Identification of Hub Gene in Cervical Cancer by Weighted Gene Co-Expression Network Analysis

Identification of Hub Gene in Cervical Cancer by Weighted Gene Co-Expression Network Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Hub Gene in Cervical Cancer by Weighted Gene Co-Expression Network Analysis huan Chen, huan Chen, Meiyuan Huang, Qunzhi Zhang, Qiong Xu, Jinjin Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-844099/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cervical cancer(CC) is one of the most common malignant tumors in gynecology. Both its incidence and mortality are high. Despite advances in screening, diagnosis, prevention, and treatment, CC is still one of the leading causes of cancer-related death in women. However, in the pathogenesis of CC, the exact molecular mechanism is still unclear. It may be a multi-gene, multi-factor, multi-step, and multi-stage complex process. Hence, the pathogenesis and molecular mechanism of CC are the keys to effective treatment of it. In this study, we tried to identify candidate biomarkers for cervical cancer through weighted gene co-expression network analysis (WGCNA). Methods: The gene expression profile of CESC was downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by the Limma package, and the gene co-expression module was constructed by WGCNA. Use the online website STRING to construct the protein interaction network of genes in significant modules, and then use Cytoscape analysis to find the 10 most important node degree genes. Results: Among these 10 genes, SOX9 expression was associated with the prognosis of cervical cancer patients. Immunohistochemical results from the online website human protein atlas showed that SOX9 protein expression was significantly higher in cervical cancer tissues than in normal cervical tissues. After collecting cancerous and paracancerous tissue specimens from 16 patients with cervical cancer, Q-PCR showed that the mRNA expression levels of SOX9 in cervical cancer tissues were significantly greater than those in normal adjacent tissues. Conclusions: The elevated expression of SOX9 is significantly related to the disease-free survival and overall survival of cervical cancer. Therefore, the SOX9 gene could be used as an indicator of cervical cancer diagnosis and prognosis. Oncology bioinformatics analysis differentially expressed genes (DEGs) weighted gene co-expression network analysis (WGCNA)，cervical cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cervical cancer(CC) is one of the most common malignant tumors in gynecology. Its incidence is second only to breast cancer[ 1 ], and it has increased significantly worldwide[ 2 ]. Both its incidence and mortality are high[ 3 ]. The global incidence rate was estimated to be 570,000 cases and nearly 311,000 deaths in 2018[ 4 ]. In China, CC is also the second most common malignant tumor in gynecology. The incidence and mortality of cervical cancer in young women are increasing[ 5 ]. The pathogenesis of CC is diverse, including infection with human papillomavirus (HPV), herpes simplex virus type 2 (HSV-2), cervical thymus (CT), and other bad habits[ 6 ]. Although the pathogenesis of CC is complicated, HPV is an important reason for the development of cervical cancer, which has been widely accepted[ 7 ]. CC is also one of the most preventable cancers[ 8 ]. The persistent infection with high-risk HPV is closely related to the development of CC. So HPV vaccination can effectively prevent it[ 9 ]. As a secondary prevention effort, early screening for CC and precursor lesions can also reduce the incidence and mortality of CC[ 10 ]. Despite advances in screening, diagnosis, prevention, and treatment, CC is still one of the leading causes of cancer-related death in women[ 11 ]. However, in the pathogenesis of CC, the exact molecular mechanism is still unclear[ 12 ]. It may be a multi-gene, multi-factor, multi-step, and multi-stage complex process.[ 13 ]. Hence, the pathogenesis and molecular mechanism of CC are the keys to effective treatment of it.[ 14 ]. In recent years, bioinformatics has been widely used at the genomic level of many cancer types to reveal the internal mechanisms of tumor progression and canceration [ 15 ]. Bioinformatics methods combine biology, mathematics, and computer science to further promote the molecular mechanism explanation and discovery of tumor-related diagnostic markers[ 16 ]. Gene expression analysis is an important part of molecular biology research[ 17 ]. The identification of differentially expressed genes (DEGs) under different conditions is an important goal of microarray-based gene expression analysis[ 18 ]. Weighted Gene Co-expression Network Analysis (WGCNA) is a new tool to explore potentially related modules in expression data[ 19 ]. WGCNA recognizes the correlation between genes through microarray samples, so it can detect clusters of highly related genes (module)[ 20 ]. On the other hand, DEGs from the TCGA database are also used for WGCNA[ 21 ]. The Cancer Genome Atlas (TCGA) project is a collaborative initiative to use existing large-scale genome-wide technologies to better understand cancer[ 22 ]. It is a publicly available data set that provides various types of genomic data[ 23 ]. At the same time, it provides detailed clinical information[ 24 ]. Therefore, the TCGA database is widely used in oncology research, providing useful information for the discovery of new tumor biological indicators and drug targets[ 25 ]. In this study, we performed a weighted gene co-expression analysis based on the different analysis results of the TCGA Cervical squamous cell carcinoma and endocervical adenocarcinoma(CESC) dataset and tried to find key gene in the development of cervical cancer . Materials And Methods Data collection Transcription level data of cervical samples and complete clinical data sets were obtained from TCGA (https://portal.gdc.cancer.gov/). The study collected a total of 306 CESC RNA sequencing data samples and survival follow-up time and 3 adjacent normal tissue sequencing data samples. The workflow is shown in Figure1. Screening for differentially expressed genes The genome-wide transcriptional expression profile was obtained from TCGA's RNA-seq Counts data. The limma[26] software package of R version 3.6.3 (http://www.r-project.org) was used to screen differentially expressed genes. Log2 (fold change)≥2.00 and false discovery rate (FDR)<0.01 were considered to indicate DEGs. Co-expression analysis of DEGs in CESC WGCNA analysis is a widely used method in systems biology and is designed for multivariate data (ie gene expression, DNA methylation, metabolites, etc.)[27]. It can reveal the correlation of genes and look for significantly related gene modules. In this study, we used the "WGCNA" program package [28,29] to construct a co-expression network based on the results of the previous differential expression analysis to analyze potential genes in CC. Identification of clinically significant modules Two methods were used to determine modules related to clinical features. According to the linear regression between clinical traits and gene expression, the log10 conversion of P value was defined as gene significance (GS), and then the module significance (MS) was calculated using the average value of all genes in one module. It was usually considered that the module with the largest absolute value of MS was the module that has the closest relationship with clinical characteristics. Module exigencies (MEs) were used as the main components of gene modules and clinical traits to determine relevant modules. Select modules that were highly relevant to a given clinical feature for further analysis. Protein-Protein Interaction (PPI) Network Establishment and Hub Gene Identification Use STRING protein database 11.0 (http://string-db.org/) to construct a PPI network based on genes in significant modules. Next, export and import the results of the string database into the Cytoscape software [30]. The cytohubba plug-in is used to find the Hub genes. Functional annotation and pathway analysis To explore