The transcription activity of OTX2 on p16 expression is significantly blocked by methylation of CpG shore in non-promoter of lung cancer cell lines

Introduction

Lung cancer is one of the most common malignant tumors globally, being a leading cause of death among cancer patients. Moreover, also it has one of the highest incidence rates, with 2.2 million new cases of lung cancer reported worldwide in 2020 (1). Although rapid progress has been made in the treatment of lung cancer, the 5-year survival rate of lung cancer remains relatively low (2). Therefore, clarifying the driving factors of lung cancer and identifying biomarkers for early prevention are effective strategies to reduce the incidence rate and mortality of lung cancer in the population.

According to previous studies, gene mutations and epigenetic variations are key drivers in tumor development (3). Abnormal epigenetic changes, such as DNA hypermethylation of specific tumor suppressor genes, serve as biomarkers of early tumor development (4). Among these genes, p16 is a well-studied tumor suppressor gene encoded by the CDKN2A and is frequently hypermethylated in cancers (5). Moreover, it is reported that p16 gene inactivation occurs in about 70% of cancer patients (6). Additionally, several studies have identified significant differences in DNA methylation levels of p16 genes between lung cancer and normal tissues (7-9). Most of these studies utilized methylation-specific polymerase chain reaction (MSP) to assess the DNA methylation level of p16, but there were various 5’-cytosine-phosphate-guanine-3’ (CpG) regions or sites being detected, and some of them might not be the key regulatory regions of p16 (10). Therefore, it is essential to identify the impacts of different CpG regions in p16 gene expression to effectively utilize p16 methylation as a biomarker of early lung cancer.

DNA methylation often occurs in C-G dinucleotide-rich regions, which is called CpG island. 5’-cytosine-phosphate-guanine-3’ island (Cgi) is mostly located in the gene promoter region (11). In the past decade, Cgi was the main region for people to study gene methylation regulation. However, recent studies have found that cancer-related DNA methylation occurs not only in Cgi, but also in the regions within 2 kb of its upstream and downstream, which is defined as Cgi shore (12). Although the CpG density of Cgi shores is lower than that of Cgi, their methylation has been shown to be important in regulating gene transcription. Rao et al. suggested that Cgi shore methylation could regulate the expression of caveolin-1 in breast cancer, potentially serving as a new prognostic marker for ERα-negative, basal-like breast cancer (13). Similarly, Bockmühl et al. demonstrated that after early-life stress, the methylation of the Cgi shore of Nr3c1 could increase the activity of its promoter (14). Currently, most studies on p16 methylation primarily focuses on its Cgi region, while the level and function of its Cgi shore methylation in lung cancer remain largely unknown.

Most studies have primarily observed a negative relationship between DNA methylation levels and gene expression, but obtaining direct evidence that DNA methylation affects the gene expression and function has been challenging. The difficulty lies in the lack of effective tools for editing DNA methylation in specific regions. However, the recent development of a DNA methylation editing method based on Clustered Regularly Interspaced Short Palindromic Repeats/dCas9 (CRISPR/dCas9) has effectively addressed this issue (15). By expressing a fusion protein of DNA methylation modifying enzymes (such as DNMT3A or TET1) and dCas9, the CRISPR/dCas9 system allows precise localization of the DNA methylation modifying enzyme to specific gene regions, thereby altering the methylation levels without cutting the DNA strands.

In this study, we employed the CRISPR/dCas9 system to investigate the regulatory effect of Cgi shore methylation on p16 expression and function. Subsequently, we examined the impact of transcriptional regulators binding to the Cgi shore of p16 after region-specific methylation. Finally, we revealed the relationship between p16 Cgi shore methylation and p16 expression, utilizing data from The Cancer Genome Atlas (TCGA) program database and patient samples from the real clinical setting. Through this study, we aim to draw more attention to the methylation level of p16 Cgi shore in lung cancer, as it holds promising potential as epigenetic biomarkers for the early detection and treatment of lung cancer. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-909/rc).

