Impact of Chondroitin Sulfate Proteoglycan 4 Pseudogene 12 Genetic Variants on Colorectal Cancer Risk: A Case-Control Study

Abstract

This study aims to investigate the correlation between the chondroitin sulfate proteoglycan 4 pseudogene 12 (CSPG4P12) polymorphism and the risk of colorectal cancer (CRC). This case–control study involved 850 patients with CRC and 850 health controls. The genotypes of CSPG4P12 (rs2880765, rs6496932, and rs8040855) were determined by the TaqMan-MGB probe method. Logistic regression model was employed to evaluate the association of CSPG4P12 single-nucleotide polymorphisms (SNPs) with the risk of CRC by calculating the odds ratio (OR) and 95% confidence interval (CI). The CSPG4P12 exhibited lower expression in CRC tissues. Our data showed that the rs6496932 variant increased CRC risk (CA vs. CC: p = 0.006; CA + AA vs. CC: p = 0.005). In contrast, the rs8040855 variant reduced the risk of CRC (CG vs. CC: p < 0.001; CG + GG vs. CC: p < 0.001). Stratification by gender and age revealed that the rs8040855 variant decreased CRC risk; however, the rs6496932 variant increased CRC risk among males (CA vs. CC: p = 0.024; CA + AA vs. CC: p = 0.014) and younger individuals (CA vs. CC: p = 0.004; CA + AA vs. CC: p = 0.010). When stratified by smoking and drinking status, the rs8040855 variant decreased CRC risk among nonsmokers (CG vs. CC: p < 0.001; CG + GG vs. CC: p < 0.001) and nondrinkers (CA vs. CC: p = 0.002; CA + AA vs. CC: p = 0.004). The rs6496932 variant increased CRC risk among nonsmokers (CA vs. CC: p = 0.016; CA + AA vs. CC: p = 0.036) and nondrinkers (CG vs. CC: p < 0.001; CG + GG vs. CC: p < 0.001). Haplotype analysis showed that the CSPG4P12 T_rs2880765C_rs6496932G_rs8040855 haplotype reduced the risk of CRC compared with the reference haplotype (CSPG4P12 A_rs2880765C_rs6496932C_rs8040855) (OR = 0.46, 95% CI = 0.26–0.82, p = 0.049). These findings highlight the potential of these genetic variants as biomarkers for CRC susceptibility, offering insights into personalized prevention strategies.

Introduction

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide (Sung et al., 2021). In China, 2022 statistics report 592,232 new CRC cases and 309,114 deaths, making it the fifth leading cause of cancer-related mortality (Xia et al., 2022). Both genetic and environmental factors play important roles in the development of CRC (Baidoun et al., 2021; Thanikachalam and Khan, 2019) with ∼35% of CRC risk attributed to genetic factors (Lichtenstein et al., 2000). Numerous case–control studies have highlighted strong associations between genetic variants and the risk of CRC, with single-nucleotide polymorphisms (SNPs) being a key area of investigation (Zhang et al., 2014).

Pseudogenes are derivatives of the homeodomain gene, sharing a high degree of sequence homology and potentially competing with the homeodomain gene for miRNA binding (Sur et al., 2013). Chondroitin sulfate proteoglycan 4 (CSPG4) is widely expressed in various malignant tumors (Beard et al., 2014), including breast cancer, melanoma (Hu et al., 2022b; Price et al., 2011), and thyroid cancer (Egan et al., 2021). Chondroitin sulfate proteoglycan 4 pseudogene 12 (CSPG4P12) is a pseudogene-derived lncRNA that is homologous to its parent gene CSPG4 (Wiest et al., 2006).

The expression and function of lncRNAs are likely influenced by SNPs (Kumar et al., 2013). For example, H19 rs2839698 is associated with the risk of bladder cancer, and SNPs in PCGEM1 (rs6434568 and rs16834898) are associated with prostate cancer risk in Chinese men (Verhaegh et al., 2008; Xue et al., 2013). Our previous study indicated that CSPG4P12 SNPs affect the expression of CSPG4P12 in lung cancer (Hu et al., 2022a). Given the potential role of CSPG4P12 in cancer development, we hypothesized that CSPG4P12 genetic variants may contribute to the risk of CRC development.

In the present study, we genotyped SNPs (rs2880765, rs6496932, and rs8040855) in GSPG4P12 and evaluated their association with the risk of developing CRC. Our study provides additional evidence supporting the limited evidence that CSPG4P12 polymorphisms are associated with CRC risk in the northern Chinese Han population.

Materials and Methods

Study population

A total of 410 pathologically diagnosed colon cancer cases and 440 rectal cancer cases were collected at the Affiliated Tangshan Gongren Hospital of North China University of Science and Technology from 2008 to 2016. The controls consisted of individuals undergoing routine health checkups at the same hospital during the same period, with no history or indications of cancer. Controls were frequency matched to the case group on a 1:1 basis by sex and age (±5 years). General demographic and clinicopathological information were gathered through medical record reviews and questionnaires, with informed consent obtained from all participants. Peripheral venous blood (2 mL) was collected from each participant for further analysis. This study was approved by the Ethics Committee of North China University of Science and Technology (No. 2022027).