the function of genes in significant modules, we used David[31,32] online analysis tool to perform go and KEGG enrichment analysis on the genes in the significant module. Then downloaded the enrichment results, and used the R language to map the results. Hub genes basic expression in normal and cancer tissues The further verification and survival analysis of these hub genes were performed by using the gene expression profile interactive analysis (GEPIA) database (http://gepia.cancer-pku.cn/index.html; Z.Tang et al., 2017). We drew the overall survival curve and Disease-Free Survival curve of the hub genes in the GEPIA database, with a p-value of <0.05. Meanwhile, we use the Human Protein Atlas database (https://www.proteinatlas.org/) to verify the protein expression of the hub genes. Tissue collection From October 2020 to January 2021, 16 samples of cervical squamous cell carcinomas and adjacent normal tissues were collected from Zhuzhou Hospital affiliated with Xiangya Medical College. All specimens were assessed by immunohistochemistry and confirmed by two independent pathologists. At the same time, fresh tissue specimens of the corresponding patients were collected and stored in an ultra-low temperature refrigerator for Q-PCR. The study was approved by the Research and Clinical Trial Ethics Committee of Zhuzhou Hospital, and all eligible participants provided written informed consent. All clinical procedures are carried out in compliance with the ethical standards of the "Declaration of Helsinki" guidelines and relevant Chinese policies. Validation of hub gene After obtaining the hub gene through the co-expression network, we verified the hub gene by Quantitative real-time polymerase chain reaction (Q-PCR). Total RNA was extracted using RNAiso Plus (9109; Takara, Dalian, China). To obtain cDNA, the Hifair® Ⅱ 1st Strand cDNA Synthesis Kit (11119ES60; Yeasen Biotechnology, Shanghai, China) was used in 1 μg of total RNA. Use the following system for mRNA amplification: 85°C for 5 minutes, then 40 cycles (95°C for 10 s, 60°C for 30 s). ACTB was employed as an internal control for mRNA evaluation. To standardize SOX9 gene expression, ACTB expression levels were assessed as housekeeping genes and comparative CT(2 −ΔCt )methods were used for the analysis. Table1 show the primers of SOX9 and ACTB Table1 The primers of SOX9 and ACTB Primer Sequence（5'-3'） Product（bp） SOX9-F2 CCCGCTCACAGTACGACTAC SOX9-R2 CTGAGCGGGGTTCATGTAGG 113 ACTB-F2 AGACCTGTACGCCAACACAG ACTB-R2 CGCTCAGGAGGAGCAATGAT 132 Results Differential gene expression in TCGA CESC At the threshold of false discovery rate (FDR)<0.01 and |log2(FC) ≥2, a total of 1265 DEGs were identified between 306 CESC samples and 3 normal samples. Select all DEGs for subsequent co-expression network construction. Weighted Co-expression Network Perform a co-expression network analysis using 306 CESC samples in TCGA. Use the "WGCNA" R package to analyze the previous DEGs. Choose β = 7 (ratio R2 = 0.92) as the soft threshold power (Figure 2A&B). Five modules were identified, the minimum size (genome) of the gene tree diagram was 30, and the tangent line of the module tree diagram was 0.25.(Figure3) Identification of Clinically Significant Module Combining the relationship between the module traits and the module significance, we finally determined that the turquoise module is the most relevant module for tumor diagnosis. The turquoise module has the highest correlation with the diagnosis level (r = 0.65, p = 3e-38, Figure 4). Therefore, we choose the turquoise module as the interest module and analyze it. GO and KEGG Pathway A total of 384 genes in the turquoise module are enriched in gene ontology (GO) and KEGG pathway analysis by using the ClusterProfifiler and the org.Hs.eg.db packages. The biological process of the turquoise module focuses on cell proliferation, cell adhesion, and protein binding processes(P<0.05). The KEGG pathway of the turquoise module significantly enriches pathways related to cancer and cell circle(Figure5A,B,C,D). PPI Network Construction To identify the hub gene, a protein-protein interaction (PPI) network was constructed. The PPI network is visualized by Cytoscape. Use the cytohubba application in Cytoscape to estimate the node degree. The 10 most important node degree genes are selected as pivot nodes because they play a vital role in the progress of CC. The selected genes were CDKN2A , KRT5 , SOX2 , KRT8 , CAV1 , EPCAM , FOXA1 , KRT14 , ARF6 , SOX9 . Identification of Hub Genes Among the 10 most important node degree genes, we found that Univariate analysis (P<0.05) showed that the expression level of SOX9 was significantly related to the OS and PFS of CC patients, Low expression of SOX9 may lead to higher survival rates and disease-free survival(Figure6A&B). It is worth noting that in CC tissues, the expression of this gene is significantly higher than that of normal tissues. As shown in the figure, in cervical cancer specimens, the protein level of SOX9 is significantly higher than that of normal tissues in the HPA database(Figure7A&B). In addition, the results of cervical cancer Q-PCR showed that in cervical cancer tissues, the expression of SOX9 at mRNA levels was higher than that in adjacent normal tissues(Figure8). Discussion In this study, we combined the TCGA databases for differential expression analysis and analyzed the DEGs by WGCNA. We found that the turquoise module genes are closely related to the diagnosis of cervical cancer. After analyzing the genes in the module was by PPI network, we used the Cytoscape software to calculate the results of the PPI network analysis and obtained 10 particularly important genes. Among them, SOX9 is related to the overall survival rate and disease-free survival of patients with cervical cancer, and the overall survival rate and disease-free survival of patients with low expression are higher than high expression patients. The official name of SOX9 is SRY-box transcription factor 9, located at 17q24.3. The protein encoded by the gene recognizes the sequence CCTTGAG with other members of the HMG-BOX DNA binding protein. It plays a role in the differentiation of chondrocytes and with steroid factor 1 to regulate the transcription of anti-Mullerian hormone (AMH) genes. Its defects lead to skeletal malformation syndrome and dysplasia, which is often reversed with sex. SOX9 has many functions. It is part of the nucleus, nucleoplasm, protein-containing complex, transcription regulator complex, and chromatin. It can enhance DNA-binding transcription factor activity (RNA polymerase II-specific), DNA-binding transcription activator activity (RNA polymerase II-specific), protein binding, DNA-binding transcription factor activity, cis-regulatory region sequence-specific DNA binding, RNA polymerase II cis-regulatory region sequence-specific DNA binding, pre-mRNA intronic binding, beta-catenin binding, sequence-specific DNA binding, bHLH transcription factor binding, protein kinase A catalytic subunit binding, chromatin binding, sequence-specific double-stranded DNA binding. It has also participated in many processes including positive regulation of epithelial cell proliferation, positive regulation of epithelial cell migration, and positive regulation of cell population proliferation.