Methods

Open-source data collection and processing

RNA sequencing and DNA methylation sequencing data of lung adenocarcinoma (LUAD) was downloaded from TCGA Xena (http://xena.ucsc.edu/), and processed using R Studio (4.0.2). UCSC website (http://genome.ucsc.edu/) and JASPAR database (https://jaspar.genereg.net/) were utilized to identify potential transcription factors (TFs) located on the Cgi shore of p16. Furthermore, a systematic literature search was conducted to validate the regulatory relationship between these TFs and the expression of p16. TFs that met the specified criteria were included in the subsequent investigations.

Clinical samples collection

A total of 15 pairs of human LUAD tissues and the matched paracancerous tissues were collected from the Affiliated Cancer Hospital and Institute of Guangzhou Medical University. All the included subjects were clinically and histopathologically diagnosed with LUAD. Tissues were snap-frozen in a −80 ℃ refrigerator located in School of Public Health, Sun Yat-sen University. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Written consents were obtained from the patients before their enrollment in this study. The human experimental study was approved by the Ethics Committee of the Affiliated Cancer Hospital & Institute of Guangzhou Medical University (No. 2020-SK05).

Cell lines and cultures

HEK293 and A549 cells were obtained from ATCC (Virginia, USA) and cultured in Dulbecco’s modified Eagle medium (DMEM, GIBCO, USA). The culture media were supplemented with 10% fetal bovine serum (Biological Industries, BeitHaemek, Israel). Cells were maintained in a 5% CO₂ and 95% air incubator.

Plasmids construction and transfection

Single guide RNA (sgRNA) was designed using Feng Zhang website (https://zlab.bio/guide-design-resources) (Table S1). pSpCas9(BB)-2A-GFP (PX458) (#48138; Addgene, USA) with sgRNA was employed to assess the binding effect of sgRNA on target sites. Lentiviral plasmids, including Fuw-dCas9-Dnmt3a-P2A-tagBFP (#84569; Addgene, USA), and pgRNA-modified (#84477; Addgene, USA), were utilized to construct stable cell lines expressing sgRNA. In addition, pcDNA3.1(+) (V790-20; Invitrogen, USA) was used to construct cell lines over-expressing OTX2.

Bisulfite sequencing PCR (BSP)

The genomic DNA of HEK293 and A549 cells were extracted according to the operating instructions of GeneJET Genomic DNA PurificationKit (K0722; ThermoFisher, USA). BSP was conducted using the standard methods as described in the Epitect Bisulfite Kit (59104; Qiagen, Germany). PCR was carried out using primers designed through the online MethPrimer2.0 software (http://www.urogene.org/methprimer2/). T-A cloning was carried out using the pMD19-T vector (3271-C1; TaKaRa, Japan).

Quantitative real-time PCR

Total RNA was extracted using TRIzol (15596018; Invitrogen, USA) according to the manufacturer’s instructions. cDNA synthesis was performed using the ReverTra Ace^® qPCR RT Kit (FSQ-101; TOYOBO, Japan). The quantitative real-time PCR (RT-qPCR) reaction system was prepared according to the instructions of SYBR^®Green Realtime PCR Master Mix (QPK-201; TOYOBO, Japan).

T7E1 test

The fragment covering the sgRNA targeting sequence was amplified using Phusion high-fidelity DNA polymerase (M0530S; NEB, USA). The purified PCR product was used as a template to prepare a hybrid reaction system. After hybridization, 1 µL of T7E1 endonuclease was added to the above 19 µL hybridization reaction system, and the system was digested for 30–90 min at 37 ℃ in PCR instrument. The electrophoresis results were then subjected to gray scale analysis using Adobe PhotoshopCC2018 software.

