Identification of POLQ as a key gene in cervical cancer progression using integrated bioinformatics analysis and experimental validation
- Authors:
- Published online on: April 26, 2023 https://doi.org/10.3892/mmr.2023.13002
- Article Number: 115
Abstract
Introduction
Cervical cancer (CC) is a serious hazard to health worldwide; it has the highest incidence and mortality rates among gynecological cancers (1). In 2020, >6 million people were diagnosed with CC and >3.4 million people with CC succumbed globally (1). The majority of cases of CC are caused by persistent infection with high-risk human papillomavirus (HPV), a virus comprising a double-stranded circular DNA that can incorporate into the host genome (2). Following the incorporation of HPV DNA, the E2 protein is deactivated, causing the dysregulated expression of the E6 and E7 oncoproteins, thereby driving the development of the cervical epithelium from normal to cervical intraepithelial neoplasia (CIN) and finally to CC (2,3). However, fully understanding the molecular mechanisms involved in CC pathogenesis is a distant achievement, and new biomarkers and therapeutic targets need to be explored.
With the development of gene sequencing technology, the abundant genome sequencing data of human diseases have been uploaded to public databases, such as the Gene Expression Omnibus database (GEO; http://www.ncbi.nlm.nih.gov/geo), and bioinformatics analysis has become a common method for assessing the molecular change in these diseases. The expression level of RNAs, such as mRNA and microRNA (miRNA), between different samples can be compared conveniently, and further information can also be obtained. In the present study, integrated bioinformatics analysis methods were used to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in the process of CC progression and to analyze the function, interaction and signaling pathways. Thereafter, a key gene network that probably has a significant influence on the progression of CC was screened out and verified in vitro. Overall, the present study may provide new insights into the molecular mechanism of CC progression and the search for new therapeutic targets.
Materials and methods
Selection of microarrays
The microarrays were obtained from the GEO database. The mRNA microarray GSE63514 is comprised of 24 normal cervical epithelium samples, 76 CIN samples and 28 CC samples, all of which have HPV infection and >90% of which have high-risk HPV infection. The miRNA microarray GSE86100 contains six samples of normal mucosa tissues with no HPV infection and six samples of CC tissues with high-risk HPV infection.
Identification of DEGs and DEMs
The present study analyzed the gene expression profiles using the analysis tool GEO2R (ncbi.nlm.nih.gov/geo/geo2r) on the GEO website to obtain putative DEGs and DEMs between different cervical samples. Normal cervical epithelium vs. CIN samples, and CIN vs. CC samples were compared to determine the DEGs. The overlapping genes exhibiting similar expression trends from the two comparisons were regarded as DEGs that were differentially expressed in both processes from normal to precancerous, and from precancerous to cancerous. Data were compared between normal tissue samples and CC tissue samples to determine the DEMs. P<0.05 and |Log FC|≥1, where FC is fold change, were set as the cut-off values.
Functional enrichment analyses of DEGs
Gene Ontology (GO) functional term enrichment, comprising biological processes, cellular components and molecular functions, as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID 6.8 (https://david.ncifcrf.gov) website.
Protein-protein interaction (PPI) network
A PPI network is a visual display of the molecular interaction among proteins encoded by DEGs. Interaction information was exported from the STRING database 10.0 (string-db.org); a combined score of >0.4 was set as the threshold value. Cytoscape 3.5.0 (http://www.cytoscape.org) software for visualizing complex networks, was used for network construction, and subnetwork analysis was performed using the plug-in MCODE (http://apps.cytoscape.org/apps/mcode). The node degrees were analyzed using the Cytoscape NetworkAnalyzer. Further functional enrichment analysis of genes in the extracted subnetworks was conducted using the DAVID 6.8 website.
miRNA-target regulatory network
The online tool WebGestalt (http://www.webgestalt.org) was used to conduct miRNA-target gene enrichment analysis. By uploading DEGs to the website, miRNA-target interaction information can be obtained immediately, and a miRNA-target regulatory network can be generated using Cytoscape.
Literature search
A literature search was performed using terms structural maintenance of chromosomes 4 (‘SMC4’), ATPase family, AAA domain-containing 2 (‘ATAD2’) and DNA polymerase θ (‘POLQ’), combined with ‘cervical cancer’ on PubMed (pubmed.ncbi.nlm.nih.gov/) up to 20th October 2022.