[ 34 ] SOX9 plays an important role in many tumor types. For example, pediatric Pendymoma[ 35 ], sporadic pheochromocytoma[ 36 ], Solid pseudopapillary tumours (SPT)[ 37 ],et al[ 38 – 62 ]. It also plays an important role in the three major gynecological malignancies. In the studies of ovarian cancer, HK2, which is regulated by the tumor microenvironment, controls the production of lactic acid and regulates the expression of MMP9/NAONG/SOX9 through the FAK/ERK1/2 signaling pathway, which leads to ovarian cancer metastasis and stem regulation[ 63 ]. SOX9 activates βIII-tubulin expression to enable ovarian cancer cells to survive under hypoxic conditions, and its abnormal expression is particularly prominent in patients with aggressive OC[ 64 ]. As a key super-enhanced regulatory transcription factor, it plays a key role in acquiring and maintaining the regulation of chemical resistance in OC[ 65 ], SOX9 is highly expressed in OC cell lines, and overexpression of SOX9 can enhance the resistance of ovarian cancer cells to DDP chemical drugs [ 66 ]. Down-regulated the expression of SOX9 through exosomal MicroRNA-30a-5p could reduce the resistance of ovarian cancer cells to DDP[ 67 ]. Compared with high-grade serous ovarian tumor effusions and solid metastases, SOX9 is more expressed in the latter, Univariate (P = 0.04) and multivariate (P = 0.049) analysis, higher SOX9 levels are associated with shorter overall survival rates, and the level of SOX9 mRNA in HGSC efusion may be a marker of clinically aggressive diseases[ 68 ]. In the study of endometrial cancer, Gabriel Gonzalez et al. found that SOX9 in the uterine epithelium can induce the development of endometrial hyperplasia. Therefore, SOX9 expression can be the formation of endometrial cancer[ 69 ]. Circ_0109046 up-regulates SOX9 expression through sponging MiR105, leading to activation of Wnt/β-catenin signaling and EC malignant growth[ 70 ]. SOX9 expression also showed a significant gradual increase from normal to grade 1 to grade 2/3 tumors[ 71 ]. SOX9 can enhance the colony-forming activity of squamous cell carcinoma cells[ 72 ]. Several studies on the SOX9 gene in cervical cancer have confirmed that it plays a role in cervical cancer. Feng et al. found that the SOX9/miR-130a/ctr1 axis can regulate the chemoresistance of cervical cancer cells to cisplatin[ 73 ]. Liu et al. found that miR-215-3p was significantly lower in cervical cancer tissue than normal cervical tissue, and SOX9 , as a direct target of MiR-215-3P, was negatively correlated with miR-215-3p. It suggests that the miR-215-3p/SOX9 axis plays an important role in the progression of cervical cancer[ 74 ]. Wu et al. found that the methylation level (methylation score) of the SOX9 gene increased significantly with the severity of cervical squamous lesions. It suggests that SOX9 methylation is often involved in cervical cancer, and it can provide valuable molecular biomarkers for the early detection of cervical cancer[ 75 ]. Masuda et al. identified two new loci related to cervical cancer. Among them, RS140991990: A > G is located at the SOX9 locus, which also suggests the role of SOX9 in the pathogenesis of cervical cancer[ 76 ]. The HPV-16 long control region (LCR) was the most variable region of the HPV-16 genome and may play an important role in the persistence of the virus and the development of cervical cancer. In the study of Xi et al., it was found that the HPV-16 long control region (LCR) G7193T and G7521A mutations in southwestern China accounted for 100% of the total number of infections, and are expected to be located at the binding sites of FOXA1 and SOX9 [ 77 ]. All these studies suggest that the SOX9 gene plays an important role in cervical cancer and maybe a universal target for multiple cancers. This study had several limitations. First, the sample size we collected was not large enough, second, in vitro cellular experiments were not performed to demonstrate the role of SOX9 gene, and third, SOX9 , as a transcription factor, should have downstream targets. Conclusions In summary, the high expression of SOX9 is significantly related to the disease-free survival and overall survival of cervical cancer. The expression of SOX9 was higher than the cancer adjacent normal tissue. Therefore, the SOX9 gene can be a good indicator of cervical cancer patient prognosis. Declarations Authors contribution HC conceived the study, participated in the design, performed the statistical analyses, and drafted the manuscript. YT conceived the study, participated in the design,collected the samples, and helped to draft the manuscript. XP-C done QPCR. MY-H, QZ-Z, JJ-W and QX collected the samples. All authors read and approved the final manuscript. Acknowledgements We thank all the staff in the laboratory. Competing interests The authors declare that they have no competing interests. Availability of data and materials The data that support the findings of this study are openly available in TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and The Human Protein Atlas（https://www.proteinatlas.org/） Consent for publication Not applicable. Ethics approval and consent to participate The research design was approved by the Ethics Committee. Zhuzhou Central Hospital(No: ZZCHEC-2020080-01; August 19, 2020). All procedures are conducted in conformity to ethical standards. Funding This research received no specific grant from anyfunding agency in the public, commercial, or not-for-profit sectors. References Li W, Tian S, Wang P, et al. The characteristics of HPV integration in cervical intraepithelial cells. J Cancer . 2019;10(12):2783-2787. Published 2019 Jun 2. doi:10.7150/jca.31450 Jin Y, Zhou X, Yao X, Zhang Z, Cui M, Lin Y. 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J Invest Dermatol . 2018;138(8):1716-1725. doi:10.1016/j.jid.2018.01.040 Tsuda M, Fukuda A, Roy N, et al. The BRG1/SOX9 axis is critical for acinar cell-derived pancreatic tumorigenesis. J Clin Invest . 2018;128(8):3475-3489. doi:10.1172/JCI94287 Drivdahl R, Haugk KH, Sprenger CC, Nelson PS, Tennant MK, Plymate SR. Suppression of growth and tumorigenicity in the prostate tumor cell line M12 by overexpression of the transcription factor SOX9. Oncogene . 2004;23(26):4584-4593. doi:10.1038/sj.onc.1207603 Cai C, Wang H, He HH, et al. ERG induces androgen receptor-mediated regulation of SOX9 in prostate cancer. J Clin Invest . 2013;123(3):1109-1122. doi:10.1172/JCI66666 Guo X, Xiong L, Sun T, et al. Expression features of SOX9 associate with tumor progression and poor prognosis of hepatocellular carcinoma. Diagn Pathol . 2012;7:44. Published 2012 Apr 19. doi:10.1186/1746-1596-7-44 Yang X, Liang R, Liu C, et al. SOX9 is a dose-dependent metastatic fate determinant in melanoma. J Exp Clin Cancer Res . 2019;38(1):17. Published 2019 Jan 14. doi:10.1186/s13046-018-0998-6 Yu CC, Tsai LL, Wang ML, et al. miR145 targets the SOX9/ADAM17 axis to inhibit tumor-initiating cells and IL-6-mediated paracrine effects in head and neck cancer [published correction appears in Cancer Res. 2015 Jul 1;75(13):2761]. Cancer Res . 2013;73(11):3425-3440. doi:10.1158/0008-5472.CAN-12-3840 Zhang XD, Wang YN, Feng XY, Yang JY, Ge YY, Kong WQ. Biological function of microRNA-30c/SOX9 in pediatric osteosarcoma cell growth and metastasis. Eur Rev Med Pharmacol Sci . 2018;22(1):70-78. doi:10.26355/eurrev_201801_14102 Sakamoto H, Mutoh H, Miura Y, Sashikawa M, Yamamoto H, Sugano K. SOX9 Is Highly Expressed in Nonampullary Duodenal Adenoma and Adenocarcinoma in Humans. Gut Liver . 2013;7(5):513-518. doi:10.5009/gnl.2013.7.5.513 Matsushima H, Kuroki T, Kitasato A, et al. Sox9 expression in carcinogenesis and its clinical significance in intrahepatic cholangiocarcinoma. Dig Liver Dis . 2015;47(12):1067-1075. doi:10.1016/j.dld.2015.08.003 Wan YP, Xi M, He HC, et al. Expression and Clinical Significance of SOX9 in Renal Cell Carcinoma, Bladder Cancer and Penile Cancer. Oncol Res Treat . 2017;40(1-2):15-20. doi:10.1159/000455145 Jiang SS, Fang WT, Hou YH, et al. Upregulation of SOX9 in lung adenocarcinoma and its involvement in the regulation of cell growth and tumorigenicity. Clin Cancer Res . 2010;16(17):4363-4373. doi:10.1158/1078-0432.CCR-10-0138 Wang X, Ju Y, Zhou MI, Liu X, Zhou C. Upregulation of SOX9 promotes cell proliferation, migration and invasion in lung adenocarcinoma. Oncol Lett . 2015;10(2):990-994. doi:10.3892/ol.2015.3303 Huang JQ, Wei FK, Xu XL, et al. SOX9 drives the epithelial-mesenchymal transition in non-small-cell lung cancer through the Wnt/β-catenin pathway. J Transl Med . 2019;17(1):143. Published 2019 May 6. doi:10.1186/s12967-019-1895-2 Voronkova MA, Rojanasakul LW, Kiratipaiboon C, Rojanasakul Y. The SOX9-Aldehyde Dehydrogenase Axis Determines Resistance to Chemotherapy in Non-Small-Cell Lung Cancer. Mol Cell Biol . 2020;40(2):e00307-19. Published 2020 Jan 3. doi:10.1128/MCB.00307-19 Zhou CH, Ye LP, Ye SX, et al. Clinical significance of SOX9 in human non-small cell lung cancer progression and overall patient survival. J Exp Clin Cancer Res . 