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Association of genetic variants in MIR17HG and in the promoter of MIR17HG with susceptibility to cancer in Chinese Han population: a systematic review and meta-analysis

BMC Cancer volume 25, Article number: 631 (2025) Cite this article

Abstract

Background

The association between microRNA 17–92 cluster host gene (MIR17HG) polymorphisms and the risk of cancer has been evaluated in studies, here, we attempted to elucidate the relationship between 6 single nucleotide polymorphisms (SNPs) of MIR17HG (rs17735387 G > A, rs7336610 C > T, rs1428 C > A, rs7318578 A > C, rs72640334 C > A, and rs75267932 A > G), 3 SNPs in the promoter of MIR17HG (rs9588884 C > G, rs982873 T > C, and rs1813389 A > G) and susceptibility to cancer in Chinese Han population.

Methods

Systematic literature research from databases were performed with strict eligibility criteria to include the relevant studies for this meta-analysis. Association between the SNPs of MIR17HG and cancer risk was estimated by pooling the odds ratios (ORs) with 95% confidence interval (95% CI) in five genetic models (allelic model, dominant model, recessive model, homozygous model, and heterozygous model).

Results

The pooled meta-analysis showed that there was no significant association between rs17735387 G > A, rs7336610 C > T, rs1428 C > A, rs7318578 A > C, rs72640334 C > A, and rs75267932 A > G and cancer risk in Chinese Han population. However, for the SNPs in the promoter of MIR17HG, rs9588884 C > G and rs982873 T > C could decrease cancer risk in most genetic models, but not rs1813389 A > G.

Conclusion

This present meta-analysis identified 2 SNPs in the promoter of MIR17HG (rs9588884 C > G and rs982873 T > C) may be protective factors against cancer in Chinese Han population.

Peer Review reports

Introduction

Cancer is a major global health threat and one of the leading causes of lethality worldwide, which is a great threat to nation’s health [1, 2]. Various risk factors are related with tumorigenesis, such as genetic regulation and environmental factors [3, 4]. Some individuals may not develop to cancer even though they are exposed to certain environmental factors, indicating the role of genetic factors in the development of cancer and the complex of cancer occurrence [5]. Normally, mutated allele and allelic variant (polymorphism) are addressed in disease genetic basis, and polymorphism is the most common genetic variation [6]. For cancer, gene variants may have certain effects on cancer occurrence, development and even its treatment [7, 8]. SNPs (single nucleotide polymorphisms), which means the change of a single nucleotide in the DNA sequence, could affect gene expression, gene production function, biological pathways and further carcinogenesis [9]. Therefore, identification of SNPs in host genes or non-coding genes will provide a better understanding for the relationship between genomic variations and cancer susceptibility.

MicroRNAs (miRNAs) are endogenous non-coding small RNA molecules possessed with 18–22 nucleotides in length [10]. They act as key post-transcriptional regulators in various physiological and pathological processes through regulating gene expression by targeting mRNAs [10, 11], exhibiting multiple functions during cancer process, such as cell growth, cell cycle and DNA damage [5, 12]. Among, 6 miRNAs (miR17, miR18a, miR19a, miR20a, miR-19b-1 and miR-92a-1) coded by microRNA 17–92 cluster host gene (MIR17HG) showed vital role in hematopoietic cancers and some solid cancers [13]. It was highly expressed in multiple cancers, such as mantle cell lymphoma (MCL) [14], hepatocellular carcinoma (HCC) [15], breast cancer [16, 17], and non-small cell lung cancer (NSCLC) [18], directing its oncogenic role in cancer. In MCL, MIR17HG could modulate cell proliferation and activate B-cell receptor (BCR) signaling to play an oncogenic role [14]. In human hepatocellular carcinoma (HCC), the overexpression of MIR17HG enhanced the tumor cell proliferation and invasiveness [15], and it may serve as a novel diagnostic and prognostic biomarker for HCC [19] and also link to multiple myeloma prognosis [20]. However, its upregulated expression in colorectal cancer exhibited inhibited role in the progression of colorectal cancer via suppressing tumor angiogenesis through targeting TGFBR2, HIF1α, and VEGFA [21]. And MIR17HG level was associated with gastric cancer grades and stages, showing tumor-suppressive function [22]. Its dual function of oncogene and tumor suppressor gene in different types of tumors indicated the complex function in cancers. Recently, the SNPs in MIR17HG have been reported to be related to cancer risk and survival, such as rs7318578 has a higher susceptibility to glioma [23] and rs17735387 was associated with a better prognosis [23]. In liver cancer, the individuals with CA genotype of rs7318578 presented with decreased risk, but the CC genotype of rs7318578 significantly increased the risk. rs7336610 and rs4284505 were also reported to be strongly associated with increased susceptibility to head and neck squamous cell carcinoma (HNSCC) [24] and multiple myeloma (MM) [25] under different genetic models. However, the exploration of SNPs in the promoter of MIR17HG mostly showed protect against cancer, which may play a role in influencing transcriptional activity, rs9588884 G allele and rs982873 C allele decreased the transcriptional activity when compared to their counterpart (rs9588884 C allele and rs982873 T allele) [26]. It showed that rs9588884 and rs982873 in the promoter of MIR17HG had a protective role in colorectal cancer (CRC) [26] and cervical squamous cell carcinoma (CSCC) [27]. These raise our interest in elucidating the association between SNPs in MIR17HG and in the promoter of MIR17HG and the susceptibility to cancer.