Cell culture and transfection
According to the data from mRNA microarray GSE63514, all the samples are infected by HPV, which meant the DEGs identified may be associated with HPV infection. Therefore, the present study selected HeLa cells, a type of human CC cell line with HPV infection, to conduct the experiment validation. HeLa cells were purchased from the American Type Culture Collection and provided by the Tianjin Medical University. Cells were cultured and maintained in DMEM supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin and 100 U/ml streptomycin in a humidified incubator at 37°C with 5% CO2. Three small interfering (si)RNAs targeting DNA polymerase θ (POLQ; si-POLQ) and negative control siRNA (si-NC) were synthesized by Shanghai GenePharma Co., Ltd.; the siRNA sequences are listed in Table I. Following the manufacturer's instruction, all siRNAs were transfected into the HeLa cells (density of 50%) at 16.7 nM using Lipofectamine® 3000 (Thermo Fisher Scientific, Inc.) at 37°C for 24 or 48 h. After transfection, the efficiency of transfection was evaluated by measuring mRNA levels at 24 h and protein levels at 48 h. Transfected cells were collected for subsequent experiments 48 h after transfection.
Reverse transcription-quantitative PCR (RT-qPCR)
Following transfection with siRNA for 24 h, the total RNA of HeLa cells (with a density of 70%) was obtained using TRIzol® reagent (Thermo Fisher Scientific, Inc.) and subsequently reverse transcribed using the GoScript Reverse Transcription System (Promega Corporation) according to the manufacturer's protocols. qPCR was performed using the GoTaq qPCR Master Mix (Promega Corporation) and a 7500 Real-Time PCR Instrument (Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. The PCR cycling conditions were as follows: an initial denaturation step at 95°C for 2 min, followed by 40 cycles of 95°C for 15 sec and an annealing and extension step at 60°C for 1 min. The 2−ΔΔCq method was used for data analysis (4); gene expression levels were normalized to that of the housekeeping gene, GAPDH. PCR primers were synthesized by Tianyi Huiyuan Biotechnology Co. Ltd. and the sequences were: POLQ forward, 5′-TCCTACACCCATTCCAACATCTG-3′ and reverse, 5′-GTCTTTGAACCCATTTCTACTCCC-3′; GAPDH forward, 5′-GGTGGTCTCCTCTGACTTCAACA-3′ and reverse, 5′-GTTGCTGTAGCCAAATTCGTTGT-3′. These experiments were replicated three times.
Western blotting
Following transfection with siRNA for 48 h, total proteins were extracted from HeLa cells using RIPA lysis buffer and determined with the BCA Protein Assay Kit (Solarbio). 20 µg of protein were subjected to SDS-PAGE separation (10% separating gel and 5% stacking gel) and transferred onto PVDF membranes. Following blocking with 5% nonfat milk at room temperature for 2 h, the membranes were incubated with primary antibodies against POLQ (cat. No. orb48495; 1:1,000 dilution; Biorbyt, Ltd.) and GAPDH (cat. No. 97166; 1:1,000 dilution; CST) overnight at 4°C. The membranes were then incubated with the appropriate secondary antibody (cat. No. ZB-5301; and cat. No. ZB-5305; both 1:5,000; both ZSGB-BIO) for 1 h. Protein bands were visualized using the Immobilon Western HRP Substrate (MilliporeSigma) and the results were analyzed using the software Image J Version 2.0.0-rc-54 (imagej.net/software/fiji/).
Cell Counting Kit (CCK)-8 assay
CCK−8 (Beyotime Institute of Biotechnology) was used to assess cell viability and proliferation. HeLa cells were seeded in 96-well plates and transfected with si-NC, si-POLQ#2 or si-POLQ#3. After 1, 2, 3, 4 and 5 days, 10 µl of CCK-8 solution was added to each well. After 1.5 h of culture, the OD value at 450 nm was determined.
Transwell assay
The cell migration assay was conducted using 24-well Transwell chambers (Corning, Inc.). The transfected cells were suspended in serum-free medium, and 1×105 cells were seeded into the upper chamber; medium supplemented with 10% FBS was added to the lower compartment. After 24 h of culture, the migrated cells were fixed with 4% paraformaldehyde for 15 min at room temperature, stained with crystal violet ammonium oxalate solution for 10 sec at room temperature. A total of six fields for each chamber were examined with a light microscope at 100× magnification for cell counting. For the invasion assay, prior to the cell suspension being prepared, 70 µl of Matrigel was added to each upper Transwell chamber at 37°C for 1 h. The subsequent procedures were the same as those used for the migration assay.