2012;31(1):18. Published 2012 Mar 3. doi:10.1186/1756-9966-31-18 Ma Y, Shepherd J, Zhao D, et al. SOX9 Is Essential for Triple-Negative Breast Cancer Cell Survival and Metastasis. Mol Cancer Res . 2020;18(12):1825-1838. doi:10.1158/1541-7786.MCR-19-0311 Pomp V, Leo C, Mauracher A, Korol D, Guo W, Varga Z. Differential expression of epithelial–mesenchymal transition and stem cell markers in intrinsic subtypes of breast cancer. Breast Cancer Res Treat . 2015;154(1):45-55. doi:10.1007/s10549-015-3598-6 Siu MKY, Jiang YX, Wang JJ, et al. Hexokinase 2 Regulates Ovarian Cancer Cell Migration, Invasion and Stemness via FAK/ERK1/2/MMP9/NANOG/SOX9 Signaling Cascades. Cancers (Basel) . 2019;11(6):813. Published 2019 Jun 12. doi:10.3390/cancers11060813 Raspaglio G, Petrillo M, Martinelli E, et al. Sox9 and Hif-2α regulate TUBB3 gene expression and affect ovarian cancer aggressiveness. Gene. 2014;542(2):173-181. doi:10.1016/j.gene.2014.03.037 Shang S, Yang J, Jazaeri AA, et al. Chemotherapy-Induced Distal Enhancers Drive Transcriptional Programs to Maintain the Chemoresistant State in Ovarian Cancer. Cancer Res. 2019;79(18):4599-4611. doi:10.1158/0008-5472.CAN-19-0215 Xiao S, Li Y, Pan Q, et al. MiR-34c/SOX9 axis regulates the chemoresistance of ovarian cancer cell to cisplatin-based chemotherapy. J Cell Biochem. 2019;120(3):2940-2953. doi:10.1002/jcb.26865 Liu R, Zhang Y, Sun P, Wang C. DDP-resistant ovarian cancer cells-derived exosomal microRNA-30a-5p reduces the resistance of ovarian cancer cells to DDP. Open Biol. 2020;10(4):190173. doi:10.1098/rsob.190173 Sherman-Samis M, Onallah H, Holth A, Reich R, Davidson B. SOX2 and SOX9 are markers of clinically aggressive disease in metastatic high-grade serous carcinoma. Gynecol Oncol. 2019;153(3):651-660. doi:10.1016/j.ygyno.2019.03.099 Gonzalez G, Mehra S, Wang Y, Akiyama H, Behringer RR. Sox9 overexpression in uterine epithelia induces endometrial gland hyperplasia. Differentiation . 2016;92(4):204-215. doi:10.1016/j.diff.2016.05.006 Li Y, Liu J, Piao J, Ou J, Zhu X. Circ_0109046 promotes the malignancy of endometrial carcinoma cells through the microRNA-105/SOX9/Wnt/β-catenin axis. IUBMB Life . 2021;73(1):159-176. doi:10.1002/iub.2415 Saegusa M, Hashimura M, Suzuki E, Yoshida T, Kuwata T. Transcriptional up-regulation of Sox9 by NF-κB in endometrial carcinoma cells, modulating cell proliferation through alteration in the p14(ARF)/p53/p21(WAF1) pathway. Am J Pathol . 2012;181(2):684-692. doi:10.1016/j.ajpath.2012.05.008 Li XM, Piao YJ, Sohn KC, et al. Sox9 is a β-catenin-regulated transcription factor that enhances the colony-forming activity of squamous cell carcinoma cells. Mol Med Rep . 2016;14(1):337-342. doi:10.3892/mmr.2016.5210 Feng C, Ma F, Hu C, et al. SOX9/miR-130a/CTR1 axis modulates DDP-resistance of cervical cancer cell. Cell Cycle . 2018;17(4):448-458. doi:10.1080/15384101.2017.1395533 Liu CQ, Chen Y, Xie BF, Li YL, Wei YT, Wang F. MicroRNA-215-3p suppresses the growth and metastasis of cervical cancer cell via targeting SOX9. Eur Rev Med Pharmacol Sci . 2019;23(13):5628-5639. doi:10.26355/eurrev_201907_18297 Wu JH, Liang XA, Wu YM, Li FS, Dai YM. Identification of DNA methylation of SOX9 in cervical cancer using methylated-CpG island recovery assay. Oncol Rep . 2013;29(1):125-132. doi:10.3892/or.2012.2077 Masuda T, Low SK, Akiyama M, et al. GWAS of five gynecologic diseases and cross-trait analysis in Japanese. Eur J Hum Genet . 2020;28(1):95-107. doi:10.1038/s41431-019-0495-1 Xi J, Chen J, Xu M, et al. Genetic variability and functional implication of the long control region in HPV-16 variants in Southwest China. PLoS One. 2017;12(8):e0182388. Published 2017 Aug 2. doi:10.1371/journal.pone.0182388 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-844099","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":49026227,"identity":"e8d6908c-57c9-43a7-8e95-5c4cfc756db9","order_by":0,"name":"huan Chen","email":"","orcid":"","institution":"Zhu Zhou Hospital Affiliated to Xiangya school of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"huan","middleName":"","lastName":"Chen","suffix":""},{"id":49026228,"identity":"b3b3472a-47c6-4873-876a-85374566142a","order_by":1,"name":"huan Chen","email":"","orcid":"","institution":"Zhu zhou Hospital Affilated to Xiangya schoolof medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"huan","middleName":"","lastName":"Chen","suffix":""},{"id":49026229,"identity":"81130f56-a5c9-488c-8a1b-de115a656d1c","order_by":2,"name":"Meiyuan Huang","email":"","orcid":"","institution":"Zhu Zhou Hospital Affiliated to Xiangya school of medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meiyuan","middleName":"","lastName":"Huang","suffix":""},{"id":49026230,"identity":"67084d19-c625-4486-81fa-e6af360acc56","order_by":3,"name":"Qunzhi Zhang","email":"","orcid":"","institution":"Xiangyang No 1 People's Hospital Affiliated to Hubei University of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qunzhi","middleName":"","lastName":"Zhang","suffix":""},{"id":49026231,"identity":"f1adb3e8-1568-4dfb-b420-945fe2bb0ef3","order_by":4,"name":"Qiong Xu","email":"","orcid":"","institution":"Zhu Zhou Hospital Affiliated to Xiangya school of medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Xu","suffix":""},{"id":49026232,"identity":"388e5c56-ceea-4021-9ccb-3965c3b80d2d","order_by":5,"name":"Jinjin Wang","email":"","orcid":"","institution":"Zhu Zhou Hospital Affilated to Xiangya school of medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinjin","middleName":"","lastName":"Wang","suffix":""},{"id":49026233,"identity":"c17d0d31-8b9d-44a6-9295-69e03d66f3a6","order_by":6,"name":"Yin Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACeYaDDQYfKmzk+NkbiNRi2Hj4QOGMM2nGkj0HiLXm8LGEz5xthxM33EggUgdj2xnDzQxngFpuPt54g6HGJpqgFnaeM8bGBRXpxjNvpxVbMBxLy20gaMuMM2bGM85Yy/bdzjGTYGw4TFgLw/035r9525gZG26eIVbLgWMJxrxtzooTbvAQqcWw4fABQ0ggA/2SQIxfkKLy8MYbH2psiHAYEjCQSCBFOUQLqTpGwSgYBaNgZAAAl9pJ3/1oFksAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6266-8768","institution":"Zhu Zhou Hospital Affiliated to Xiangya school of medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2021-08-25 04:51:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-844099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-844099/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12993757,"identity":"62d68c06-95f8-48a0-b55b-7e22368bf794","added_by":"auto","created_at":"2021-09-01 22:55:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57329,"visible":true,"origin":"","legend":"A flowchart for analysis.GO Gene Ontology annotation, KEGG the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, PPI protein–protein interaction","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/8aca14d6da7182a9d1b51f1c.png"},{"id":12993756,"identity":"473e3b78-dd70-4c76-ab82-bbccd6747bfa","added_by":"auto","created_at":"2021-09-01 22:55:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27101,"visible":true,"origin":"","legend":"Determination of soft thresholds and testing of scale-free networks\nA shows the correlation between log( K) and log[P( k)] corresponding to different soft thresholds. The higher the coefficient, the more the network conforms to the distribution of the scale-free network.\nB denotes the mean value of gene neighbor coefficients in the gene network corresponding to different soft thresholds, which reflects the average connectivity level of the network.