The sorting of target cells by flow cytometer

The ultra-high-speed flow sorting system (Beckman Coulter, USA) in the Experimental Center of Zhongshan School of Medicine, Sun Yat-sen University was employed to sort the target cells containing fluorescent protein markers. The cell lines with high expression of DNMT3A were screened based on the fluorescence intensity of tagBFP. Similarly, the cell lines with high expression of sgRNA were screened using the fluorescence intensity of mCherry.

Chromatin immunoprecipitation (ChIP)

The ChIP assay was performed using the Pierce(tm) Magnetic ChIP Kit (26157; Invitrogen, USA), according to the manufacturer’s instructions. In brief, 5×10⁶ A549 cells were fixed in 1% formaldehyde for 10 min at room temperature. The fixed cells were then harvested, lysed, and sonicated using Sonics VCX130 (Sonics & Materials, USA). Immunoprecipitation was conducted using antibodies against SP1 (07-645; Merck millipore, Germany), ZNF148 (ab69933; Abcam, UK), YY1 (46395; CST, USA), OTX2 (AB9566; Merck Millipore, Germany) and rabbit IgG (Thermo Fisher, USA). PCR amplification of the precipitated DNA was performed.

MTS, transwell migration and wound healing test

For the cell proliferation assay, cells were equally seeded in 96-well plates. Cell viability was measured by MTS for 24, 48, 72 and 96 h, and the absorbance values at OD450 were measured using multi-function enzyme labeling instrument (BIOTEK, USA). Each group was analyzed in triplicate. For the analysis of cell migration, 1×10⁶ cells were seeded in the upper chambers of Transwell plates (Corning, New York, USA). After incubation for 48 h, cells were fixed in 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet for 30 min. Migrating cells were observed under microscope, and cells counts were performed using ImageJ. For the wound healing test, when the degree of cell fusion reached 100%, vertical scratches were made on the surface of the dish using a 1 mL tip. Pictures were taken at 0, 6, 12 and 24 h using a microscope, and changes in the scratch area were analyzed using ImageJ. Each group was set up with three replicates.

Statistical analysis

The data were presented as mean ± standard deviation (SD) and derived from a minimum of three independent experiments. Multiple experimental groups were compared using one-way analysis of variance (ANOVA), and pairwise comparisons were conducted using t-tests. P values less than 0.05 were defined as statistically significance.

Results

Methylation of p16 Cgi shore guided by CRISPR-dCas9 system inhibited p16 expression in HEK293 cells

The Cgi of p16 is located in the 132–450-nt upstream of its transcriptional start site, while the region approximately 2 kb upstream and downstream of Cgi was defined as its Cgi shore. To investigate the regulatory relationship between Cgi shore methylation and p16 mRNA expression, we designed 2 sgRNAs (sgRNA1–2) to target the upstream and downstream regions of p16 Cgi shore (Figure 1A). The T7E1 assay results confirmed the successful binding of both sgRNAs to their target regions (Figure S1A). The time points for detecting the expression of DNMT3A and p16 after instantaneous transfection were determined by capturing images using an inverted fluorescence microscope at different time intervals (Figure S1B-S1G). Besides, sgRNA1 and sgRNA2 significantly reduced p16 mRNA levels after transient co-transfection with dCas-DNMT3A in HEK293 cells (Figure S1H). HEK293 cells stably expressed dCas9-DNMT3A (Figure S1I-S1K) and sgRNA1/sgRNA2 cell lines were established. The results confirmed that sgRNA1 and sgRNA2 suppressed the expression levels of p16 by 60.00% and 48.37%, respectively, compared with the control (Figure 1B). BSP revealed that the basal level of DNA methylation in the entire regions, including Cgi and Cgi shores of p16, was 3.33%. The expression of dCas9-DNMT3A and scramble RNA (scRNA) had little effect on it. However, in DNMT3A-sgRNA1 and DNMT3A-sgRNA2 cells, the DNA methylation levels of p16 increased to 9.86% and 19.72% in these regions, respectively, which were significantly higher than that in the DNMT3A-scRNA (Figure 1C). It was noteworthy that sgRNA1 predominantly altered the DNA methylation of the upstream shore of Cgi (U-shore), while sgRNA2 primarily affected the downstream shore of Cgi (D-shore) (Figure 1C). Compared with scRNA control, sgRNA1 and sgRNA2 increased the DNA methylation levels of U- and D-shore by 32.69% and 39.28%, respectively (Figure 1C). These findings indicated that the Cgi shore might be the key regulatory regions of DNA methylation in p16.

