TIM3 and CTLA4 immune checkpoint polymorphisms are associated with acute myeloid leukemia in Saudi Arabia

ABSTRACT

Background

Immune checkpoints are receptors on the surface of T cells that function crucially in suppressing the immune response, and they are implicated in autoimmunity and cancer diseases.

Aim

The present study aimed to investigate the relationship between functional single nucleotide polymorphisms (SNPs) of two immune checkpoint molecules, CTLA-4 and TIM-3, and acute myeloid leukemia (AML) in a Saudi population.

Methods

Two SNPs in CTLA-4 (rs231775, A > G) and TIM-3 (rs10515746, A > C) were genotyped in 229 subjects, including 98 patients and 131 healthy controls, from the Saudi population using TaqMan assay methods. Differential expression of these two genes was performed using in silico analysis.

Results

An association was found between polymorphisms in TIM-3 (OR: 6.01; 95% CI: 3.99–9.05, P < 0.0001) and the risk of AML. Inversely, the rs231775 SNP in the CTLA-4 gene was found to protect against AML in allelic, dominant, and additive models (P < 0.05). A significantly higher expression of TIM-3 in the blood of individuals with AML was observed.

Conclusion

This is the first study focusing on single nucleotide polymorphisms (SNPs) for CTLA-4 and TIM-3 in acute myeloid leukemia patients in a Saudi community and could be a potential new prognostic factor for this disease.

KEYWORDS:

• Hematological malignancy
• CTLA-4
• TIM-3‌
• Immune checkpoint molecules
• single nucleotide polymorphism
• acute myeloid leukemia
• Saudi Arabia

1. Introduction

Leukemia refers to a group of malignancies that arise from hematopoietic stem cells and progenitor cells located inside the bone marrow. This condition leads to an atypical increase in the number of leukocytes, hence diminishing the count of erythrocytes, thrombocytes, and other cellular components that are in a healthy state [1]. Leukemia can be categorized into acute and chronic subtypes, which are distinguished by the rate of disease progression. There are four primary classifications of leukemia, including acute myeloid leukemia (AML), chronic myeloid leukemia (CML), acute lymphoblastic leukemia (ALL), and chronic lymphocytic leukemia (CLL).

Leukemia is a serious issue for world health. According to the World Health Organization, leukemia ranked 14th among all cancers with a global incidence of 474,519 cases reported globally in 2020 and 67,784 in North America with a mortality rate of about 3.2. ALL and AML [2]. According to national cancer registry data, leukemia ranks as the fifth most prevalent malignancy in Saudi Arabia, with an estimated yearly incidence of around 3.5 cases per 100,000 individuals [3]. Acute myeloid leukemia (AML) is a very aggressive hematological malignancy that arises from the myeloid progenitor cells. It is distinguished by the aberrant proliferation of blast cells in the bone marrow and various other tissues. Acute myeloid leukemia (AML) is the prevailing form of acute leukemia observed in the adult population, exhibiting a notable escalation in occurrence as individuals advance in age. Consequently, this condition imposes a substantial healthcare burden on the senior demographic [1]. Acute myeloid leukemia (AML) is characterized by molecular aberrations occurring at the cellular level, namely somatic mutations that interfere with the processes of cellular maturation and death. The presence of a wide range of mutations across individuals plays a significant role in the variability observed in disease progression and treatment outcomes across different subtypes of acute myeloid leukemia (AML). Chromosomal translocations, such as t(8;21) and t(15;17), play a significant role in defining key diagnostic categories [4]. However, the precise combination of mutations associated with these translocations offers crucial prognostic insights that can aid clinical decision-making.

