
==== Front
Ann Med
Ann Med
Annals of Medicine
0785-3890
1365-2060
Taylor & Francis

39221756
10.1080/07853890.2024.2397569
2397569
Version of Record
Research Article
Medical Genetics & Genomics
Assessment of LINC-PINT genetic polymorphisms and esophageal squamous cell carcinoma risk in the Hainan Han population
R. Tu et al.
Tu Ruisha a*
Zhong Dunjing b*
Li Ping c*
Li Yongyu b
Chen Zhuang b
Hu Feixiang a
Yuan Guihong b
Chen Zhaowei b
Yu Shuyong a
Song Jian d
a Department of Gastrointestinal Surgery, Hainan Cancer Hospital, Haikou, Hainan, China
b Department of Gastroenterology, Hainan Cancer Hospital, Haikou, Hainan, China
c Department of Digestive Endoscopy Center, Hainan Cancer Hospital, Haikou, Hainan, China
d Department of Gastroenterology, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
* These authors have contributed equally to this work.

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07853890.2024.2397569.

CONTACT Jian Song  songjian0532@sina.com  6019 Liuxian Dadao, Nanshan District, Shenzhen, Guangdong Province, China
2 9 2024
2024
2 9 2024
56 1 239756910 1 2024
4 5 2024
9 5 2024
KnowledgeWorks Global Ltd.30 8 2024
published online in a building issue30 8 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Objectives

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor with high incidence and mortality rates worldwide. This study aimed to investigate the correlation between LINC-PINT polymorphisms and ESCC risk in the Hainan Han population.

Methods

A total of 391 patients with ESCC and 452 healthy controls were enrolled to evaluate the effect of LINC-PINT SNPs (single nucleotide polymorphisms) on ESCC susceptibility. Associations were evaluated by calculating odds ratios (OR) and 95% confidence intervals (CIs). Multifactor dimensionality reduction analysis was performed to explore the association between SNP-SNP interactions and ESCC susceptibility. We further determined the correlation between clinical indicators and SNP in patients with ESCC.

Results

Our study showed that rs157916 (OR 0.63, p = 0.011) and rs157928 (OR 0.80, p = 0.021) were associated with a decreased risk of ESCC. Stratified analysis indicated that rs157916 could decrease the risk of ESCC in people aged >64 years, in males, and non-drinkers (OR 0.58, p = 0.042; OR 0.58, p = 0.010; OR 0.62, p = 0.025, respectively). Rs16873842 was related to a decreased risk of ESCC in males (OR 0.70, p = 0.015). Rs7801029 was associated with ESCC risk in females (OR 0.39, p = 0.033) and non-drinkers (OR 0.68, p = 0.040). Rs7781295 decreased the ESCC risk in smokers (OR 0.58, p = 0.046) and drinkers (OR 0.58, p = 0.046). In addition, rs157928 played a protective role in ESCC risk in females (OR 0.39, p = 0.033) and non-smokers (OR 0.32, p = 0.006). Additionally, the best predictive model for ESCC was a combination of rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928.

Conclusion

Our study revealed that LINC-PINT polymorphisms were associated with ESCC risk.

Keywords

Esophageal squamous cell carcinoma
LINC-PINT
genetic polymorphisms
risk
Hainan Provincial Major Science and Technology Project in 2020 ZDKJ202005 the specific research fund of The Innovation Platform for Academinicians of Hainan Province YSPTZX202029 This study was supported by the Hainan Provincial Major Science and Technology Project in 2020 [ZDKJ202005] and the specific research fund of The Innovation Platform for Academinicians of Hainan Province [YSPTZX202029].
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pmcIntroduction

Esophageal carcinoma (EC) is one of the tenth most common cancers worldwide [1]. According to the Global Cancer Statistics 2020, EC ranks seventh in terms of incidence (604,000 new cases) and sixth in overall mortality (544,000 deaths), the latter signifying that EC is responsible for one in every 18 cancer deaths in 2020 [2]. The latest cancer statistics in China showed that EC ranked as the sixth leading type of cancer and the fourth leading cause of death, with an estimated 324,000 new cases and 301,000 million deaths, respectively [3]. EC has two main pathological subtypes, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). ESCC is the major histological subtype of EC, accounting for 80% of total EC [4]. China holds the highest ESCC incidence areas in the world, with some areas of incidence over 100 per 100,000 [5]. The variety of factors that contribute to ESCC development in China, including genetic variations, gene-environment interactions, dietary factors such as alcohol and tobacco use, pickled vegetables and salted meat, hot food or drink consumption, and environmentally related factors [6]. Genetically related risk factors were the most important factors in the occurrence and progression of ESCC [7,8]. However, the pathogenesis of ESCC is still not fully understood. Long non-coding RNAs (lncRNAs) are non-coding RNA molecules longer than 200 nucleotides. Emerging evidence indicates that lncRNAs can regulate the expression of downstream genes, thereby influencing some biological functions in ESCC, such as proliferation, migration, and metastasis [9–11].

LINC-PINT, also known as long intergenic non-protein coding RNA p53 induced transcript, has been proven to be a tumor suppressor lncRNA that reduces the invasive phenotype of cancer cells, such as glioma, non-small cell lung cancer, melanoma, and pancreatic cancer [12–15]. The LINC-PINT gene is located on 7q32.3. As a long non-coding RNA, LINC-PINT does not encode proteins itself. Its main function is to regulate gene expression through RNA-RNA interactions, RNA-DNA interactions, and RNA-protein interactions [16]. LINC-PINT predominantly localizes to the cell nucleus and exhibits downregulation in tumor cells [17]. A recent study indicated that the downregulation of LINC-PINT is likely to contribute to the postoperative recurrence of ESCC [18]. Another previous study demonstrated that the low expression level of LINC-PINT is correlated with poor prognosis and may participate in gastric cancer by promoting proliferation, migration, and invasion of gastric cancer cells [19]. Additionally, the decreased expression of LINC-PINT is associated with a worse prognosis and can increase cell proliferation and migration in esophageal cancer [20]. These results suggest that LINC-PINT also plays an anti-cancer role in ESCC development. Single nucleotide polymorphisms (SNPs) are the most common type of human heritable variation and are likely to be a biomarker for predicting the susceptibility of human disease or studying the function of genetic variants in disease progression [21,22]. Evidence has shown that genetic polymorphisms in LINC-PINT are functionally associated with human diseases. For instance, a case-control study discovered that rs157916, rs16873842, and rs7781295 in LINC-PINT are significantly associated with steroid-induced osteonecrosis of the femoral head in a Chinese population [23]. A strong relationship between rs157928 in LINC-PINT and high-altitude pulmonary edema susceptibility in the Chinese population has been reported in the literature [24]. Data from one study suggest that LINC-PINT polymorphism (rs10228040) plays a critical role in the immunopathogenesis of both endemic and sporadic forms [25]. A multistage genome-wide association study (GWAS) identified a susceptibility allele of rs6971499 at 7q32.3 in an intron of LINC-PINT for pancreatic cancer in European populations [26]. Another important finding was that rs4593472 in the LINC-PINT gene was associated with breast cancer in Chinese Han women [27]. Moreover, Yu et al. revealed that rs157916 and rs16873842 reduced susceptibility to liver cancer [28]. Taken together, these data suggest that LINC-PINT polymorphisms play a key role in the occurrence of ESCC. In reviewing the literature, no data were found on the association between LINC-PINT polymorphisms and ESCC risk in the Chinese population.