Figure 1 Increased methylation level of p16 Cgi shore by CRISPR-dCas9 system decreased p16 expression in HEK293 cells. (A) The blue section shows the positions of the 5'UTR, the first exon, and the first intron of p16. The green part shows the location of p16 Cgi, Cgi shore and the region targeted by sgRNA. sgRNA targeting specific sequence of p16; sgRNA1 target to Cgi adjacent to upstream-shore (U-shore), sgRNA2 target to downstream-shore (D-shore). (B) The mRNA expression level of DNMT3A/p16 in cell stably transfected of double plasmid. (C) Changes of methylation levels in targeted regions caused by targeted methylation in HEK293 cells. Each BSP sample has eight repeats, each row of circles represents the BSP sequencing results of one sample, the black solid circle indicates that the site is methylated, and the hollow circle indicates that no methylation is detected at the site. **, P<0.01; ***, P<0.001; ****, P<0.0001. Cgi, 5’-cytosine-phosphate-guanine-3’ island; CRISPR-dCas9, Clustered Regularly Interspaced Short Palindromic Repeats/dCas9; 5'UTR, 5'untranslated region; sgRNA, single guide RNA; BSP, bisulfite sequencing PCR; PCR, polymerase chain reaction.

Hypermethylation of p16 Cgi D-shore induced by CRISPR-dCas9 system significantly promoted malignant phenotype of A549 cells

To validate the results obtained in HEK293 cells, we also constructed A549 cell lines with hypermethylated p16 Cgi shore using the same method. Similarly, we observed significant suppression of p16 mRNA levels by both sgRNA1 (29.62%) and sgRNA2 (82.25%) (Figure 2A). Similarly, we found consistent results at the protein level (Figure 2B). The transfection efficiency of sgRNA was verified by inverted fluorescence microscopy and flow cytometry (Figure S2A,S2B). Furthermore, we assessed DNA methylation ratio of the U-shore and D-shore in DNMT3A-sgRNA1 and DNMT3A-sgRNA2 cells. The methylation ratio of U-shore in DNMT3A-sgRNA1 A549 cells increased to 9.62% compared with 1.92% in scRNA cells. Similarly, the methylation ratio of the D-shore in DNMT3A-sgRNA2 A549 cells increased to 26.35% compared with 1.01% in scRNA cells. However, both sgRNAs did not induce changes the methylation level of p16 Cgi (Figure 2C).

Figure 2 Increased methylation level of p16 Cgi shore by CRISPR-dCas9 system decreased p16 expression in A549 cells. (A) The mRNA expression level of DNMT3A in cell lines stably transfected of double plasmid and the effect of sgRNA transfection on mRNA expression level of p16. (B) p16 protein level presented in A549 wild-type, DNMT3A-scRNA, DNMT3A-sgRNA1 and DNMT3A-sgRNA2 cells. (C) Changes of methylation levels in targeted regions caused by targeted methylation in A549 cells. Each BSP sample has eight repeats, each row of circles represents the BSP sequencing results of one sample, the black solid circle indicates that the site is methylated, and the hollow circle indicates that no methylation is detected at the site. *, P<0.05; **, P<0.01; ****, P<0.0001. Cgi, 5’-cytosine-phosphate-guanine-3’ island; CRISPR-dCas9, Clustered Regularly Interspaced Short Palindromic Repeats/dCas9; sgRNA, single guide RNA; BSP, bisulfite sequencing PCR; PCR, polymerase chain reaction.

