EREG, HOPX and SYNGR3 influence the Pathogenesis and Prognosis of Cervical Cancer through immune cells infiltration

doi:10.21203/rs.3.rs-2453153/v1

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EREG, HOPX and SYNGR3 influence the Pathogenesis and Prognosis of Cervical Cancer through immune cells infiltration

https://doi.org/10.21203/rs.3.rs-2453153/v1

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Background

As a competitive endogenous RNA (ceRNA), circular RNA (circRNA) plays a significant role in the pathogenesis and progression of cervical cancer. A circRNA-associated ceRNA regulation network was built in this study, providing a new biological target for the treatment and prognosis of cervical cancer.

Methods

The expression profiles (GSE102686, GSE86100, and GSE7803) of circRNAs, miRNAs, and mRNAs were downloaded from the GEO database, and differentially expressed (DE) RNAs (DEcircRNAs, DEmiRNAs, and DEmRNAs) were acquired. The circRNA-miRNA and miRNA-mRNA regulatory links were retrieved from the CSCD and TargetScan databases, respectively. Then, a regulatory network for circRNA-associated ceRNA has been developed. On the basis of the ceRNA network, GO analysis, KEGG analysis, survival analysis, and sub-network creation were done. We verified the hub gene affecting prognosis through qRT-PCR. Finally, we analyzed the relationship between the four hub genes and immune cell infiltration in cervical cancer patients by the single sample gene set enrichment analysis method.

Results

A total of 13 DEcircRNAs, 330 DEmiRNAs, and 74 DEmRNAs were found, as well as 6 circRNA-miRNA pairings and 42 miRNA-mRNA pairings predicted. The ceRNA regulatory network (circRNA-miRNA-mRNA) was constructed, which included 3 circRNA, 4 miRNA, and 27 mRNA. The prognostic sub-network consists of 3 circRNAs (hsa_circ_0027821, hsa_circ_0046290, hsa_circ_0000745), 4 miRNAs (hsa-miR-766-3p, hsa-miR-96-5p, hsa-miR-362-5p, hsa-miR-1227-5p) and 4 mRNAs (CDA, EREG, HOPX and SYNGR3) that are associated with survival and prognosis of cervical cancer. Immune infiltration analysis shown that neutrophils were positively correlated with EREG gene and HOPX gene, but negatively correlated with SYNGR3 gene.

Conclusions

In this research, we established a circRNA-associated ceRNA regulation network for cervical cancer and discovered that hub genes (EREG, HOPX, and SYNGR3) influence the pathogenesis and clinical prognosis of cervical cancer by immune cells infiltration.

circRNA

ceRNA network

prognosis

pathogenesis

cervical cancer

immune cells infiltration

1. The key mRNAs impact the prognosis of cervical cancer were screened.

2. These genes influence the pathogenesis and prognosis of cervical cancer through immune cells infiltration.

Cervical cancer is one of the most life-threatening malignancies in women, with a dismal likelihood of survival if it has progressed to an advanced stage[1]. According to a recent World Health Organization report, the number of people newly diagnosed with cervical cancer has climbed to over 560,000, and the incidence and mortality rates of cervical cancer are 13 and 7 per 10 million people, respectively[2]. Cervical cancer is more common in young women, emphasizing the necessity of disease prevention and treatment studies[3]. Researchers have discovered probable genetic abnormalities and genetic diseases that play a key role in the pathogenesis and prognosis of malignant tumors thanks to the advancement of precision medicine. As a result, we must make every effort to find trustworthy biomarkers and treatment targets for cervical cancer.

