Introduction

Cervical cancer (CC) ranks as the fourth most prevalent cancer among women worldwide.1 If no action is taken, the World Health Organization (WHO) estimates that the annual number of new CC cases will rise from 570,000 to 700,000 between 2018 and 2030, with deaths increasing from 311,000 to 400,000 per year.1,2 China has a heavy burden of CC, and the incidence and mortality of CC have shown an increasing trend over the past 20 years,3 with 109,000 new CC cases and 59,000 deaths in 2020, accounting for 18.2% and 17.3% of global cases, respectively.4

The occurrence, development, metastasis, and drug resistance of CC have been linked to various cell signaling pathways, including the PI3K/AKT, Wnt, Notch, NF-κB, MAPK-ERK, Hedgehog, and JAK/STAT pathways.5–11 AMP-activated protein kinase (AMPK) serves as a receptor for cellular energy status, monitoring changes in ATP levels and phosphorylating substrates accordingly to regulate ATP precisely.12,13 ATP is essential for cellular activities,14,15 and the occurrence and development of malignant tumors rely heavily on an abundant supply of cellular energy. Thus, abnormal regulation of AMPK is closely associated with tumor development.16,17

miRNAs play a crucial role in cell differentiation and proliferation by binding to complementary target mRNAs, inhibiting mRNA translation, or degrading it.18,19 miRNA undergoes two processing steps in the nucleus and cytoplasm for maturation, forming a complex and precise regulatory network. This network allows for a single miRNA to regulate expression of multiple genes and achieve fine regulation of gene expression through the combined regulation of multiple miRNAs.20–23

Numerous studies have reported the involvement of miRNAs in regulating components of the AMPK signaling pathway. These miRNAs may lead to abnormal expression of their target genes, resulting in signaling pathway disorders and related diseases. For instance, miR-27a,24 miR-449a,25 miR-216a,26 miR-96,27 and miR-519628 have been implicated in the occurrence of diseases by targeting the AMPK signaling pathway. Additionally, miR-612,29 miR-3622b,30 and miR-62731 have been linked to the occurrence of tumors in the human reproductive system, with their putative target genes potentially playing a role in the AMPK pathway.

Genetic variation plays a significant role in miRNA maturation, with SNPs being the most common human genome genetic polymorphisms.32,33 Pathway-associated miRNA gene profile expression changes caused by SNPs might be important for explaining the pathomechanism driving cancers.34 For example, our previous studies reported that rs4636297 in pri-miR-126, rs11614913 in miR-196a2, rs107822 in miR-219a and rs2292832 in miR-149 are associated with CC risk.8,35 In addition, Gilam A et al reported that rs1071738 in miR-96 is associated with the risk of breast cancer metastasis.36 Therefore, SNPs are notable factors in regulating miRNAs, which have been shown to be associated with diseases.37

In the current study, we first employed bioinformatics tools to identify miRNAs associated with CC. Then, we predicted their target genes and conducted pathway enrichment analysis of the genes. Finally, a total of nine SNPs located in eight miRNAs of the AMPK pathway were selected, and the association between SNPs and the risk of CC in a Han Chinese population was investigated. Our results provide more data for further elucidating the molecular genetic mechanisms of CC.

Methods Subjects

A total of 421 patients with CIN and 871 patients with CC were randomly selected from the Third Affiliated Hospital of Kunming Medical University. The inclusion criteria were as follows: diagnosis of cervical malignant tumor or CIN II or CIN III confirmed by biopsy or surgery, complete clinical data, good compliance, absence of other concurrent tumors, no previous history of other tumors, and no prior antitumor therapy such as radiotherapy or chemotherapy. Diagnostic criteria were based on Clinical Diagnosis and Treatment Guidelines for Obstetrics and Gynecology and National Guidelines for Diagnosis and Treatment of Cervical Cancer 2022 in China.38 Simultaneously, 928 healthy individuals were randomly selected as the healthy control group. All subjects belonged to the Han Chinese population. All samples were collected with informed consent from the patients and their families, and the study was approved by the Ethics Committee of the Third Affiliated Hospital of Kunming Medical University (No. KYCS2021193).