Six SNPs (rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928) in the LINC-PINT gene were selected from the 1000 Genomes Project and genotyped using the MassARRAY platform. We further examined the relationship between SNPs and ESCC susceptibility in the Hainan Han population. Stratification analysis was used to explore the correlations between polymorphisms and ESCC risk in subgroups, including age, sex, alcohol consumption, and tobacco use. Finally, we tested the association between clinical indicators and SNP genotypes in ESCC patients. The present work provides a new perspective for understanding the role of LINC-PINT polymorphisms in the pathogenesis of ESCC.

Materials and methods

Study participants

In the present study, we recruited 843 unrelated Chinese subjects, including 391 ESCC cases and 452 ethnicity-matched healthy controls, from the Hainan Cancer Hospital between September 2020 and March 2022. All participants were informed of the purpose of our study, and signed an informed consent form before the start of the study. Patients in the case group were newly diagnosed with ESCC, which was confirmed by histopathological examination. Patients with any autoimmune diseases, history of anti-cancer treatment, and any other tumors, including ESCC, were excluded. All healthy controls matched with ESCC cases in terms of age and sex were selected from cancer-free subjects with a physical examination during the same period as the cases. Detailed information on age stratification (every 10 years) in both the ESCC and control groups is shown in Table S1. Control individuals with immunological or infectious disorders and a family history of cancer were excluded. The basic characteristics of each participant, including age, sex, carcinoembryonic antigen (CEA), carbohydrate antigen (CA50), carbohydrate antigen 199 (CA199), Alpha-fetoprotein (AFP), smoking status, alcohol use, pathological grade, lymph node metastasis status, and body mass index (BMI), were obtained from questionnaires or medical records. Our study was approved by the Ethics Committee of Hainan Cancer Hospital (2020 (Scientific Research) No. (08)). All procedures performed in the study involving human participants were in accordance with the ethical standards of the Hainan Cancer Hospital and Declaration.

SNP selection and genotyping

Six SNPs (rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928) in LINC-PINT were selected from the Han population in Southern China (CHS) of the 1000 Genomes Project. The detailed steps of LINC-PINT SNPs selection are as follows: (1) We obtained the physical position of the LINC-PINT gene on the chromosome 7:130791264-131110176 through the human e!GRCh37 database (http://asia.ensembl.org/Homo_sapiens/Info/Index). In the VCF to PED Converter window (http://grch37.ensembl.org/Homo_sapiens/Tools/VcftoPed), we entered the gene location, selected the CHS, and downloaded the ped and info file for the SNPs of LINC-PINT. We obtained 444 SNPs from the database. (2) We used Haploview software for quality control (minor allele frequency (MAF) > 5%, min genotype >75%, r2<0.8, and Hardy-Weinberg equilibrium (HWE) > 0.05) to select tag-SNPs. (3) The call rate for each SNP was greater than 95%. Other SNPs in LINC-PINT did not meet the above standards. Six SNPs (rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928) were selected for further investigation. A DNA extraction kit (Xi’an GoldMag Co. Ltd., Xi’an, China) was used to extract genomic DNA from the peripheral blood samples. Primers for PCR amplification were designed using the Agena Bioscience Assay Design software. The selected SNPs were genotyped using the Agena MassARRAY iPLEX platform (Agena Bioscience Inc., CA, USA). Additionally, the genotyping data were organized and analyzed using Agena Bioscience TYPER software (version 4.0).

Bioinformatics analysis

The potential functions of the six selected SNPs were predicted through HaploReg v4.2 online software (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php). Additionally, the mRNA expression of LINC-PINT in EC was analyzed using the UALCAN online software (https://ualcan.path.uab.edu/analysis.html).

Statistical analyses

All statistical analyses were conducted using SPSS 20.0 software. Statistical significance was defined as a two-tailed p-value <0.05. All continuous variables (including age, CEA, CA50, CA199, and AFP) were examined for normal distributions using the Kolmogorov-Smirnov test, and all data were normally distributed. The differences in the continuous variables (age, CEA, CA50, CA199, and AFP) and non-continuous variable (sex) between cases and controls were compared by using Student’s t-test and Pearson’s χ2 test, respectively. Fisher’s exact test was performed to evaluate the p-values of the Hardy-Weinberg equilibrium (HWE) in controls. The association between LINC-PINT polymorphisms and the risk of ESCC was examined by calculating OR and 95% CI using logistic regression analysis. The analyses were adjusted adjustments for age, sex, smoking, drinking, and BMI. We also explored the correlations between LINC-PINT polymorphisms and ESCC risk using stratification analysis. A false-positive report probability (FPRP) analysis was performed to verify the findings of this study. In addition, the SNP-SNP interactions with the risk of ESCC were analyzed by multifactor dimensionality reduction (MDR), in which the interaction model with the highest cross-validation consistency (CVC) and testing accuracy was considered the best. Finally, comparisons of CEA, CA50, CA199, and AFP in EC patients with SNP genotypes were tested by ANOVA test and one-way analysis.

Results

Basic characteristics

Our study included 391 ESCC patients and 452 healthy controls, and the basic characteristics of each subject are presented in Table 1. The average age was 63.91 ± 9.17 years in cases and was 63.92 ± 8.27 years in controls. There were no significant differences in age or sex between the cases and controls (p = 0.977 and p = 0.943, respectively). Statistically significant differences in carcinoembryonic antigen (CEA), carbohydrate antigen 50 (CA50), carbohydrate antigen 199 (CA199), and alpha-fetoprotein (AFP) levels were found between the cases and controls (all p < 0.001).

Table 1. Basic characteristics in ESCC cases and controls.