SNP selection

In the initial design of this study, we selected SNPs from the National Center for Biotechnology Information dbSNP database, focusing on those with a minor allele frequency >0.05 in the Chinese Han Beijing population. To further evaluate the potential impact of these SNPs on CSPG4P12 expression levels in the colorectum, we utilized the GTEx database. This analysis led us to select three SNPs (rs2880765, rs6496932, and rs8040855) for further investigation. However, as the databases were updated during the course of our research, we found that the rs6496932 variant did not significantly affect CSPG4P12 expression levels. While a decreasing trend in expression was observed in both the sigmoid and the transverse colon, these changes were not statistically significant. Despite this, we decided to include the rs6496932 SNP in our study to maintain the integrity of our initial selection criteria.

DNA extraction and genotyping of CSPG4P12

DNA was extracted using a DNA extraction kit provided by Tiangen Biochemical Technology (China). Genotyping was performed using TaqMan MGB probe method. The primers and probes used in our study were custom designed and synthesized by Ranrun Jikang (China) Biotechnology using the following process: (1) Target Sequence Selection: The target sequences for the CSPG4P12 SNPs (rs2880765, rs6496932, and rs8040855) were identified using available genomic data (Ensembl); (2) Primer and Probe Design: Using the target sequences, primers, and probes were designed with the assistance of Primer Express^® software (Thermo Fisher Scientific, USA) to ensure specificity and efficiency in amplifying the regions of interest; (3) Validation: The designed primers and probes were validated through in silico analysis using BLAST (basic local alignment search tool) to confirm their binding specificity and to avoid potential off-target effects. Oligo Analyzer was used to predict and avoid the formation of secondary structures and primer-dimers; (4) Synthesis: Following validation, the primers and probes were then synthesized by Ranrun Jikang. The PCR primers and probes used for detecting CSPG4P12 rs2880765, rs6496932, and rs8040855 variants are listed in Table 1. PCR was carried out using an ABI 7900HT Fast Real-Time PCR instrument (Thermo Fisher Scientific). The 5 µL reaction system included 0.2 µL of each probe (2 µM), 0.15 µL of each upstream and downstream primer (10 µM), 1 µL of genomic DNA (0.1–20 ng), and 2× PCR mix (TaqMan Universal Master Mix II, ABI, USA). For the genotyping of CSPG4P12 rs2880765, rs6496932, and rs8040855 polymorphisms, the thermal cycling reactions included a preliminary melting step at 95°C for 10 min, followed by 50 cycles of denaturation at 95°C for 15 s and annealing at 58°C for 1 min. The results were analyzed using ABI SDS 2.4 software. For quality control, negative controls and two randomized duplicate samples were included in each assay.

Table 1.

Sequence Information for Fluorescent Probes and Companion Primers

SNPs	Primers or probes	Sequences
CSPG4P12 rs2880765	rs2880765-F	5′-GCCCTATCAATACAGATGTGAAGAAA-3′
	rs2880765-R	5′-GGTAAAAAGGCGCTTGTGTGAG-3′
	rs2880765-PF	FAM-CAGCCTCACACTGAG-MGB
	rs2880765-PH	HEX-CAGCCTCACTCTGAGA-MGB
CSPG4P12 rs6496932	rs6496932-F	5′-CTAAACTTCTCCATCCTTGCTGC-3′
	rs6496932-R	5′-CATTCATGTGAAATGCTATTCATAACTG-3′
	rs6496932-PF	FAM-ACTTTGCTCCCTGTTT-MGB
	rs6496932-PH	HEX-ACTTTGCTACCTGTTTC-MGB
CSPG4P12 rs8040855	rs8040855-F	5′-AAGGCTTCCTTGTGACACAGAAG-3′
	rs8040855-R	5′-AGACCAAGGTTTGTATGCCCAC-3′
	rs8040855-PF	FAM-CACATCTAGTTCTTTCTT-MGB
	rs8040855-PH	FAM-CACATCTACTTCTTTCTT-MGB

Bioinformatics methods

The online GEPIA program (http://gepia.cancer-pku.cn) was used to analyze the differential expression of CSPG4P12 between colorectal cancer tissues and adjacent normal tissues. The online GTEx program (https://www.gtexportal.org) was used to assess the effects of rs2880765, rs6496932, and rs8040855 on the expression of CSPG4P12.

Haplotype and linkage disequilibrium analyses

Haplotypes for the different SNPs were constructed using SHEsis online software (http://analysis.bio-x.cn/myAnalysis.php). To analyze linkage disequilibrium (LD), including generating the LD plot, we used Haploview 4.2 software (USA).

