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Abstract

Several genetic association studies have analyzed the link between the catalase (CAT) C262T variant and different cancers, but the findings remain controversial. Our research centered on establishing a comprehensive correlation between the C262T variant and different cancers. This study was conducted using RevMan 5.4 software following the PRISMA 2020 guidelines. For this meta-analysis, 53 case-control studies (18,258 cases and 47,476 controls) were chosen. The analysis revealed that three genetic models were statistically linked (P < 0.05) to overall cancer susceptibility in codominant model 2 (COD2): odds ratio (OR) = 1.16, COD3: OR = 1.21, recessive model (RM): OR = 1.20). After stratification by ethnicity, a significant link (P < 0.05) was found in Caucasians (COD2: OR = 1.18, COD3: OR = 1.17, over-dominant model (ODM): OR = 1.19) and Asians (COD3: OR = 1.49). Subgroup analyses revealed a significant correlation (P < 0.05) with blood-and-bone-marrow-related cancer, skin cancer, gastrointestinal-tract-related cancer, prostate cancer, and gynecologic cancer. Three genetic models in population-based controls (COD2: OR = 1.19, COD3: OR = 1.17, RM: OR = 1.19) and two genetic models in hospital-based controls (COD3: OR = 1.40, RM: OR = 1.24) were found to be significantly correlated (P < 0.05) with cancer. Also, three genetic models for polymerase chain reaction-restriction fragment length polymorphism (COD3: OR = 1.46; RM: OR = 1.34, ODM: OR = 0.80) and three models for MALDI-TOF + MassARRAY (COD2: OR = 1.32, RM: OR = 1.26, allele model: OR = 1.14) genotyping methods showed significant association (P < 0.05) with cancer. The meta-regression showed that the quality scores might be a source of significant heterogeneity under the COD2 model (coefficient = 0.176, P = 0.029). Trial sequential analysis also validated the adequacy of the sample size on overall findings. Our results indicate that CAT C262T variant is associated with overall cancer susceptibility, especially in Caucasians and Asians. This variant may also be associated with blood-and-bone-marrow-related, GIT-related, prostate, skin, and gynecological cancers.

Keywords

Catalase Polymorphism Meta-analysis Cancer

Introduction

Cancer is a major worldwide public health issue at present. The prevalence of cancer and cancer-related morbidity and mortality are rising at an alarming rate. Based on the GLOBOCAN estimation, in 2020, more than 19 million people were supposed to be affected by different cancers and approximately 10 million people were supposed to die due to cancer.¹ In 2020, the most common cancer was breast cancer (2.3 million new cases) and the fifth-highest reason for global death. The second most frequent cancer and the leading cause of mortality was lung cancer, with an estimated 2.2 million cases and 1.8 million deaths in 2020. Colorectal carcinoma was the third-highest prevalent cancer and the second most common cause of death in the world. Cancer progresses for multiple reasons, including genetic, racial, environmental, lifestyle, infection, and other associated factors.^1,3

Superoxide anion radicals, hydroxyl radicals, hydrogen peroxide, and singlet oxygen are examples of reactive oxygen species (ROS) that play significant roles in the regulation of cellular function. They may cause serious damage to macromolecules (DNA, protein, and lipids) at high or prolonged amounts, resulting in cancer.⁴ As a result, radicals are now regarded as an important cause of malignancies. Endogenous antioxidants (e.g., superoxide dismutase (SOD), glutathione peroxidase, catalase (CAT), butyrylcholinesterase, and thioredoxin-peroxiredoxin) and non-enzymatic defenders (e.g., glutathione, vitamins A, ascorbic acid, tocopherol, and flavonoids) counteract the effects of ROS. SODs are enzymes that convert superoxide radicals into hydrogen peroxide and oxygen.⁵ The CAT enzyme is an endogenous heme enzyme that converts H₂O₂ to H₂O to neutralize ROS.⁵

CAT is an essential protein commonly found in erythrocytes, liver, and kidneys. This 240 kDa tetrameric enzyme with four porphyrin heme (iron) groups is mostly found in peroxisomes. The CAT gene, which spans 34 kb and is made up of 13 exons and 12 introns, is located in the 11p13 locus.⁶ Several polymorphisms in the CAT gene have been discovered, with rs1001179 (C262T) being the most widely investigated. It produces a cytosine (C) to thymine (T) alteration in the promoter region at position −262 (262 C > T). Many studies have revealed that genetic polymorphisms can affect protein expression and function, and they have shown a possible link between CAT C262T polymorphism and cancer risk. According to several studies, the CAT C262T genetic variant has been linked to prostate cancer, invasive cervical cancer, breast cancer, hepatocellular carcinoma, and endometriosis, among other cancers.^7,10 However, these findings were inconsistent and inconclusive.

Based on the inconsistencies of the previously available genetic association studies, a meta-analysis is much needed to achieve a comprehensive outcome. Therefore, the goal of this study was to find a possible connection between CAT C262T polymorphism and the risk of various cancers.

Materials & methods

Methods of literature search

We followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 principles for our meta-analysis.¹¹ From October 15, 2021 to December 07, 2021, the articles were carefully reviewed in electronic literature resources such as PubMed, ScienceDirect, the Wiley Online Library, BMC, and the Cochrane Library. Duplicate studies were eliminated using EndNote × 7.0 software. The following terms—“catalase”, “CAT”, “C262T”, “rs1001179”, “polymorphism”, “variant”, “cancer”, “tumor”, “malignancy”—were used in searches, either alone or in combination. In this meta-analysis, no language or country restrictions were applied, and only online literature was considered for evaluation. We looked over the reference lists of all of the papers we selected to see if any studies were missing.

Study eligibility

For studies to be included, the following criteria were implemented: (a) human-sample-based case-control or cohort studies; (b) cancer was assessed as the major outcome of the study; (c) studies analyzed CAT C262T polymorphism and various cancers; and (d) the publications provided enough data to assess odds ratio (OR) and 95% confidence interval (CI) values.