To explore the influence of p16 Cgi shore hypermethylation on the phenotype of A549 cells, cell proliferation and migration tests were conducted. MTS results indicated that both DNMT3A-sgRNA1 and DNMT3A-sgRNA2 cells showed higher proliferation rates than scRNA cells 96 h after seeding (Figure 3A). Moreover, the wound healing test revealed a significant increase in wound closure in DNMT3A-sgRNA1 cells and DNMT3A-sgRNA2 cells compared to scRNA cells after 12 h (Figure 3B,3C). Additionally, the Transwell assay showed that cell migratory and invasive capabilities of DNMT3A-sgRNA1 and DNMT3A-sgRNA2 cells were significantly enhanced in comparison to scRNA cells (Figure 3D,3E). These findings indicated that the editing of DNA methylation levels in the Cgi shore of p16 in A549 ells resulted in significant alterations in both the expression and biological function of p16.

Figure 3 Increased methylation level of p16 Cgi shore by CRISPR-dCas9 system in A549 cells affects its malignant phenotype. (A) The cell viability of three cell lines was detected by MTS at 24, 48, 72 and 96 h, respectively. (B,C) Wound healing test results for three cell lines were observed at four time points (0, 6, 12 and 24 h) after vertical scratches using a microscope and analyzed with ImageJ. (D,E) Three kinds of cell lines were seeded in Transwell 24-well plate at a density of 2×10⁴ cells per well, and the results of 24 h migration experiment were stained with crystal violet and observed under a microscope. *, P<0.05; **, P<0.01; ****, P<0.0001. Cgi, 5’-cytosine-phosphate-guanine-3’ island; CRISPR-dCas9, Clustered Regularly Interspaced Short Palindromic Repeats/dCas9; sgRNA, single guide RNA; BSP, bisulfite sequencing PCR; MTS, 3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethoxyphenyl)-2-(4-sulfopheny)-2H-tetrazolium, inner salt; PCR, polymerase chain reaction.

The promotive effect of OTX2 on p16 expression was inhibited by Cgi shore methylation

Since DNA hypermethylation of the D- or U-Cgi shore can suppress the expression of p16 and enhance the malignancy of A549 cells, the regulatory mechanism of DNA methylation on p16 expression was investigated. Firstly, the TFs that bind to the regions of Cgi shore of p16 were predicted by JASPAR CORE database. Subsequently, a literature retrieval was used to select the TFs that might be involved in the regulation of p16 gene expression. Four TFs including YY1, SP1, ZNF148 and OTX2 were eventually screened out. According to the prediction of JASPAR CORE, there were 9 binding sites of these 4 TFs located in the Cgi shore of p16 (Figure 4A). ChIP-qPCR assays were performed in DNMT3A-sgRNA1/sgRNA2 cells to verify whether DNA methylation changes could interfere with the binding of these TFs. The results showed that sgRNA1 could only inhibit the binding of YY1 limitedly in U-shore, while sgRNA2 could inhibit the binding of OTX2 in D-shore (Figure 4B). sgRNA1 decreased YY1 binding level in the U-shore by 14.92%, while sgRNA2 decreased OTX2 binding level in the D-Shore by 19.35%. In order to verify the regulatory effect of OTX2 on p16, OTX2 was over-expression in A549 DNMT3A-sgRNA2 cell lines. Remarkably, the high expression of OTX2 partially restored the expression of p16 in A549 DNMT3A-sgRNA2 cell line by 19.04% (Figure 4C). Taken together, DNA methylation on p16 Cgi shore may regulate its gene expression by affecting the binding of OTX2 to p16.