Methods

Protocol and registration

The implementation of this meta-analysis was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [28] (Supplementary Table 1). The systematic review was registered in (PROSPERO: CRD420250650213).

Searching strategy

The systematic literature search was performed in PubMed, EBSCO, Web of Science, SinoMed (Chinese Biomedical Literature Database), China National Knowledge Infrastructure (CNKI) electronic database and Wanfang database to get the relevant studies published before February 2025. The following key words were used without restrictions: (“miRNA 17–92 cluster” OR “MIR17HG” OR “MicroRNA 17–92 HG”) AND (“polymorphism” OR “variant”) AND (“cancer” OR “tumor”). Additional search of grey literature and unpublished literature was performed by using Cochrane, ClinicalTrial.gov, OpenGrey and DataCite Commons. All the qualified studies were determined by reading the title, abstract and full text of the literature.

Eligibility criteria

The following inclusion criteria were used for the studies selection: (1) association of MIR17HG polymorphisms with cancer risk in Chinese Han population; (2) case-control studies; (3) human studies with available allelic frequency and/or genotype data; (4) the control group genotype subjected to HWE (Hardy-Weinberg equilibrium). Additionally, the exclusion criteria were: (1) duplicate studies and repetitive data; (2) studies on the association between MIR17HG SNPs and other disease; (3) SNPs in the binding sites of MIR17HG.

Literature quality assessment

The Newcastle-Ottawa scale (NOS) was applied to assess article quality (https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp), which consists of three perspectives: selection (definition, representativeness, selection, and definition), comparability, and exposure (ascertainment, method, and rate). Each perspective contains specific items that were scored to determine the overall quality of a study. The maximum score of NOS is 9 points. Studies with higher scores indicated their higher methodological quality. Studies with scores ≥ 5 points were considered to be high-quality ones, and with scores <5 points were considered low quality ones [29]. Two authors (Xu and Yang) independently evaluated the quality scores of the included studies, the consistency of quality assessment among authors was evaluated by Cohen’s Kappa coefficient (κ = 0.889). Any disagreement in the assessments were resolved through discussion. Moreover, study for the SNP that was deviated from HWE in the control group was excluded by χ2 goodness of fit (p<0.05).

Data extraction

The following informations were extracted from each included study: (1) first author; (2) publication year; (3) type of cancers; (4) genotyping methods; (5) number of genotypes and alleles of cases and controls. The counts of alleles A and a were derived directly from the genotype data (AA, Aa, aa) reported in the included studies if raw allele counts were not provided. The allele frequencies were calculated by using the Hardy-Weinberg equilibrium (HWE) formula based on genotype counts: For the allele frequency of A: f(A) = 2×NAA+NAa/2×(NAA+NAa+Naa). For the allele frequency of a: f(a) = 1 − f(A), NAA, NAa, and Naa represent the number of individuals with each genotype in cases and controls (“A” represents the major allele and “a” represents the minor allele). Two authors (Yang and Zhang) independently collected the above data, and the third author (Tong) rechecked the data and participated in the discussion to solve disagreements.