The criteria for exclusion were: (a) reviews, expert opinions, letters to the editor, and case reports; (b) duplicate or overlapped research; (c) articles that examined other CAT polymorphisms; and (d) animal studies.

Data extraction

The data were collected individually by two authors (MAB and SJ), followed by the above-mentioned criteria. They individually performed a literature review and evaluation, and entered data into an Excel spreadsheet. If a disagreement appeared during the inquiry, it was resolved through discussion by other investigators (MAA and MSI). The first author's name, publication year, country of publication, ethnicity, cancer types, age of cases and controls, cancer stages, genotyping methods, type of control, number of patients and controls, genotypes, and P-value from HWE were selected for data collection from each study. To select publications for the meta-analysis, the Rayyan QCRI was applied.¹²

Methodological quality assessment

To assess the quality of the included studies, the Newcastle–Ottawa Scale (NOS) was applied.¹³ The Jadad scale was used to verify data integrity in randomized controlled trials, if necessary.¹⁴

Statistical analysis, heterogeneity, and publication bias

The Review Manager 5.4 (RevMan 5.4) software was applied to investigate the link between CAT C262T polymorphism and various malignancies. To assess the significance of the link, seven distinct genotypic models were used: codominant model 1 (COD1), COD2, COD3, dominant model (DM), over-dominant model (OD), recessive model (RM), and allele model (AM). We employed the χ²-based I²-statistic and the Q-test to assess potential heterogeneity. If the P-value was lower than 0.1, the Q results indicated significant heterogeneity, and a random-effects model was applied. In other cases (P > 0.1), the fixed-effects model was applied. Sensitivity assessments were carried out by deleting chosen studies one at a time in order to classify any substantial risk differences across studies for each parameter. The funnel plot asymmetry, Begg–Mazumdar rank correlation, and Egger's test were applied to evaluate the publication biases. The threshold for statistically significant publication bias was P ≤ 0.05. In addition, various subgroups were implemented in our investigation, including ethnicity, cancer types, genotyping methods, and type of control sources (hospital or population).

The meta-regression analysis was also performed to evaluate the impact of the quality scores of studies on overall findings or to identify the source heterogeneity. In addition, trial sequential analysis (TSA) was applied to test whether the estimated sample sizes are adequate enough to generate a statistically significant result.

Results

Characteristics and quality assessment of eligible studies

There were 321 entries identified in five databases during the primary search. A total of 185 documents were eliminated due to redundancy; 25 articles were excluded for being discursive studies; 21 studies were eliminated after reading the manuscript title and full abstract; 15 were removed from the rest 90 studies for different arguments and reasons. Finally, based on the inclusion and elimination characteristics, 53 complete studies^7,9,15,58 with 18,258 cancer patients and 47,476 controls, were included for the assessment in this meta-analysis (Figure 1 and Table 1).

Figure 1.

Study selection process according to PRISMA guidelines.

Table 1.

Characteristics of the selected studies for detecting the connection of CAT C262T (rs1001179) polymorphism with cancer.