The evasion of immune surveillance is becoming recognized as a significant characteristic of the development of acute myeloid leukemia (AML). The activation of T cells plays a crucial role in anti-tumor immunity since it involves the integration of signals originating from antigen detection, co-stimulation, and immunological checkpoint suppression. The process of enhancing the expression of specific immunological checkpoint proteins allows cancer cells to evade T cell responses by means of inhibitory signalling [5,6]. The Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and T-cell immunoglobulin mucin-3 (Tim-3) are the major targetable co-inhibitory receptors on T cell genes. They are responsible for encoding essential immunological checkpoint receptors that play a crucial role in the regulation of T cell activation [5,7]. CTLA-4 functions by engaging in a competitive interaction with CD28 for the purpose of binding to CD80/CD86 ligands found on antigen-presenting cells. This interaction ultimately leads to the inhibition of T cell proliferation and the generation of cytokines [8]. The protein TIM-3 interacts with its ligands, phosphatidylserine and galectin-9, resulting in the inhibition of Th1 immunity. Both receptors have intricate functions in the modulation of anti-tumor immune responses. [9] The presence of genetic variations in immune checkpoint genes, leading to modifications in protein structure and expression, has the potential to impact the immune response to cancer diseases [10]. The presence of the CTLA-4 (also referred to as CTLA-4 +49 A/G polymorphism) variant results in a threonine to alanine mutation, which causes a reduction in inhibitory activity due to a drop-in surface expression [11]. The minor rs231775 GG genotype is associated with improved outcomes of many cancer diseases, including solid and hematological malignancies [11–14]. The TIM-3 rs10515746 (also referred to as TIM-3 −574 A > C polymorphism) is located in the promoter region of the gene and the observed polymorphic allele change could result in a different regulation of TIM-3 function [15]. This polymorphism has been reported to be associated with many diseases, including cancers and the potential impairment in T cell functionality [16,17].

This study was conducted to explore the association between SNPs of two inhibitory immune checkpoint receptors (CTLA-4 rs231775 and TIM-3 rs10515746) and the risk of acute myeloid leukemia in the Saudi population.

2. Material and methods

2.1. Patients and healthy control

The current study included 98 Saudi patients with acute myeloid leukemia (AML), of which 44 females (44.90%) and 54 males (55.10%), were recruited from King Khaled Hospital. AML patients were diagnosed with a comprehensive analysis including full blood count, bone marrow examination, and flow cytometry. In addition to these tests, chromosomal and fluorescent in situ hybridization (FISH) were performed to verify the presence of AML. Blood sample collection from AML patients started in April 2018 and ended in February 2020 due to COVID-19 lockdown directives. To guarantee that only AML cases were included for analysis, participants who had been diagnosed with other cancers or chronic diseases were excluded from this study.

The control group consisted of 131 healthy volunteers including 49 females (37.40%) and 82 males (62.60%) matched for age and sex. The mean age of the study population was 27.56 ± 18.59 years for patients with AML and 29.35 ± 18.91 years for healthy controls. All the control subjects have no personal or family history with AML or any other chronic or immunological diseases.

All procedures performed in the study that involved human participants were in accordance with the Ethics Committee of the Faculty of Medicine, King Saud University, Riyadh, Saudi Arabia (Ref. No. 20/0800/IRB). All procedures were performed according to the 1964 Helsinki Declaration, and all subjects provided written informed consent.

3) SNP genotyping.

2.2. Samples collection and DNA extraction

Three ml of blood samples were drawn aseptically from each study group and stored at −20°C in ethylenediaminetetraacetic acid (EDTA)-containing tubes before analysis. The DNA of peripheral blood from AML patients and healthy controls was extracted by QIAamp DNA Mini Kit (Qiagen, Germany) according to the manufacturer’s instructions. The concentration of DNA was measured with a Nanodrop ND-2000c spectrophotometer (Thermo Scientific, Wilmington, DE, USA).

2.3. Single nucleotide polymorphisms (SNPs) selection

In this paper, two single nucleotide polymorphisms (SNPs) were identified in the immune checkpoint genes CTLA-4 (rs231775) and TIM-3 (rs10515746) through dbSNP databases (https:// www. NCBI.nlm.nih.gov/snp/). SNPs were selected according to their minor allele frequency (MAF) ≥ 5%.; Hardy-Weinberg equilibrium (HWE) P-value cut-off >0.005 (Table 1).

Table 1. Characteristics of selected polymorphisms involved in the CTLA-4 rs231775 and TIM-3 rs105157460.

Gene	SNP ID	Chromosome position	Nucleotide change	Region	MAF in Human populations (1000genomes Study)
					Global	European	American	South Asian	East Asian	Qatar
CTLA-4	rs231775	Chr2/ 203867991	A > G	Exon1	0.43	0.36	0.46	0.31	0.64	0.20
TIM-3	rs10515746	Chr5/ 157109557	A > C	promoter at −574	0.15	0.19	0.12	0.04	0.02	0.30

MAF: minor allele frequency;

Genotyping was performed by the VIC and FAM labelled allelic discrimination method, using assay-on-demand TaqMan assays ordered from Applied Biosystems according to the manufacturer’s instructions using an ABI Prism 7500 Real-time PCR system (Applied Biosystems, Foster City, USA). Real-time PCR was implemented in 10 μl a reaction system containing 0.26 µl 2x SNP Genotyping Assay, 5.5 µl 2xPower Taq MasterMix Mix, 2.24 µl Nuclease-Free Water, and 2 µl DNA template (100 ng/μl).