Statistical analysis

STATA version 12.0 was used to perform all the statistical analyses. Outcome measure was evaluated by the pooled odds ratios (ORs) with their corresponding 95% CIs, which reflect the pooled estimates of the all studies and uncertainties. A narrower CI suggests a precise estimate with low uncertainty, while a wider CI reflects uncertainty. The CIs for each SNP OR address the strength and reliability of the observed genetic association in our meta-analysis. Meta-analyses were conducted by using the following five genetic models: allelic model (a vs. A) (It compares the frequency of one allele versus the other allele in cases versus controls, which assumes a multiplicative effect of the risk allele on disease susceptibility.), dominant model (Aa + aa vs. AA) (It assumes that one copy of the risk allele could increase disease risk, which is appropriate when the risk allele exerts a dominant effect.), recessive model (aa vs. AA + Aa) (It assumes that homozygous genotype are required to increase disease risk.), heterozygous model (Aa vs. AA) (It specifically examines the effect of heterozygous genotype compared to homozygous allele carriers.) and homozygous models (aa vs. AA) (It compares individuals with homozygous genotype against those with two copies of the allele.) (“A” represents the major allele and “a” represents the minor allele). It was considered to be statistically significant when the p-value was less than 0.05. The Cochran’s Q statistical test and I2 values (representing the proportion of between-study variability attributable to heterogeneity rather than chance, ranging between 0 and 100%) were used to evaluate the heterogeneity of the eligible studies. No obvious heterogeneity was interpreted if p>0.1 and I2 ≤ 50% with the application of fixed-effect model, otherwise, random-effect model was used. Meta-regression analysis was conducted to find the potential source of heterogeneity by considering some common factors, including publication year, methodological method, cancer type. It was considered to be statistically different when p<0.05. Begg’s funnel plot and Egger’s test were carried out to detect publication bias, no obvious publication bias was considered with the quantitative method when p>0.05. Sensitivity analyses was carried out to evaluate the effect of individual study on the stability of pooled ORs an 95% CIs.

Results

Characteristics of the included studies

Total 440 articles were obtained from the public datasets (PubMed, EBSCO, Web of Science, SinoMed, CNKI and Wanfang database) using keywords indicated in the searching strategy. Grey literature and ongoing trials were searched by using Cochrane, ClinicalTrials.gov, DataCite Commons and OpenGrey. After the initial screening to exclude duplicates (n = 168) and obvious unrelative studies (n = 253) by screening the titles and abstracts, 19 potentially relevant studies were remained for full review. Based on our topic on exploring the association between MIR17HG SNPs and cancer risk in Chinese Han population, the studies that focused on other diseases (including depression [30], systemic lupus erythematosus [31], osteonecrosis [32], IgA nephropathy [33], high-attitude pulmonary edema [34], alopecia areata [35]) were excluded, and a study for the genetic phenomenon (miRNA-17-92 polymorphism in China Guangxi population) was excluded because of no mentioned disease [36]. Another two studies were excluded because one was performed in Caucasian [37] and one focused on the SNPs in the binding site of MIR17HG [38]. Finally, 6 articles [5, 23,24,25, 39, 40] including 6 SNPs (rs17735387 G > A, rs7336610 C > T, rs1428 C > A, rs7318578 A > C, rs72640334 C > A, and rs75267932 A > G) in MIR17HG and 4 articles [26, 27, 41, 42] including 3 SNPs (rs9588884 C > G, rs982873 T > C, and rs1813389 A > G) in the promoter of MIR17HG were finally included for quality evaluation (Fig. 1). The 6 included studies [5, 23,24,25, 39, 40] were published between 2019 and 2021, and the 4 included studies [26, 27, 41, 42] were published during 2017–2024. Main characteristics of these included articles were respectively shown in Tables 1 and 2, the first author, published year, cancer types, genotyping methods, the number of allele and genotype in cases and controls, and HWE in the control population of each study were reflected. As it shown in Table 1, the numbers of genotype and allele of cases and controls for rs7336610 C > T and rs1428 C > A in Chen’s article were not included for the analysis because of the deviation from HWE in the control group. NOS was conducted to assess the quality of individual studies. As it shown in Supplementary Table 2, all the included studies met the threshold for high quality (scores ≥ 5 points).