DNA Extraction

DNA extraction was performed using QIAamp DNA Blood Mini Kit (Qiagen NV, Venlo, Netherlands). The concentration and purity of the extracted DNA were assessed using a MultiskanTM GO full-wavelength spectrophotometer.

miRNA Selection, Target Gene Prediction and Signal Pathway Enrichment

The miRSNP database (http://bioinfo.life.hust.edu.cn/miRNASNP/#!) was used to retrieve miRNAs associated with CC, as were the HMDD database (http://www.cuilab.cn/hmdd) and the miRWalk database (http://129.206.7.150/). By integrating the data from these three databases, a total of 151 miRNAs were obtained. The target genes of these miRNAs were predicted using the TargetScan 8.0 database (https://www.targetscan.org/vert_80/) and TarBase v8 database (https://bio.tools/tarbase#!). Pathway enrichment analysis was performed using the mirPath database (https://dianalab.e-ce.uth.gr/html/mirpathv3/index.php?R=mirpath#mirnas=hsa-miR-101-3p=Tarbase&selection=0) and the DAVID database (https://david.ncifcrf.gov/).

SNP Selection and Genotyping

Based on the results of pathway analysis, miRNA genes located in the AMPK signaling pathway and SNPs in the mature sequence, precursor sequence, or transcriptional regulatory sequence of these miRNAs were selected. A minor allele frequency (MAF) higher than 0.05 was used as a screening criterion. Following the screening process, a total of nine SNPs were chosen as candidates for this study: rs895819 located in the promoter region of miR-27a, rs10061133 located in the mature sequence of miR-449b, rs41291179 located in the enhancer region of miR-216a, rs76481776 located in the enhancer region of miR-182, rs10406069 located in the non-coding transcript exon region of miR-5196, rs12803915 located in the miR-612 precursor sequence (pre-miRNA), rs550894 in the non-coding transcript exon region, rs66683138 located in the mature sequence of miR-3622b, and rs2620381 located in the mature sequence of miR-627 (Table 1 and Figure 1).

Table 1 The Basic Information of the SNPs

Figure 1 The miRNAs associated with AMPK signaling pathways in the current study.

The genotyping reagent TaqMan Genotyping Master Mix and the genotyping probe TaqManSNP Genotyping Assays were purchased from ABI, USA. For the genotyping process, please refer to our previous study.39 In brief, add 2.5 µL of 2× Master Mix, 0.25 µL of 40× probe, 1.25 µL of nuclease-free water, and 1 µL of template DNA to each well of PCR plate, resulting in a final reaction volume of 5 µL. The reaction is then carried out on the LightCycler®480 instrument with the following program settings: PCR initial heat activation at 95°C for 2min, denaturation at 95°C for 5s, combined annealing/extension at 60°C for 30s and the number of cycles is 40. For quality control, 14.6% of the samples were randomly selected for repeated detection, and 1.7% of the samples were randomly selected for DNA sequencing analysis to verify the accuracy of the TaqMan probe method.

Statistical Analysis

The genotyping results of all SNP loci were summarized using Microsoft Excel software. To assess the age bias, we conducted one-way ANOVA using GraphPad Prism 9.4 software. The Hardy‒Weinberg equilibrium (HWE), distribution of alleles and genotypes of the nine SNPs among different groups, and odds ratio were calculated using SHEsis software40 through the chi-square test and Fischer’s exact test. The inheritance model of the nine SNPs was analyzed using SNPstats41 under five different models: codominant, dominant, recessive, overdominant, and log-additive models. The optimal inheritance model for each SNP was determined based on the Akaike information criterion (AIC) and Bayesian information criterion (BIC), whereby smaller AIC and BIC values indicate lower expected entropy and higher model accuracy.42 To account for multiple comparisons, Bonferroni correction was applied to the P value, which the Pc value was set at 0.05 (P = 0.05/n, n = 9).