Circular RNAs (circRNAs), a diverse group of non-coding RNAs, are covalently closed via mechanisms involving the splicing machinery and thus lack 5' or 3' ends[4]. High-throughput RNA sequencing (RNA-Seq) analyses have identified vast numbers of circRNAs, which can act as miRNA sponges in cells, to relieve the inhibition of miRNA on its target genes[5]. The competitive endogenous RNA (ceRNA) theory, which proposes that non-coding RNA, mRNA, and other transcripts can competitively bind miRNA response elements (MREs) to mutually regulate the expression of their respective genes, is central to the interaction mechanism between RNAs[6]. A large number of studies have discovered that aberrant circRNA regulation causes a variety of diseases, including cervical cancer[7–9]. Through bioinformatics research and multiple databases, such as the Gene Expression Omnibus (GEO) database, the cancer-specific circRNA database (CSCD), and the TargetScan database, we attempt to create the circRNA-miRNA-mRNA-related ceRNA regulation network for cervical cancer. GO analysis, KEGG analysis and survival analysis were conducted on the mRNAs in the ceRNA network and a prognostic sub-network was constructed.

2.1. Data acquisition

The Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) plays an important role in many fields, including comparative genomic analysis, proteomics, non-coding RNA, single nucleotide polymorphism genome and gene methylation status analysis[10]. A search was conducted from the GEO dataset for microarrays that met the following requirements: using “cervical carcinoma” OR “cervical tumor” OR “cervical neoplasia” OR “cervical cancer” AND “circRNA”.

After the selection process, three datasets have been downloaded from the GEO database: One circRNA expression profiling dataset [GSE102686 (GPL19978 platform)], consisted of 5 cervical squamous cell carcinoma tissues and 5 paired-paracancerous cervical tissues; One miRNA expression profiling dataset [GSE86100 (GPL19730 platform)], consisted of 6 cervical cancer tissues and 6 normal mucosa tissues; And one mRNA expression profiling dataset [GSE7803 (GPL96 platform)], consisted of 10 normal squamous cervical epitheilia samples, 31 high grade squamous intraepithelial lesions or invasive squamous cell carcinomas of the cervix (Table 1). Our research process is shown in Fig. 1.

Table 1

Basic information of three gene microarray datasets of cervical cancer
Data source	Series	Platform	Sample size (Control/Tumor)
circRNA	GSE102686	GPL19978	5/5
miRNA	GSE86100	GPL19730	6/6
mRNA	GSE7803	GPL96	10/31

2.2. Data processing

The genes used for constructing the ceRNA network must be differentially expressed (DE) between cervical carcinoma and healthy tissue samples. Perl software version 5.30.0.1 was used to convert the probe matrix into an RNA matrix. DEcircRNA, DEmiRNA, and DEmRNA were obtained using the “limma” package in R version 4.0.3. The following screening criteria were considered statistically significant: | log₂ fold change |>2 and adjusted P-value < 0.05. The pheatmap package in R is used for the visual analysis of differentially expressed RNA.

2.3 Construction of the ceRNA network

Based on the theory that circRNAs act as miRNA sponges in mammalian cells, we constructed a circRNA-miRNA-mRNA regulatory network. The circBase database and Cancer-Specific CircRNA database (CSCD, https://gb.whu.edu.cn/CSCD/) were used to predict miRNA binding sites (MREs) [11]. CircBase supplied the circRNAs' chromosomal location, as well as the chromosomal location and length of the RNA required for the study. Interactions between intersection miRNAs and target mRNAs were predicted based on the TargetScan database[12]. Finally, the data for the regulatory network (circRNA-miRNA-mRNA) were processed through Perl software, and then a visual ceRNA regulatory network was established by using Cytoscape 3.8.0 software.

2.4 KEGG and GO pathway enrichment analysis

We performed GO function annotation and KEGG pathway analysis to further investigate the pathway and mechanism of DEmRNA in the circRNA-associated ceRNA network affecting cervical cancer by using the “cluster profiler” package in the R software. GO analysis covers three areas, cellular components (CC), molecular functions (MF) and biological processes (BP). Each category explains the biological function of genes at different levels[13]. KEGG pathway enrichment analysis is used to measure the degree of enrichment of differential genes in pathway terms.