The PCR conditions for CTLA-4 rs231775 and TIM-3 rs10515746 were one cycle at 95 °C for 10 min followed by 40 cycles (95 °C for 15 s, 55 °C for 30 s, 72 °C for 30 s) and a final extension at 72 °C. Celsius for 5 min. For confirmation, about 5% of the samples were randomly selected for genotyping repeat.

2.4. In Silico gene expression profile and its relationship with survival prognostic

The difference in gene expression profiles and patient survival information were analysed with Gene expression profile Interactive Analysis (GEPIA) (Http:/gepia.cancer-pku.cn, accessed on August 2023) website based on TCGA transcription database developed by Peking University. Comparisons were performed between 173 patients and 70 controls from TCGA and GTEx [18]. The effect of the gene expression level (high vs low expression) of CTLA-4 and TIM-3 on the overall survival rate of patients with acute myeloid leukemia (AML) was performed using the online tool of Kaplan-Meier plot in the UALCAN database (http://ualcan.path.uab.edu/, accessed on 18 August 2023)

2.5. Statistical analysis

The relative risk associated with alleles and genotypes was calculated as an odds ratio (OR) with a 95% confidence interval (CI) for five inheritance models including co-dominant, dominant, recessive, over-dominant and log-additive models were performed using the web-based SNPStats software program [19] (accessed on 25 July 2023). The Akaike information criterion (AIC) was calculated to compare different models that fit the same data. The model with the lowest AIC is considered to be the best one.

All SNPs were tested for deviation to Hardy–Weinberg equilibrium using the Chi-square test. The threshold of significance was association was considered for P < 0.05.

3. Results

3.1. Association of CTLA-4 (rs231775A > G) polymorphisms with AML

Comparative distribution of the CTLA-4 + 49 A/G alleles and genotypes between patient and control groups in codominant, dominant, recessive and additive are shown in Table 2. Our results indicated that the allele frequency is lower in patients than in control (0.21 vs. 0.3). The MAF in the control group is in the range of many populations, including South Asian and European, but higher than Qatari and lower than American and East Asian populations (Table 1). Statistical analyses show a protective effect of the SNP polymorphism for AML in our population in dominant and additive models (Table 2). The GG and AG genotypes are more frequent in controls than in patients. The rs231775 G allele was associated with a decreased risk of developing AML (OR = 0.622; P = 0.031). In addition, in dominant model (AG + GG vs AA) is associated with a protective effect for AML (OR = 0.52; P = 0.017). Finally, the additive model confirms the overall association of rs231775 polymorphism with a protective effect for AML (OR = 0.59; P = 0.023). Based on the AIC value, the dominant model seems to be the best fitted one.

Table 2. Association between AML and CTLA-4 rs10515746 SNP.

Locus	Model	Genotype	Control (%) N = 131	AML (%) N = 98	OR (95% CI)	P*-value	AIC
CTLA-4 rs231775 A > G	Alleles	A	0.7	0.79	1.00	0.031
		G	0.3	0.21	0.622 (0.404–0.959)
	Codominant	AA	58 (45.3%)	60 (61.2%)	1.00	0.058	309.6
		AG	62 (48.4%)	34 (34.7%)	0.53 (0.31–0.92)
		GG	8 (6.2%)	4 (4.1%)	0.48 (0.14–1.69)
	Dominant	AA	58 (45.3%)	60 (61.2%)	1.00	0.017	307.6
		AG + GG	70 (54.7%)	38 (38.8%)	0.52 (0.31–0.90)
	Recessive	AA + AG	120 (93.8%)	94 (95.9%)	1.00	0.47	312.8
		GG	8 (6.2%)	4 (4.1%)	0.64 (0.19–2.18)
	Log-Additive				0.59 (0.37–0.94)	0.023	308.1

AML, acute myeloid leukemia; OR, odds ratio; CI, 95% confidence interval; AIC, akaike information criterion; significant associations are depicted in bold.