Apoptosis and cell cycle analysis
HeLa cells transfected with siRNAs were collected with 25% trypsin without EDTA and washed with PBS. The annexin V-FITC/PI Apoptosis kit (BD Biosciences) was used for apoptosis detection. The cells were resuspended in 1X binding buffer and adjusted to 1×106 cells/ml. Thereafter, 5 µl of annexin V-FITC and 5 µl propidium iodide (PI) were added to 100 µl of cell suspension. After incubation for 15 min in the dark, 400 µl of the 1X binding buffer was added to the cell suspension. Apoptosis (early + late apoptotic cells) was detected within 1 h using a flow cytometer (Beckman Coulter, Inc.). For cell cycle analysis, HeLa cells were fixed with 75% cold ethanol, resuspended in 100 µl RNase A and incubated at 37°C for 30 min. Subsequently, cells were stained with 400 µl PI buffer and incubated in the dark at 4°C for 30 min. The DNA content was determined using flow cytometry.
Statistical analysis
The data were analyzed using one-way ANOVA followed by Dunnett's multiple comparisons test with GraphPad Prism 8.0 (Dotmatics). P<0.05 was considered to indicate a statistically significant difference.
Results
DEGs and DEMs
Based on the GSE63514 dataset, a total of 1,318 DEGs were screened upon comparison of the CIN sample data with those of normal cervical epithelium samples. Furthermore, 1,575 DEGs were screened between CC and CIN tissues. The expression heat maps of the top 100 up- and downregulated DEGs are shown in Fig. 1A and B. Among them, 250 genes, including 118 upregulated and 132 downregulated genes (data not shown), were common to both comparisons and presented the same expression trends (Fig. 1C). Based on the miRNA dataset, GSE86100, 166 DEMs were screened, including 74 upregulated and 92 downregulated DEMs, and the top 50 are shown in Fig. 1D.
GO and KEGG enrichment analyses
Using the online tool, DAVID, the GO term enrichments in terms of biological processes, cellular components and molecular functions, and KEGG pathway enrichment were analyzed; the top five significant enrichment terms for each category are presented in Fig. 2. For the biological processes, the upregulated DEGs were mainly enriched in ‘nuclear division’ and ‘mismatch repair’ pathway (Fig. 2A), whereas the downregulated DEGs were mainly enriched in the process of ‘icosanoid metabolic process’ and ‘arachidonic acid metabolism’ pathway (Fig. 2B).
PPI network
As depicted in Fig. 3A, the PPI network consisted of 123 nodes (including 67 nodes of upregulated DEGs and 56 nodes of downregulated DEGs) and 283 edges. Based on the NetworkAnalyzer tool, the top 15 hub proteins were RFC4, SMC4, STAT1, KNTC1, ATAD2, FBXO5, TRIP13, ESR1, CD44, CENPN, ANLN, CDK2, ECT2, KIF14 and POLQ (Table II). Using the plug-in MCODE, three significant subnetworks with scores >4 were extracted (Fig. 3B-D). For the first significant subnetwork, it contains 72 edges and 13 nodes, while 11 of these nodes belong to the top 15 hub proteins of the PPI network. For the second significant subnetwork, it contains 33 edges and 9 nodes, including IFIT3, USP18, IFI44L, STAT1, GBP5, IFI44, EPSTI1, RSAD2, GBP1. And for the third significant subnetwork, it is composed of 20 edges and 9 nodes, and contains the only two downregulated DEGs (ESR1 and AR) in these three significant subnetworks. The top five biological processes in the GO analysis of each subnetwork are presented in Fig. 3E-G.
miRNA-target regulatory network
The miRNA-target regulatory network is presented in Fig. 4A. This network comprises 66 nodes, including 30 DEGs and 36 miRNAs, and 137 edges. The majority of DEGs in this regulatory network were upregulated and were included in the PPI network. It was found that approximately one-third of the miRNAs in the regulatory network were also the screened DEMs, aforementioned. Collectively, SMC4, ATAD2 and POLQ in the miRNA-target regulatory network were not only identified as hub genes in the PPI network, but are also the nodes of the first significant subnetwork (Table II and Fig. 3B). Furthermore, these genes were modulated by miR-106b, miR-17-5p, miR-20a and miR-20b, which were identified as DEMs in the CC samples (Figs. 4B and 1D). Therefore, it was hypothesized that the three DEGs (SMC4, ATAD2 and POLQ) may interact closely with each other in CC progression.