Characteristics	Cases (n = 391)	Controls (n = 452)	p	
Age, years (mean ± SD)	63.91 ± 9.17	63.92 ± 8.27	0.977a	
 >64	193 (49%)	219 (48%)	 	
 ≤64	198 (51%)	233 (52%)	 	
Sex	 	 	0.943b	
 Male	291 (74%)	321 (71%)	 	
 Female	100 (26%)	131 (29%)	 	
CEA (ng/ml)	12.79 ± 4.84	2.25 ± 3.02	<0.001a	
CA50 (U/ml)	2.99 ± 2.68	5.59 ± 3.37	<0.001a	
CA199 (U/ml)	20.12 ± 10.24	10.53 ± 6.08	<0.001a	
AFP (ng/ml)	9.73 ± 4.24	3.20 ± 3.40	<0.001a	
LN metastasis	 	 	 	
 Node-positive	50 (13%)	 	 	
 Node-negative	341 (87%)	 	 	
Smoking status	 	 	<0.001b	
 Never	196 (50%)	92 (20%)	 	
 Ever	195 (50%)	360 (80%)	 	
Alcohol use status	 	 	0.090b	
 Never	298 (76%)	367 (81%)	 	
 Ever	93 (24%)	85 (19%)	 	
BMI, kg/m2	 	 	0.001b	
 ≤24	336 (86%)	349 (77%)	 	
 >24	55 (14%)	103 (23%)	 	
Pathological grade	 	 	 	
 III/IV	154 (53%)	 	 	
 I/II	138 (47%)	 	 	
CEA: carcinoembryonic antigen; CA50: carbohydrate antigen 50; CA199: carbohydrate antigen 199; AFP: alpha fetoprotein; LN: lymph node; ESCC: esophageal squamous cell carcinoma; BMI: body mass index.

The pa value was calculated by Student’s t-test. The pb value was calculated by Pearson’s χ2 test. p < 0.05 indicates statistical significance.

The correlation analyses between LINC-PINT polymorphisms and ESCC risk

In this case-control study, six SNPs (rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928) in LINC-PINT were successfully detected. The allele frequency of each SNP is listed in Table 2. The selected SNPs in the controls conformed to HWE (all p > 0.05). And we observed that minor allele ‘C’ of rs157916 significantly decreased the risk of ESCC (OR = 0.80, 95% CI = 0.65–0.97, p = 0.021). We further investigated the effect of LINC-PINT polymorphisms on ESCC risk after adjusting for age, sex, smoking, drinking, and BMI (Table 3). Our result showed that rs157916 polymorphism could significantly decrease the risk of ESCC in the codominant model (GG vs. AA, OR = 0.63, 95% CI = 0.44–0.90, p = 0.011) and dominant model (GA-GG vs. AA, OR = 0.64, 95% CI = 0.46–0.91, p = 0.012).

Table 2. Basic information for LINC-PINT polymorphisms.

SNP ID	Chromosome position	Role	Alleles (minor/major)	MAF	O (HET)	E (HET)	pa-HWE	OR (95% CI)	pb	
Case	Control	
rs157916	chr7: 130884634	Intron	G/A	0.428	0.452	0.541	0.496	0.058	0.91 (0.75–1.10)	0.317	
rs16873842	chr7: 130885731	Intron	A/G	0.175	0.199	0.336	0.319	0.302	0.85 (0.67–1.09)	0.210	
rs7801029	chr7: 130890856	Intron	G/C	0.210	0.229	0.365	0.353	0.594	0.89 (0.71–1.13)	0.341	
rs7781295	chr7: 130894403	Intron	A/G	0.203	0.210	0.347	0.332	0.390	0.96 (0.75–1.22)	0.729	
rs28662387	chr7: 130895393	Intron	A/T	0.051	0.056	0.108	0.107	1.000	0.90 (0.59–1.38)	0.633	
rs157928	chr7: 130896599	Intron	C/T	0.396	0.452	0.506	0.496	0.704	0.80 (0.65–0.97)	0.021	
SNP: Single nucleotide polymorphisms; MAF: minor allele frequency; HWE: Hardy–Weinberg equilibrium.

pa values were calculated by exact test. pa <0.05 are excluded. pbvalues were calculated by two-sided χ2. pb<0.05 indicates statistical significance. Significant p-values are in bold.

Table 3. Association of LINC-PINT polymorphisms with ESCC risk.