Figure 4 The elevated methylation level of p16 Cgi shore affects the binding of key transcription factors. (A) Specific binding sites of four transcription factors on p16 Cgi shore was described. The circle corresponding to the plus represent the CpG site contained in the transcription factor binding sequence. (B) The effects of elevated methylation levels of p16 Cgi shore on the binding levels of four transcription factors were detected by CHIP-qPCR. The results of CHIP-qPCR were standardized by the enrichment fold of sgRNA scramble group. (C) OTX2 was highly expressed in DNMT3A-sgRNA2, and the expression levels of OTX2 and p16 were detected. *, P<0.05; **, P<0.01. Cgi, 5’-cytosine-phosphate-guanine-3’ island; CpG, 5’-cytosine-phosphate-guanine-3’; CHIP-qPCR, chromatin immunoprecipitation-quantitative polymerase chain reaction; sgRNA, single guide RNA.

The negative correlation between p16 expression and p16 Cgi D-shore methylation was verified by the TCGA database and in LUAD tissues

To obtain the DNA methylation level of p16 in human lung cancer, DNA methylation profiles of lung cancer tissues detected by Illumina Infinium Human Methylation 450 in TCGA database were analyzed (Figure 5A). The probes used to measure the DNA methylation level of p16 cover a total of nine CpG sites. Among these nine CpG sites, cg04026675 is located in the D-shore of p16. Therefore, the correlation between DNA methylation and mRNA expression of p16 was analyzed in a total of 453 LUAD tissues. Overall, the average DNA methylation of the total nine CpG sites is not negatively correlated with the mRNA level of p16. However, cg04026675 in the D-shore showed remarkable negative correlation with p16 mRNA level (r=−0.13, P=0.0044).

Figure 5 The importance of p16 Cgi D-shore methylation for gene regulation was verified in TCGA program database and LUAD tissues. (A) In cancer patients, the relationship between methylation levels at p16 Cgi D-shore and p16 expression levels was shown in a scatter plot, in which linear fitting was performed and correlation coefficients and P values were marked. The second scatter plot shows the relationship between the average methylation level of all methylation test sites and p16 expression in LUAD patients in the TCGA database. (B) The p16 expression, p16 Cgi D-shore methylation and p16 Cgi methylation of 15 pairs samples were detected by qPCR or BSP; P stands for paracancerous tissue and C for cancer tissue. *, P<0.05; **, P<0.01. 5'UTR, 5'untranslated region; Cgi, 5’-cytosine-phosphate-guanine-3’ island; TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma.

To further investigate the potential inverse correlation between p16 expression and Cgi D-shore methylation, we examined the levels of p16 expression, p16 Cgi methylation and p16 Cgi D-shore methylation in 15 paired LUAD and paracancerous tissues (the clinical pathological features of these samples are presented in Table 1). However, these clinical characteristics were not significantly associated with the level of p16 Cgi D-shore methylation (P>0.05) (Table 1). A pilot study was conducted on 15 pairs of LUAD tissues. The results showed that p16 was downregulated in 10 out of 15 pairs (66.67%, 2/3) of the LUAD tissues. In parallel, we observed that p16 Cgi shore methylation was upregulated in 6 out of 15 pairs (40%, 2/5) compared with their adjacent control tissues. p16 Cgi is difficult to play an important role in the regulation of p16 due to the limited changes in the level of methylation which increased by less than 20% (Figure 5B). We suggested that Cgi D-shore methylation of p16 may play a more important role in the regulation of p16 function than that in Cgi through the results of population sample analysis of LUAD.