Literature search of SMC4 and ATAD2 in CC
Using ‘SMC4’, ‘ATAD2’ and ‘POLQ’, combined with ‘cervical cancer’, a total of six articles on the roles of SMC4 and ATAD2 in CC were obtained from PubMed (5–10). As in Table III, SMC4 expression was upregulated in CC and recognized as a hub gene of miRNA-transcription factor-mRNA network of HPV-positive CC (5–7). Moreover, SMC4 can negatively regulate CTCF which mediates the ribosomal RNA gene transcription in CC, and promote proliferation, migration, invasion, epithelial-mesenchymal transition, spheroid formation of CC cells (7,8). ATAD2 is also identified as one hub gene of the PPI network and involved in the circRNA-miRNA-mRNA network in CC (9). Besides, ATAD2 is overexpressed in CC tissues and related to adverse clinicopathologic features of CC patients, and the knockdown of ATAD2 can reduce the growth, invasion, migration and clonogenic potential of CC cells (10). To the best of the authors’ knowledge, the involvement of POLQ in CC has not yet been reported. As a result, the present study proceeded to validate the effect of POLQ on the biological behavior of HeLa cells.
Effects of POLQ knockdown on CC cells
siRNAs were used to knock down the expression of POLQ in HeLa cells. The efficiency of three POLQ siRNAs was assessed using western blotting (Fig. 5A and B) and RT-qPCR (Fig. 5C). si-POLQ#2 and si-POLQ#3 exhibited greater efficiency and were selected for subsequent experimental validation. Cell proliferative ability was determined using the CCK-8 test, and the data showed that HeLa cells transfected with si-POLQ#2 and si-POLQ#3 had lower absorbance values compared with those transfected with si-NC (Fig. 5D. Thus, POLQ may be important for the proliferation of HeLa cells. To determine the effect of POLQ knockdown in migration and invasion, Transwell assays were performed and showed that the knockdown of POLQ inhibited the migration and invasion of HeLa cells (Fig. 5E-G). Apoptosis and cell cycle progression were assessed using flow cytometry. The percentage of apoptotic cells increased when POLQ was downregulated compared with that in the si-NC group (Fig. 5H and I). Furthermore, the percentage of cells arresting in the G2 phase were increased in the si-POLQ#2 group and si-POLQ#3 group than in the si-NC group (Fig. 5J and K), indicating that POLQ knockdown may impede HeLa cell cycle progression.
Discussion
CC is the most common gynecological malignancy and causes the most mortality associated with gynecological malignancy worldwide (1). To explore the molecular mechanism of CC progression, bioinformatics analysis among normal cervical epithelium samples, CIN samples and CC samples was performed based on the microarray datasets of GSE63514 and GSE86100. A total of 118 upregulated and 132 downregulated DEGs were identified in the process of progression from normal cervical epithelium to CIN and from CIN to CC. In addition, 166 DEMs were identified based on an analysis of the normal cervical epithelium and CC data. Based on the results of GO term and KEGG pathway enrichment analyses, PPI network analysis and miRNA-target regulatory network construction, the upregulated DEGs, SMC4, ATAD2 and POLQ, were identified as hub genes in the PPI network and as genes involved in the first significant subnetwork. Notably, in the miRNA-target regulatory network, these three DEGs were all regulated by miR-106B, miR-17-5P, miR-20A and miR-20B, which were identified as DEMs in the present study and which were shown to be modulated by the HPV proteins E6/E7 in a previous study (11–14). Therefore, it was hypothesized that SMC4, ATAD2 and POLQ may interact closely with each other and serve vital roles in CC progression.