Fig. 1
figure 1

Flow diagram of articles selection process

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Table 1 The main characteristics of the included studies of MIR17HG SNPs in cancers
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Table 2 The main characteristics of the included studies of SNPs in the promoter region of MIR17HG in cancers
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Overall meta-analysis

Firstly, 6 SNPs (rs17735387 G > A, rs7336610 C > T, rs1428 C > A, rs7318578 A > C, rs72640334 C > A and rs75267932 A > G) were analyzed to explore the association between MIR17HG variations with cancer susceptibility. Respectively, 6 studies (with 2729 cases and 2685 controls) for rs17735387 G > A, 3 studies (with 1346 cases and 1255 controls) for rs7336610 C > T, 2 studies (with 754 cases and 753 controls) for rs1428 C > A, 4 studies (with 2075 cases and 1975 controls) for rs7318578 A > C, 4 studies (with 2075 cases and 1975 controls) for rs72640334 C > A, and 3 studies (with 1561 cases and 1465 controls) for rs75267932 A > G were enrolled. Based on the assumptions about the genetic model and relationship between genotypes and phenotypes, five genetic models, including allelic model, dominant model, recessive model, heterozygous model and homozygous model, are commonly used in the genetic association studies, particularly in the relationship between polymorphisms and cancer risk. Each model is suitable for different genetic hypotheses by providing unique insight into the relationship between genetic variants and cancer risk. And the use of different genetic model could help us to adequately understand the role of genetic variants in cancer. In our meta-analysis, each SNP was conducted to evaluate their association with cancer risk under the above five genetic models. The main results were concluded in Table 3. The overall meta-analysis showed that there was no significant association between all the 6 SNPs in MIR17HG with cancer risk under any of the tested models.

Then we analyzed the association between the 3 SNPs in the promoter of MIR17HG (rs9588884 C > G, rs982873 T > C, and rs1813389 A > G) and cancer risk. Totally, 4 studies (with 2042 cases and 2361 controls) for both rs9588884 C > G and rs982873 T > C and 3 studies (with 1644 cases and 1941 controls) for rs1813389 A > G were enrolled. The overall analysis results were presented in Table 4, as it shown, both rs9588884 C > G and rs982873 T > C showed a protective role against cancer. rs9588884 C > G could significantly decrease cancer risk under all the five genetic models. The pooled ORs were 0.75 (95% CI: 0.66–0.85; p = 0.000) under dominant model and 0.83 (95% CI: 0.72–0.94; p = 0.000) under heterozygous model by using the fixed-effect model with no heterogeneity, respectively indicating a 25% risk reduction (with the true effect between 15% and 34% reduction) and a 17% risk reduction (with the true effect between 6% and 28% reduction). And the pooled OR were respectively 0.77 (95% CI: 0.71–0.84, p = 0.000) under allelic model and 0.62 (95% CI: 0.53–0.74, p = 0.000) under recessive model with a moderate heterogeneity (allelic model: I2 = 33.2%, p = 0.213; recessive model: I2 = 50.0%, p = 0.112) by using the fixed-effect model. Random-effect model (I2 = 51.9%, p = 0.101) was used under homozygous model (pooled OR = 0.56, 95% CI: 0.43–0.75) (Fig. 2). rs982873 T > C was also a protective factor in several genetic models: pooled OR = 0.87, 95% CI: 0.77–0.98 in allelic model, pooled OR = 0.74, 95% CI: 0.56–0.99 in recessive model, and pooled OR = 0.71, 95% CI: 0.52–0.97 in homozygous model by using the random-effect model, and pooled OR = 0.87, 95% CI: 0.77–0.99 in dominant model by using the fixed-effect model (Fig. 3). These 95% CIs for the SNP ORs were narrower, indicating a precise estimate with low uncertainty. However, no significant association was observed between rs1813389 A > G and cancer risk.