2.5 Survival analysis of hub genes

We conducted a survival analysis to explore whether DEmRNA in the circRNA-associated ceRNA network affects the overall survival of cervical carcinoma patients. The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) is currently the world’s largest public database of cancer genetic information. It aims to help researchers gain a deeper understanding of cancer so that cancer prevention, diagnosis and treatment can be improved through high-throughput genome analysis technology. The TCGA database contains gene expression data, miRNA expression data, frequency, and standardized clinical data with large sample sizes. Perl software was used to extract the information from the TCGA database, and then “survival and survminer” packages were used to analyze the relationship between target genes and the survival rate of patients to obtain mRNAs related to the survival and prognosis of cervical carcinoma. Then, using Cytoscape software, a prognostic sub-network was constructed for the prognostic-related genes, and the miRNA and circRNA exerting a regulatory relationship with them were confirmed.

2.6 Quantitative RT-PCR (qRT-PCR) verification

We obtained 8 cervical cancer tissues from patients and 8 normal mucosa tissues from normal healthy volunteers. Total RNA was isolated from tissues using Trizol and cDNA was synthesized using reverse transcription kits (Takara, Beijing, China) according to the manufacturer's instructions. Quantitative reverse transcription PCR was performed using SYBR Green Mix on an ABI7900HI (Thermo Fisher Scientific). The program was set to be a two-step method, 95℃ for 5 s, 60℃ for 30 s, and 40 cycles. The gene expression results were analyzed using the 2^−ΔΔCT method, and GAPDH was used as an endogenous control for mRNA expression. The primer sequence information of the qRT-PCR experiment is shown in Supplementary Table S1.

2.7 Correlation analysis between cervical cancer hub genes and immune infiltrating cells

The single sample gene set enrichment analysis (ssGSEA) method was then used to evaluate the abundance of 23 immune cells in cervical cancer to further investigate the correlation. Then we analyzed the correlation between hub genes and cervical cancer-related immune cells. The heat map and the vioplpt were constructed by the “ggplot2” package and the “ggpubr” package to visualize the features.

3.1. Extraction of DEcircRNA, DEmiRNA and DEmRNA

Human-derived RNA datasets (GSE102686, GSE86100, and GSE7803) containing cervical carcinoma tissues and normal cervical carcinoma were selected. The differential expression of the data set was analysed with the "limma" package. In total, 13 DEcircRNAs were obtained, of which 9 were downregulated and 4 were upregulated. Of the 330 DEmiRNAs, 187 were downregulated and 143 were upregulated. There were 74 DEmRNAs, 53 were downregulated and 21 were upregulated. As shown in Fig. 2, we selected the top 50 downregulated and upregulated DERNAs for heat map analysis.

3.2 Identification of target miRNA and mRNA of circRNA

Through the CSCD database, we predicted a total of 612 target miRNAs that bind to 10 DEcircRNAs. The structure pattern diagrams and basic characteristics of the 10 circRNAs, are shown in Table 2 and supplementary figures S1A-J. The fluorescent green part represents the protein that circRNA may encode, namely Open Reading Frame (ORF). The blue part represents the location of circRNA bound to the protein, and the position of the red small triangle represents the binding position of the circRNA to the miRNA. Subsequently, using the Venn diagram method, the predicted target miRNA and DEmiRNA were intersected, and 6 intersection miRNAs were obtained, as shown in supplementary figure S1K. Next, genes identified in the TargetScan database were selected as potential target mRNAs. Then, the above-mentioned intersection miRNAs were predicted to obtain target 36094 mRNAs. By intersecting the target mRNA and DEmRNA, 42 intersection mRNAs were obtained, as shown in supplementary figure S1L.