Statistical methods

SPSS 23.0 software (SPSS, Inc. Chicago, IL) was used for data analysis. The distribution difference of categorical variables (e.g., sex, age, smoking, and drinking status) and genotypes of each SNP between cases and controls was compared using the χ ² test. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using unconditional logistic regression to evaluate the association between genetic variants and the risk of colon and rectal cancer. Hardy–Weinberg equilibrium (HWE) in controls was assessed using the Pearson goodness-of-fit test. All statistical tests were two-sided, with a significance level of p < 0.05.

Results

Differential expression of CSPG4P12 in CRC and SNP selection

Bioinformatics analysis revealed that the pseudogene CSPG4P12 was poorly expressed in many types of cancer tissues, including colorectal cancer (Fig. 1a, b). To demonstrate if CSPG4P12 genetic variation affects gene expression, we analyzed the data from the GTEx database. The rs2880765 TT or AT genotype was associated with a significant increase in CSPG4P12 expression in sigmoid colon (p = 0.024) compared with the AA genotype, but no significant difference was observed in transverse colon (p = 0.483) (Fig. 1c). For the rs6496932 variant, the AA or CA genotypes did not exhibit any significant differences in CSPG4P12 expression levels compared with the CC genotype, although a decreasing trend was observed in both the sigmoid and the transverse colon (p = 0.434 and p = 0.761) (Fig. 1d). The rs8040855 GG or CG genotypes significantly increased the CSPG4P12 expression levels compared with the CC genotype in both the sigmoid and the transverse colon (p = 0.029 and p = 0.006) (Fig. 1e).

FIG. 1.

CSPG4P12 expression patterns in colorectal cancer. (a) Pan-cancer analysis illustrating the expression levels of CSPG4P12 across various cancer types. (b) The expression of CSPG4P12 in CRC tissues. (c–e) Impact of different CSPG4P12 genetic variants on its expression: (c) rs2880765 (d) rs6496932, and (e) rs8040855. CSPG4P12, chondroitin sulfate proteoglycan 4 pseudogene 12.

Basic characteristics of subjects in case–control study

The basic information of 850 patients with CRC and 850 health controls is summarized in Table 2. There was no statistically significant difference in sex, age, smoking, and drinking status between the case and control groups (p > 0.05, Table 2), which means that the case and control groups were comparable.

Table 2.

Distributions of Selected Characteristics in Cases and Control Subjects

Variables	Controls (850)		Cases (850)		χ ²	p ^a
Variables	N	(%)	N	(%)	χ ²	p ^a
Gender					0.039	0.844
Male	500	58.9	496	58.4
Female	350	41.1	354	41.6
Age					0.680	0.410
≤60	441	51.9	424	49.9
≤60	409	48.1	426	50.1
Smoking status					0.529	0.467
Nonsmoker	630	74.1	643	75.6
Smoker	220	25.9	207	24.4
Drinking status					0.027	0.869
Nondrinker	627	73.8	624	73.4
Drinker	223	26.2	226	26.6

Two-sided χ ² test.

Association of CSPG4P12 variants with the risk of CRC

For these three SNPs, we undertook a HWE calculation in both the control and case groups. No significant deviations were observed in both groups, indicating that that the subjects included in this study were representative of the population (all p > 0.05). We used three models (codominant, dominant, and recessive) to assess the association between CSPG4P12 SNPs (rs2880765, rs6496932, and rs8040855) and CRC risk (Table 3). The results indicated that the rs6496932 variant increased CRC risk in codominant (CA vs. CC: OR = 1.36, 95% CI= 1.11–1.66, p = 0.006) and dominant models (CA + AA vs. CC: OR = 1.32, 95% CI = 1.09–1.60, p = 0.005) but not in recessive model (AA vs. CC + CA: OR = 0.99, 95% CI = 0.72–1.35, p = 0.948). The rs8040855 variant decreased the risk of CRC in codominant (CG vs. CC: OR = 0.47, 95% CI = 0.32–0.69, p < 0.001) and dominant model (CG + GG vs. CC: OR = 0.48, 95% CI = 0.33–0.70, p < 0.001). The rs8040855 GG genotype was present in only one case within the case group, making it unsuitable for analysis in the recessive model. For the rs2880765 variant, no significant association with CRC risk was observed across the codominant, dominant, or recessive models (all p > 0.05).

Table 3.