3.2. Association of TIM-3 (rs10515746A > C) polymorphisms with AML

Analysis of alleles and genotypes distribution among AML and healthy control for the rs10515746A > C polymorphism in the promoter at position −574 in the HAVCR2 gene is reported in Table 3. Our results indicated that the TIM-3 C allele was associated with more than 6 times to develop AML (p < 0.0001). The homozygous CC genotype is present in patients (38%) 4.8 times than in healthy (8%) causing a very high risk of developing the disease (OR  = 327.75; p < 0.0001). The increased risk of developing AML was observed also for the heterozygous AC genotype (OR = 7.47; CI: 3.29-16.99). The risk of AML was also tested and confirmed in dominant (A/C–C/C vs AA) and recessive models (C/C vs A/C-AA). In the dominant model, the risk of developing AML was 12.78 and 81 times for the recessive model. According to AIC criteria, the codominant model is considered the best fitted one.

Table 3. Association between TIM-3 rs10515746 SNP with AML.

Locus	Model	Genotype	Control (%) N = 131	AML (%) N = 98	OR (95% CI)	P*-value	AIC
TIM-3 rs10515746A > C	Alleles	A	0.76	0.35	1.00	<0.0001
		C	0.24	0.65	6.01(3.99–9.05)
	Codominant	AA	69 (53.1%)	8 (8.2%)	1.00	<0.0001	221.4
		AC	60 (46.1%)	52 (53.1%)	7.48 (3.29–16.99)
		CC	1 (0.8%)	38 (38.8%)	327.75 (39.48–2720)
	Dominant	AA	69 (53.1%)	8 (8.2%)	1.00	<0.0001	259.1
		AC + CC	61 (46.9%)	90 (91.8%)	12.73 (5.71–28.35)
	Recessive	AA + AC	129 (99.2%)	60 (61.2%)	1.00	<0.0001	249.5
		CC	1 (0.8%)	38 (38.8%)	81.70 (10.96–609.20)
	Log-Additive				11.89 (5.97–23.69)	<0.0001	222.6

AML, acute myeloid leukemia; OR, odds ratio; CI, 95% confidence interval; AIC, akaike information criterion; significant associations are depicted in bold.

3.3. Stratified analysis based on gender and age

The potential association between CTLA-4 rs231775, TIM-3 rs10515746 and risk for AML was evaluated by stratified analysis based on gender (Table 4) and age (Table 5). For all models and genotypes, no associations were observed with the studied polymorphisms and risk for AML.

Table 4. Correlation between CTLA-4 rs231775 and TIM-3 rs10515746 SNPs with AML after gender stratification.

Locus	Model	Genotype	AML Female N = 44	AML Male N = 54	OR (95% CI)	P-value	AIC
CTLA-4 rs231775A > G	Alleles	A	0.81	0.77	Ref	0.6
		G	0.19	0.23	1.25 (0.62–2.51)
	Codominant	AA	29 (65.9%)	31 (57.4%)	1.00	0.62	139.9
		AG	13 (29.6%)	21 (38.9%)	1.51 (0.64–3.56)
		GG	2 (4.5%)	2 (3.7%)	0.94 (0.12–7.08)
	Dominant	AA	29 (65.9%)	31 (57.4%)	1.00	0.39	138.1
		AG + GG	15 (34.1%)	23 (42.6%)	1.43 (0.63–3.27)
	Recessive	AA + AG	42 (95.5%)	52 (96.3%)	1.00	0.83	138.8
		GG	2 (4.5%)	2 (3.7%)	0.81 (0.11–5.98)
		AG	13 (29.6%)	21 (38.9%)	1.52 (0.65–3.54)
	Log-Additive				1.27 (0.63–2.57)	0.51	138.4
TIM-3 rs10515746A > C	Alleles	A	0.34 (30)	0.35 (38)	ref	0.88
		C	0.66 (58)	0.65 (70)	1.05(.58–1.86)
	Codominant	CC	18 (40.9%)	20 (37%)	1.00	0.85	140.5
		AC	22 (50%)	30 (55.6%)	1.23 (0.53–2.85)
		AA	4 (9.1%)	4 (7.4%)	0.90 (0.20–4.14)
	Dominant	CC	18 (40.9%)	20 (37%)	1.00	0.7	138.7
		AC + AA	26 (59.1%)	34 (63%)	1.18 (0.52–2.66)
	Recessive	CC + AC	40 (90.9%)	50 (92.6%)	1.00	0.76	138.7
		AA	4 (9.1%)	4 (7.4%)	0.80 (0.19–3.40)
		AC	22 (50%)	30 (55.6%)	1.25 (0.56–2.78)
	Log-Additive				1.06 (0.55–2.03)	0.86	138.8

AML acute myeloid leukemia, OR odds ratio, 95% CI 95% confidence interval, *p < 0.05 was considered significant and are depicted in bold.