Table 3 Analyses of the association between MIR17HG variations and cancers risk
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Table 4 Analyses of the association between variations in MIR17HG promoter and cancers risk
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Fig. 2
figure 2

ORs with 95% CI for the association between rs9588884 C> G and cancer in Chinese Han population by using fixed-effect model. (A) Allelic model: G vs. C; (B) Dominant model: CGGG vs. CC; (C) Recessive model: GG vs. CCCG; (D) Homozygous model: GG vs. CC; (E) Heterozygous model: CG vs. CC

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Fig. 3
figure 3

ORs with 95% CI for the association between rs982873 T> C and cancer in Chinese Han population. (A) Allelic model: C vs. T, random-effect model; (B) Dominant model: TCCC vs. TT, fixed-effect model; (C) Recessive model: CC vs. TTTC, random-effect model; (D) Homozygous model: CC vs. TT, random-effect model

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Heterogeneity, sensitivity analysis and publication bias

For rs982873 T> C, although significant association was observed between this polymorphism and decreased cancer risk, there was obvious heterogeneity in allelic model (I2 = 50.1%, p = 0.111), recessive model (I2 = 63.4%, p = 0.042), and homozygous model (I2 = 62.1%, p = 0.048) (Table 4). But no heterogeneity was observed for rs982873 T> C under dominant model and rs9588884 C> G under all the five genetic models (Table 4). The limited representation of certain cancer types and ethnicities in the included studies could not support the performance of subgroup analysis stratified by cancer type and ethnicity. The common variables, including cancer type, publication year, and methodological method, were used to perform meta-regression analysis to find the potential source of heterogeneity using the data of homozygous model under random effect model for rs982873 T> C. It showed that cancer type and publication year were not correlated with heterogeneity (p>0.05). But the methodological method may cause the high heterogeneity (p<0.05). However, higher heterogeneity was still observed by the stratified analysis according to the methodological method. While it was considered a potential contributor to heterogeneity, the interaction with other unmeasured covariates (e.g., contextual factors) may explain why subgroup analysis based on methodological method did not fully resolve the heterogeneity. More importantly, the small number of studies may result in insufficient statistical power for the meta-regression, limiting its ability to identify true associations.

Sensitivity analysis was applied to determine the stability of the results. Overall, the corresponding pooled ORs were not substantially altered for rs9588884 C > G (ORs ranged from 0.74 (95% CI: 0.67–0.81) to 0.81 (95% CI: 0.72–0.91)) and rs982873 T > C (ORs ranged from 0.83 (95% CI: 0.76–0.91) to 0.89 (95% CI: 0.81–0.98)) in allelic model (Fig. 4), indicating that the overall results remained consistent across the sensitivity analysis and the results were robust to the exclusion of individual studies.

Egger’s test and Begg’s funnel plot were conducted to evaluate the publication bias of the studies involved in this meta-analysis for s9588884 C > G and rs982873 T > C (Fig. 5). As it shown in the funnel plot, a symmetrical distribution of studies was presented around the pooled effect size (Fig. 5B and D) (for s9588884 C > G: Begg’s test: z = -0.34, p = 1.00; and for rs982873 T > C: Begg’s test: z = 0.34, p = 0.734). Egger’s test further supported the absence of significant publication bias for s9588884 C > G (Egger’s test: t = 0.64, p = 0.59) and for rs982873 T > C (Egger’s test: t = 0.60, p = 0.609), indicating our findings were unlikely to be largely impacted by severe publication bias (Fig. 5A and C).

Fig. 4
figure 4

Sensitivity analysis for rs9588884 C> G pooled with the fixed-effect model (allelic model: G vs. C) (A), and for rs982873 T> C pooled with the random-effect model (allelic model: C vs. T) (B), showing the influence of the exclusion of individual study on the pooled OR. Central vertical axis: Represents the pooled OR. Vertical axes on both sides: Display the corresponding 95% CIs. Open circles: Indicate the pooled OR when the left study is omitted. The two ends of each line represent the correspond to the 95% CI of the pooled OR

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Fig. 5
figure 5

Egger’s regression (left) and Begg’s funnel plot (right) to explore the publication bias for rs9588884 C> G (allelic model: G vs. C) (A: Egger’s test: t = 0.64, p = 0.59, B: Begg’s test: z = -0.34, p = 1.00) and rs982873 T> C (allelic model: C vs. T) (C: Egger’s test: t = 0.60, p = 0.609, and D: Begg’s test: z = 0.34, p = 0.734)