Study ID	Country	Ethnicity	Cancer type	Age (Mean ± SD)		Cancer stage	Genotyping method	Control type	Cases	Controls	Cases			Controls			HWE P-value
Study ID	Country	Ethnicity	Cancer type	Cases	Controls	Cancer stage	Genotyping method	Control type	Cases	Controls	TT	TC	CC	TT	TC	CC	HWE P-value
Abbas 2021	Iraq	Caucasian	BC	NIA	NIA	NIA	PCR-RFLP	PB	70	30	5	18	47	2	0	28	0.000
Ahn 2005	USA	Caucasian	BC	58.60	56.10	NIA	MALDI-TOF	PB	1008	1056	45	349	614	42	335	679	0.933
Aynali 2013	Turkey	Caucasian	Laryngeal cancer	NIA	NIA	NIA	PCR-RFLP	HB	25	23	2	10	13	0	11	12	0.132
Banescu 2014	Romania	Caucasian	CML	51.60 ± 13.28	47.80 ± 14.40	NIA	PCR-RFLP	PB	168	321	14	49	105	21	132	168	0.466
Banescu 2016	Romania	Caucasian	AML	51.70 ± 16.76	46.46 ± 14.50	NIA	PCR-RFLP	PB	102	303	6	41	55	19	123	161	0.482
Castaldo 2015	Portugal	Caucasian	CC	45.01 ± 13.07	50.29 ± 14.86	NIA	PCR-RFLP	PB	119	106	36	25	58	14	27	65	0.001
Cebrian 2006	UK	Caucasian	BC	NIA	NIA	NIA	TaqMan	PB	2171	2262	113	707	1351	113	787	1362	0.960
Ceylan 2016	Turkey	Caucasian	PC	64.90 ± 6.70	53.20 ± 14.70	NIA	PCR-RFLP	HB	78	75	17	30	31	6	25	44	0.372
Choi-1 2007	USA	Caucasian	PC	NIA	NIA	NIA	MALDI-TOF	PB	463	1233	25	157	281	56	445	732	0.261
Choi-2 2007	USA	African	PC	NIA	NIA	NIA	MALDI-TOF	PB	27	120	0	3	24	0	11	109	0.599
Cosma 2019	Romania	Caucasian	NHL	NIA	NIA	NIA	PCR-RFLP	PB	91	315	5	32	54	20	130	165	0.401
Cuchra 2015	Poland	Caucasian	BC	57.00 ± 12.00	67.00 ± 14.00	36 (I), 90 (II), 44 (III)	PCR-RFLP	HB	126	126	5	51	70	2	71	53	0.000
Datkhile 2020	India	Asian	CC	48.67 ± 13.78		NIA	PCR-RFLP	HB	350	400	118	199	33	114	244	42	0.000
Ding 2012	China	Asian	PC	71.30 ± 8.10	62.10 ± 10.00	NIA	MassARRAY	PB	1417	1008	2	99	1316	1	67	940	0.863
Ebrahimpour 2014	Iran	Caucasian	GC	57.30 ± 12.80	56.80 ± 10.00	NIA	PCR-RFLP	PB	160	241	6	49	105	8	86	147	0.281
Eras 2021	Turkey	Caucasian	Leukemia	51.30	49.30	NIA	PCR-RFLP	PB	102	112	15	28	59	7	44	61	0.802
Ezzikouri 2010	Morocco	Caucasian	HCC	59.30 ± 14.10	56.40 ± 10.10	NIA	PCR-RFLP	PB	96	222	6	14	76	4	45	173	0.593
Farawela 2012	Egypt	Caucasian	NHL	NIA	NIA	38 (IA), 3 (IB), 17 (IIA), 2 (IIB), 28 (IIIA), 6 (IIIB), 3 (IVA), 3 (IVB)	PCR-RFLP	PB	100	100	25	49	26	19	53	28	0.492
Franko 2018	UK	Caucasian	MM	NIA	NIA	NIA	TaqMan	HB	123	83	15	64	44	5	47	31	0.020
Funke 2009	Germany	Caucasian	CRC	NIA	NIA	NIA	Pyrosequencing	PB	632	605	23	235	374	26	231	348	0.108
Geybels 2015	Netherland	Caucasian	PC	72.30 ± 5.30	NIA	952 (III/IV), 595 (IV)	MassARRAY	PB	1529	25184	103	539	887	1282	8108	15794	0.000
He-1 2010	USA	Caucasian	BCC	NIA	NIA	NIA	TaqMan	PB	270	796	12	97	161	32	252	512	0.887
He-2 2010	USA	Caucasian	Melanoma	NIA	NIA	NIA	TaqMan	PB	211	796	7	75	129	32	252	512	0.887
He-3 2010	USA	Caucasian	CSCC			NIA	TaqMan	PB	266	796	10	96	160	32	252	512	0.887
Ho 2006	China	Asian	LC	55.60 ± 11.90	54.80 ± 12.60	204 (III-IV)	PCR-RFLP	HB	230	240	2	19	209	0	23	217	0.436
Jamhiri 2017	Iran	Caucasian	CRC	54.40 ± 14.00	53.40 ± 10.80	NIA	PCR-RFLP	PB	204	239	5	67	132	8	85	146	0.300
Kagita 2021	India	Asian	CML	NIA	NIA	NIA	PCR-RFLP	PB	125	200	8	50	67	13	71	116	0.634
Kakkoura 2016	Cyprus	Caucasian	BC	NIA	NIA	NIA	TaqMan	PB	1056	1142	79	369	608	71	399	672	0.257
Karunasinghe 2012	New Zealand	Mixed	PC	66.45	57.40	NIA	TaqMan	PB	258	434	15	99	144	16	160	258	0.145
Kopp 2017	Denmark	Caucasian	BC	NIA	NIA	NIA	KASP	HB	703	703	44	251	408	41	253	409	0.821
Li 2009	USA	Caucasian	BC	NIA	NIA	NIA	TaqMan	PB	497	493	26	176	295	23	167	303	0.999
Lightfoot 2006	UK/USA	Mixed	NHL	52.40 ± 9.80	54.90 ± 12.30	NIA	TaqMan	PB	909	1437	57	298	554	72	498	867	0.964
Liu 2015	China	Asian	HCC	49.38 ± 11.14	46.50 ± 6.94	NIA	PCR-RFLP	HB	266	248	0	27	239	1	24	223	0.684
Moradi 2018	Iran	Caucasian	OC	48.00 ± 5.00	NIA	NIA	AS-PCR	HB	74	153	8	38	28	2	53	98	0.078
Nahon 2011	France	Caucasian	HCC	55.93	NIA	NIA	TaqMan	HB	84	55	1	21	62	4	19	32	0.617
Pascual-Geler 2019	Spain	Caucasian	PC	NIA	NIA	NIA	TaqMan	HB	150	145	9	40	101	8	48	89	0.651
Patil 2020	India	Asian	GC	59.00 ± 13.33	57.46 ± 11.64	NIA	PCR-RFLP	HB	200	400	66	44	90	116	178	106	0.029
Quick-1 2008	USA	Caucasian	BC	63.30 ± 8.40	63.70 ± 8.50	NIA	MALDI-TOF	PB	569	974	27	197	345	43	333	598	0.696
Quick-2 2008	USA	Mixed	BC	63.3 ± 8.4	63.70 ± 8.50	NIA	MALDI-TOF	PB	47	108	0	13	34	1	10	97	0.222
Rajaraman-1 2008	USA	Caucasian	Glioma	51.20	49.20	NIA	TaqMan	HB	330	438	11	124	195	23	164	251	0.569
Rajaraman-2 2008	USA	Caucasian	Meningioma	54.80	49.20	NIA	TaqMan	HB	120	438	8	39	73	23	164	251	0.569
Rajaraman-3 2008	USA	Caucasian	AN	51.70	49.20	NIA	TaqMan	HB	63	438	3	17	43	23	164	251	0.569
Saadat 2015	Iran	Caucasian	BC	45.20 ± 10.70	43.90 ± 8.80	NIA	PCR-RFLP	PB	407	395	17	129	261	23	132	240	0.396
Sousa 2016	Brazil	Mixed	HCC	63.00 ± 8.60	52.50 ± 11.80	NIA	TaqMan	HB	106	139	3	35	68	4	32	103	0.440
Su-1 2015	China	Asian	HCC	NIA	NIA	NIA	PCR-RFLP	HB	301	186	1	27	273	0	18	168	0.488
Su-2 2015	China	Asian	HCC	NIA	NIA	NIA	PCR-RFLP	HB	99	294	0	7	92	1	29	264	0.831
Sukiennicki 2019	Poland	Caucasian	LC	67.94	67.62	59 (I), 38 (II), 68 (III), 26 (26)	TaqMan	PB	200	200	13	77	110	8	77	115	0.265
Tang 2010	USA	Mixed	Pancreatic cancer	NIA	NIA	NIA	TaqMan	PB	551	602	28	174	349	29	207	366	0.969
Tefik 2013	Turkey	Caucasian	PC	64.30 ± 7.47	62.80 ± 7.58	NIA	PCR-RFLP	PB	155	195	33	64	58	20	68	107	0.071
Titov 2017	Russia	Caucasian	LC	59.00 ± 7.10	57.00 ± 8.10	140 (I), 98 (III-IV)	TaqMan	PB	238	256	19	79	140	24	89	143	0.071
Trifa 2016	Romania	Caucasian	MPN	NIA	NIA	NIA	PCR-RFLP	PB	328	363	15	119	194	28	157	178	0.411
Tsai 2012	Taiwan	Asian	BC	49.60 ± 11.3	49.10 ± 14.80	32 (0), 77 (I), 100 (II), 51 (III)	TaqMan	HB	260	224	0	35	225	0	22	202	0.440
Vodusek 2015	Slovenia	Caucasian	TC	NIA	NIA	NIA	TaqMan	HB	24	24	1	7	16	1	7	16	0.834
Total									18,258	47,476	1114	5658	11,486	2483	15,429	29,564

