > 2.0 and ANOVA p < 0.05). The Volcano plot (Supplementary Figure S1A) shows that the distribution of the differential genes between the lymphoma patient before chemotherapy and healthy control. The scatter plot data (Supplementary Figure S1B) shows the distribution of signal values between the lymphoma patient before chemotherapy and healthy control on the plane of the rectangular coordinate system. The Cluster heat map (Supplementary Figure S1C) shows the differentially expressed genes.

We focused our analysis on the TLR signaling pathway, which includes 110 genes. In the TLRs pathways, there was a significant change (>2-fold; p < 0.05) in the expression profiles of 31 genes in patients before chemotherapy as compared to healthy control. Among these genes, 30 genes were up regulated (Figure 1b), including (PIK3R1, MAP3K8, IL6, IL8, CCL3, CCL4, NFKB1, TLR8, CD14, TNF, CXCL10, PIK3CG, LY96, MYD88, STAT1, TLR6, TLR3, IRAF3, IRAK1, RELA, MAP2K2, MAP2K4, IFNAR1, RAC1, MAP2K3, IKBKE, PI3K3CA, MAPK1, MAP2K3 and CCL5 and 1 gene was down regulated (TLR2) (Figure 1a).

Figure 1. Venn diagrams and heat map of expression levels of TLRs pathway genes (110 genes) among lymphoma patients (before, during and after chemotherapy) and healthy control: (a)down regulated and (b) up regulated(c) healthy control vs. before chemotherapy (d) before vs. during chemotherapy (e) during vs. after chemotherapy.

Lymphoma chemotherapy alters the expression of certain genes in the TLR pathways

In patient during chemotherapy, there were 9 genes which were up regulated (Figure 1b) and 11 genes were down regulated when compared to patients before chemotherapy (Figure 1a). When the fold change of patient before chemotherapy vs. healthy control were compared to with the fold change of patients after 4 cycles vs. healthy control, we found that chemotherapy tend to increase the expression of CD86 by 5.5-fold, FOS by 19.4-fold, and NF-κB2 by 2.42-fold. Interestingly, however, the expression of these genes in patient before chemotherapy was similar to their expression in the control.

Interestingly, after chemotherapy, there was a decrease in the number of up regulated genes (Figure 2a) and an increase in the number of down regulated genes (Figure 2b) than in the patient before chemotherapy. There were 16 up regulated genes and 9 down regulated after chemotherapy vs. patient before chemotherapy (Figure 3a,b). Additionally, we found an increase in the expression levels of two genes (TLR8 and STAT1) in the three patients before, during and after chemotherapy (Figure 1).

Figure 2. Venn diagrams and heat map of TLRs genes. Venn diagrams among lymphoma patient before and after chemotherapy in (a) up regulated and (b) down regulated genes (c) heat map between genes expression levels in patient before vs. after chemotherapy.

Figure 3. Differentially expressed genes in patients before vs. patient after chemotherapy. (a) Up regulated genes. (b) Down regulated genes.

Lymphoma chemotherapy alters the expression profiles of TL9 signaling pathway

TLR9 gene expression in the patient before chemotherapy showed no significance difference as compared to healthy control (1.2-fold change). There was no significant difference in the gene expression of TLR9 in patients during and after chemotherapy, which showed 1.-fold, and 1.2-fold, respectively. As compared to control, the gene expression of NF-κB1 showed significant increases in the patient before (33.9-fold), during (10.7-fold) and after (3.-fold) chemotherapy, while the expression profile of MyD88 showed increases by 2.96-, 4.86-, and 15.9-fold, respectively. After chemotherapy, however, the expression profile of NF-κB1 showed significant decrease as compared to patient before 11.1-fold). This was in contrast to of MyD88, which showed significant increase in its expression by 3.39-fold (Figure 4). Furthermore, we found that NF-κB1 is down regulated and Myd88 is up regulated after chemotherapy as compare to before chemotherapy.

Figure 4. The expression levels of selected genes (TLR9, MyD88 and NF-κB1) in NHL patients using microarray.

QRT-PCR of selected gene

Three genes were selected to validate the array results. The fold changes of mRNA in the three groups of NHL were normalized to controls in qRT-PCR Figure 5 and Table 4. The results of qRT-PCR was consistent with the findings recorded by gene array analysis above (Figure 6).