Genotype Frequencies of SNPs in CSPG4P12 Genes and Their Association with CRC Risk

Genotypes	Cases		Controls		OR (95% CI)^a	p ^*
Genotypes	N	(%)	N	(%)	OR (95% CI)^a	p ^*
CSPG4P12 rs2880765
Codominant model
AA	517	60.8	489	57.5	1.00 (Ref.)
AT	280	32.9	320	37.7	0.82 (0.67–1.01)	0.124
TT	53	6.3	41	4.8	1.21 (0.79–1.85)	0.382
Dominant model
AA	517	60.8	489	57.5	1.00 (Ref.)
AT + TT	145	35.4	361	42.5	0.87 (0.72–1.05)	0.153
Recessive model
AA + AT	797	93.7	809	95.2	1.00 (Ref.)
TT	53	6.3	41	4.8	1.30 (0.86–1.98)	0.220
CSPG4P12 rs6496932
Codominant model
CC	347	40.8	405	47.6	1.00 (Ref.)
CA	413	48.6	354	41.7	1.36 (1.11–1.66)	0.006
AA	90	10.6	91	10.7	1.16 (0.84–1.60)	0.383
Dominant model
CC	347	40.8	405	47.6	1.00 (Ref.)
CA + AA	503	59.2	445	52.4	1.32 (1.09–1.60)	0.005
Recessive model
CC + CA	760	89.4	759	89.3	1.00 (Ref.)
AA	90	10.6	91	10.7	0.99 (0.72–1.35)	0.948
CSPG4P12 rs8040855
Codominant model
CC	808	95.1	767	90.2	1.00 (Ref.)
CG	41	4.8	83	9.8	0.47 (0.32–0.69)	<0.001
GG	1	0.1	0	0	—	—
Dominant model
CC	808	95.1	767	90.2	1.00 (Ref.)
CG + GG	42	4.9	83	9.8	0.48 (0.33–0.70)	<0.001
Recessive model
CC + CG	849	99.9	850	100	1.00 (Ref.)
GG	1	0.1	0	0	—	—

Data were calculated by unconditional logistic regression and adjusted for sex, age, smoking status, and drinking status.

p-Values were FDR-corrected.

CI, confidence interval; CRC, colorectal cancer; OR, odds ratio; SNP, single-nucleotide polymorphism.

Stratification analysis of the CSPG4P12 variants and CRC risk

The stratification analysis results are listed in Tables 4–6. In codominant and dominant models, the rs6496932 variant increased the risk of CRC in males (CA vs. CC: OR = 1.40, 95% CI = 1.08–1.83, p = 0.024; CA + AA vs. CC: OR = 1.41, 95% CI = 1.10–1.82, p = 0.014) and younger individuals (CA vs. CC: OR = 1.57, 95% CI = 1.18–2.10, p = 0.004; CA + AA vs. CC: OR = 1.49, 95% CI = 1.13–1.96, p = 0.010) (Table 4). Similarly, the rs6496932 variant increased the risk of CRC among nonsmokers (CA vs. CC: OR = 1.37, 95% CI = 1.09–1.73, p = 0.016; CA + AA vs. CC: OR = 1.31, 95% CI = 1.05–1.63, p = 0.036) and nondrinkers (CA vs. CC: OR = 1.50, 95% CI = 1.18–1.90, p = 0.002; CA + AA vs. CC: OR = 1.42, 95% CI = 1.13–1.78, p = 0.004) in codominant and dominant models (Table 4). However, the rs6496932 variant did not influence the risk of CRC in any of the subgroups in the recessive model (all p > 0.05) (Table 4).

Table 4.

Stratified Analysis of the Association Between CSPG4P12 rs6496932 and CRC Risk

Variables	Genotypes (controls/cases)			CA/CC OR (95% CI)^a	P *	CA+AA/CC OR (95% CI) ^a	P *	AA/CC+CA OR (95% CI)^a	p *
Variables	CC	CA	AA	CA/CC OR (95% CI)^a	P *	CA+AA/CC OR (95% CI) ^a	P *	AA/CC+CA OR (95% CI)^a	p *
Gender
Male	241/199	214/244	45/53	1.40 (1.08–1.83)	0.024	1.41 (1.10–1.82)	0.014	1.23 (0.81–1.87)	0.343
Female	164/148	140/169	46/37	1.32 (0.96–1.82)	0.087	1.20 (0.89–1.62)	0.244	0.71 (0.45–1.15)	0.326
Age
≤60	212/165	179/214	50/45	1.57 (1.18–2.10)	0.004	1.49 (1.13–1.96)	0.010	0.93 (0.61–1.44)	0.754
>60	191/182	175/199	41/45	1.24 (0.77–2.00)	0.370	1.24 (0.94–1.63)	0.129	1.12 (0.71–1.75)	1.000
Smoking status
Smoker	106/85	92/97	22/25	1.27 (0.85–1.92)	0.247	1.30 (0.89–1.92)	0.180	1.27 (0.68–2.36)	0.902
Nonsmoker	299/262	262/316	69/65	1.37 (1.09–1.73)	0.016	1.31 (1.05–1.63)	0.036	0.91 (0.64–1.31)	0.618
Drinking status
Drinker	97/94	104/105	22/27	1.07 (0.72–1.62)	0.731	1.11 (0.75–1.64)	0.605	1.23 (0.66–2.29)	1.000
Nondrinker	308/253	250/308	69/63	1.50 (1.18–1.90)	0.002	1.42 (1.13–1.78)	0.004	0.92 (0.64–1.32)	0.653

Data were calculated by unconditional logistic regression and adjusted for sex, age, smoking status, and drinking status.

p-Values were FDR-corrected.