AML: acute myeloid leukemia; AN: acoustic neuroma; BC: breast cancer; BCC: basal cell carcinoma; CC: cervical cancer; CML: chronic myeloid leukemia; CRC: colorectal cancer; CSCC: cutaneous squamous cell carcinoma; GC: gastric cancer; HB: hospital-based; HCC: hepatocellular carcinoma; LC: lung cancer; MM: malignant mesothelioma; MPN: myeloproliferative neoplasms; NHL: non-Hodgkin lymphoma; NIA: no information available; OC: ovarian cancer; PB: population-based; PC: prostate cancer; TC: testicular cancer.

In this meta-analysis, 38 studies of Caucasians, 9 of Asians, and 6 of mixed and other populations were analyzed. The included studies were also categorized based on cancer as: 5 studies were blood-and-bone-marrow-related cancer (chronic myeloid leukemia + acute myeloid leukemia + leukemia + myeloproliferative neoplasms), 3 lung, 11 breast, 3 non-Hodgkin lymphoma, 4 gastrointestinal tract (GIT)-related (gastric cancer + colorectal cancer), 8 prostate, 4 head and neck cancer and central nervous system cancer (glioma + meningioma + acoustic neuroma + laryngeal cancer), 3 skin (basal cell carcinoma + melanoma + cutaneous squamous cell carcinoma), 3 gynecological (cervical cancer + ovarian cancer), 6 hepatocellular, and 3 other cancers (pancreatic cancer + testicular cancer + multiple myeloma). A total of seven different genotyping methods (polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), MALDI-TOF, TaqMan, MassARRAY, pyrosequencing, kompetitive allele-specific PCR (KASP), and allele-specific-PCR (AS-PCR)) were used for the identification of polymorphisms in patients and controls. The sample sizes ranged from 24 to 2,171 in cases and from 23 to 25,184 in controls. The general properties of all studies are listed in Table 1.

According to the quality assessment tool (NOS), the maximum studies included had a score of more than 5 (high quality). Only one study was of moderate quality (score 5; Table S1).

Meta-analysis on overall cancer and ethnicity

Our findings demonstrated that the CAT C262T SNP was significantly linked with higher cancer risk in three genotype models: COD2 (CC vs. TT: OR = 1.16, CI = 1.03–1.32, P = 0.018), COD3 (CC vs. TC: OR = 1.21, CI = 1.11–1.33, P = 1.82 × 10⁻⁵), and RM (CC vs. TT + TC: OR = 1.20, CI = 1.10–1.31, P = 1.95 × 10⁻⁵) (Table 2, Figure 2). The connection between C262T variant and cancer risk in the Caucasian population was evaluated in 38 case-control studies with a number of 13,112 cancer patients and 41,436 controls. As shown in Table 2, COD2 (OR = 1.18, CI = 1.03–1.38, P = 0.027), COD3 (OR = 1.17, CI = 1.06–1.29, P = 0.003), and the ODM (OR = 1.19, CI = 1.04–1.36, P = 0.011) showed a significant link (P > 0.05) with cancer. In Asians, nine articles with 3,248 patients and 3,200 controls were assessed. Only one genetic model, COD3 (OR = 1.49, CI = 1.17−1.91, P = 0.002), showed a strong association with cancer. No statistically significant relationship was observed in any genetic model in the mixed and other populations, as shown in Table 2 and Figure S1.

Figure 2.

Table 2.

Meta-analysis for detecting the connection of CAT C262T (rs1001179) polymorphism with overall cancer and ethnicity.