\nC distribution of the connectivity of each node in the network; \nD The scatter plot of log( K) vs.log[ P( k) ], the linear regression results show that the correlation coefficient is 0.92","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/128054c114bca1f6985a340e.png"},{"id":12993960,"identity":"50f6194d-9d6a-4e2d-acdb-129e73641986","added_by":"auto","created_at":"2021-09-01 22:58:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186143,"visible":true,"origin":"","legend":"Classification of gene clustering trees by dynamic tree cut\nA total of 5 gene modules were obtained, with different colors indicating different gene modules.The gray color indicates the genes that do not belong to any known module.","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/a89246e14a4d77908bcdfb3b.png"},{"id":12993758,"identity":"7e2f08d6-6698-4ffa-a554-a8b0477156e1","added_by":"auto","created_at":"2021-09-01 22:55:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":173058,"visible":true,"origin":"","legend":"The relationship between the module traits and the module significance\nThe turquoise module has the highest correlation with the diagnosis level (r = 0.65, p = 3e-38)","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/bf617eb4333853ed0bd382f6.png"},{"id":12993762,"identity":"d818b076-9542-4e20-8f8d-580344ed20d6","added_by":"auto","created_at":"2021-09-01 22:55:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173570,"visible":true,"origin":"","legend":"GO and KEGG pathway analysis\nA shows BP of GO, B shows CC of GO, C shows MC of GO, D shows the KEGG pathway of the turquoise module significantly enriches pathways related to cancer and cell circle","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/2c8bf2a478564f9b151d91ea.png"},{"id":12993760,"identity":"56a508fa-c106-4a22-8621-d6dff14279eb","added_by":"auto","created_at":"2021-09-01 22:55:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59797,"visible":true,"origin":"","legend":"Association between SOX9 gene expression and overall survival, disease-free survival of cervical cancer patients in TCGA\nA shows low SOX9 gene expression patient had high over all survive rate(P=0.038)\nB shows low SOX9 gene expression patient had high disease-free survival(P=0.049)","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/360c095dc5c315ec8a31740e.png"},{"id":12993761,"identity":"25aeeb91-7ad7-4596-933a-b0e0c5be51ff","added_by":"auto","created_at":"2021-09-01 22:55:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":618432,"visible":true,"origin":"","legend":"SOX9 immunohistochemical images of normal cervical and cervical cancer tissues on the HPA website\nA normal cervix, B cervical cancer ","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/a86fd8ffa4a333c4e972e1e7.png"},{"id":12993763,"identity":"b0c961e3-c4d5-4f0b-9364-ec776c808f32","added_by":"auto","created_at":"2021-09-01 22:55:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5315,"visible":true,"origin":"","legend":"Quantitative RT-PCR for cancer and adjacent normal cervix tissue\nQuantitative RT-PCR assay showed significantly increased SOX9 mRNA level in cervical cancer tissues compared with adjacent normal cervix tissues(p=0.031)\n","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/01fb0843338674b7dade3755.png"},{"id":13712398,"identity":"769d0bb9-6890-4259-8f8d-2dbefa039f62","added_by":"auto","created_at":"2021-09-17 14:28:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1738955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-844099/v1/1467272e-005f-42cd-927c-81f664df8b7a.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIdentification of Hub Gene in Cervical Cancer by Weighted Gene Co-Expression Network Analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer(CC) is one of the most common malignant tumors in gynecology. Its incidence is second only to breast cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and it has increased significantly worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Both its incidence and mortality are high[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The global incidence rate was estimated to be 570,000 cases and nearly 311,000 deaths in 2018[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In China, CC is also the second most common malignant tumor in gynecology. The incidence and mortality of cervical cancer in young women are increasing[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The pathogenesis of CC is diverse, including infection with human papillomavirus (HPV), herpes simplex virus type 2 (HSV-2), cervical thymus (CT), and other bad habits[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although the pathogenesis of CC is complicated, HPV is an important reason for the development of cervical cancer, which has been widely accepted[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. CC is also one of the most preventable cancers[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The persistent infection with high-risk HPV is closely related to the development of CC. So HPV vaccination can effectively prevent it[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As a secondary prevention effort, early screening for CC and precursor lesions can also reduce the incidence and mortality of CC[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite advances in screening, diagnosis, prevention, and treatment, CC is still one of the leading causes of cancer-related death in women[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, in the pathogenesis of CC, the exact molecular mechanism is still unclear[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It may be a multi-gene, multi-factor, multi-step, and multi-stage complex process.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hence, the pathogenesis and molecular mechanism of CC are the keys to effective treatment of it.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, bioinformatics has been widely used at the genomic level of many cancer types to reveal the internal mechanisms of tumor progression and canceration [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Bioinformatics methods combine biology, mathematics, and computer science to further promote the molecular mechanism explanation and discovery of tumor-related diagnostic markers[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Gene expression analysis is an important part of molecular biology research[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The identification of differentially expressed genes (DEGs) under different conditions is an important goal of microarray-based gene expression analysis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Weighted Gene Co-expression Network Analysis (WGCNA) is a new tool to explore potentially related modules in expression data[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. WGCNA recognizes the correlation between genes through microarray samples, so it can detect clusters of highly related genes (module)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. On the other hand, DEGs from the TCGA database are also used for WGCNA[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The Cancer Genome Atlas (TCGA) project is a collaborative initiative to use existing large-scale genome-wide technologies to better understand cancer[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It is a publicly available data set that provides various types of genomic data[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. At the same time, it provides detailed clinical information[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, the TCGA database is widely used in oncology research, providing useful information for the discovery of new tumor biological indicators and drug targets[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we performed a weighted gene co-expression analysis based on the different analysis results of the TCGA Cervical squamous cell carcinoma and endocervical adenocarcinoma(CESC) dataset and tried to find key gene in the development of cervical cancer .\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscription level data of cervical samples and complete clinical data sets were obtained from TCGA (https://portal.gdc.cancer.gov/). The study collected a total of 306 CESC RNA sequencing data samples and survival follow-up time and 3 adjacent normal tissue sequencing data samples. The workflow is shown in Figure1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening for differentially expressed genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genome-wide transcriptional expression profile was obtained from TCGA\u0026apos;s RNA-seq\u0026nbsp;Counts\u0026nbsp;data. The limma[26]\u0026nbsp;software package of R version 3.6.3 (http://www.r-project.org)\u0026nbsp;was used to screen differentially expressed genes.