Table 5.

Stratified Analysis of the Association Between CSPG4P12 rs8040855 and CRC Risk

Variables	Genotypes (controls/cases)			CG/CC OR (95% CI)^a	p *	CG + GG/CC OR (95% CI)^a	p *
Variables	CC	CG	GG	CG/CC OR (95% CI)^a	p *	CG + GG/CC OR (95% CI)^a	p *
Gender
Male	455/470	45/25	0/1	0.54 (0.33–0.90)	0.019	0.57 (0.34–0.93)	0.026
Female	312/338	38/16	0/0	0.38 (0.21–0.71)	0.004	0.38 (0.21–0.71)	0.004
Age
≤60	394/404	47/20	0/0	0.40 (0.23–0.69)	0.002	0.40 (0.23–0.69)	0.002
>60	373/404	36/21	0/1	0.53 (0.30–0.93)	0.026	0.55 (0.32–0.96)	0.034
Smoking status
Smoker	201/193	19/13	0/1	0.68 (0.32–1.43)	0.308	0.73 (0.35–1.51)	0.391
Nonsmoker	566/615	64/28	0/0	0.40 (0.25–0.64)	<0.001	0.40 (0.25–0.64)	<0.001
Drinking status
Drinker	207/211	16/14	0/1	0.79 (0.34–1.72)	0.557	0.85 (0.39–1.81)	0.665
Nondrinker	560/597	67/27	0/0	0.37 (0.24–0.59)	<0.001	0.37 (0.24–0.59)	<0.001

Data were calculated by unconditional logistic regression and adjusted for sex, age, smoking status, and drinking status.

p-Values were FDR-corrected.

Table 6.

Stratified Analysis of the Association Between CSPG4P12 rs2880765 and CRC Risk

Variables	Genotypes (controls/cases)			AT/AA OR (95% CI)^a	p *	AT + TT/AA OR (95% CI)^a	p *	TT/AA + AT OR (95% CI)^a	p *
Variables	AA	AT	TT	AT/AA OR (95% CI)^a	p *	AT + TT/AA OR (95% CI)^a	p *	TT/AA + AT OR (95% CI)^a	p *
Gender
Male	294/297	185/167	21/32	0.90 (0.69–1.17)	0.414	0.65 (0.74–1.23)	0.691	1.48 (0.84–2.62)	0.358
Female	195/220	135/113	20/21	0.77 (0.56–1.05)	0.200	0.79 (0.58–1.08)	0.27	1.08 (0.57–2.04)	0.807
Age
≤60	262/255	161/144	18/25	0.93 (0.70–1.23)	0.594	0.97 (0.74–1.28)	0.832	1.41 (0.75–2.64)	0.572
>60	227/262	159/136	23/28	0.75 (0.56–1.00)	0.104	0.71 (0.59–1.03)	0.166	1.13 (0.63–2.01)	0.683
Smoking status
Smoker	136/130	74/68	10/9	0.97 (0.64–1.47)	0.902	0.98 (0.66–1.46)	0.916	1.02 (0.40–2.60)	0.963
Nonsmoker	353/387	246/212	31/44	0.78 (0.62–0.99)	0.082	0.84 (0.67–1.05)	0.258	1.43 (0.89–2.30)	0.276
Drinking status
Drinker	135/135	78/78	10/13	1.07 (0.71–1.62)	0.737	1.12 (0.75–1.66)	0.589	1.40 (0.59–3.34)	0.443
Nondrinker	354/382	242/202	31/40	0.78 (0.61–0.98)	0.072	0.83 (0.66–1.04)	0.192	1.33 (0.82–2.15)	0.504

Data were calculated by unconditional logistic regression and adjusted for sex, age, smoking status, and drinking status.

p-Values were FDR-corrected.

As shown in Table 5, stratified analyses showed that individuals carrying at least one rs8040855 G allele had a lower risk of CRC in any of the subgroups in both codominant and dominant models. When stratified by smoking or drinking status, results indicated that the G allele carriers had a lower risk of CRC among nonsmokers (CG vs. CC: OR = 0.40, 95% CI = 0.25–0.64, p < 0.001; CG + GG vs. CC: OR = 0.40, 95% CI = 0.25–0.64, p < 0.001) and nondrinkers (CG vs. CC: OR = 0.37, 95% CI = 0.24–0.59, p < 0.001; CG + GG vs. CC: OR = 0.37, 95% CI = 0.24–0.59, p < 0.001) but not among smokers and drinkers (all p > 0.05).