Genetic Model	No. of studies	Test of association			Test of heterogeneity			No. of studies	Test of association			Test of heterogeneity
Genetic Model	No. of studies	OR	95% CI	P-value	Model	P-value	I² (%)	No. of studies	OR	95% CI	P-value	Model	P-value	I² (%)
Overall								Caucasians
COD1	53	0.97	0.90–1.05	0.448	Random	<0.001	56.29	38	0.97	0.90–1.05	0.505	Random	0.001	48.23
COD2		1.16	1.03–1.32	0.018	Random	0.017	32.04		1.18	1.03–1.38	0.027	Random	0.005	41.4
COD3		1.21	1.11–1.33	1.82 × 10⁻⁵	Fixed	0.281	9.6		1.17	1.06–1.29	0.003	Fixed	0.244	13.09
DM		1.01	0.94–1.09	0.836	Random	<0.0001	58.91		1.01	0.93–1.09	0.877	Random	<0.001	59.06
RM		1.20	1.10–1.31	1.95 × 10⁻ ⁵	Fixed	0.262	10.62		0.96	0.90–1.03	0.242	Random	0.009	38.95
ODM		0.95	0.89–1.02	0.152	Random	<0.001	50.65		1.19	1.04–1.36	0.011	Random	0.045	29.9
AM		1.04	0.98–1.11	0.156	Random	<0.001	57.98		1.04	0.97–1.12	0.261	Random	<0.001	64.45
Asian								Mixed and other population
COD1	9	0.88	0.62–1.25	0.474	Random	0.0001	75.02	6	1.14	0.88–1.47	0.330	Random	0.024	61.37
COD2		0.91	0.67–1.22	0.524	Fixed	0.522	0		1.22	0.93–1.61	0.151	Fixed	0.873	0
COD3		1.49	1.17–1.91	0.002	Fixed	0.370	7.74		1.26	0.95–1.67	0.110	Fixed	0.761	0
DM		0.93	0.71–1.23	0.626	Random	0.005	63.73		1.14	0.90–1.44	0.277	Random	0.041	56.74
RM		1.23	0.98–1.54	0.070	Fixed	0.961	0		1.24	0.95–1.62	0.121	Fixed	0.903	0
ODM		0.87	0.65–1.17	0.366	Random	0.0005	71.3		1.12	0.87–1.45	0.386	Random	0.022	62
AM		1.00	0.89–1.13	0.986	Fixed	0.259	20.74		1.05	0.95–1.16	0.338	Fixed	0.110	44.36

AM: allele model (T vs. C); COD1: codominant 1 (TC vs. CC); COD2: codominant 2 (TT vs. CC); COD3: codominant 3 (TT vs. TC); DM: dominant model (TT + TC vs. CC); ODM: over-dominant model (TC vs. TT + CC); RM: recessive model (TT vs. TC + CC).

Meta-analysis based on cancer types

Three genetic models, including COD1 (OR = 0.77, CI = 0.64−0.93, P = 0.008), DM (OR = 0.80, CI = 0.67−0.95, P = 0.013), and ODM (OR = 0.78, CI = 0.65−0.94, P = 0.009) showed a significant association between C262T SNP and blood-and-bone-marrow-related cancer. Only one genetic model, ODM (OR = 0.86, CI = 0.76−0.98, P = 0.023), showed a significant link between C262T and GIT-related cancer. The C26 T variant was also linked with the risk of prostate cancer in all genotypic models: COD1 (OR = 1.13, CI = 1.04−1.24, P = 0.005), COD2 (OR = 1.51, CI = 1.27−1.80, P = 3.15 × 10⁻⁶), COD3 (OR = 1.30, CI = 1.08–1.55, P = 0.005), RM (OR = 1.43, CI = 1.21−1.70, P = 3.85 × 10⁻⁵), ODM (OR = 1.09, CI = 1.00−1.19, P = 0.048), and AM (OR = 1.21, CI = 1.03–1.43, P = 0.021), except DM. Three genetic models, including COD1 (OR = 1.21, CI = 1.02−1.44, P = 0.033), DM (OR = 1.19, CI = 1.00−1.41, P = 0.044), and ODM (OR = 1.21, CI = 1.02−1.45, P = 0.032) revealed a substantial link with the risk of skin carcinoma. Our findings also revealed that the rs1001179 variant is significantly linked with gynecological malignancy in four genetic models: COD2 (OR = 2.94, CI = 1.06−8.16, P = 0.039), DM (OR = 1.73, CI = 1.01−2.97, P = 0.045), RM (OR = 2.51, CI = 1.01−6.19, P = 0.047), and AM (OR = 1.73, CI = 1.04–2.88, P = 0.036) (Table 3, Figure S2).

Table 3.

Meta-analysis for detecting the connection of CAT C262T (rs1001179) polymorphism with different cancer subtypes.