Results Clinical Characteristics

A total of 2,220 participants were enrolled in this study. There were 421 patients in the CIN group, whose average age was 45.61±9.60, including 48 patients in grade II and 373 in grade III. The CC group comprised 871 patients, with an average age of 46.28±9.88 years, including 155 patients with adenocarcinoma (AC) and 716 patients with squamous cell carcinoma (SCC). According to CC staging, 563 patients were in stage I, 248 in stage II and 60 in stage III–IV. There were 928 participants in the healthy control group, whose average age was 46.37±9.28. There was no statistically significant difference in age observed among the three groups (F = 0.958, P = 0.384) (Table 2).

Table 2 The Clinical Characteristics of Subjects Enrolled in Current Study

Signaling Pathway Enrichment

Signaling pathway analysis of 151 miRNAs revealed that 41 miRNAs, which may be associated with the development of CC, were significantly enriched in the AMPK signaling pathway (hsa04152). The enrichment analysis yielded an adjusted P value of 5.09×10−5.

Association of Nine SNPs in Eight miRNAs with CIN and CC

Except for rs76481776, the genotypic distribution of the other eight SNPs was consistent with HWE (P > 0.05), indicating that the sample selection of this study was representative. The distribution and comparison of alleles and genotypes for each SNP locus in different groups are presented in Table 3. Notably, significant differences were observed in the distribution of alleles and genotypes for the rs41291179 locus in miR-216a and the rs12803915 locus in miR-612 between the control group and the CIN group, as well as between the control group and the CC group (control group vs CIN group, P values were 5.88×10−5 and 1.30 × 10−5, respectively; control group vs CC group, P values were 0.003 and 3.73 × 10−4, respectively). Furthermore, statistically significant differences were found in the distribution of alleles and genotypes for rs550894 between the control group and the CC group (P value = 0.001). Additionally, the A allele of rs41291179 and the G allele of rs12803915 were associated with a reduced risk of CIN (OR = 0.05, 95% CI: 0.01–0.39; OR = 0.61, 95% CI: 0.49–0.76, respectively) and CC (OR = 0.08, 95% CI: 0.01–0.66; OR = 0.71, 95% CI: 0.59–0.86, respectively). Conversely, the C allele of rs550894 was associated with an increased risk of CC (OR = 1.29, 95% CI: 1.11–1.50).

Table 3 The Allele and Genotype Distribution of 9 SNPs in Control, CIN and CC Groups

The results of the inheritance model analysis indicated that the optimal model for rs12803915 and rs550894 in comparison between the control group and the CC group was the log-additive model. Specifically, compared to the GG genotype, the rs12803915 genotype 2AA+AG may be a risk factor for cervical cancer (OR = 1.39, 95% CI: 1.15–1.67); the rs550894 genotype 2AA+AC may act as a protective factor against CC compared to the CC genotype (OR = 0.77, 95% CI: 0.66–0.90). The optimal model for rs12803915 in the control group compared with the CIN group was dominant, with the AG+AA genotype relative to the GG genotype being a potential risk factor for CIN (OR = 1.85, 95% CI: 1.43–2.38). Due to the absence of the T/T genotype of rs41291179 in this study, analysis of the five inheritance models could not be conducted. However, compared with the A/A genotype, the T/A genotype was found to be associated with an increased risk of CC and CIN (OR = 12.50, 95% CI: 1.54–100.00; OR = 20.00, 95% CI: 2.56–100.00, respectively). See Tables 4 and 5.