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Discussion

MiRNAs exhibited significant effect on various physiological processes and diseases mainly by targeting the 3’-untranslated region (3’-UTR) of mRNAs to inhibit the translation or decrease their stability, even though they do not code proteins [43]. They regulated approximately 30% of the human coding protein genes [44], and the aberrant expression of miRNAs participated in many diseases, including cardiovascular disease [45], major depressive disorder [46], rheumatic diseases [47], cancers [48, 49], etc. In the context of cancer, miRNAs could act as tumor promoters or suppressors to play a role and some miRNAs have been suggested to act as biomarkers for cancer diagnosis, prognosis and treatment [50,51,52]. The miR-17-92 gene cluster (MIR17HG) is a highly conserved gene cluster including six individual members (miR17, miR18a, miR19a, miR20a, miR-19b-1 and miR-92a-1) and exhibits broad function in multiple physiological and pathological processes, including cancer, immunology, neurology, pulmonology, and cardiology [53]. This gene cluster came into our focus because of its vital role in cancer development, normally regarding as an oncogene [54]. It can act as a diagnostic marker in NSCLC [55], and sex-determining region Y-box 4 (SOX4) could transcriptionally upregulate miR-17-92 cluster and promote prostate cancer [56]. However, MIR17HG could suppress cervical cancer proliferation and invasion by inhibiting CDC-10 dependent transcript-2 (Cdt2) [57] and also exhibits tumor-suppressive role in breast cancer [58], indicating its dual role in different cancer context. Particularly, each member of this cluster could play a role in different cancers, miR-18a and miR-92a could promote cell proliferation, migration and cycle progression of NSCLS by targeting Sprouty 4 (SPRY4) [18]. MiR-20a could promote prostate cancer invasion and migration by targeting ABL2 (ABL family non-receptor tyrosine kinase) [59].

SNPs were germline variant alleles that occur naturally and exhibited certain effect on human health [60]. SNPs in MIR17HG participated in different diseases, including systemic lupus erythematosus [31], high-altitude pulmonary edema [34], nephropathy [33], cancer [37]. Most studies focused on cancers and no systematic meta-analysis was performed. Our study was the first comprehensive meta-analysis for the effect of MIR17HG SNPs on cancer risk. Totally, our pooled results indicated that no significant association was observed between rs17735387 G > A, rs7336610 C > T, rs1428 C > A, rs7318578 A > C, rs72640334 C > A, and rs75267932 A > G and cancer risk in Chinese Han population (Table 3). However, during the search of literatures, the data in an article with two cohort studies explored in Caucasian [37] were also extracted to try to analyze the association between the SNPs (rs17735387 G > A, rs7336610 C > T, rs1428 C > A, and rs4284505 A > G) and cancer risk. The quality of the two studies was also assessed by NOS according to the evaluation annotation for cohort study. The NOS scales included three perspectives: selection (representativeness, selection, ascertainment, and definition), comparability, and outcome (assessment, duration, and adequacy), also with the maximum score of 9 points. Both the two eligible studies were high-quality owing to the fact that NOS scores were 7 points (higher than 5 points). The overall analysis results based on the data that were extracted from our included 6 studies and the two studies in Caucasian showed that no significant association between rs17735387 G > A and rs1428 C > A and cancer susceptibility was observed (data not shown). However, rs7336610 C > T may be linked to higher cancer risk in dominant model (pooled OR = 1.37, 95% CI:1.00-1.88) and heterozygous model (pooled OR = 1.30, 95% CI:1.01–1.92), but with higher heterogeneity (dominant model: I2 = 71.7%, p = 0.007, heterozygous model: I2 = 70.1%, p = 0.010) (Supplementary Fig. 1). In addition, the analysis for rs4284505 A > G was also performed based on the data in Wu’s study [25] and Chacon-Cortes’s studies. The results showed that rs4284505 A > G was associated with increased cancer risk in all the five genetic models with no heterogeneity, respectively with a 17%, 56%, 39%, 77%, and 43% risk increase for cancer under allelic, dominant, recessive, homozygous, and heterozygous model (Supplementary Fig. 2).

Apart from the above SNPs, genetic variants in the promoter and binding sites of MIR17HG were also explored to investigate their association with the risk or prognosis of different cancers in some case-control studies. One study investigated the association with six SNPs in the binding site of MIR17HG (rs12594531, rs1366600, rs1804506, rs3741779, rs3763763, and rs8323) and esophageal squamous cell carcinoma risk in Chinese population [38]. And 4 studies explored the association between SNPs (rs9588884, rs982873, and rs1813389) in the promoter of MIR17HG and cancer risk, including colorectal cancer [26], endometrial cancer [41], cervical squamous cell carcinoma [27] and breast cancer [42] in Chinese Han population. The pooled analysis showed that rs9588884 C > G and rs982873 T > C in the promoter of MIR17HG could decrease cancer risk in Chinese Han population (Table 4).