Genetic Model	No. of studies	Test of association			Test of heterogeneity			No. of studies	Test of association			Test of heterogeneity
Genetic Model	No. of studies	OR	95% CI	P-value	Model	P-value	I² (%)	No. of studies	OR	95% CI	P-value	Model	P-value	I² (%)
Blood-and-bone-marrow-related cancer (CML + AML + leukemia + MPN)								LC
COD1	5	0.77	0.64–0.93	0.008	Fixed	0.143	41.82	3	0.95	0.74–1.23	0.694	Fixed	0.833	0
COD2		0.92	0.64–1.32	0.634	Fixed	0.141	42.12		1.08	0.64–1.82	0.763	Fixed	0.256	26.72
COD3		1.24	0.72–2.15	0.436	Random	0.087	50.72		1.15	0.67–1.98	0.605	Fixed	0.338	7.88
DM		0.80	0.67–0.95	0.013	Fixed	0.178	36.52		0.97	0.76–1.24	0.834	Fixed	0.711	0
RM		1.02	0.72–1.46	0.905	Fixed	0.121	45.23		1.10	0.66–1.83	0.717	Fixed	0.280	21.48
ODM		0.78	0.65–0.94	0.009	Fixed	0.116	46.02		0.94	0.73–1.21	0.655	Fixed	0.910	0
AM		0.87	0.75–1.00	0.052	Fixed	0.147	41.15		1.00	0.82–1.23	0.990	Fixed	0.520	0
BC								NHL
COD1	11	1.02	0.90–1.17	0.730	Random	0.005	60.15	3	0.92	0.78–1.08	0.302	Fixed	0.694	0
COD2		1.08	0.93–1.26	0.308	Fixed	0.938	0		1.21	0.88–1.66	0.238	Fixed	0.626	0
COD3		1.08	0.93–1.27	0.317	Fixed	0.605	0		1.31	0.96–1.80	0.095	Fixed	0.870	0
DM		1.04	0.92–1.18	0.568	Random	0.004	60.91		0.96	0.82–1.12	0.566	Fixed	0.540	0
RM		1.09	0.93–1.26	0.285	Fixed	0.946	0		1.25	0.93–1.69	0.143	Fixed	0.711	0
ODM		1.02	0.89–1.16	0.808	Random	0.005	60.44		0.90	0.77–1.05	0.179	Fixed	0.787	0
AM		1.04	0.94–1.14	0.465	Random	0.028	50.43		1.01	0.89–1.14	0.885	Fixed	0.406	0
GIT-related cancer (GC + CRC)								Prostate cancer
COD1	4	0.67	0.41–1.11	0.118	Random	<0.001	86.81	8	1.13	1.04–1.24	0.005	Fixed	0.124	38.4
COD2		0.74	0.54–1.01	0.055	Fixed	0.858	0		1.51	1.27–1.80	3.15 × 10⁻⁶	Fixed	0.120	40.74
COD3		1.29	0.70–2.38	0.409	Random	0.046	62.55		1.30	1.08–1.55	0.005	Fixed	0.857	0
DM		0.74	0.52–1.04	0.085	Random	0.006	76.16		1.18	0.99–1.42	0.068	Random	0.016	59.34
RM		1.06	0.80–1.41	0.681	Fixed	0.671	0		1.43	1.21–1.70	3.85 × 10⁻⁵	Fixed	0.374	7.07
ODM		0.86	0.76–0.98	0.023	Fixed	0.552	0		1.09	1.00–1.19	0.048	Fixed	0.460	0
AM		0.70	0.45–1.10	0.125	Random	<0.001	84.71		1.21	1.03–1.43	0.021	Random	0.004	66.57
HNC and CNS cancer (glioma + meningioma + AN + laryngeal cancer)								Skin cancer (BCC + melanoma + CSCC)
COD1	4	0.86	0.69–1.08	0.202	Fixed	0.562	0	3	1.21	1.02–1.44	0.033	Fixed	0.985	0
COD2		0.85	0.51–1.40	0.522	Fixed	0.474	0		1.03	0.67–1.59	0.884	Fixed	0.844	0
COD3		1.00	0.60–1.68	0.999	Fixed	0.338	11.07		0.85	0.55–1.32	0.477	Fixed	0.877	0
DM		0.86	0.70–1.07	0.177	Fixed	0.662	0		1.19	1.00–1.41	0.044	Fixed	0.957	0
RM		0.90	0.55–1.48	0.687	Fixed	0.412	0		0.97	0.63–1.48	0.872	Fixed	0.851	0
ODM		0.87	0.70–1.09	0.234	Fixed	0.485	0		1.21	1.02–1.45	0.032	Fixed	0.994	0
AM		0.89	0.75–1.07	0.212	Fixed	0.684	0		1.13	0.98–1.30	0.101	Fixed	0.913	0
Gynecological cancer (CC + OC)								Other cancers (pancreatic cancer + TC + MM)
COD1	3	1.39	0.78–2.46	0.264	Random	0.051	81.13	3	0.90	0.72–1.12	0.341	Fixed	0.953	0
COD2		2.94	1.06–8.16	0.039	Random	0.011	17.75		1.16	0.72–1.87	0.543	Fixed	0.503	0
COD3		2.12	0.96–4.67	0.062	Random	0.056	10.01		1.31	0.80–2.13	0.282	Fixed	0.567	0
DM		1.73	1.01–2.97	0.045	Random	0.044	9.49		0.92	0.74–1.15	0.471	Fixed	0.851	0
RM		2.51	1.01–6.19	0.047	Random	0.009	10.51		1.22	0.76–1.95	0.411	Fixed	0.488	0
ODM		1.07	0.62–1.85	0.801	Random	0.022	65.8		0.88	0.70–1.09	0.243	Fixed	0.961	0
AM		1.73	1.04–2.88	0.036	Random	0.001	6.99		0.98	0.82–1.17	0.806	Fixed	0.600	0
HCC
COD1	6	0.95	0.72–1.24	0.682	Fixed	0.233	26.91
COD2		1.23	0.55–2.78	0.612	Fixed	0.212	29.72
COD3		1.28	0.55–2.98	0.573	Fixed	0.211	29.88
DM		0.96	0.74–1.24	0.746	Fixed	0.201	31.3
RM		1.24	0.55–2.79	0.597	Fixed	0.223	28.24
ODM		0.95	0.73–1.24	0.697	Fixed	0.258	23.4
AM		0.97	0.77–1.23	0.828	Fixed	0.128	41.54

AM: allele model (T vs. C); AML: acute myeloid leukemia; AN: acoustic neuroma; BC: breast cancer; BCC: basal cell carcinoma; CC: cervical cancer; CI: confidence interval; CML: chronic myeloid leukemia; CNS: central nervous system; COD1: codominant 1 (TC vs. CC); COD2: codominant 2 (TT vs. CC); COD3: codominant 3 (TT vs. TC); CRC: colorectal cancer; CSCC: cutaneous squamous cell carcinoma; DM: dominant model (TT + TC vs. CC); GC: gastric cancer; GIT: gastrointestinal tract; HCC: hepatocellular carcinoma; HNC: head and neck cancer; LC: lung cancer; MM: malignant mesothelioma; MPN: myeloproliferative neoplasms; NHL: non-Hodgkin lymphoma; OC: ovarian cancer; ODM: over-dominant model (TC vs. TT + CC); OR: odds ratio; PC: prostate cancer; RM: recessive model (TT vs. TC + CC); TC: testicular cancer.