Table 5. Correlation between CTLA-4 rs231775 and TIM-3 rs10515746 SNPs with AML after age stratification.

Locus	Model	Genotype	AML ≥ 29 (%) N = 55	AML < 29 (%) N = 43	OR (95% CI)	P-value	AIC
CTLA-4 rs231775A > G	Alleles	A	0.81	0.75	Ref	0.38
		G	0.19	0.25	1.36 (0/69–2.71)
	Codominant	AA	35 (63.6%)	25 (58.1%)	1.00	0.42	138.7
		AG	19 (34.5%)	15 (34.9%)	1.11 (0.47–2.58)
		GG	1 (1.8%)	3 (7%)	4.20 (0.41–42.77)
	Dominant	AA	35 (63.6%)	25 (58.1%)	1.00	0.58	138.1
		AG + GG	20 (36.4%)	18 (41.9%)	1.26 (0.56–2.85)
	Recessive	AA + AG	54 (98.2%)	40 (93%)	1.00	0.2	136.7
		GG	1 (1.8%)	3 (7%)	4.05 (0.41–40.39)
	Overdominant	AA + GG	36 (65.5%)	28 (65.1%)	1.00	0.97	138.4
		AG	19 (34.5%)	15 (34.9%)	1.02 (0.44–2.35)
	Log-Additive				1.38 (0.69–2.79)	0.36	137.5
TIM-3 rs10515746A > C	Alleles	A	0.69	0.64	ref	0.76
		C	0.31	0.36	1.12 (0.61–2.00)
	Codominant	CC	21 (38.2%)	17 (39.5%)	1.00	0.5	139
		AC	31 (56.4%)	21 (48.8%)	0.84 (0.36–1.95)
		AA	3 (5.5%)	5 (11.6%)	2.06 (0.43–9.87)
	Dominant	CC	21 (38.2%)	17 (39.5%)	1.00	0.89	138.4
		AC + AA	34 (61.8%)	26 (60.5%)	0.94 (0.42–2.14)
	Recessive	CC + AC	52 (94.5%)	38 (88.4%)	1.00	0.27	137.2
		AA	3 (5.5%)	5 (11.6%)	2.28 (0.51–10.13)
	Overdominant	CC + AA	24 (43.6%)	22 (51.2%)	1.00	0.46	137.8
		AC	31 (56.4%)	21 (48.8%)	0.74 (0.33–1.65)
	Log-Additive				1.14 (0.59–2.18)	0.7	138.2

AML acute myeloid leukemia, OR odds ratio, 95% CI 95% confidence interval, *p < 0.05 was considered significant and are depicted in bold.

3.4. In silico analysis of mRNA differential expression and prognostic

The expression levels of CTLA-4 and TIM-3 mRNA in AML samples and corresponding normal samples were analysed using the GEPIA database. This analysis included 173 AML and 70 normal individuals. While an over expression of CTLA-4 in AML is observed, this difference was statistically not significant CTLA-4 (Figure 1A). For the TIM-3, significant over expression in the patient group compared to the control was observed (Figure 2A). Kaplan-Meier analysis showed no significant association between CTLA-4 and TIM-3 gene expression and survival rate (Figure 1B and Figure 2B).

Figure 1. A, Expression of CTLA-4 in blood of AML patients (red) and normal blood (black). Each bar indicates the average level of expression of CTLA-4. Data were obtained from the GEPIA database were extracted (http://gepia.cancer-pku.cn). B: Kaplan-Meier analysis obtained plot from in the UALCAN database (http://ualcan.path.uab.edu/).

Figure 2. A: Expression of TIM-3 (HAVCR2) in blood of AML patients (red) and normal blood (black). Each bar indicates the average level of expression of TIM-3. Data were obtained from the GEPIA database were extracted (http://gepia.cancer-pku.cn). B: Kaplan-Meier analysis obtained plot of TIM-3 obtained from in the UALCAN database (http://ualcan.path.uab.edu/).