Discussion

Previous studies revealed that the Cgi of p16 located in its promoter 132–450-nt upstream of its transcriptional start site was correlated with the expression of p16 gene in human cancers and associated with the tumor staging and prognosis (16). Pezzuto et al. investigated the prognostic value of p16 in 256 patients with non-small cell lung cancer (NSCLC) who underwent curative surgery. The research findings indicated that p16 expression was associated with tumor grading and staging (P<0.05) and had an impact on overall survival (OS). The average OS was 36 months, but after stratifying patients based on p16 expression levels, the OS increased to 54 months. Staging stratification showed significant prognostic value for early-stage p16 expression (P<0.014). P16 significantly influenced prognosis, particularly in early-stage cases, along with other variables such as tumor grading and staging (17). Although this study did not find a significant relationship between p16 methylation levels and other variables due to the relatively small sample size (P>0.05), a significant regulatory effect of p16 methylation on its expression level was observed. Methylation-mediated changes in p16 expression levels may play a crucial role in affecting the prognosis of NSCLC. In their review, Pezzuto et al. summarized that aberrant expression of the p16 gene was mainly observed in NSCLC, with p16 gene methylation being the most common. High methylation of the p16 gene, along with p53 and KRAS mutations, has been reported to promote lung cancer development in smokers. Furthermore, the promoter hypermethylation of the p16 gene leads to gene silencing, which is of great significance in confirming the downregulation of p16 protein expression in NSCLC (18). However, more than half of NSCLC failed to be detected with p16 Cgi methylation, indicating that the inactivation of p16 in lung cancer might be only partially caused by abnormal DNA methylation that occurs in the p16 Cgi region (19). Actually, methylation of Cgi shore has been shown to be inversely associated with gene expression, similar to the function of Cgi (20), However, few studies have focused on the changes of DNA methylation in regions outside the Cgi of p16. For example, hypermethylation of Cgi shore in HOXA2 and GATA2 has been inversely associated with their transcription in colon cancer (21). In our study, a new regulatory methylation region in Cgi shore of p16 that could lead to the inhibition of its gene expression was screened. Furthermore, the analytical results with TCGA database and LUAD clinical samples demonstrated that the methylation of the p16 Cgi D-shore was more important for the regulation of p16 expression compared to p16 Cgi. Although the accurate data of p16 methylation in lung cancer need further detection, our findings provide new evidence for the study of p16 Cgi shore methylation in lung cancer.

The regulatory mechanism of DNA methylation is considered to interfere with the binding of TFs, leading to the suppression of gene transcription (22). Numerous studies have reported a negative correlation between the global DNA methylation level and p16 expression in lung cancer; however, the detailed mechanism was not fully clarified (23,24). Previous studies have indicated YY1, SP1, CTCF, ZNF148 as important transcriptional factors of p16 (6). In this study, four TFs of which the binding sites were located in the Cgi shore of p16 according to the prediction results from the JASPAR database, were screened for further exploration. Previous studies have indicated that SP1, YY1, ZNF148, and OTX2 can directly or indirectly regulate the expression of p16, however, the underlying mechanisms by which these TFs regulate p16 expression remain unclear (25-29). Previous studies have shown that SP1 and OTX2 are known transcriptional activators, while YY1 and ZNF148 function as transcriptional suppressors. The more significant decrease in p16 expression observed in DNMT3A-sgRNA2 cell line may be attributed to the decrease in OTX2 binding levels as a transcriptional activator in the D-shore. However, it is essential to acknowledge that the level of gene expression is not solely determined by TF binding, as multiple regulatory mechanisms can influence gene expression, such as epigenetic modifications, chromatin accessibility, and post-transcriptional regulation (30). This could be a potential explanation for why the decrease in YY1 binding did not result in an increase in p16 mRNA expression level. Additionally, several studies have consistently shown that ZNF148 plays a crucial role as a TF in regulating p16 expression (26,31,32). However, the impact of methylation on ZNF148's function remains uncertain. The binding sites of ZNF148 on the p16 gene are predominantly concentrated in the promoter region and its upstream regions. Methylation alterations downstream of the p16 promoter may not significantly influence ZNF148’s ability to regulate p16 expression. Based on our results, we believed that the methylation of Cgi shore probably disrupted the binding of TFs described above, which might be the main reason resulting in the repression of p16 expression.