In addition, the rs2880765 variant was not associated with the risk of CRC in any of the subgroups in all three models (all p > 0.05) (Table 6).

LD and haplotype analysis

We further conducted haplotype and LD analyses. The LD analysis revealed a significant association between rs2880765 and rs6496932 (D′ = 0.648, r ² = 0.063) (Fig. 2). Haplotype analysis showed that the CSPG4P12 T_rs2880765C_rs6496932G_rs8040855 haplotype was associated with a reduced risk of CRC compared with the reference haplotype (CSPG4P12 A_rs2880765C_rs6496932C_rs8040855) (OR = 0.46, 95% CI = 0.26–0.82, p = 0.049) (Table 7). No other haplotypes showed a significant association with CRC risk (p > 0.05).

FIG. 2.

LD plot about LD for the three tag SNPs (rs2880765, rs6496932, and rs8040855) in CSPG4P12. Numbers in squares indicate 100-fold D′ values for each pair of SNPs. LD, linkage disequilibrium; SNPs, single-nucleotide polymorphisms.

Table 7.

Haplotype Frequencies of CSPG4P12 Among Cases and Controls and Their Association with CRC Risk

Haplotypes	Cases (2n = 1700) (%)	Controls (2n = 1700) (%)	OR (95% CI)	p *
A_rs2880765C_rs6496932C_rs8040855	757 (44.6)	775 (45.6)	1.00 (Ref.)
A_rs2880765C_rs6496932G_rs8040855	14 (0.8)	28 (1.7)	0.51 (0.27–0.98)	0.140
A_rs2880765A_rs6496932C_rs8040855	535 (31.5)	483 (28.4)	1.13 (0.97–1.33)	0.280
A_rs2880765A_rs6496932G_rs8040855	7 (0.4)	11 (0.7)	0.65 (0.25–1.69)	0.525
T_rs2880765C_rs6496932C_rs8040855	319 (18.7)	323 (19.0)	1.01 (0.84–1.22)	0.907
T_rs2880765C_rs6496932G_rs8040855	17 (1.0)	38 (2.2)	0.46 (0.26–0.82)	0.049
T_rs2880765A_rs6496932C_rs8040855	46 (2.7)	36 (2.1)	1.31 (0.84–2.05)	0.417
T_rs2880765A_rs6496932G_rs8040855	5 (0.30)	6 (0.30)	0.85 (0.26–2.81)	0.926

p-Values were FDR-corrected.

Discussion

Pseudogenes are involved in the development of CRC. For example, DUXAP8 promotes CRC cell proliferation, invasion, and migration (He et al., 2020), and CTNNAP1 inhibits CRC growth (Chen et al., 2016). CSPG4P12 is a pseudogene-derived lncRNA that is strongly homologous to its parent gene, CSPG4 (Wiest et al., 2006). In cancer, SNPs in different regions of gene perform distinct functions through various mechanisms (Deng et al., 2017). SNPs can disrupt the function of lncRNAs by directly interfering with transcription factor binding or indirectly affecting the expression of regulatory factors (Abdi et al., 2022). In addition, SNPs in the distal region may regulate remote cis effects, thereby altering gene transcription (He et al., 2015). Numerous studies have demonstrated that genetic polymorphisms in pseudogenes are significantly associated with CRC risk. For example, MYLKP1 variants (rs12490683 and rs12497343) significantly increased the risk of colon cancer in African Americans (Lynn et al., 2018).

Currently, it remains uncertain whether CSPG4P12 polymorphisms are associated with CRC risk. This case–control study revealed that the CSPG4P12 rs6496932 variant increased CRC risk, while the rs8040855 variant significantly decreased the risk of CRC. This difference may be attributed to the distinct physiological characteristics and pathogenesis of colon and rectal cancers, among other factors (Paschke et al., 2018; Tamas et al., 2015).

Epidemiology studies have shown that the development of CRC is influenced by a combination of demographic characteristics, genetic factors, and environmental factors, with interactions between them (Archambault et al., 2022). In this study, our results also demonstrated that CSPG4P12 rs6496932 genetic variant increased the risk of colorectal cancer in males and younger individuals, while the rs8040855 genetic variant decreased the risk of colorectal cancer in sex- and age-stratified analyses. Consistent with other studies, the relationship between genetic variants and CRC risk is influenced by sex and age (Leberfarb et al., 2020; Wu et al., 2019). This highlights the importance of considering the interaction of sex, age, and genetic variants in the development of CRC.