\u0026nbsp;Log2 (fold change)\u0026ge;2.00 and false discovery rate (FDR)\u0026lt;0.01 were considered to indicate DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-expression analysis of DEGs in CESC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGCNA analysis is a widely used method in systems biology and is designed for multivariate data (ie gene expression, DNA methylation, metabolites, etc.)[27]. It can reveal the correlation of genes and look for significantly related gene modules. In this study, we used the \u0026quot;WGCNA\u0026quot; program package [28,29] to construct a co-expression network based on the results of the previous differential expression analysis to analyze potential genes in CC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of clinically significant modules\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo methods were used to determine modules related to clinical features. According to the linear regression between clinical traits and gene expression, the log10 conversion of P value was defined as gene significance (GS), and then the module significance (MS) was calculated using the average value of all genes in one module. It was usually considered that the module with the largest absolute value of MS was the module that has the closest relationship with clinical characteristics. Module exigencies (MEs) were used as the main components of gene modules and clinical traits to determine relevant modules. Select modules that were highly relevant to a given clinical feature for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-Protein Interaction (PPI) Network Establishment and Hub Gene Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse STRING protein database 11.0 (http://string-db.org/) to construct a PPI network based on genes in significant modules. Next, export and import the results of the string database into the Cytoscape software [30]. The cytohubba plug-in is used to find the Hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotation and pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the function of genes in significant modules, we used David[31,32] online analysis tool to perform go and KEGG enrichment analysis on the genes in the significant module. Then downloaded the enrichment results, and used the R language to map the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHub genes basic expression in normal and cancer tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe further verification and survival analysis of these hub genes were performed by using the gene expression profile interactive analysis (GEPIA) database (http://gepia.cancer-pku.cn/index.html; Z.Tang et al., 2017). We drew the overall survival curve and Disease-Free Survival curve of the hub genes in the GEPIA database, with a p-value of \u0026lt;0.05. Meanwhile, we use the Human Protein Atlas database (https://www.proteinatlas.org/) to verify the protein expression of the hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom October 2020 to January 2021, 16 samples of cervical squamous cell carcinomas and adjacent normal tissues were collected from Zhuzhou Hospital affiliated with Xiangya Medical College. All specimens were assessed by immunohistochemistry and confirmed by two independent pathologists. At the same time, fresh tissue specimens of the corresponding patients were collected and stored in an ultra-low temperature refrigerator for Q-PCR. The study was approved by the Research and Clinical Trial Ethics Committee of Zhuzhou Hospital, and all eligible participants provided written informed consent. All clinical procedures are carried out in compliance with the ethical standards of the \u0026quot;Declaration of Helsinki\u0026quot; guidelines and relevant Chinese policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of hub gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter obtaining the hub gene through the co-expression network, we verified the hub gene by Quantitative real-time polymerase chain reaction (Q-PCR). Total RNA was extracted using RNAiso Plus (9109; Takara, Dalian, China). To obtain cDNA, the Hifair\u0026reg; Ⅱ 1st Strand cDNA Synthesis Kit (11119ES60; Yeasen Biotechnology, Shanghai, China) was used in 1 \u0026mu;g of total RNA. Use the following system for mRNA amplification: 85\u0026deg;C for 5 minutes, then 40 cycles (95\u0026deg;C for 10 s, 60\u0026deg;C for 30 s). ACTB was employed as an internal control for mRNA evaluation. To standardize \u003cem\u003eSOX9\u003c/em\u003e gene expression, ACTB expression levels were assessed as housekeeping genes and comparative CT(2\u003csup\u003e\u0026minus;\u0026Delta;Ct\u003c/sup\u003e)methods were used for the analysis. Table1 show the primers of SOX9 and ACTB\u003c/p\u003e\n\u003cp\u003eTable1 The primers of SOX9 and ACTB\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.413597733711047%\"\u003e\n \u003cp\u003ePrimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.69121813031161%\"\u003e\n \u003cp\u003eSequence（5\u0026apos;-3\u0026apos;）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.89518413597734%\"\u003e\n \u003cp\u003eProduct（bp）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSOX9-F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCCCGCTCACAGTACGACTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSOX9-R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCTGAGCGGGGTTCATGTAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eACTB-F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAGACCTGTACGCCAACACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eACTB-R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCGCTCAGGAGGAGCAATGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression in TCGA CESC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the threshold of false discovery rate (FDR)\u0026lt;0.01 and |log2(FC) \u0026ge;2, a total of 1265 DEGs were identified between 306 CESC samples and 3 normal samples. Select all DEGs for subsequent co-expression network construction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Co-expression Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform a co-expression network analysis using 306 CESC samples in TCGA. Use the \u0026quot;WGCNA\u0026quot; R package to analyze the previous DEGs. Choose \u0026beta; = 7 (ratio R2 = 0.92) as the soft threshold power (Figure 2A\u0026amp;B). Five modules were identified, the minimum size (genome) of the gene tree diagram was 30, and the tangent line of the module tree diagram was 0.25.(Figure3)\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eIdentification of Clinically Significant Module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCombining the relationship between the module traits and the module significance, we finally determined that the turquoise module is the most relevant module for tumor diagnosis. The turquoise module has the highest correlation with the diagnosis level (r = 0.65, p = 3e-38, Figure 4). Therefore, we choose the turquoise module as the interest module and analyze it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO and KEGG Pathway\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 384 genes in the turquoise module are enriched in gene ontology (GO) and KEGG pathway analysis by using the ClusterProfifiler and the org.Hs.eg.db packages.