Table 2

The fundamental properties of the ten differentially expressed circRNAs that bind to target miRNAs
CircRNA ID	Strand	Position	length	Fold change	Gene symbol
hsa_circ_0084927	+	chr8:95676924–95677424	500	287	ESRP1
hsa_circ_0027821	+	chr12:97886238–97924637	38399	897	RMST
hsa_circ_0020926	+	chr11:3988781–3988912	131	131	STIM1
hsa_circ_0002762	-	chr12:96717725–96728643	10918	312	CDK17
hsa_circ_0000745	+	chr17:20107645–20109225	1580	1580	SPECC1
hsa_circ_0065898	-	chr3:51454240–51456330	2090	367	VPRBP
hsa_circ_0070190	+	chr4:79747190–79772210	25020	705	BMP2K
hsa_circ_0031027	+	chr13:114164552–114193822	29270	652	TMCO3
hsa_circ_0046290	+	chr17:79937059–79943483	6424	272	ASPSCR1
hsa_circ_0043280	+	chr17:35797838–35804870	7032	412	TADA2A

3.3 Construction of the regulatory network

Only genes that meet the following criteria will be selected for inclusion in the ceRNA network: (1) All genes must be differentially expressed; (2) circRNAs and mRNAs have a binding relationship with miRNAs at the same time; (3) RNAs (circRNA, mRNA) and miRNAs that meet the above binding relationship must be negatively regulated. Then, through Cytoscape software, a ceRNA regulatory network (circRNA-miRNA-mRNA) was constructed, including 3 circRNA (hsa_circ_0027821, hsa_circ_0046290, hsa_circ_0000745), 4 miRNA (hsa-miR-766-3p, hsa-miR-96-5p, hsa-miR-362-5p, hsa-miR-1227-5p), and 27 mRNA (Fig. 3). Meanwhile, the pheatmap package in R was used to visualize the circRNA, miRNA, and mRNA in the ceRNA regulatory network (Figs. 4A-4C).

3.4 GO function annotation and KEGG pathway analysis

In the ceRNA regulatory network, GO analysis of 27 mRNAs indicated a major enrichment in: epidermis development, keratinocyte differentiation (BPs) and desmosome (CCs). The histogram is displayed in Figs. 5A-5C. The results of the KEGG pathway analysis indicated that the main enrichment occurred in the complex and coagulation cascade, staphylococcus aureus infection (Fig. 5D).

3.5. Survival analysis and prognostic sub-network construction

Through the analysis of 72 mRNAs in the ceRNA regulatory network by the survival package in R, it was found that 4 mRNAs (CDA, EREG, HOPX and SYNGR3) were related to the prognosis of cervical cancer (Figs. 6A-6D). At the same time, based on these four genes, we established a circRNA-miRNA-mRNA prognostic sub-network. The CDA, EREG and HOPX gene high expression groups have a worse prognosis compared with the low expression groups (P = 0.009, P < 0.009, P = 0.049). The SYNGR3 gene high expression groups have a better prognosis compared with the low expression groups, respectively (P = 0.009). Meanwhile, a circRNA-miRNA-mRNA prognostic sub-network was constructed based on the four mRNAs mentioned above (Fig. 7).

3.6 qRT-PCR verified the hub genes affecting prognosis

The expression of the four hub genes which affect the prognosis of cervical cancer (CDA, EREG, HOPX and SYNGR3) was analyzed by quantitative RT-PCR. Between the cervical cancer and control groups, we found that the CDA, EREG and HOPX genes had significantly increased expression, while the SYNGR3 gene had significantly decreased expression in cervical cancer patients (Fig. 8).

3.7 Correlation analysis between cervical cancer hub genes and immune infiltrating cells

Considering that tumors have been shown to be infiltrated with a large number of immune cells in previous studies[14]. We analyzed the difference of immune cell infiltration between the cervical cancer group and the normal group (Figure S2, Figure S3). Moreover, we analyzed the relationship between the expression of the four hub genes and immune cell infiltration in cervical cancer patients by the ssGSEA method (Fig. 9). The results were as follows: EREG was positively correlated with neutrophil. HOPX was positively correlated with neutrophil and negatively correlated with T cell. SYNGR3 was negatively correlated with neutrophil. CDA levels were not statistically significantly correlated with immune cell infiltration.