Table 4 The Inheritance Model Analysis of 9 SNPs Between CC and Control Groups

Table 5 The Inheritance Model Analysis of 9 SNPs Between CIN and Control Groups

Association of Nine SNPs in Eight miRNAs with Different Pathological Types of CC

To ascertain whether the selected SNPs are associated with different pathological types of CC, the distribution of the nine SNPs in CC patients was analyzed by stratifying them according to pathological type. As shown in Table 6, significant differences were observed in the distribution of alleles and genotypes of rs12803915 and rs550894 between the SCC group and the control group (P values were 0.001). The results of the inheritance model analysis are displayed in Table 7, revealing that the optimal model for both loci is the log-additive model. Based on this model, compared to the GG genotype, the 2AA+AG genotype of rs12803915 may act as a risk factor for SCC (OR = 1.37, 95% CI: 1.14–1.67); relative to the CC genotype, the 2AA+AC genotype of rs550894 may serve as a protective factor against SCC (OR = 0.75, 95% CI: 0.64–0.88). Furthermore, the distribution of alleles and genotypes of rs41291179 exhibited statistically significant differences between the AC group and the control group, with the A allele and A/A genotype of this locus potentially acting as protective factors for AC (OR = 0.03, 95% CI: 0.003–0.28). However, there were no significant differences in the distribution of alleles and genotypes between the AC group and the SCC group.

Table 6 The Allele and Genotype Distribution of 9 SNPs in Different Pathological Types of CC

Table 7 The Inheritance Model Analysis of rs12803915 and rs550894 Between SCC and Control Groups

Association of Nine SNPs in Eight miRNAs with Different Stages of CC and CIN Groups

To investigate whether the selected SNPs are associated with disease progression, the CIN group was categorized into grade I and grade II, while the CC group was divided into stage I and stage II+III+IV. As shown in Table 8, significant differences were observed in the distribution of alleles and genotypes of rs41291179 and rs12803915 between the CIN II group and the CIN III group (P values were 0.002 and 1.39 × 10−4, respectively). The A allele of rs41291179 and the G allele of rs12803915 were associated with a reduced risk of CIN II progressing to CIN III (OR = 0.16, 95% CI: 0.04–0.59; OR = 0.41, 95% CI: 0.26–0.66, respectively). There were no statistically significant differences observed in the distribution of all SNP loci among different stages of CC, Pc > 0.05 (see Table S1).

Table 8 The Allele and Genotype Distribution of 9 SNPs in CIN II and CIN III Groups

Discussion

Aberrant activation of the AMPK signaling pathway has been implicated in various cancers, contributing to tumor cell proliferation, invasion, metastasis, and other malignant behaviors.43,44 Any change in miRNA expression level or function may be related to signaling pathways.34 Abnormal expression of miRNAs has been identified as a significant cause of dysregulated signaling pathways, and SNPs in these miRNA genes are known to influence their expression.37 In the current study, we investigated the association between SNPs located in miRNA genes of the AMPK signaling pathway and the risk of CC in a Han Chinese population. Our results revealed that the target genes of miRNAs were significantly enriched in the AMPK signaling pathway and that three SNPs of AMPK pathway-associated miRNAs, rs41291179, located in the enhancer region of miR-216a, and rs12803915 and rs550894, located in the pre-miRNA sequence of miR-612, are associated with the occurrence and development of CC.

miR-216a has been implicated in the development of various human tumors, with demonstrated antitumor properties.45–47 Predictions suggest that miR-216a regulates the AMPK signaling pathway by targeting the gene stearoyl-CoA desaturase (SCD). Studies have shown that SCD promotes the proliferation, migration, and metastasis of cancer cells.48–50 Xing et al reported that SCD expression was significantly upregulated in patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) compared to the control group.51 In the current study, our results showed that the SNP rs41291179 in miR-216a is associated with the development of CC and CIN. According to the Ensembl database (https://asia.ensembl.org/index.html), this variant is located in an enhancer binding site. Hence, this SNP might enhance miR-216a expression, reducing SCD expression and being associated with the occurrence and progression of CC. However, no association between this SNP and disease susceptibility was reported in previous studies.52–54 Considering the low MAF of this SNP, at only 0.08, the sample size had a significant impact on the statistical results, which might be an important reason for the discrepancies between our study and previous studies. Furthermore, we analyzed the association between rs41291179 and different pathological types of CC, with significant differences found between the control group and the AC group in the distribution of alleles and genotypes. Growing evidence reveals that AC has hidden lesions, an unclear starting site, and shorter overall survival than SCC.55–57 Thus, the polymorphisms of this SNP in miR-126a should be further studied, especially regarding the pathogenic mechanism for AC.