Meta-analysis based on control sources

The connection between the CAT C262T variant with cancer, depending on population-based control sources, was evaluated in 33 studies with 14,546 cancer patients and 42,644 controls. Three genetic models showed a link between the C262T SNP and the risk of cancer in COD2 (OR = 1.19, CI = 1.08−1.32, P = 0.0003), COD3 (OR = 1.17, CI = 1.06−1.29, P = 0.003), and RM (OR = 1.19, CI = 1.08−1.31, P = 0.0004). In contrast, an analysis of hospital-based control sources was evaluated in 20 studies with 3,712 cancer patients and 4,832 controls, which revealed that two genetic models, COD3 (OR = 1.40, CI = 1.16−1.70, P = 0.0005) and RM (OR = 1.24, CI = 1.04−1.48, P = 0.018), were correlated with the risk of cancer (Table S2).

Meta-analysis based on genotyping methods

Subgroup analysis depending on the genotyping methods (Table S3) showed that three genetic association models, including COD3, RM, and ODM were significantly associated with cancer risk for PCR-RFLP from 23 studies (OR = 1.46, CI = 1.15−1.86, P = 0.002, OR = 1.34, CI = 1.08−1.67, P = 0.009, and OR = 0.80, CI = 0.70−0.93, P = 0.003, respectively) and three genetic association models—COD2, RM, and AM—were significantly associated with cancer risk for MALDI-TOF + MassARRAY from 7 studies (OR = 1.32, CI = 1.11−1.56, P = 0.001, OR = 1.26, CI = 1.07−1.49, P = 0.005, and OR = 1.14, CI = 1.07−1.21, P = 5.68 × 10⁻⁵, respectively). No statistically significant correlation was found for TaqMan (20 studies) and other genotyping methods (3 studies).

Heterogeneity and publication bias analysis

From the analysis, we identified that different genetic models showed significant heterogeneity (P < 0.1) in which random-effects models were implemented (Table 2, Table 3, Table S2, Table S3). In our meta-analysis, we used both Begg's funnel plot and Egger's test to investigate the publication bias, which revealed that there was no bias (Table S4). A similar result is shown in the funnel plots of Figure S3.

Meta-regression and sensitivity analysis

Based on the quality scores of studies as a source of heterogeneity, we performed a meta-regression as shown in Table S5. The analysis showed that the quality scores might be a source of significant heterogeneity under the COD2 genetic model (coefficient = 0.176, CI = 0.018–0.335, P = 0.029). During the sensitivity study, individual papers in the meta-analysis were removed one by one to see whether individual data changed the pooled ORs; our results were found to be consistent among all genetic models (Figure S4).

TSA

We performed a TSA for CAT C262T in the allele models for the overall study, Caucasian, Asian, and mixed and other populations as depicted in Figure S5(a) to (d) with a sample size of 26,251, 30,517, 7,403, and 6,367 subjects, respectively. As per the analysis, the cumulative Z-curve (indicated by the blue line) exceeded the futility boundaries (indicated by the red line) in the overall, Caucasians, and Asians, and the total sample were firm enough to validate our results. Therefore, our findings were sufficient, and no further analysis was required for validation.

Discussion

Cancer is the most severe public health problem in the present world and the second-highest cause of mortality in this century. In this meta-analysis, we looked at 53 case-control studies with 65,734 samples (18,258 cases and 47,476 controls) from 18 countries using seven genetic models and found that there is a link between the CAT C262T variant and the risk of various cancers.

External variables, such as ROS or ionizing radiation, build up over time and may impact DNA expression in hematopoietic stem cells by causing double strand breaks. The CAT gene has many SNPs, including rs7943316, rs769214, and rs1001179.^59,60 In the regulatory regions of the CAT gene, a C → T mutation at nucleotide −262 in the 5′ non-coding region is a common polymorphism (C262T/ rs1001179). This variant within the promoter region has been demonstrated to alter transcription factor binding. As a result, the CAT gene's transcription and expression are altered. CAT C262T polymorphism is linked not only to cancer, but also to many other disorders.^61,63 In the present meta-analysis, three genotype models, namely COD2 (OR = 1.16, P = 0.018), COD3 (OR = 1.21, P = 1.82 × 10⁻⁵), and RM (OR = 1.20, P = 1.95 × 10⁻⁵) showed a statistically significant association with cancer. Also, a significant association was found between the C262T variant and cancer in three genotypic models of the Caucasian population (COD2: OR = 1.18, P = 0.027; COD3: OR = 1.17, P = 0.003; ODM: OR = 1.19, P = 0.011) and one genotypic model of the Asian population (COD3: OR = 1.49, P = 0.002).

Significant amount of oxygen radicals trigger the destruction of cellular structures and functions, as well as the death of a cell. As a result, ROS-induced DNA damage in cells can cause aberrant genetic changes in cells, which can contribute to cancer initiation and progression. The CAT gene is essential in the neutralization of reactive species and is involved in antioxidant defense systems. A study in Romania demonstrated that the CAT C262T polymorphism is correlated with chronic myeloid leukemia (OR = 0.59, P = 0.01)⁴¹; however, another study found no significant association with the CAT −21A/T (P = 0.037) polymorphism with the risk of CML⁴⁵ and leukemia.⁴⁴ In our meta-analysis, we found significant associations between the CAT C262T polymorphism and blood-and-bone-marrow-related cancer in three genetic models (COD1: OR = 0.77, P = 0.008; DM: OR = 0.80, P = 0.013; ODM: OR = 0.78, P = 0.009) and skin cancer in three genetic models (COD1: OR = 1.21, P = 0.033; DM: OR = 1.19, P = 0.044; ODM: OR = 1.21, P = 0.032). Previous research also revealed a link between the C262T variant and a higher risk of skin cancer (CT + TT vs. CC: OR = 1.19, P = 0.04; CT vs. CC: OR = 1.21, P = 0.03).²³ Several studies have found no link between the C262T variant and the incidence of gastric³⁶ and colorectal cancer.^37,49 In our meta-analysis, we found that only one genetic model (ODM: OR = 0.86, P = 0.023) was significantly connected with GIT-related cancer. Populations from Europe, Australia, New Zealand, and North America have the greatest rates of colon cancer, with Hungary and Norway ranked top for men and women, respectively.¹