SNP ID	Model	Genotype	Case
N	Control
N	Without adjusted	With adjusted	
OR (95% CI)	pa	OR (95% CI)	pb	
rs157916	Codominant	AA	125	125	1	 	1	 	
 	GA	195	244	0.80 (0.59 − 1.09)	0.158	0.63 (0.44 − 0.90)	0.011	
GG	69	82	0.84 (0.56–1.26)	0.404	0.70 (0.44–1.12)	0.134	
Dominant	AA	125	125	1	 	1	 	
GA-GG	264	326	0.81 (0.60–1.09)	0.163	0.64 (0.46–0.91)	0.012	
Recessive	AA-GA	320	369	1	 	1	 	
GG	69	82	0.97 (0.68–1.38)	0.867	0.93 (0.62–1.41)	0.739	
Log-additive	–	–	–	0.90 (0.74–1.10)	0.302	0.80 (0.64–1.01)	0.061	
rs16873842	Codominant	GG	268	286	1	 	1	 	
GA	109	152	0.77 (0.57–1.03)	0.078	0.81 (0.57–1.14)	0.223	
AA	14	14	1.07 (0.50–2.28)	0.867	0.99 (0.40–2.44)	0.977	
Dominant	GG	268	286	1	 	1	 	
GA-AA	123	166	0.79 (0.59–1.05)	0.108	0.82 (0.59–1.15)	0.251	
Recessive	GG-GA	377	438	1	 	1	 	
AA	14	14	1.16 (0.55–2.47)	0.697	1.05 (0.43–2.58)	0.910	
Log-additive	–	–	–	0.85 (0.66–1.09)	0.207	0.87 (0.65–1.16)	0.338	
rs7801029	Codominant	CC	238	266	1	 	1	 	
CG	142	165	0.96 (0.72–1.28)	0.789	0.81 (0.58–1.13)	0.208	
GG	11	21	0.59 (0.28–1.24)	0.162	0.53 (0.22–1.28)	0.160	
Dominant	CC	238	266	1	 	1	 	
GC-GG	153	186	0.92 (0.70–1.21)	0.551	0.78 (0.56–1.08)	0.129	
Recessive	CC-GC	380	431	1	 	1	 	
GG	11	21	0.59 (0.28–1.25)	0.169	0.58 (0.24–1.38)	0.216	
Log-additive	–	–	–	0.89 (0.70–1.13)	0.326	0.78 (0.59–1.03)	0.084	
rs7781295	Codominant	GG	247	272	1	 	1	 	
GA	123	153	0.89 (0.66–1.19)	0.416	0.94 (0.67–1.33)	0.733	
AA	17	16	1.17 (0.58–2.37)	0.662	1.48 (0.65–3.35)	0.353	
Dominant	GG	247	272	1	 	1	 	
GA-AA	140	169	0.91 (0.69–1.21)	0.524	0.99 (0.71–1.38)	0.953	
Recessive	GG-GA	370	425	1	 	1	 	
AA	17	16	1.22 (0.61–2.45)	0.575	1.50 (0.67–3.39)	0.325	
Log-additive	–	–	–	0.96 (0.75–1.22)	0.727	1.04 (0.79–1.38)	0.773	
rs28662387	Codominant	TT	352	402	1	 	1	 	
TA	38	49	0.89 (0.57–1.39)	0.595	0.98 (0.58–1.68)	0.951	
AA	1	1	1.14 (0.07–18.33)	0.925	0.19 (0.01–5.41)	0.335	
Dominant	TT	352	402	1	 	1	 	
TA-AA	39	50	0.89 (0.57–1.39)	0.609	0.95 (0.56–1.61)	0.844	
Recessive	TT-TA	390	451	1	 	1	 	
AA	1	1	1.16 (0.07–18.55)	0.918	0.19 (0.01–5.42)	0.335	
Log-additive	–	–	–	0.90 (0.59–1.38)	0.632	0.92 (0.55–1.53)	0.739	
rs157928	Codominant	TT	138	133	1	 	1	 	
TC	196	228	0.83 (0.61–1.12)	0.227	0.83 (0.59–1.19)	0.317	
CC	57	90	0.61 (0.41–0.92)	0.018	0.68 (0.42–1.09)	0.109	
Dominant	TT	138	133	1	 	1	 	
TC-CC	153	318	0.77 (0.57–1.03)	0.072	0.79 (0.57–1.11)	0.176	
Recessive	TT-TC	334	361	1	 	1	 	
CC	57	90	0.68 (0.48–0.98)	0.041	0.76 (0.50–1.16)	0.198	
Log-additive	–	–	–	0.79 (0.65–0.96)	0.019	0.83 (0.66–1.04)	0.103	
ESCC: Esophageal squamous cell carcinoma; CI: confidence interval; OR: odds ratio; SNP: single nucleotide polymorphism.

pa: Values were calculated by unconditional logistic regression analysis without adjustment. pb: Values were calculated by unconditional logistic regression analysis with adjustment for age, sex, smoking, drinking, and BMI. p < 0.05 indicates statistical significance. Significant p-values are in bold.

The LINC-PINT polymorphisms and ESCC with stratification analyses

We also examined the correlations between LINC-PINT polymorphisms and ESCC risk by stratifying the participants by age, sex, smoking status, and alcoholic use. Since the average age of the case and control groups was 64 years in our study, we stratified them by the age of 64 years. As is presented in Table 4, the association analysis based on age stratification showed that rs157916 was dramatically associated with a decreased risk of ESCC susceptibility at age >64 years (GA genotype: OR = 0.58, 95% CI = 0.34–0.98, p = 0.042; GA-GG genotype: OR = 0.59, 95% CI = 0.36–0.97, p = 0.038), no significant associations were observed in patients with aged ≤64 years. The data for sex-based stratification analysis are shown in Table 5. It was found that rs157916 was correlated with a decreased risk of ESCC in males (GA genotype: OR = 0.58, 95% CI = 0.39–0.88, p = 0.010; GA-GG genotype: OR = 0.61, 95% CI = 0.42–0.90, p = 0.014). Rs16873842 was related to a decreased susceptibility to ESCC in males (A allele: OR = 0.70, 95% CI = 0.52–0.93, p = 0.015). In females, rs7801029 (GG genotype: OR = 0.07, 95% CI = 0.01–0.54, p = 0.011; GC-GG: OR = 0.39, 95% CI = 0.16–0.93, p = 0.033) and rs157928 (C allele: OR = 0.62, 95% CI = 0.42–0.90, p = 0.012; CC genotype: OR = 0.19, 95% CI = 0.05–0.64, p = 0.007; TC-CC: OR = 0.35, 95% CI = 0.13–0.94, p = 0.036) played a protective role in ESCC risk. Moreover, we analyzed the associations between SNPs and ESCC risk in smoking status and alcoholic use subgroups (Table 6). When stratified by smoking status, we observed that A allele of rs7781295 was associated with a decreased susceptibility to ESCC in smokers (OR = 0.58, 95% CI = 0.34–0.99, p = 0.046). The rs157928 polymorphism significantly decreased the risk of ESCC in non-smokers (CC genotype: OR = 0.32, 95% CI = 0.14–0.73, p = 0.006; TC-CC: OR = 0.52, 95% CI = 0.28–0.98, p = 0.038). When stratified by drinking status, the results showed that rs7781295 (OR = 0.58, 95% CI = 0.34–0.99, p = 0.046) was significantly associated with the risk of ESCC in drinkers. In non-drinkers, rs157916 (GA genotype: OR = 0.62, 95% CI = 0.41–0.94, p = 0.025; GA-GG: OR = 0.62, 95% CI = 0.42–0.92, p = 0.019) and rs7801029 (GC-GG: OR = 0.68, 95% CI = 0.46–0.98, p = 0.040) significantly decreased the risk of ESCC.

Table 4. Association between SNPs and ESCC susceptibility stratified by age.