\u003c/p\u003e\n\u003cp\u003eThe biological process of the turquoise module focuses on cell proliferation, cell adhesion, and protein binding processes(P\u0026lt;0.05). The KEGG pathway of the turquoise module significantly enriches pathways related to cancer and cell circle(Figure5A,B,C,D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI Network Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the hub gene, a protein-protein interaction (PPI) network was constructed. The PPI network is visualized by Cytoscape. Use the cytohubba application in Cytoscape to estimate the node degree. The 10 most important node degree genes are selected as pivot nodes because they play a vital role in the progress of CC. The selected genes were \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eKRT5\u003c/em\u003e, \u003cem\u003eSOX2\u003c/em\u003e, \u003cem\u003eKRT8\u003c/em\u003e, \u003cem\u003eCAV1\u003c/em\u003e, \u003cem\u003eEPCAM\u003c/em\u003e, \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eKRT14\u003c/em\u003e, \u003cem\u003eARF6\u003c/em\u003e, \u003cem\u003eSOX9\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of Hub Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 10 most important node degree genes, we found that Univariate analysis (P\u0026lt;0.05) showed that the expression level of \u003cem\u003eSOX9\u003c/em\u003e was significantly related to the OS and PFS of CC patients, Low expression of \u003cem\u003eSOX9\u003c/em\u003e may lead to higher survival rates and disease-free survival(Figure6A\u0026amp;B). It is worth noting that in CC tissues, the expression of this gene is significantly higher than that of normal tissues. As shown in the figure, in cervical cancer specimens, the protein level of \u003cem\u003eSOX9\u003c/em\u003e is significantly higher than that of normal tissues in the HPA database(Figure7A\u0026amp;B). In addition, the results of cervical cancer Q-PCR showed that in cervical cancer tissues, the expression of \u003cem\u003eSOX9\u003c/em\u003e at mRNA levels was higher than that in adjacent normal tissues(Figure8).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we combined the TCGA databases for differential expression analysis and analyzed the DEGs by WGCNA. We found that the turquoise module genes are closely related to the diagnosis of cervical cancer. After analyzing the genes in the module was by PPI network, we used the Cytoscape software to calculate the results of the PPI network analysis and obtained 10 particularly important genes. Among them, \u003cem\u003eSOX9\u003c/em\u003e is related to the overall survival rate and disease-free survival of patients with cervical cancer, and the overall survival rate and disease-free survival of patients with low expression are higher than high expression patients.\u003c/p\u003e \u003cp\u003eThe official name of \u003cem\u003eSOX9\u003c/em\u003e is SRY-box transcription factor 9, located at 17q24.3. The protein encoded by the gene recognizes the sequence CCTTGAG with other members of the HMG-BOX DNA binding protein. It plays a role in the differentiation of chondrocytes and with steroid factor 1 to regulate the transcription of anti-Mullerian hormone (AMH) genes. Its defects lead to skeletal malformation syndrome and dysplasia, which is often reversed with sex. \u003cem\u003eSOX9\u003c/em\u003e has many functions. It is part of the nucleus, nucleoplasm, protein-containing complex, transcription regulator complex, and chromatin. It can enhance DNA-binding transcription factor activity (RNA polymerase II-specific), DNA-binding transcription activator activity (RNA polymerase II-specific), protein binding, DNA-binding transcription factor activity, cis-regulatory region sequence-specific DNA binding, RNA polymerase II cis-regulatory region sequence-specific DNA binding, pre-mRNA intronic binding, beta-catenin binding, sequence-specific DNA binding, bHLH transcription factor binding, protein kinase A catalytic subunit binding, chromatin binding, sequence-specific double-stranded DNA binding. It has also participated in many processes including positive regulation of epithelial cell proliferation, positive regulation of epithelial cell migration, and positive regulation of cell population proliferation.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cem\u003eSOX9\u003c/em\u003e plays an important role in many tumor types. For example, pediatric\u003c/p\u003e \u003cp\u003ePendymoma[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], sporadic pheochromocytoma[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], Solid pseudopapillary tumours (SPT)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e],et al[\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. It also plays an important role in the three major gynecological malignancies.\u003c/p\u003e \u003cp\u003eIn the studies of ovarian cancer, HK2, which is regulated by the tumor microenvironment, controls the production of lactic acid and regulates the expression of MMP9/NAONG/SOX9 through the FAK/ERK1/2 signaling pathway, which leads to ovarian cancer metastasis and stem regulation[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. \u003cem\u003eSOX9\u003c/em\u003e activates βIII-tubulin expression to enable ovarian cancer cells to survive under hypoxic conditions, and its abnormal expression is particularly prominent in patients with aggressive OC[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. As a key super-enhanced regulatory transcription factor, it plays a key role in acquiring and maintaining the regulation of chemical resistance in OC[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], \u003cem\u003eSOX9\u003c/em\u003e is highly expressed in OC cell lines, and overexpression of \u003cem\u003eSOX9\u003c/em\u003e can enhance the resistance of ovarian cancer cells to DDP chemical drugs [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Down-regulated the expression of \u003cem\u003eSOX9\u003c/em\u003e through exosomal MicroRNA-30a-5p could reduce the resistance of ovarian cancer cells to DDP[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Compared with high-grade serous ovarian tumor effusions and solid metastases, \u003cem\u003eSOX9\u003c/em\u003e is more expressed in the latter, Univariate (P\u0026thinsp;=\u0026thinsp;0.04) and multivariate (P\u0026thinsp;=\u0026thinsp;0.049) analysis, higher \u003cem\u003eSOX9\u003c/em\u003e levels are associated with shorter overall survival rates, and the level of \u003cem\u003eSOX9\u003c/em\u003e mRNA in HGSC efusion may be a marker of clinically aggressive diseases[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn the study of endometrial cancer, Gabriel Gonzalez et al. found that \u003cem\u003eSOX9\u003c/em\u003e in the uterine epithelium can induce the development of endometrial hyperplasia. Therefore, \u003cem\u003eSOX9\u003c/em\u003e expression can be the formation of endometrial cancer[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Circ_0109046 up-regulates \u003cem\u003eSOX9\u003c/em\u003e expression through sponging MiR105, leading to activation of Wnt/β-catenin signaling and EC malignant growth[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. \u003cem\u003eSOX9\u003c/em\u003e expression also showed a significant gradual increase from normal to grade 1 to grade 2/3 tumors[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSOX9\u003c/em\u003e can enhance the colony-forming activity of squamous cell carcinoma cells[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Several studies on the \u003cem\u003eSOX9\u003c/em\u003e gene in cervical cancer have confirmed that it plays a role in cervical cancer. Feng et al. found that the SOX9/miR-130a/ctr1 axis can regulate the chemoresistance of cervical cancer cells to cisplatin[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Liu et al. found that miR-215-3p was significantly lower in cervical cancer tissue than normal cervical tissue, and \u003cem\u003eSOX9\u003c/em\u003e, as a direct target of MiR-215-3P, was negatively correlated with miR-215-3p. It suggests that the miR-215-3p/SOX9 axis plays an important role in the progression of cervical cancer[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Wu et al. found that the methylation level (methylation score) of the \u003cem\u003eSOX9\u003c/em\u003e gene increased significantly with the severity of cervical squamous lesions. It suggests that \u003cem\u003eSOX9\u003c/em\u003e methylation is often involved in cervical cancer, and it can provide valuable molecular biomarkers for the early detection of cervical cancer[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Masuda et al. identified two new loci related to cervical cancer. Among them, RS140991990: A\u0026thinsp;\u0026gt;\u0026thinsp;G is located at the \u003cem\u003eSOX9\u003c/em\u003e locus, which also suggests the role of \u003cem\u003eSOX9\u003c/em\u003e in the pathogenesis of cervical cancer[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. The HPV-16 long control region (LCR) was the most variable region of the HPV-16 genome and may play an important role in the persistence of the virus and the development of cervical cancer. In the study of Xi et al., it was found that the HPV-16 long control region (LCR) G7193T and G7521A mutations in southwestern China accounted for 100% of the total number of infections, and are expected to be located at the binding sites of \u003cem\u003eFOXA1\u003c/em\u003e and \u003cem\u003eSOX9\u003c/em\u003e[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. All these studies suggest that the \u003cem\u003eSOX9\u003c/em\u003e gene plays an important role in cervical cancer and maybe a universal target for multiple cancers.