4. Discussion

Numerous co-stimulatory pathway molecules are known to function as immune checkpoint molecules, regulating the intensity of the immune response by providing conflicting signals to activated T cells. SNP Polymorphisms, particularly in immune genes, have been reported to play a critical role in the development and progression of cancer [20]. Understanding the functional effect of some genetic variants in genes coding for immune checkpoint molecules can help to inform personalized medicine approaches for more effective treatments of cancers [14,21]. In this context. cancer immunotherapy relies mainly on the manipulation of T-lymphocyte molecules that regulate the stimulatory or inhibitory function [22,23].

Recently, it has been noted that the prevalence of leukemia in the Kingdom of Saudi Arabia has increased compared to previous years. The number of cases in 2018 reached approximately 437,033 cases. In 2020, the number of new cases reached 27,885 [3]. Given the relatively high prevalence of leukemia in Saudi Arabia, especially among children, the association between some immune checkpoint receptors (CTLA-4 and TIM-3) and susceptibility to AML was discussed in this study.

Our results support a protective effect of the CTLA-4 rs231775 polymorphism from AML. Different studies have reached conflicting results about the association between 49 A > G polymorphism and cancers. Several investigations have demonstrated the significant immunoregulatory function that CTLA-4 polymorphisms play in hematological malignancies; nevertheless, the outcomes were conflicting. The first study was mentioned by Pavkovic et al. [24] who reported a higher frequency of G allele in chronic lymphocytic leukemia patients from Macedonia who had developed autoimmune hemolytic anemia. However, this study included only 30 patients and was not confirmed on a larger number of individuals. A strong association of the G allele with non-Hodgkin’s lymphoma was observed in the Italy population [25,26]. Recently [11] reported that patients with a CTLA4 rs231775 AA/AG genotype showed a median progression-free survival significantly lower than those with GG genotype. However, no correlations were found between this CTLA-4 polymorphism and ALL in the China population [27] and B-CLL in the Polish population [28]. A metanalysis including conducted by Dai et al. [29] including 9 case–control studies (7 Caucasians and 2 Asians reports) did not find an association between CTLA-4 A > G polymorphism and susceptibility to develop lymphoid malignancy. In a recent metanalysis study, the relationship between CTLA-4 rs231775 genetic variants and cancer risk was investigated for 87 case–control studies including 35,858 controls and 29,464 cases [30]. The overall analysis showed that the G polymorphism appears to decrease cancer risk. However, there were significant associations between CTLA-4 polymorphisms and two types of cancers including colorectal cancer and thyroid cancer but, significantly decreased associations were detected in six types of cancers; breast cancer, liver cancer, cervical cancer, bone cancer; head and neck and pancreatic cancer [30]. When malignancies were categorized according to their systems, rs231775 was linked to an elevated chance of developing digestive tract cancer, but a decreased risk of developing orthopedic, urinary, and gynecological tumors. Ethnic stratification revealed that rs231775 conferred protection to both Asians and Caucasians. This discrepancy could be explained by the lowering CTLA-4 function via reduced surface expression, the G allele may strengthen anti-tumor immune responses, thereby decreasing cancer risk for certain cancers [31]. However, this could also break self-tolerance and increase autoimmunity in some individuals, inadvertently increasing cancer risk through chronic inflammation [32]. Potential determinants of the impact include tumor immunogenicity or mutations, individual immunological factors, and gene-pathway interactions [32].

For the TIM-3 rs10515746A > C, our results indicated a strong association between this polymorphism and the risk for AML. Studies on the association between this polymorphism and other cancer diseases are scarce. It was shown that the rs10515746 polymorphism had a considerably greater risk effect for renal cell carcinoma [33] and stomach cancer [34]. In contrast to these results, no correlation between the risk of developing pancreatic cancer [35], colorectal cancer [36], epithelial ovarian cancer [37], oesophageal squamous cell carcinoma [17] and invasive breast cancer [38]. The two studied genes were no longer associated with AML following stratifications based on gender and age. This could indicate that gender and age may not significantly influence the link between polymorphism and AML.