Although sensitivity analysis revealed the stability of the results (Fig. 4), and no publication bias was observed by using Egger’s test and Begg’s test (Fig. 5), there were still limitations need to be considered. Firstly, the numbers of included studies for each SNP was not abundant, which may take higher heterogeneity, a larger sample size need to be followed up and explored in the future to strengthen the results. Secondly, the tumor types explored in the included studies were different (including glioma [23], head and neck squamous cell carcinoma [24] multiple myeloma [25], liver cancer [5], colorectal cancer [26, 39], ESCC [40], breast cancer [37, 42], endometrial cancer [41], CSCC [27]). The available data do not support the stratified analysis by cancer types. Thirdly, the SNPs were mostly explored in Chinese Han Population, which limited the generalizability of the results to other ethnic groups. It is necessary to have more detailed subgroup data and perform stratified analyses by population or ethnicity in the future, which would provide valuable insights into the generalizability of our findings.

In summary, our meta-analysis provided insight into the association between MIR17HG SNPs and cancer risk in Chinese population, which was valuable for understanding the genetic variants to cancer in Chinese population. But one limitation was that the included studies were mainly performed in Chinese population, the results may reflect the genetic architecture and phenomenon specific to this group. Genetic variants, including SNPs, can indeed show differences in frequency and effect sizes across populations due to multiple factors, including history, evolution, and environment [61, 62]. Our findings may not fully represent and capture the genetic diversity present in other ethnic groups. Therefore, the potential population-specific effects and regional genetic variation should be considered, more research and larger sample size should be performed to investigate the association of MIR17HG SNPs with cancer susceptibility in different races around the world.

Conclusion

Here, this meta-analysis implied two SNPs in the promoter of MIR17HG (rs9588884 C > G, rs982873 T > C) may decrease cancer risk in Chinese Han population in multiple genetic models, but because of the limitation of available sample size, further researches including a broader range of population and types of cancers are necessary to perform succeeding research.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Abbreviations

OR:

Odds ratio

CI:

Confidence interval

SNP:

Single nucleotide polymorphism

MIR17HG:

MicroRNA 17–92 cluster host gene

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

HWE:

Hardy-Weinberg equilibrium

NOS:

Newcastle-Ottawa scale

ESCC:

Esophageal squamous cell carcinoma

CSCC:

Cervical squamous cell carcinoma

HNSCC:

Head and neck squamous cell carcinoma

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Acknowledgements

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Funding

This work was supported by project ZR2024QH657 supported by Shandong Provincial Natural Science Foundation, Binzhou Medical University Scientific Research Start-up Fund (Grant No. BY2022KYQD12), Fundamental Research Projects of Science&Technology Innovation and development Plan in Yantai City (Grant No.2024JCYJ096), and Shandong Province Medical and Health Technology Development Plan Project (Grant No.202404070013).

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  1. Binghua Tong and Zhaonan Zhang contributed equally to this work.

Authors and Affiliations

  1. School of Pharmacy, Binzhou Medical University, Yantai, 264003, China

    Binghua Tong & Zhaowei Xu

  2. Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 256603, China

    Zhaonan Zhang

  3. School of Basic Medicine, Binzhou Medical University, Yantai, 264003, China

    Yangyang Yang

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Y. Y. and Z. X. contributed to conceiving, writing and editing of the manuscript, B. T. and Z. Z. contributed to the publications’ screen and evaluation, Y. Y., B. T. and Z. Z. contributed to data extraction, data analysis, figures and tables preparation, Z. Z. and Z. X. revised the manuscript. All the authors read and approved the final manuscript.

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Tong, B., Zhang, Z., Xu, Z. et al. Association of genetic variants in MIR17HG and in the promoter of MIR17HG with susceptibility to cancer in Chinese Han population: a systematic review and meta-analysis. BMC Cancer 25, 631 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14018-x

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BMC Cancer

ISSN: 1471-2407

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