Additionally, environmental factors such as smoking, alcohol consumption, and bacterial infections are considered as risk factors for CRC (Keum and Giovannucci, 2019). Ethanol, found in all types of alcoholic beverages, is a well-established risk factor for CRC and smoke-induced dysbiosis of the gut microbiota alters intestinal metabolites and impairs the intestinal barrier, promoting CRC (Bai et al., 2022; Grosse et al., 2019). CSPG4P12 may interact with the environmental factors, contributing to CRC development. Stratified analysis by smoking status showed that CSPG4P12 rs8040855 G allele reduced the risk of CRC among nonsmokers but not among smokers, whereas the rs6496932 A allele increased the risk of colorectal cancer among nonsmokers but not among smokers. Similarly, stratified analysis by alcohol consumption revealed that CSPG4P12 rs8040855 G allele reduced the risk of CRC among non-drinkers but not among drinkers, while the rs6496932 A allele increased the risk of colorectal cancer among nondrinkers. These findings suggest that CSPG4P12 rs6496932 and rs8040855 genetic variations may affect the risk of CRC through gene–environment interactions.

Our haplotype analysis revealed that the CSPG4P12 T_rs2880765C_rs6496932G_rs8040855 haplotype was significantly associated with a reduced risk of CRC. No other haplotypes showed significant associations with CRC risk. This finding is consistent with our single SNP analysis, where the rs8040855 G allele was associated with a reduced risk of CRC. The presence of the G allele in the protective haplotype further supports its potential role in reducing cancer risk. The protective effect observed for the T_rs2880765C_rs6496932G_rs8040855 haplotypes may be related to the combined influence of these SNPs on CSPG4P12 expression.

Conclusions

CSPG4P12 gene polymorphisms, particularly rs6496932 and rs8040855 genetic variants, significantly influence colorectal cancer risk. These findings highlight the potential of these genetic variants as biomarkers of CRC susceptibility and offer insights into personalized prevention strategies.

Footnotes

Acknowledgment

The authors thank the patients and their families for their involvement in the study.

Authors’ Contributions

X. Zhou: Conceptualization, methodology, formal analysis, investigation, and writing—original draft preparation. L.G.: Data curation, visualization, and investigation. Z.Y.: Visualization, data curation, and supervision. H.X.: Validation and formal analysis. Z.Z.: Investigation and resources. X. Zhang: Conceptualization, resources, supervision, project administration, writing, reviewing, editing, and funding acquisition. All authors revised the text and approved the final article.

Disclosure Statement

All the authors declare that they have no competing interests.

Funding Information

This study was supported by the Natural Science Foundation of Hebei Province (Grant No. H2023105018) and Tangshan Municipal Science and Technology Program (Grant No. 23130232E).

Supplementary Material

Supplementary Data S1

References

Abdi

, Latifi-Navid

. Long noncoding RNA polymorphisms and colorectal cancer risk: Progression and future perspectives. Environ Mol Mutagen, 2022; 63(2):98–112; doi: 10.1002/em.22477

Archambault

, Jeon

, Lin

, et al. Risk stratification for early-onset colorectal cancer using a combination of genetic and environmental risk scores: An international multi-center study. J Natl Cancer Inst, 2022; 114(4):528–539; doi: 10.1093/jnci/djac003

Bai

, Wei

, Liu

, et al. Cigarette smoke promotes colorectal cancer through modulation of gut microbiota and related metabolites. Gut, 2022; 71(12):2439–2450; doi: 10.1136/gutjnl-2021-325021

Baidoun

, Elshiwy

, Elkeraie

, et al. Colorectal cancer epidemiology: Recent trends and impact on outcomes. Curr Drug Targets, 2021; 22(9):998–1009; doi: 10.2174/1389450121999201117115717

Beard

, Zheng

, Lagisetty

, et al. Multiple chimeric antigen receptors successfully target chondroitin sulfate proteoglycan 4 in several different cancer histologies and cancer stem cells. J Immunother Cancer, 2014; 2:25; doi: 10.1186/2051-1426-2-25

Chen

, Zhu

, Wu

, et al. Downregulated pseudogene CTNNAP1 promote tumor growth in human cancer by downregulating its cognate gene CTNNA1 expression. Oncotarget, 2016; 7(34):55518–55528; doi: 10.18632/oncotarget.10833

Deng

, Zhou

, Fan

, et al. Single nucleotide polymorphisms and cancer susceptibility. Oncotarget, 2017; 8(66):110635–110649; doi: 10.18632/oncotarget.22372

Egan

, Stefanova

, Ahmed

, et al. CSPG4 is a potential therapeutic target in anaplastic thyroid cancer. Thyroid, 2021; 31(10):1481–1493; doi: 10.1089/thy.2021.0067

Grosse

, Lajoie

, Billard

, et al. Development of a database on tumors and tumor sites in humans and in experimental animals for ‘group 1 agents identified through volume 109 of the IARC monographs. J Toxicol Environ Health B Crit Rev, 2019; 22(7–8):237–243; doi: 10.1080/10937404.2019.1642601

10.

, Li

, Liyanarachchi

, et al. Multiple functional variants in long-range enhancer elements contribute to the risk of SNP rs965513 in thyroid cancer. Proc Natl Acad Sci U S A, 2015; 112(19):6128–6133; doi: 10.1073/pnas.1506255112

11.