It is known that aberrant DNA methylation is one of the early events that occurs during human cancer development (33). The hypomethylation of the genomic DNA and the hypermethylation of specific genes are the characteristics of tumors relative to normal tissues. Thus, it is widely accepted that abnormal DNA methylation changes is probably the driving factors for tumorigenesis. P16, being a well-studied tumor suppressor gene, is frequently inactivated in cancers through aberrant hypermethylation. However, it remains unclear whether this hypermethylation is the “driver” or “passenger” for cancer development, mainly due to lack of technology for editing DNA methylation in specific regions. In a previous study, engineered zinc finger methyltransferase was uses to increase the promoter methylation of p16, resulting in a significant decrease in p16 expression by 50.6% and 57.1% in HEK293 and BGC823 cells, respectively (21). However, the previous editing approach using engineered zinc finger methyltransferase was non-specific, leading in an increase in the methylation level of approximately 900bp regions in both the p16 promoter and exon-1. To address this limitation, the CRISPR/Cas9 system provides a promising approach for studying the methylation regulatory machinery of specific regions in the target DNA, allowing for more precise and efficient regulation of DNA methylation. According to data from TCGA, the methylation levels of p16 were found to be 9.2% and 10.7% in normal and primary tumor tissues, respectively, in LUAD. Therefore, the significant role of p16 in the development and progression of LUAD cannot be solely attributed to these minor changes in methylation levels. Therefore, it is difficult to differentiate between LUAD and normal tissues solely based on p16 Cgi methylation, which is a commonly used method for detecting cancer in humans (19). To gain a deeper understanding, it is essential to investigate which specific region of p16 is crucial for DNA methylation to exert its function, as along with exploring the underlying mechanism involved.

There is still room for improvement in this study. For instance, cell experiments cannot fully replicate the cellular environment within the human body, which may result in variations in the expression level of p16 between the A549 cell line and lung adenocarcinoma tissue. LUAD is known for its significant genetic heterogeneity, and different patients may harbor distinct genetic alterations (34). This variability in genetic profiles among LUAD tissues might result in diverse p16 expression patterns. The tumor microenvironment in LUAD tissues can influence gene expression levels (35). Interactions between tumor cells and surrounding stromal and immune cells can affect the expression of various genes, including p16. Regulatory mechanisms, such as mRNA stability, alternative splicing, and microRNA-mediated regulation, can impact gene expression levels. These mechanisms may differ between LUAD tissues and cell lines, leading to varied p16 expression. Epigenetic changes, including DNA methylation and histone modifications, can affect gene expression (36). Differences in epigenetic patterns between LUAD tissues and cell lines may contribute to the observed variation in p16 expression. While cell experiments may not fully replicate the human environment, they can offer valuable insights to guide subsequent animal and clinical studies. Furthermore, the impact of p16 promoter methylation on its expression should be investigated with larger sample sizes and across various disease types to offer more comprehensive guidance for early clinical diagnosis and targeted therapy.

Conclusions

In conclusion, our study highlights the significance of DNA methylation in the Cgi D-shore of p16 as a crucial regulator of its gene expression and function in lung cancer. The hypermethylation of p16 Cgi D-shore suppresses its expression, thereby promoting the development of lung cancer by disrupting the binding of TF OTX2. Our findings have deepened the understanding of the regulation mechanisms of p16 DNA methylation and discovered its potential as a promising target for the diagnosis and treatment of lung cancer.

Acknowledgments

Funding: This work was supported by National Natural Science Foundation of China (No. 81872656); National Natural Science Foundation of China (No. 82073583); National Key Research and Development Program of China (No. SQ2019YFC1604603).

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-909/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-909/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-909/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-909/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Written consents were obtained from patients before participation in this study. The human experimental study has received approval from the Ethics Committee of the Affiliated Cancer Hospital & Institute of Guangzhou Medical University (No. 2020-SK05).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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