Figure 5. The expression levels of the TLRs/GAPDH mRNA in PBMCs of non-Hodgkin lymphoma (NHL). A: TLR9. B: MyD88. C: NF-KB1 Fold change ≥ 1.5, p < 0.05.

Figure 6. Q RT-PCR validation of the gene chip array result. Ratio of gene expression (a) healthy control vs before chemotherapy (b) healthy control vs. after chemotherapy by qRT-PCR (blue); and gene chip (violet).

Table 4. RT-PCR results of TLR9, NFKB1 and Myd88 genes show the expression in the groups of study.

Genes	Healthy control	Patients before chemotherapy	Patients during chemotherapy	Patients after chemotherapy	P value	F
TLR9	1.0 ± 0.07	1.08 ± 0.09^a-	0.92 ± 0.09^a- b-	1.09 ± 0.13^a-b-c-	0.58	0.66
NF-KB1	1.0 ± 0.07	3.75 ± 0.09^a+	2.33 ± 0.08^a+ b+	1.5 ± 0.12^a+ b+ c+	<0.0001	171.0
MyD88	1.0 ± 0.09	1.66 ± 0.14^a+	2.23 ± 0.07^a+ b+	3.2 ± 0.08^a+ b+ c+	<0.0001	89.11

^aNon-significant when compared with healthy control.

^bNon-significant when compared with lymphoma patients before chemotherapy.

^cNon-significant when compared with lymphoma patients during chemotherapy.

^a+Significant when compared with Healthy control.

^b+Significant when compared with lymphoma patients before chemotherapy.

^c+Significant when compared with lymphoma patients during chemotherapy.

Discussion

TLRs are expressed on innate immune cells like dendritic cells, macrophages, monocytes and neutrophils, and natural killer cells as well as in adaptive immune cells, including B and T (CD4+ and CD8+) lymphocytes [34]. Besides, TLRs are expressed on some cancer cells where their activation can induce cell death [35]. As such, TLRs play crucial roles in tumor rejection and anticancer immunity [36]. In the current study, we found alteration in the differential expression level of most of TLR signaling pathway genes, where most of them were up regulated in lymphoma patients before chemotherapy. Some of these genes were altered upon treating with the conventional chemotherapy, where expression of MyD88 and NF-κB1 genes was up regulated and down regulated, respectively, while TLR9 showed no differential expression.

Since the TLR9/MyD88/NF-κB signaling pathway may similarly regulate various diseases, it might serve as the pharmacological basis for the majority of medications that cure diseases by controlling the amounts of cytokines, inflammatory agents, and chemokines [37] According to several studies on TLRs and cancer, TLR9 was found to be expressed in several types of cancers such as oral cancer [38], colorectal cancer [39], pancreatic cancer [40], and marginal zone (MZ) B cells [41]. Previous studies reported that high expression of TLR9 is associated with tumor growth and migration, making it as a prognostic biomarker in some types of cancers. For instance, patients with peripheral T-cell lymphomas have been reported to have a poorer prognosis when TLR9 expression was high [42]. In the present study, we analyzed the expression level of TLR9 in NHL, where we could not find statistically significant difference between the expression level of TLR9 in before chemotherapy as compared to the health control. Our findings are similar to those of previous studies [43] who found no significant TLR9 expression between different lymphoma subtypes, except for diffuse large B-cell lymphoma (DLBCL) which showed low level of its expression [44].

After chemotherapy, TLR9 might modulate the anti-tumor immune response because it acts as DNA sensor, promoting maturation and migration of the professional antigen-presenting cells such as dendritic cells from the tumor microenvironment to the regional lymph nodes, where they subsequently activate tumor-specific CTLs leading to effective tumor control [45]. Moreover, activation of TLR signaling pathway leads to the up regulation of DNA repair genes, resulting in increasing of functional DNA repair system [46]. Indeed, this suggestion of the association between TLR 9 signaling and functional DNA repair is possible due to the presence of its binding sites in the promoter regions of various DNA repair genes of all the nucleotide excision repair (NER) genes, which contain a binding site for activator protein-1 (AP-1) [47]. The latter itself is a transcription factor that induced by TLR9 signaling. Our data demonstrate that there was no significant difference in TLR9 expression during and after chemotherapy as compared to patients before chemotherapy, indicating that critical impact of chemotherapy on the TLR9 more than the effect of the tumor itself.