SNP	Allele/Genotype	OR (95% CI)	p	OR (95% CI)	p	
Age	 	>64	 	≤64	 	
rs157916	A	1	 	1	 	
G	0.92 (0.70–1.22)	0.574	0.89 (0.68–1.17)	0.396	
AA	1	 	1	 	
GA	0.58 (0.34–0.98)	0.042	0.71 (0.43–1.19)	0.191	
GG	0.63 (0.33–1.22)	0.172	0.78 (0.39–1.54)	0.475	
GA-GG	0.59 (0.36–0.97)	0.038	0.73 (0.45–1.18)	0.200	
rs16873842	G	1	 	1	 	
A	0.95 (0.67–1.35)	0.764	0.77 (0.55–1.09)	0.146	
GG	1	 	1	 	
GA	0.80 (0.48–1.34)	0.403	0.85 (0.52–1.37)	0.501	
AA	1.15 (0.35–3.75)	0.817	0.72 (0.16–3.20)	0.671	
GA-AA	0.84 (0.52–1.37)	0.485	0.84 (0.53–1.34)	0.462	
rs7801029	C	1	 	1	 	
G	0.86 (0.62–1.19)	0.351	0.93 (0.67–1.29)	0.673	
CC	1	 	1	 	
CG	0.70 (0.43–1.15)	0.158	0.93 (0.59–1.48)	0.762	
GG	0.51 (0.16–1.67)	0.268	0.57 (0.15–2.17)	0.410	
GC-GG	0.68 (0.43–1.09)	0.112	0.90 (0.57–1.41)	0.637	
rs7781295	G	1	 	1	 	
A	1.05 (0.74–1.48)	0.802	0.89 (0.64–1.24)	0.492	
GG	1	 	1	 	
GA	0.99 (0.59–1.65)	0.966	0.94 (0.59–1.51)	0.807	
AA	1.39 (0.48–4.04)	0.543	1.62 (0.44–5.99)	0.468	
GA-AA	1.04 (0.64–1.69)	0.875	0.98 (0.62–1.55)	0.948	
rs28662387	T	1	 	1	 	
A	0.87 (0.46–1.67)	0.676	0.93 (0.53–1.63)	0.799	
TT	1	 	1	 	
TA	/	/	0.93 (0.44–1.96)	0.841	
AA	/	/	0.24 (0.01–7.77)	0.418	
TA-AA	0.98 (0.45–2.14)	0.965	0.88 (0.42–1.82)	0.721	
rs157928	T	1	 	1	 	
C	0.81 (0.62–1.07)	0.137	0.78 (0.59–1.02)	0.073	
TT	1	 	1	 	
CT	1.03 (0.61–1.76)	0.906	0.71 (0.43–1.15)	0.162	
CC	0.70 (0.36–1.38)	0.304	0.65 (0.32–1.29)	0.218	
TC-CC	0.93 (0.56–1.53)	0.765	0.69 (0.43–1.10)	0.122	
ESCC: esophageal squamous cell carcinoma.

p Values were calculated by logistic regression adjusted by sex, smoking, drinking, and BMI. p < 0.05 indicates statistical significance. Significant p-values are in bold.

Table 5. Correlation between SNPs and ESCC susceptibility stratified by sex.

SNP	Allele/Genotype	OR (95% CI)	p	OR (95% CI)	p	
Sex	 	Male	 	Female	 	
rs157916	A	1	 	1	 	
G	0.86 (0.69–1.08)	0.206	1.04 (0.71–1.50)	0.857	
AA	1	 	1	 	
GA	0.58 (0.39–0.88)	0.010	0.95 (0.34–2.63)	0.924	
GG	0.71 (0.42–1.21)	0.205	0.43 (0.12–1.51)	0.188	
GA-GG	0.61 (0.42–0.90)	0.014	0.78 (0.30–2.03)	0.603	
rs16873842	G	1	 	1	 	
A	0.70 (0.52–0.93)	0.015	1.44 (0.91–2.28)	0.119	
GG	1	 	1	 	
GA	0.77 (0.52–1.13)	0.183	1.26 (0.49–3.26)	0.629	
AA	0.54 (0.17–1.72)	0.297	12.85 (0.29–56.54)	0.186	
GA-AA	0.75 (0.51–1.09)	0.130	1.53 (0.61–3.84)	0.371	
rs7801029	C	1	 	1	 	
G	0.95 (0.73–1.25)	0.737	0.75 (0.48–1.17)	0.207	
CC	1	 	1	 	
CG	0.82 (0.57–1.20)	0.311	0.47 (0.19–1.16)	0.099	
GG	0.83 (0.32–2.17)	0.706	0.07 (0.01–0.54)	0.011	
GC-GG	0.82 (0.57–1.19)	0.299	0.39 (0.16–0.93)	0.033	
rs7781295	G	1	 	1	 	
A	0.82 (0.62–1.09)	0.173	1.48 (0.92–2.37)	0.106	
GG	1	 	1	 	
GA	0.91 (0.62–1.34)	0.644	1.73 (0.65–4.61)	0.272	
AA	1.36 (0.56–3.34)	0.498	1.05 (0.11–9.75)	0.963	
GA-AA	0.96 (0.66–1.39)	0.813	1.64 (0.64–4.16)	0.301	
rs28662387	T	1	 	1	 	
A	0.88 (0.53–1.46)	0.627	0.96 (0.43–2.14)	0.917	
TT	1	 	1	 	
TA	0.84 (0.46–1.55)	0.586	3.58 (0.54–23.93)	0.188	
AA	/	/	/	/	
TA-AA	0.82 (0.45–1.50)	0.513	3.70 (0.56–24.25)	0.173	
rs157928	T	1	 	1	 	
C	0.87 (0.69–1.09)	0.224	0.62 (0.42–0.90)	0.012	
TT	1	 	1	 	
CT	0.93 (0.62–1.39)	0.708	0.46 (0.16–1.34)	0.155	
CC	0.85 (0.49–1.45)	0.546	0.19 (0.05–0.64)	0.007	
TC-CC	0.91 (0.62–1.33)	0.616	0.35 (0.13–0.94)	0.036	
ESCC: esophageal squamous cell carcinoma.

p Values were calculated by logistic regression adjusted by age, smoking, drinking, and BMI. p < 0.05 Indicates statistical significance. Significant p-values are in bold.

Table 6. Correlation between SNPs and ESCC risk stratified by smoking and drinking status.