Recent studies have highlighted involvement of miR-612 as a tumor suppressor in progression of various human tumors, such as CC,29 hepatocellular carcinoma58 and colorectal cancer.59 In the current study, based on the results of TarBase,60,61 we predict that Sirtuin-1 (SIRT1), a target gene of miR-612, is involved in the AMPK signaling pathway and influences disease progression. SIRT1 plays various roles in biological processes, including gene regulation, proliferation, and tumorigenesis.62–64 Our current results showed that the distribution of the rs12803915 allele, located in the precursor sequence of miR-612, differed between the control group and the CIN group, as well as between the control group and the CC group. Therefore, the G allele of rs12803915 may act as a protective factor for CC. In 2012, Kim et al observed an allelic effect of rs12803915 on expression of mature miR-612 in a series of cell lines, and the A allele increased expression in most prostate cancer cell lines but decreased it in five colon cancer cell lines,65 indicating that the A allele of this SNP affects expression of SIRT1 by regulating miR-612 maturation and thus might play roles in CIN and CC.

Moreover, our results illustrated differences in the rs550894 located in miR-612 between the control and CC groups, suggesting its association with cervical lesions. The C allele of rs550894 may act as a risk factor for CC, which is similar to the conclusion of Zhao et al with regard to lung cancer, whereby the A allele of rs550894 is associated with good prognosis in patients with advanced non-small cell lung cancer.66 Kim et al also reported that the A allele of rs550894 can increase expression of mature miR-612 in most prostate cancer cell lines while decreasing it in most colon cancer cell lines and prostate cancer cell lines, even though the effect was absent in four breast cancer cell lines.65 Currently, there is no research about rs550894 in CC cell lines. Therefore, analysis of effects of this SNP in the context of CC cell lines is needed.

Conclusions

miRNAs associated with the development of CC were identified using bioinformatics tools and enriched in the AMPK signaling pathway. Our current study systematically analyzed the role of miRNA gene polymorphisms targeting the AMPK signaling pathway in CC and found that such polymorphisms may impact the development of CC through the AMPK/SCD (rs41291179 in miR-216a) and AMPK/SIRT1 (rs12803915 and rs550894 in miR-612) pathways. However, there are some limitations in this study. We only collected HPV infection status for some of the patients, as well as other clinical information, which prevented us from conducting relevant analyses of SNP genotyping and HPV infection status. Future research should focus on how SNP alleles affect the maturation of miRNAs and are associated with CC development in vitro and in vivo.

Abbreviations

CC, cervical cancer; CIN, cervical intraepithelial neoplasia; WHO, World Health Organization; miRNA, microRNA; SNP, Single nucleotide polymorphisms; AMPK, AMP-activated protein kinase; AIC, Akaike information criterion; BIC, Bayesian information criterion; AC, adenocarcinoma; SCC, squamous cell carcinoma.

Ethical Approval and Informed Consent

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of Third Affiliated Hospital of Kunming Medical University. Samples were collected after obtaining written informed consent from all patients.

Funding

The current study was supported by grants from the CAMS Innovation Fund for Medical Sciences (CIFMS) (2021-I2M-1-004), the National Science Foundation of China (82103190), the Association Foundation Program of Yunnan Provincial Science and Technology Department and Kunming Medical University (202201AY070001-139), and Yunnan Province “Xingdian Talent Support Program” (XDYC-CYCX-2023-0074). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Disclosure

The authors report no conflicts of interest in this work.

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