Our data demonstrated statistically significant increase in NF-κB1expression in patients before chemotherapy as compared to the healthy control. Similar to our studies; NF-κB has shown a vital role in facilitating the oncogenesis of B-cell-like diffuse large B-cell lymphoma [48]. Although we have not addressed the mechanisms of how NF-κB1 impacts NHL prognosis, recent research suggests that dysregulated NF-κB activity contributes to both cancer and inflammatory illnesses, and NF-κB has long been suggested as a possible target for therapeutic intervention [49]. Moreover, it has been reported that there is a constitutive activation of NF-κB in cells of certain malignant tumors, including NHL [50]. Clinical evidence suggests that NF-κB1 has predictive significance in stomach cancer since elevated expression of the protein is associated with larger tumors and a higher chance of metastatic lymphogenesis [51,52]. Moreover, NF-κB is a key transcription factor that required for induction of a large number of pro inflammatory cytokines, such as (IL-6, IL-12, IL-10, TNF-α) and chemokines [53]. Numerous chemotherapeutics and targeted medications have now been demonstrated to change NF-κB1expression, which in turn affects tumor growth. For instance, it was stated that taxanes prevented NF-κB1from being activated, preventing metastases formation [54,55]. Doxorubicin which damages DNA, also activates NF-κB1pathway and makes tumor cells resistant to this anticancer agent. This is because doxorubicin inhibits the synthesis of DNA and RNA as well as the enzyme DNA topoisomerase II, preventing the transcription and replication of nucleic acids [56,57]. Indeed, our study found statistically significant increase in NF-κB1 expression in patients during and after chemotherapy than control, but still less than patients before chemotherapy. These data indicate that chemotherapy itself alters the expression of NHL-induced changes in NF-κB1 expression. In this regard, it has been demonstrated that NF-κB1 activation is connected to tumor resistance to different therapeutic substances [58]. Taken together, we can suggest that the decreased expression level of NF-κB1 after chemotherapy as compared to before and during chemotherapy could be due to the decrease in DNA damage.

MyD88 expression has been observed to be elevated in parenchymal cells across a range of cancer types [59,60]. MyD88 has been found to be essential for survival of DLBCL [61]. Additionally, in mature B-cell NHL and DLBCL, a correlation was observed between MyD88 overexpression and low clinical risk [62]. Patients with DLBCL often express and have mutations in the MyD88 gene, which might impact their prognosis and survival [63]. In our study, there was an increase in the MyD88 expression in before chemotherapy as compared to the healthy control. MyD88 is essential for the expression of the excision repair cross-complementation group 1 enzyme (ERCC1), the major DNA repair enzyme and it is an important element of the NER machinery. Moreover, NER play a vital role in removal and repair of the DNA damages in cancer cells which exposed to DNA damaging agents, thus protecting it from death [64]. As such, we may suggest that the increase in MyD88 expression levels in lymphoma patient could be a defensive mechanism to lower cell damage and increase repair, resulting in chemo resistance. MyD88 inhibition leads to defective ERCC1-dependent DNA repair and to accumulation of DNA damage, resulting in cancer cell death via p53 [65]. After chemotherapy, however, we found increases in MyD88 expression in lymphoma patients, especially after chemotherapy, which might explain some of the mechanisms mediating the anti-cancer effects of chemotherapy through enhancing MyD88.

In conclusion, this study suggests that targeting of TLR9/MyD88/NF-KB pathway may represent a novel therapeutic strategy that can help in treatment of NHL lymphomas. In addition, TLRs signaling pathway could become potential biological markers for predicting the response to chemotherapy in NHL patients.

Limitations of study

Small sample size.

Acknowledgments

The authors would like to thank Dr. Sohaila Galal, Lecturer of Immunology, Zoology Department, Faculty of Science, Tanta University, Egypt for her kind help in the technical part related to the transcriptome analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/2314808X.2024.2347129

Additional information

Funding

This study was supported by a grant [ID#5245], from the Science and Technology Development Fund, Ministry of Scientific Research Egypt to Prof. Mohammed L. Salem, the Principal Investigator of this project, and the funding Director of Center of Excellence in Cancer Research, Teaching Hospital, Medical Campus, Tanta, Egypt.