SNP	Allele/Genotype	OR (95% CI)	p	OR (95% CI)	p	
Smoking status	 	Smoking	 	Non-smoking	 	
rs157916	A	1	 	1	 	
G	1.09 (0.72–1.65)	0.695	0.86 (0.69–1.07)	0.172	
AA	1	 	1	 	
GA	0.62 (0.39–1.00)	0.050	0.70 (0.36–1.34)	0.281	
GG	0.75 (0.42–1.35)	0.340	0.52 (0.22–1.24)	0.138	
GA-GG	0.66 (0.42–1.02)	0.061	0.65 (0.35–1.23)	0.180	
rs16873842	G	1	 	1	 	
A	0.62 (0.36–1.06)	0.078	0.93 (0.71–1.23)	0.613	
GG	1	 	1	 	
GA	0.90 (0.57–1.41)	0.643	0.80 (0.44–1.45)	0.463	
AA	0.49 (0.13–1.80)	0.281	2.43 (0.29–20.77)	0.416	
GA-AA	0.85 (0.55–1.32)	0.470	0.87 (0.49–1.55)	0.630	
rs7801029	C	1	 	1	 	
G	1.28 (0.77–2.11)	0.342	0.81 (0.62–1.05)	0.114	
CC	1	 	1	 	
CG	0.81 (0.52–1.25)	0.342	0.65 (0.37–1.15)	0.137	
GG	0.51 (0.16–1.65)	0.262	0.54 (0.12–2.39)	0.417	
GC-GG	0.78 (0.51–1.19)	0.240	0.64 (0.37–1.11)	0.110	
rs7781295	G	1	 	1	 	
A	0.58 (0.34–0.99)	0.046	1.10 (0.84–1.43)	0.502	
GG	1	 	1	 	
GA	1.17 (0.74–1.85)	0.499	0.87 (0.49–1.54)	0.633	
AA	1.31 (0.51–3.40)	0.576	1.61 (0.18–14.04)	0.667	
GA-AA	1.19 (0.77–1.84)	0.430	0.90 (0.51–1.58)	0.710	
rs28662387	T	1	 	1	 	
A	0.91 (0.37–2.24)	0.836	0.90 (0.55–1.45)	0.656	
TT	1	 	1	 	
TA	0.86 (0.43–1.73)	0.680	1.79 (0.64–5.02)	0.271	
AA	/	/	0.57 (0.01–64.53)	0.816	
TA-AA	/	/	1.71 (0.62–4.66)	0.290	
rs157928	T	1	 	1	 	
C	0.75 (0.49–1.15)	0.182	0.82 (0.66–1.02)	0.068	
TT	1	 	1	 	
CT	1.00 (0.63–1.59)	1.000	0.62 (0.32–1.20)	0.158	
CC	1.12 (0.60–2.09)	0.729	0.32 (0.14–0.73)	0.006	
TC-CC	1.03 (0.66–1.60)	0.910	0.52 (0.28–0.98)	0.038	
Drinking status	 	Drinking	 	Non–drinking	 	
rs157916	A	1	 	1	 	
G	1.09 (0.72–1.65)	0.695	0.86 (0.69–1.07)	0.172	
AA	1	 	1	 	
GA	0.66 (0.29–1.51)	0.326	0.62 (0.41–0.94)	0.025	
GG	1.01 (0.34–2.98)	0.984	0.63 (0.37–1.08)	0.093	
GA-GG	0.73 (0.33–1.62)	0.444	0.62 (0.42–0.92)	0.019	
rs16873842	G	1	 	1	 	
A	0.62 (0.36–1.06)	0.078	0.93 (0.71–1.23)	0.613	
GG	1	 	1	 	
GA	0.64 (0.29–1.41)	0.266	0.90 (0.61–1.34)	0.615	
AA	0.52 (0.09–2.96)	0.462	1.16 (0.38–3.50)	0.796	
GA-AA	0.62 (0.30–1.31)	0.210	0.92 (0.63–1.35)	0.681	
rs7801029	C	1	 	1	 	
G	1.28 (0.77–2.11)	0.342	0.81 (0.62–1.05)	0.114	
CC	1	 	1	 	
CG	1.03 (0.50–2.15)	0.929	0.71 (0.48–1.04)	0.079	
GG	1.53 (0.21–11.02)	0.675	0.42 (0.15–1.16)	0.094	
GC-GG	1.07 (0.52–2.18)	0.855	0.68 (0.46–0.98)	0.040	
rs7781295	G	1	 	1	 	
A	0.58 (0.34–0.99)	0.046	1.10 (0.84–1.43)	0.502	
GG	1	 	1	 	
GA	0.71 (0.32–1.59)	0.406	1.10 (0.74–1.62)	0.640	
AA	1.21 (0.22–6.60)	0.829	1.61 (0.62–4.19)	0.332	
GA-AA	0.77 (0.36–1.64)	0.497	1.14 (0.78–1.66)	0.492	
rs28662387	T	1	 	1	 	
A	0.91 (0.37–2.24)	0.836	0.90 (0.55–1.45)	0.656	
TT	1	 	1	 	
TA	0.84 (0.27–2.66)	0.772	1.06 (0.58–1.93)	0.854	
AA	/	/	/	/	
TA-AA	0.82 (0.26–2.55)	0.731	1.06 (0.58–1.94)	0.840	
rs157928	T	1	 	1	 	
C	0.75 (0.49–1.15)	0.182	0.82 (0.66–1.02)	0.068	
TT	1	 	1	 	
CT	0.65 (0.30–1.38)	0.262	0.94 (0.62–1.42)	0.765	
CC	1.11 (0.36–3.40)	0.860	0.67 (0.39–1.15)	0.146	
TC-CC	0.72 (0.35–1.49)	0.382	0.86 (0.58–1.27)	0.453	
ESCC: esophageal squamous cell carcinoma.

p Values were calculated by logistic regression adjusted by age, sex, smoking/drinking, and BMI. p < 0.05 Indicates statistical significance. Significant p-values are in bold.

The potential functions of the SNPs

As presented in Table S2, the bioinformatics analysis showed that rs157916 and rs7781295 were related to the regulation of promoter histone marks, enhancer histone marks, DNAse, proteins bound, motifs changed, and selected eQTL hits. Rs16873842 was related to the regulation of Enhancer histone marks, DNAse, Motifs changed, and Selected eQTL hits. Rs7801029 was associated with the regulation of promoter histone marks, enhancer histone marks, DNAse, proteins bound, and motifs changed. Rs28662387 and rs157928 might be involved in the regulation of Enhancer histone marks, DNAse, Proteins bound, and Motifs changed.

FPRP results

We preset 0.2 as the FPRP threshold. As shown in Table S3, with a prior probability of 0.25, all the significant findings for the associations of LINC-PINT polymorphisms with ESCC susceptibility in the whole group, as well as the subgroups aged >64 years, male, female, smoking, non-smoking, drinking, and non-drinking remained noteworthy (all FPRP < 0.2), except for the rs7801029 polymorphism in females. These results indicated that the most significant association findings are noteworthy.

Analysis of SNP-SNP interaction by using MDR

The results of the MDR analysis are listed in Table 7. Our data showed that the combination of rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928 was the best predictive model for ESCC (CVC = 10/10, testing accuracy = 0.5078, p < 0.0001). The best two-locus model was the combination of rs157916 and rs7781295 (CVC = 5/10; testing accuracy = 0.4871; p = 0.020). The three-locus model included rs157916, rs7781295, and rs157928 (CVC = 6/10; testing accuracy = 0.4858; p < 0.0001). The combination of rs157916, rs7801029, rs7781295 and rs157928 formed a four-locus model (CVC = 10/10; testing accuracy = 0.4987; p < 0.0001). The five-locus model consisted of rs157916, rs7801029, rs7781295, rs28662387 and rs157928 (CVC = 9/10; testing accuracy = 0.5000; p < 0.0001). Thus, the best model was the six-locus model, a combination of rs157916, rs16873842, rs7801029, rs7781295, rs28662387 and rs157928, with the highest CVC and highest testing accuracy. As shown in Figure 1, the interaction map shows that the interaction between rs7781295 and rs157916 had a high value of positive entropy or synergism (0.31%, shown in red).