Based on traditional treatments, increasingly novel technologies, such as targeted therapy, have improved the prognosis of cervical cancer patients to some extent. However, cervical cancer is still one of the most common gynecological cancers affecting women's health, necessitating ongoing research and the identification of new therapy options[15]. Cancer's pathophysiology is intimately linked to gene mutation and dysfunction. CircRNA affects gene transcription and regulation by interacting with miRNA, mRNA, or proteins via the ceRNA mechanism[16]. More evidence has emerged in recent years suggesting deregulation of circRNA expression plays a vital function in the ceRNA regulatory network and is a primary pathogenesis of malignancies such as colon cancer and bladder cancer[17–19]. However, there are few studies on the effect of circRNA on cervical cancer occurrence and prognosis.

To discover the hub genes that contribute to cervical cancer and to try to determine cervical cancer prognostic indicators, we used bioinformatics analysis to develop a circRNA-associated ceRNA regulatory network. Firstly, three data sets related to cervical cancer were downloaded from the GEO database, and their differential expressions were analyzed. CSCD and miRTarget were used to extract the regulatory connections between circRNA-miRNA and miRNA-mRNA. The ceRNA regulatory network was then built to examine the GO function and KEGG pathway of differentially expressed genes.

Using GO functional annotation, we discovered that the most enriched genes were involved in epidermis development, keratinocyte differentiation (BPs), and desmosome (CCs). These variables have been implicated in the pathogenesis of cervical cancer and other cancers in numerous studies. Through bioinformatics research, it was discovered that epidermal growth plays a role in the progression of cervical cancer before it is diagnosed[20, 21]. Cervical cancer and HPV-associated carcinogenesis may be aided by keratinocyte differentiation with additional transcription factors[22]. It has been found that desmosome can indirectly leads to the occurrence of malignant tumors through the upregulation of kazrin F[23]. The KEGG pathway analysis showed that the differently expressed genes were mainly enriched in complex and coagulation cascade, as well as staphylococcus aureus infection. A recent study discovered that one of the mechanisms leading to cervical cancer is the complex and coagulation cascade pathway, which is consistent with our findings[24]. According to Chen's research, staphylococcus aureus infection may elicit an inflammatory response that unwittingly increases HPV-induced malignant progression, increasing the chance of cervical cancer formation[25]. These studies further prove that the genes in our circRNA-associated ceRNA regulatory network may play an important role in the pathogenesis of the cervix.

Simultaneously, we used a circRNA-miRNA-mRNA regulatory network to predict the prognosis of cervical cancer patients. Finally, we hypothesized that the four mRNAs of EREG, HPOX, CDA, and SYNGR3 were strongly linked to cervical cancer prognosis. At the same time, we have verified the hub gene through qRT-PCR. Previous research has linked these mRNAs (excluding SYNGR3) to the occurrence and prognosis of a variety of cancers, including cervical cancer. The EREG (Epiregulin)-EGFR (epidermal growth factor receptor)-C-Myc pathway was identified as a critical axis driving the carcinogenesis of head and neck squamous cell carcinoma (HNSCC) in Liu's study, and EREG expression was suggested as a functional marker to predict HNSCC susceptibility to erlotinib treatment[26]. Other studies have discovered that EREG, as a cancer signature gene, is associated with cervical cancer malignant transformation, progression, and survival rate[27, 28]. Sebastian's team found that the EREG gene can reliably predict the prognosis of adenocarcinoma of the colon or rectum by selecting 331 patients from 472 participants with metastatic colorectal cancer[29]. HPOX deficiency can result in the loss of tumor suppressive activity and differentiation phenotype in human cells, suggesting that HPOX is both a major regulator of epigenetic dynamics and a critical determinant of differentiation[30]. According to recent studies, HOPX may also have a significant role in modulating differentiation phenotype and tumor suppressive activity, as well as altering prognosis via affecting downstream genes[31]. According to studies on pancreatic cancer, the CDA gene has been reported to cause persistent alterations in the endoplasmic reticulum, resulting in overexpression of the mucin 1 (MUC1) protein in cancer and promoting treatment resistance[32]. These miRNAs have been linked to a number of malignancies, including cervical cancer, according to our findings as well as previous studies. Therefore, we speculate that these four hub genes affecting prognosis may be indirectly linked through a certain protein pathway, thus jointly affecting the occurrence and prognosis of cervical cancer.