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, the sample size we collected was not large enough, second, in vitro cellular experiments were not performed to demonstrate the role of \u003cem\u003eSOX9\u003c/em\u003e gene, and third, \u003cem\u003eSOX9\u003c/em\u003e, as a transcription factor, should have downstream targets.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the high expression of \u003cem\u003eSOX9\u003c/em\u003e is significantly related to the disease-free survival and overall survival of cervical cancer. The expression of \u003cem\u003eSOX9\u003c/em\u003e was higher than the cancer adjacent normal tissue. Therefore, the \u003cem\u003eSOX9\u003c/em\u003e gene can be a good indicator of cervical cancer patient prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHC conceived the study, participated in the design, performed the statistical analyses, and drafted the manuscript. YT conceived the study, participated in the design,collected the samples, and helped to draft the manuscript. XP-C done QPCR. MY-H, QZ-Z, JJ-W and QX collected the samples. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the staff in the laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and The Human Protein Atlas（https://www.proteinatlas.org/）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research design was approved by the Ethics Committee. Zhuzhou Central Hospital(No: ZZCHEC-2020080-01; August 19, 2020). All procedures are conducted in conformity to ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from anyfunding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLi W, Tian S, Wang P, et al. The characteristics of HPV integration in cervical intraepithelial cells. \u003cem\u003eJ Cancer\u003c/em\u003e. 2019;10(12):2783-2787. Published 2019 Jun 2. doi:10.7150/jca.31450\u003c/li\u003e\n \u003cli\u003eJin Y, Zhou X, Yao X, Zhang Z, Cui M, Lin Y. MicroRNA-612 inhibits cervical cancer progression by targeting NOB1. \u003cem\u003eJ Cell Mol Med\u003c/em\u003e. 2020;24(5):3149-3156. doi:10.1111/jcmm.14985\u003c/li\u003e\n \u003cli\u003eLi C, Ao H, Chen G, Wang F, Li F. 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Transcriptional up-regulation of Sox9 by NF-\u0026kappa;B in endometrial carcinoma cells, modulating cell proliferation through alteration in the p14(ARF)/p53/p21(WAF1) pathway. \u003cem\u003eAm J Pathol\u003c/em\u003e. 2012;181(2):684-692. doi:10.1016/j.ajpath.2012.05.008\u003c/li\u003e\n \u003cli\u003eLi XM, Piao YJ, Sohn KC, et al. Sox9 is a \u0026beta;-catenin-regulated transcription factor that enhances the colony-forming activity of squamous cell carcinoma cells. \u003cem\u003eMol Med Rep\u003c/em\u003e. 2016;14(1):337-342. doi:10.3892/mmr.2016.5210\u003c/li\u003e\n \u003cli\u003eFeng C, Ma F, Hu C, et al. SOX9/miR-130a/CTR1 axis modulates DDP-resistance of cervical cancer cell. \u003cem\u003eCell Cycle\u003c/em\u003e. 2018;17(4):448-458. doi:10.1080/15384101.2017.1395533\u003c/li\u003e\n \u003cli\u003eLiu CQ, Chen Y, Xie BF, Li YL, Wei YT, Wang F. MicroRNA-215-3p suppresses the growth and metastasis of cervical cancer cell via targeting SOX9. \u003cem\u003eEur Rev Med Pharmacol Sci\u003c/em\u003e. 2019;23(13):5628-5639. doi:10.26355/eurrev_201907_18297\u003c/li\u003e\n \u003cli\u003eWu JH, Liang XA, Wu YM, Li FS, Dai YM. Identification of DNA methylation of SOX9 in cervical cancer using methylated-CpG island recovery assay. \u003cem\u003eOncol Rep\u003c/em\u003e. 2013;29(1):125-132. doi:10.3892/or.2012.2077\u003c/li\u003e\n \u003cli\u003eMasuda T, Low SK, Akiyama M, et al. GWAS of five gynecologic diseases and cross-trait analysis in Japanese. \u003cem\u003eEur J Hum Genet\u003c/em\u003e. 2020;28(1):95-107. doi:10.1038/s41431-019-0495-1\u003c/li\u003e\n \u003cli\u003eXi J, Chen J, Xu M, et al. Genetic variability and functional implication of the long control region in HPV-16 variants in Southwest China. PLoS One. 2017;12(8):e0182388. Published 2017 Aug 2. doi:10.1371/journal.pone.0182388\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bioinformatics analysis, differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA)，cervical cancer","lastPublishedDoi":"10.21203/rs.3.rs-844099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-844099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eCervical cancer(CC) is one of the most common malignant tumors in gynecology. Both its incidence and mortality are high. Despite advances in screening, diagnosis, prevention, and treatment, CC is still one of the leading causes of cancer-related death in women. However, in the pathogenesis of CC, the exact molecular mechanism is still unclear. It may be a multi-gene, multi-factor, multi-step, and multi-stage complex process. Hence, the pathogenesis and molecular mechanism of CC are the keys to effective treatment of it. In this study, we tried to identify candidate biomarkers for cervical cancer through weighted gene co-expression network analysis (WGCNA). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe gene expression profile of CESC was downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by the Limma package, and the gene co-expression module was constructed by WGCNA. Use the online website STRING to construct the protein interaction network of genes in significant modules, and then use Cytoscape analysis to find the 10 most important node degree genes. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong these 10 genes, \u003cem\u003eSOX9\u003c/em\u003e expression was associated with the prognosis of cervical cancer patients. Immunohistochemical results from the online website human protein atlas showed that \u003cem\u003eSOX9\u003c/em\u003e protein expression was significantly higher in cervical cancer tissues than in normal cervical tissues. After collecting cancerous and paracancerous tissue specimens from 16 patients with cervical cancer, Q-PCR showed that the mRNA expression levels of \u003cem\u003eSOX9\u003c/em\u003e in cervical cancer tissues were significantly greater than those in normal adjacent tissues. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe elevated expression of \u003cem\u003eSOX9\u003c/em\u003e is significantly related to the disease-free survival and overall survival of cervical cancer. Therefore, the \u003cem\u003eSOX9\u003c/em\u003e gene could be used as an indicator of cervical cancer diagnosis and prognosis.\u003c/p\u003e","manuscriptTitle":"Identification of Hub Gene in Cervical Cancer by Weighted Gene Co-Expression Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-01 22:55:39","doi":"10.21203/rs.3.rs-844099/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ef7c51e-1348-4805-854c-2c3aa3a128e3","owner":[],"postedDate":"September 1st, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6878428,"name":"Oncology"}],"tags":[],"updatedAt":"2021-09-01T22:55:40+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-01 22:55:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-844099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-844099","identity":"rs-844099","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}