Figure 1. Interaction map among SNPs in the LINC-PINT gene on the risk of esophageal squamous cell carcinoma. This graphical model describes the percentage entropy (information gain) explained by each SNP or 2-way interaction. Values in the nodes represent the information gain of individual attributes (main effects), whereas values between nodes represent the information gain of each pair of attributes (interaction effects).

Table 7. Summary of SNP-SNP interactions on the risk of ESCC analyzed by MDR method.

Model	Testing Bal. Acc.	CVC	p	
rs157928	0.4871	9/10	0.122	
rs157916,rs7781295	0.4871	5/10	0.020	
rs157916,rs7781295,rs157928	0.4858	6/10	<0.0001	
rs157916,rs7801029,rs7781295,rs157928	0.4987	10/10	<0.0001	
rs157916,rs7801029,rs7781295,rs28662387,rs157928	0.5000	9/10	<0.0001	
rs157916,rs16873842,rs7801029,rs7781295,rs28662387,rs157928	0.5078	10/10	<0.0001	
ESCC: esophageal squamous cell carcinoma; Bal. Acc.: balanced accuracy; CVC: cross-validation consistently; MDR: multifactor dimensionality reduction.

The model with the maximum testing accuracy and maximum CVC was considered the best model. p Values were validated by 1000 permutation tests. Significant p-values are in bold.

The interaction of clinical indicators with SNP genotypes

Finally, we detected potential associations between SNPs and the clinical indicators of ESCC. Significant differences in AFP levels were observed among the AA (9.293 ± 3.84 ng/ml), GA (9.265 ± 4.19 ng/ml), and GG (11.767 ± 4.57 ng/ml) genotypes in rs157916 (p = 0.004). Concerning the levels of CEA, there were notable differences among the AA (17.260 ± 10.91 ng/ml), AG (11.995 ± 3.63 ng/ml), and GG (12.822 ± 4.62 ng/ml) genotypes in rs16873842 (p = 0.016). Additionally, the concentrations of CA50 showed significant variance among the AT (2.359 ± 1.59 U/ml), TT (3.023 ± 2.71 U/ml), and AA (10.110 ± 0.00 U/ml) genotypes in rs28662387 (p = 0.016), as detailed in Table 8.

Table 8. Comparisons of clinical characteristics among ESCC patients with SNP genotypes.

SNP	CEA (ng/ml)	CA50 (U/ml)	CA199 (U/ml)	AFP (ng/ml)	
rs157916	 	 	 	 	
AA	12.982 ± 4.72	2.936 ± 2.53	19.902 ± 9.14	9.293 ± 3.84	
GA	12.258 ± 4.18	3.167 ± 2.92	20.500 ± 11.79	9.265 ± 4.19	
GG	13.836 ± 6.38	2.629 ± 2.31	19.565 ± 7.74	11.767 ± 4.57	
p	0.215	0.567	0.876	0.004	
rs16873842	 	 	 	 	
AA	17.260 ± 10.91	2.116 ± 1.32	16.308 ± 5.13	11.170 ± 4.65	
AG	11.995 ± 3.63	3.056 ± 3.16	19.783 ± 10.23	10.322 ± 4.60	
GG	12.822 ± 4.62	3.015 ± 2.57	20.442 ± 10.45	9.457 ± 4.09	
p	0.016	0.642	0.524	0.285	
rs7801029	 	 	 	 	
CC	13.013 ± 5.12	2.992 ± 2.80	19.134 ± 8.94	9.588 ± 4.05	
GC	12.512 ± 4.43	2.941 ± 2.29	21.865 ± 12.15	9.869 ± 4.51	
GG	12.003 ± 4.39	3.486 ± 4.60	17.813 ± 4.13	10.724 ± 5.06	
p	0.706	0.877	0.167	0.740	
rs7781295	 	 	 	 	
AA	15.431 ± 10.94	2.523 ± 1.40	18.717 ± 3.19	9.928 ± 5.42	
AG	12.246 ± 4.31	2.995 ± 3.20	19.322 ± 7.99	9.933 ± 4.48	
GG	12.895 ± 4.46	3.004 ± 2.48	20.268 ± 10.84	9.637 ± 4.08	
p	0.170	0.873	0.771	0.892	
rs28662387	 	 	 	 	
AA	15.840 ± 0.00	10.110 ± 0.00	15.500 ± 0.00	11.470 ± 0.00	
AT	13.146 ± 5.43	2.359 ± 1.89	18.990 ± 8.31	9.946 ± 5.44	
TT	12.739 ± 4.79	3.023 ± 2.71	20.284 ± 10.48	9.696 ± 4.11	
p	0.769	0.016	0.778	0.890	
rs157928	 	 	 	 	
TT	13.012 ± 3.88	2.589 ± 1.89	17.755 ± 7.40	9.764 ± 4.01	
CT	12.905 ± 5.59	3.223 ± 3.07	21.629 ± 11.57	10.090 ± 4.31	
CC	11.856 ± 3.65	3.039 ± 2.64	20.062 ± 9.92	8.257 ± 4.33	
p	0.528	0.313	0.054	0.116	
ESCC: esophageal squamous cell carcinoma; CEA: carcinoembryonic antigen; CA50: carbohydrate antigens 50; CA199: carbohydrate antigens 199; AFP: alpha fetoprotein.

p Values were calculated by the Kruskal-Wallis H test. p < 0.05 Indicates statistical significance. Significant p-values are in bold.

LINC-PINT mRNA expression

Through bioinformatics analysis, it was found that the expression of LINC-PINT was significantly lower in esophageal carcinoma compared to normal tissues (Fig S1).

Discussion

In this study, we explored the association between LINC-PINT polymorphisms and ESCC susceptibility in a Hainan Han population. We discovered that LINC-PINT polymorphisms are dramatically related to the risk of ESCC, providing scientific evidence for the prevention and diagnosis of ESCC in the Hainan Han population.