, Yu

, Huang

, et al. The pseudogene DUXAP8 promotes colorectal cancer cell proliferation, invasion, and migration by inducing epithelial-mesenchymal transition through interacting with EZH2 and H3K27me3. Onco Targets Ther, 2020; 13:11059–11070; doi: 10.2147/OTT.S235643

12.

, Wu

, Li

, et al. Pseudogene CSPG4P12 affects the biological behavior of non-small cell lung cancer by Bcl-2/Bax mitochondrial apoptosis pathway. Exp Ther Med, 2022a;24(6):734; doi: 10.3892/etm.2022.11670

13.

, Zheng

, Yang

, et al. Co-Expression and combined prognostic value of CSPG4 and PDL1 in TP53-aberrant triple-negative breast cancer. Front Oncol, 2022b;12:804466; doi: 10.3389/fonc.2022.804466

14.

Keum

, Giovannucci

. Global burden of colorectal cancer: Emerging trends, risk factors and prevention strategies. Nat Rev Gastroenterol Hepatol, 2019; 16(12):713–732; doi: 10.1038/s41575-019-0189-8

15.

Kumar

, Westra

, Karjalainen

, et al. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet, 2013; 9(1):e1003201; doi: 10.1371/journal.pgen.1003201

16.

Leberfarb

, Degtyareva

, Brusentsov

, et al. Potential regulatory SNPs in the ATXN7L3B and KRT15 genes are associated with gender-specific colorectal cancer risk. Per Med, 2020; 17(1):43–54; doi: 10.2217/pme-2019-0059

17.

Lichtenstein

, Holm

, Verkasalo

, et al. Environmental and heritable factors in the causation of cancer–analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med, 2000; 343(2):78–85; doi: 10.1056/NEJM200007133430201

18.

Lynn

, Sun

, Ayshiev

, et al. Single nucleotide polymorphisms in the MYLKP1 pseudogene are associated with increased colon cancer risk in African Americans. PLoS One, 2018; 13(8):e0200916; doi: 10.1371/journal.pone.0200916

19.

Paschke

, Jafarov

, Staib

, et al. Are colon and rectal cancer two different tumor entities? A proposal to abandon the term colorectal cancer. Int J Mol Sci, 2018; 19(9); doi: 10.3390/ijms19092577

20.

Price

, Colvin Wanshura

, Yang

, et al. CSPG4, a potential therapeutic target, facilitates malignant progression of melanoma. Pigment Cell Melanoma Res, 2011; 24(6):1148–1157; doi: 10.1111/j.1755-148X.2011.00929.x

21.

Sung

, Ferlay

, Siegel

, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021; 71(3):209–249; doi: 10.3322/caac.21660

22.

Sur

, Saha

, Tisa

, et al. Characterization of pseudogenes in members of the order Frankineae. J Biosci, 2013; 38(4):727–732; doi: 10.1007/s12038-013-9356-1

23.

Tamas

, Walenkamp

, de Vries

, et al. Rectal and colon cancer: Not just a different anatomic site. Cancer Treat Rev, 2015; 41(8):671–679; doi: 10.1016/j.ctrv.2015.06.007

24.

Thanikachalam

, Khan

. Colorectal cancer and nutrition. Nutrients, 2019; 11(1); doi: 10.3390/nu11010164

25.

Verhaegh

, Verkleij

, Vermeulen

, et al. Polymorphisms in the H19 gene and the risk of bladder cancer. Eur Urol, 2008; 54(5):1118–1126; doi: 10.1016/j.eururo.2008.01.060

26.

Wiest

, Hyrenbach

, Bambul

, et al. Genetic analysis of familial connective tissue alterations associated with cervical artery dissections suggests locus heterogeneity. Stroke, 2006; 37(7):1697–1702; doi: 10.1161/01.STR.0000226624.93519.78

27.

, He

, Han

, et al. Analysis of MIR155HG variants and colorectal cancer susceptibility in Han Chinese population. Mol Genet Genomic Med, 2019; 7(8):e778; doi: 10.1002/mgg3.778

28.

Xia

, Dong

, Li

, et al. Cancer statistics in China and United States, 2022: Profiles, trends, and determinants. Chin Med J (Engl), 2022; 135(5):584–590; doi: 10.1097/CM9.0000000000002108

29.

Xue

, Wang

, Kang

, et al. Association between lncRNA PCGEM1 polymorphisms and prostate cancer risk. Prostate Cancer Prostatic Dis, 2013; 16(2):139–144, S1; doi: 10.1038/pcan.2013.6

30.

Zhang

, Civan

, Mukherjee

, et al. Genetic variations in colorectal cancer risk and clinical outcome. World J Gastroenterol, 2014; 20(15):4167–4177; doi: 10.3748/wjg.v20.i15.4167

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

3.50 MB