SMC4 serves a role in chromosome condensation and segregation (15–17). As SMC4 contains an ATPase domain, its inactivation can cause cell death (15). The overexpression and tumor-promoting function of SMC4 in certain types of cancer, such as pancreatic ductal adenocarcinoma (16), endometrial cancer (17), lung adenocarcinoma (18) and glioma (19), have been reported in a number of studies. SMC4 has been identified as a crucial gene in CC development in other studies (5,6). Through in vitro experiments, knockdown of SMC4 has been shown to inhibit cell proliferation, migration and invasion, as well as colony formation and EMT in CC (7). In addition, the expression of stem cell markers, spheroid formation and activation of NF-κB pathways were reduced (7). In addition, SMC4 can negatively regulate CCCTC-binding factor (CTCF)-enhanced rRNA gene transcription (8).
As a member of the ATPase family, ATAD2 participates in chromatin dynamics, DNA replication and gene transcription (20). The overexpression of ATAD2 is detected in numerous advanced types of human cancer, including pancreatic cancer (21), gastric cancer (22), ovarian cancer (23), colorectal cancer (24) and esophageal carcinoma (25). Furthermore, ATAD2 is regarded as an epigenetic reader, transcriptional co-regulator, proliferation driver, apoptosis negative regulator and cell cycle progression inhibitor in various types of cancer as it regulates signaling pathways, such as MAPK, AKT, HIF1α, hedgehog and Rb/E2F-cMyc (20,26,27). The potential and prospects of ATAD2 as a drug target for cancer have been identified (20,26). Consistent with the present study, ATAD2 has been identified as a hub gene and regulated by miR-106B in CC in a study by Yi et al (9). Furthermore, the regulatory effect of miR-106B on ATAD2 was previously verified in papillary thyroid cancer by Sun et al (28). In addition, according to Zheng et al (10), ATAD2 may be important in the promotion of growth and metastasis in CC.
POLQ exhibits ATPase activity similar to ATAD2 and SMC4 (29–33). POLQ has been revealed to serve a vital role in DNA replication and the repair of DNA double-strand breaks induced by different factors, such as endonucleases and Cas9 nickase (29). Furthermore, POLQ expression is upregulated in various types of cancer, including hepatocellular carcinoma (30), lung adenocarcinoma (31), breast cancer (32) and colorectal cancer (33), and it is associated with the poor prognosis (32–34). POLQ is necessary for cell proliferation and migration, and inhibits apoptosis (30), and it is recognized as a promising drug target for the treatment of cancer (35). In addition, POLQ can be downregulated by miR-20B in XP-V tumor cells, which accelerates mismatch in DNA replication repairing (36). However, the role of POLQ in CC remains to be elucidated. Therefore, upon identification of POLQ as one of the key genes in the progression of CC, the present study proceeded to perform experimental validation in vitro, using si-POLQ transected HeLa cells. Results from the present study showed that proliferation, migration and invasion of HeLa cells were inhibited by POLQ knock down. In addition, apoptosis was increased and cell cycle was impeded at the G2 phase when POLQ was downregulated. These results suggested that POLQ may be important in the progression of CC.
In conclusion, based on the results of integrated bioinformatics analyses, the gene network comprising SMC4, ATAD2 and POLQ, which are all genes associated with ATPase activity and related to the genome regulation, may serve important roles in the progression of CC. Furthermore, the effect of these genes may be modulated by the HPV proteins E6/E7 and their regulatory miRNAs (miR-106B, miR-17-5P, miR-20A and miR-20B). To the best of our knowledge, this is the first study to elucidate the potential role of this gene network and to verify the promoting effect of POLQ in CC progression through in vitro experiments. Such findings may provide novel insights into the progression mechanism and novel therapeutic targets of CC. However, the present study has some limitations. The sample size of the miRNA microarray GSE86100 dataset was not large enough, and differences in terms of HPV infection were found between samples in the two microarrays, which may affect the accuracy of the results. The relationships between HPV infection and these DEGs were not well demonstrated and further studies are needed.
Acknowledgements
Not applicable.
Funding
The present study was supported by The National Natural Science Foundation of China (grant no. 81972448) and The Science and Technology Project of Tianjin, China (grant no. 20JCZDJC00330).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
YZ was responsible for conceiving the study, methodology, data analysis and writing the manuscript. RZ was responsible for the methodology and writing the manuscript. TW was responsible for data analysis and manuscript revision. YG performed experiments. LC and SL were responsible for data curation. YW and FX conceived the study, reviewed and edited the final manuscript, and were responsible for funding acquisition. YZ and RZ confirm the authenticity of all the raw data. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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