ESCC is a multifactorial disease caused by both environmental and genetic factors. The pathogenesis of this disease is not fully understood. The current study found that lncRNAs are closely related to the survival and progression of EC and can be used as non-invasive biomarkers for the early detection of ESCC [29]. LINC-PINT is a type of tumor suppressor lncRNA that plays an anti-cancer role in many cancers, including ESCC [17,18,20,30]. The expression level of the LINC-PINT gene in EC was much lower than that in normal tissues, based on the UALCAN database. Numerous studies have strongly supported that genetic variants can significantly affect ESCC susceptibility including ADH1A, MTHFR, TNFAIP2, and CDH1 [31–34]. In addition, a previous study found that lncRNA genetic polymorphisms are correlated with the initiation and progression of human diseases by affecting their expression and functions [35,36]. Thus, we speculate that LINC-PINT polymorphisms may be associated with ESCC progression.

In the overall analysis, we found that rs157916 and rs157928 in LINC-PINT were associated with a decreased susceptibility to ESCC. This is consistent with a previous report that showed that rs157916 can reduce the risk of steroid-induced osteonecrosis of the femoral head and liver cancer [23,28]. The rs157928 polymorphism was associated with a decreased risk of high-altitude pulmonary edema [24]. In addition, rs2048672 and rs4593472 have a protective role in breast cancer development [27,37]. Furthermore, rs6971499 has been identified as a protective factor against pancreatic cancer [26]. These findings suggest that LINC-PINT polymorphisms may play an important role in the progression of human diseases. Age and sex are common risk factors for ESCC [38]. Age-stratified analyses indicated that rs157916 significantly decreased the ESCC risk in people aged >64 years. When stratified by sex, analyses showed that rs157916 and rs16873842 were correlated with a decreased risk of ESCC in males. The rs7801029 and rs157928 polymorphisms decreased genetic susceptibility to ESCC risk in females. Although there were no studies focusing on the associations of LINC-PINT polymorphisms with ESCC risk based on age and sex stratification, we carried out FPRP analysis to detect whether the positive findings in our study were just chance or noteworthy observations. The data showed that all significant findings remained noteworthy, which indicates that our results make sense. Our study suggests that the association between SNPs, especially in rs157916, rs16873842, rs7801029 and rs157928, and ESCC risk may be influenced by age and sex.

Alcohol consumption and tobacco use are equally important in ESCC progression. We found that rs7781295 was associated with a decreased susceptibility to ESCC in smokers and drinkers. Rs7801029 was greatly associated with ESCC risk in non-drinkers. Rs157928 showed a protective role in ESCC susceptibility in non-drinkers. Although no studies have focused on the association of LINC-PINT polymorphisms with ESCC risk stratified by smoking and drinking, FPRP analysis showed that all significant findings remained noteworthy, which indicates that our results make sense. We speculate that these SNPs may also be crucial loci for ESCC progression and should be validated in future studies. A previous study demonstrated that LINC-PINT is downregulated in esophageal cancer patients compared to healthy controls [18]. In addition, a recent study found low expression of LINC-PINT in recurrent patients but not in non-recurrent patients, suggesting that LINC-PINT expression can be used as a prognostic predictor of ESCC progression [39]. Rs157916, rs16873842, rs7801029, rs7781295, and rs157928 are located in the intronic region of the LINC-PINT gene. The bioinformatics analysis revealed that rs157916 and rs7781295 were related to the regulation of promoter histone marks, enhancer histone marks, DNAse, proteins bound, motifs changed, and selected eQTL hits. Rs16873842 was related to the regulation of Enhancer histone marks, DNAse, Motifs changed, and Selected eQTL hits. Rs7801029 was associated with the regulation of promoter histone marks, enhancer histone marks, DNAse, proteins bound, and motifs changed. And rs157928 might be involved in the regulation of Enhancer histone marks, DNAse, Proteins bound, Motifs changed, which suggests their potential functions in ESCC. Several previous studies have strongly supported that the intronic variant can modify gene function by regulating gene expression [40–42]. We propose that LINC-PINT polymorphisms may contribute to ESCC progression by affecting the gene expression. Further studies are needed to confirm this hypothesis. Given that ESCC is a complex disorder affected by the interaction of genetic and environmental factors, SNP-SNP interaction studies may help to identify risk factors for ESCC. Our study indicates that the combination of rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928 is the best predictive model for ESCC.

Serum carbohydrate antigen 50 (CA50) is a clinically detected tumor biomarker, and its level is always increased in ESCC. For example, recent evidence has confirmed that the CA50 level in digestive tract tumors is significantly higher than that in normal controls [43]. Another study has indicated that the CA50 level in the ESCC was higher than that in health controls [44]. This suggests that increased serum CA50 levels are associated with ESCC occurrence. We observed that the concentration of CA50 was significantly different among the genotypes of rs28662387.

Our study had some limitations. First, we evaluated the association between LINC-PINT polymorphisms and ESCC risk, which will be explored in future studies. Second, the SNP genotype-based mRNA expression analysis will be performed in future studies to verify the present results. Third, our study is a basic research, and the molecular mechanisms of LINC-PINT polymorphisms in ESCC will be explored in the future. Despite these limitations, this is the first report to focus on the association between LINC-PINT polymorphisms and ESCC susceptibility, which provides a new perspective for the prevention and diagnosis of ESCC.

Conclusion

In summary, our study is the first to demonstrate that LINC-PINT genetic polymorphisms correlate with ESCC susceptibility in the Hainan Han population, indicating that LINC-PINT may serve as a candidate biomarker for the diagnosis of ESCC.

Supplementary Material

Supplemental Material

Acknowledgments

The authors thank all participants and volunteers for this study. We also thank the Hainan Cancer Hospital for their help with sample collection.

Ethical approval

Our study was approved by the Ethics Committee of the Hainan Cancer Hospital. All procedures performed in this study involving human participants were in accordance with the ethical standards of the Hainan Cancer Hospital and Helsinki Declaration, and informed consent was obtained from all individual participants included in the study.

Author contributions

Jian Song conceived and designed the experiments, and revised the manuscript. Ruisha Tu contributed to experiments and wrote the manuscript. Dunjing Zhong contributed to recruit and collect the study samples. Ping Li contributed to recruit and collect the study samples. Yongyu Li contributed to recruit and collect the study samples. Zhuang Chen contributed to select the SNPs and design the primers. Feixiang Hu contributed to select the SNPs and design the primers. Guihong Yuan contributed to analyze the data. Zhaowei Chen contributed to analyze the data. Shuyong Yu contributed to analyze the data. All authors have read and approved the final manuscript and no other person made a substantial contribution to the paper.

Disclosure statement

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

Data availability statement

The participants’ informed consent statements did not seek consent for data to be made publicly available. However, data could be obtained by contacting the corresponding author upon reasonable request.
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