Previous studies have shown that mRNA may affect the proliferation of cervical cancer cells through immune infiltrating cells[33]. Zhao et al. demonstrated that the INHBA gene is a prognostic biomarker and correlated with immune cell infiltration in cervical cancer [34]. In our study, neutrophils were positively correlated with the EREG gene and the HOPX gene, but negatively correlated with the SYNGR3 gene, which is consistent with our previous analysis of prognosis. Therefore, we speculate that the hub gene may affect the pathophysiology and prognosis of cervical cancer through neutrophils.

The innovation of our study is that the results are based not only on the data obtained from the GEO database, but also on subsequent bioinformatics analysis (circRNA-associated ceRNA network), which also combines the source data of TCGA, including a large number of samples and detailed information related to prognosis. At the same time, our research also has some deficiencies. It lacks detailed data for long-term follow-up to check the mRNAs that predict prognosis. However, our research indirectly proves that these key RNAs may affect the progression of cervical carcinoma and provides a good foundation for further research.

Collectively, our study proved that circRNA (hsa_circ_0027821, hsa_circ_0046290, hsa_circ_0000745) has an important role in the occurrence, progression and prognosis of cervical cancer through the ceRNA regulatory network. At the same time, the mRNAs in the regulatory network were analyzed by GO and KEGG, to more accurately find the potential molecular biological mechanisms leading to carcinogenesis. Finally, hub genes (EREG, HOPX, and SYNGR3) influence the pathogenesis and clinical prognosis of cervical cancer by immune cells infiltration, which contributes to a deeper understanding and further exploration of the molecular mechanisms that influence the occurrence of cervical cancer.

Author Contributions: Conceptualization, Pei Zhang and Yan Wang; methodology, Pei Zhang; software, Hengliang Zhang and Li Ren; validation, Pei Zhang, Yan Wang and Li Ren; formal analysis, Li Ren; investigation, Pei Zhang; resources, Pei Zhang; data curation, Hengliang Zhang; writing—original draft preparation, Pei Zhang and Hengliang Zhang; writing—review and editing, Pei Zhang and Hengliang Zhang; visualization, Pei Zhang and Hengliang Zhang; supervision, Yan Wang; project administration, Pei Zhang and Hengliang Zhang.

Funding: No funding was used in this study.

Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional ethics committee of the first affiliated hospital of henan university of science and technology.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: The three datasets (GSE102686, GSE86100, and GSE7803) in this study were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/gds/?term=).

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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No competing interests reported.

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EREG, HOPX and SYNGR3 influence the Pathogenesis and Prognosis of Cervical Cancer through immune cells infiltration

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Abstract

Background

Methods

Results

Conclusions

Figures

Headings

1. Introduction

2. Materials And Methods

2.1. Data acquisition

2.2. Data processing

2.3 Construction of the ceRNA network

2.4 KEGG and GO pathway enrichment analysis

2.5 Survival analysis of hub genes

2.6 Quantitative RT-PCR (qRT-PCR) verification

2.7 Correlation analysis between cervical cancer hub genes and immune infiltrating cells

3. Results

3.1. Extraction of DEcircRNA, DEmiRNA and DEmRNA

3.2 Identification of target miRNA and mRNA of circRNA

3.3 Construction of the regulatory network

3.4 GO function annotation and KEGG pathway analysis

3.5. Survival analysis and prognostic sub-network construction

3.6 qRT-PCR verified the hub genes affecting prognosis

3.7 Correlation analysis between cervical cancer hub genes and immune infiltrating cells

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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