According to Liu et al. 2016, CAT gene polymorphisms are related to an elevated risk of prostate cancer.⁶⁴ The TT genotype of C262T showed a considerably higher prostate cancer risk than the CC genotype. When compared to the CC genotype, the TT genotype exhibited 1.94- and 3.83-fold higher susceptibility to disease and malignancy, respectively.³¹ A significant association was also reported by another study conducted in New Zealand.²⁵ In our meta-analysis, we found that six genetic models of C262T (COD1: OR = 1.13, P = 0.005; COD2: OR = 1.51, P = 3.15 × 10⁻⁶; COD3: OR = 1.30, P = 0.005; RM: OR = 1.43, P = 3.85 × 10⁻⁵; ODM: OR = 1.09, P = 0.048; AM: OR = 1.21, P = 0.021) have a significant connection with prostate cancer. According to a study conducted in Portugal, the C262T polymorphism showed a higher risk of cervical cancer (OR = 3.03, P = 0.003).⁹ In our meta-analysis, we found that four genetic models (COD2: OR = 2.94, P = 0.039; DM: OR = 1.73, P = 0.045; RM: OR = 2.51, P = 0.047; AM: OR = 1.73, P = 0.036) have a significant connection with gynecologic cancer.

Different studies revealed that genetic variations in the CAT gene were not associated with lung cancer,²⁴ breast cancer,^26,29 acoustic neuroma,²⁸ malignant mesothelioma,¹⁷ hepatocellular carcinoma,²⁰ and non-Hodgkin lymphoma.^21,33 However, we did not find any significant risk of the CAT C262T polymorphism and lung cancer, non-Hodgkin lymphoma, head and neck cancer, central nervous system cancer, breast cancer, and hepatocellular carcinoma. Since the late 1970s, the case-fatality percentages of hepatocellular carcinoma have dropped in many Eastern and South-East Asian nations, including China, Taiwan, the Republic of Korea, and the Philippines.⁶⁵ In our meta-analysis, we also found that three genetic models (COD2: OR = 1.19, P = 0.0003; COD3: OR = 1.17, P = 0.003; RM: OR = 1.19, P = 0.0004) in population-based controls and two genetic models (COD3: OR = 1.40, P = 0.0005; RM: OR = 1.24, P = 0.018) in hospital-based controls have a statistically significant connection with cancer. In addition, three genetic models for PCR-RFLP (COD3: OR = 1.46, P = 0.002; RM: OR = 1.34, P = 0.009; ODM: OR = 0.80, P = 0.003) and three models for MALDI-TOF + MassARRAY (COD2: OR = 1.32, P = 0.001; RM: OR = 1.26, P = 0.005; AM: OR = 1.14, P = 5.68 × 10⁻⁵) genotyping methods showed a significant association with cancer progression.

The consistency and repeatability of our results across all of the genetic models confirmed their validity. We used heterogeneity analysis, publication bias assessment, meta-regression, and sensitivity analyses to conduct our meta-analysis. Also, the TSA results suggest that no additional studies are required to validate our findings. Despite the fact that we approached this meta-analysis with caution, there are several drawbacks that must be noted. First, only published case-control studies were included. Second, the total number of studies included in the analysis is rather limited (53), despite a large number of patients (cases + controls = 49,027). Third, we stratified studies only based on ethnicity, cancer type, genotyping methods, and source of control—no other characteristics. With the exception of these limitations, the included case-control publications are of excellent quality, the analysis of the data is robust, and the findings of this study are acceptable.

Conclusion

In summary, our findings support the hypothesis that the CAT C262T polymorphism is significantly associated with overall cancer, especially in Caucasians and Asians. This variant may also be associated with other cancers such as blood-and-bone-marrow-related, GIT-related, prostate, skin, and gynecological cancers. To confirm the risk identified in the current statistical analysis, large-scale investigations should be undertaken in the future.

Supplemental Material

sj-docx-1-jbm-10.1177_03936155221104128 - Supplemental material for Catalase C262T genetic variation and cancer susceptibility: A comprehensive meta-analysis with meta-regression and trial sequential analysis

Supplemental material, sj-docx-1-jbm-10.1177_03936155221104128 for Catalase C262T genetic variation and cancer susceptibility: A comprehensive meta-analysis with meta-regression and trial sequential analysis by Md Abdul Barek, Sarah Jafrin, Md. Abdul Aziz and Mohammad Safiqul Islam in The International Journal of Biological Markers

Supplemental Material

sj-docx-2-jbm-10.1177_03936155221104128 - Supplemental material for Catalase C262T genetic variation and cancer susceptibility: A comprehensive meta-analysis with meta-regression and trial sequential analysis

Supplemental material, sj-docx-2-jbm-10.1177_03936155221104128 for Catalase C262T genetic variation and cancer susceptibility: A comprehensive meta-analysis with meta-regression and trial sequential analysis by Md Abdul Barek, Sarah Jafrin, Md. Abdul Aziz and Mohammad Safiqul Islam in The International Journal of Biological Markers

Footnotes

Acknowledgements

Not applicable.

Ethical approval

Not applicable.

This work received no funding.

Data availability

All data related to the manuscript were added in the manuscript main file, figures, tables and supplementary materials. The corresponding author will provide additional information on a valid request if required.

Author contributions

The literature search and data collection were performed by Md Abdul Barek and Sarah Jafrin. Mohammad Safiqul Islam performed the statistical analyses for this meta-analysis. The initial draft of the manuscript was co-written by Md Abdul Barek and Md. Abdul Aziz. Mohammad Safiqul Islam reworked the manuscript, critically updated it for important intellectual substance, and made substantial contributions to conceptualization and design. The paper was read and approved for submission by all the authors.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mohammad Safiqul Islam

Supplemental material

Supplemental material for this article is available online.

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