The in-silico analysis did not show significant over expression of CTLA-4 in blood. Nonetheless, a number of studies have shown mRNA, protein, or cell surface upregulation of the immunological checkpoint CTLA-4 in several kinds of cancers relative to healthy tissues, indicating that it may play a role in tumor immune evasion. In CRC, CTLA-4 expression levels in tumor tissues were markedly increased compared to non-tumor tissues [12,39,40]. An examination of melanoma of the skin revealed that CTLA-4 expression was considerably elevated in tumor tissues in comparison to healthy skin [41,42]. Significantly elevated CTLA-4 expression was seen in head and neck squamous cell carcinoma (HNSCC) tissues as opposed to normal tissues; furthermore, high CTLA-4 expression was associated with inferior overall survival [43,44]. CTLA-4 expression was considerably increased in diffuse large B-cell lymphoma of the lymphoid neoplasm compared to normal tissues [45]. The expression of CTLA-4 was notably elevated in pancreatic tumor tissues including pancreatic adenocarcinoma [46,47]. Furthermore, lung cancer cells and tissues had substantially greater quantities of CTLA-4 protein and mRNA than normal lung tissues [48]. Lastly, compared to normal controls, breast cancer tumor-infiltrating lymphocytes and tumor tissues exhibited elevated CTLA-4 expression; high CTLA-4 levels were associated with shorter survival [49,50].

Whereas the majority of studies establish a link between higher CTLA-4 and cancer, a limited number of investigations failed to establish a link between CTLA-4 expression and cancer across a spectrum of tumor types, suggesting that it is context-dependent. This is the case of gastric cancer, thyroid cancer [51], prostate cancer [52,53], and cervical cancer [54]. Our in-silico analysis of TIM-3 expression in the blood of AML patients shows significantly higher expression of TIM-3 in the blood of AML patients than normal ones. It has been demonstrated that elevated TIM-3 expression is related to advanced disease and a bad prognosis in a number of malignancies [13]. The expression of TIM-3 is predominantly detected intratumorally, while expression in regulatory T cells and peripheral T cells is limited. Recently, Qin et al, Qin et al. [55] performed an exhaustive meta-analysis and database assessment to examine the predictive significance of TIM-3 expression in solid tumors. A comprehensive compilation of 21 research, including 3072 individuals, was utilized to analyze the protein expression of TIM-3 in a range of malignancies, including lung, gastric, colorectal, and liver. High TIM-3 expression, specifically on tumor cells, was shown to be strongly related to poor overall survival across all solid tumors, according to the meta-analysis. TIM-3 was associated with a worse OS in ESCC, NSCLC, gastric, and other types of cancers, according to subgroup analysis. Additionally, lymph node metastases, greater tumor grade, and co-expression of PD-1 on immune cells were all associated with increased TIM-3 expression. Although no significant correlation was found with disease-free survival (DFS) in general, individuals with NSCLC who had a high TIM-3 score had a lower DFS. Furthermore, a survival study of TIM-3 mRNA levels in databases revealed a significant correlation between elevated expression and worse survival in both stomach and lung cancers, which is similar to the findings regarding protein expression. In summary, our results offer initial proof that genetic differences in TIM-3 and CTLA-4 could affect immune surveillance pathways and thereby affect AML etiology. There were possible correlations between the CTLA-4 + 49A > G and TIM-3 −575A > C polymorphisms with risk for AML. Our study does, however, have certain shortcomings. The sample size of AML patients was modest because it was a pilot project. Greater numbers of participants are required to confirm these genotype-phenotype associations and more fully understand the functions of the TIM-3 and CTLA-4 polymorphisms. To clarify the underlying molecular processes by which these SNPs may modify immune function and protein production, more functional research is also necessary. In addition, more in-depth information may be obtained in the future for multivariate model analysis that accounts for clinical variables.

Author contributions

Conceptualization, LM; methodology, MA, LM, AA; validation, SAO, LM; investigation, MA, LM, AA and SAO.; resources, LM and SA; data curation, MA and LM.; writing original draft preparation, LM and MA; supervision LM and SA. All authors have read and agreed to the published version of the manuscript.

Institutional review board statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethic Committee of King Saud University under the number, 21-6508 and dated 05 April 2022.

Informed consent statement

Informed consent was obtained from all subjects involved in the study.

Acknowledegments

The authors extend their appreciation to the Researchers Supporting Project number (RSP2024R75), King Saudi University, Riyadh, Saudi Arabia.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

All data relevant to the study are included in the article.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.