Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced HCC

Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced HCC | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced HCC Anyin Yang, Jianping Liu, Mengru Li, Hong Zhang, Xulei Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4117465/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 Hepatocellular carcinoma (HCC), as a malignant tumor, is expected to become the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths globally by 2018. It is estimated that approximately 50–80% of HCC cases worldwide are caused by hepatitis b virus (HBV) infection, and other pathogenic factors have been shown to promote the development of HCC when coexisting with HBV. Understanding the molecular mechanisms of HBV-induced hepatocellular carcinoma (HBV-HCC) is crucial for the prevention, diagnosis, and treatment of the disease. In this study, we analyzed the molecular mechanisms of HBV-induced HCC by combining bioinformatics and deep learning methods. Firstly, we collected a gene set related to HBV-HCC from the GEO database, performed differential analysis and WGCNA analysis to identify genes with abnormal expression in tumors and high relevance to tumors. We used three deep learning methods, Lasso, random forest, and SVM, to identify key genes RACGAP1, ECT2, and NDC80. By establishing a diagnostic model, we determined the accuracy of key genes in diagnosing HBV-HCC. In the training set, RACGAP1 (AUC: 0.976), ECT2 (AUC: 0.969), and NDC80 (AUC: 0.976) showed high accuracy. They also exhibited good accuracy in the validation set: RACGAP1 (AUC: 0.878), ECT2 (AUC: 0.731), and NDC80 (AUC: 0.915). The key genes were found to be highly expressed in liver cancer tissues compared to normal liver tissues, and survival analysis indicated that high expression of key genes was associated with poor prognosis in liver cancer patients. This suggests a close relationship between key genes RACGAP1, ECT2, and NDC80 and the occurrence and progression of HBV-HCC. Molecular docking results showed that the key genes could spontaneously bind to the anti-hepatocellular carcinoma drugs Lenvatinib, Regorafenib, and Sorafenib with strong binding activity. Therefore, ECT2, NDC80, and RACGAP1 may serve as potential biomarkers for the diagnosis of HBV-HCC and as targets for the development of targeted therapeutic drugs. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatocellular carcinoma is a malignant tumor, and significant progress has been made in the epidemiology, risk factors, and molecular characteristics of HCC in many countries around the world over the past few decades. However, in many countries worldwide, the incidence and cancer-specific mortality rate of HCC continue to increase(Fitzmaurice et al., 2017). The main risk factors for hepatocellular carcinoma include chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), aflatoxin-contaminated food, heavy alcohol consumption, and type 2 diabetes. Chronic infection with hepatitis B virus (HBV) is considered a major risk factor for the occurrence and progression of HCC, accounting for more than half of global HCC cases(Jia et al., 2020). In patients with hepatitis B, the incidence of hepatocellular carcinoma increases with viral load, duration of infection, and severity of liver disease(C. J. Chen et al., 2006). Numerous studies have shown that the presence of other pathogenic factors in conjunction with HBV can increase the incidence of hepatocellular carcinoma(Jiang et al., 2021). HBV can increase the genomic instability of host cells, leading to epigenetic reshaping of host DNA, chromosomal reshaping, and abnormal expression of oncogenes and tumor suppressor genes through integration or induction of host gene mutations. It can also induce malignant transformation of liver cells by activating various cancer-related signaling pathways, regulating cell metabolism, and other mechanisms. The liver microenvironment undergoes changes induced by chronic inflammation and interactions between the virus and innate immune cells, adaptive immune cells, helping the virus evade immune surveillance and promoting the progression of the disease from inflammation to tumor formation(Jiang et al., 2021). As a high-risk factor for inducing HCC, HBV influences the occurrence and progression of tumors. Therefore, further research on the molecular mechanisms of HBV infection-induced HCC can help improve the prevention, diagnosis, and treatment of HCC. With the continuous development of computer technology, artificial intelligence (AI) is also becoming increasingly mature. Machine learning, as a branch of AI, focuses on using mathematical algorithms to identify patterns in data for prediction. Deep learning, as a subfield of machine learning, specifically utilizes multi-layer neural network algorithms inspired by the structure of the brain for prediction(LeCun et al., 2015). Due to the increasing availability and integration of various types of data such as genomics, transcriptomics, and pathology, cancer treatment is shifting towards precision medicine. Deep learning models have the potential to identify relevant granular features from multiple data types. Deep learning is being applied in the diagnosis, prognosis, and treatment of tumors, providing meaningful insights(Kleppe et al., 2021). In this study, we aim to analyze the pathogenic mechanisms of HBV-induced HCC using a combination of bioinformatics and deep learning methods. We seek to identify valuable diagnostic biomarkers and hope to provide new insights for the diagnosis and treatment of HBV-induced HCC. Methods Differentially Expressed Gene Screening Retrieve gene chip data with the keyword "HBV-HCC" from the National Center for Biotechnology Information (NCBI) public Gene Expression Omnibus (GEO) platform ( https://www.ncbi.nlm.nih.gov/geo/ ), download data with a sample size greater than 100, filter differentially expressed genes using the R software by reading the downloaded matrix file, analyze the tumor group and control group using the "limma" package, obtain differentially expressed genes (DEGs), and screen DEGs with criteria: |logFC|>2, P < 0.05. Functional Enrichment Analysis GO function enrichment analysis and KEGG pathway enrichment analysis were performed using the ' clusterProfiler ' package of R language to discover the biological functions and pathways that DEGS may be involved in. Both of them used P < 0.05 as the screening index. WGCNA co-expression analysis The 'WGCNA 'R package in R language was used to locate the co-expressed genes in the HBV-HCC dataset.The sample clustering tree algorithm is used to eliminate outlier samples, and the pick Soft Threshold function is used to select the best soft threshold β to ensure the construction of the scale-free network. The blockwiseModules function in the WGCNA package is used to construct the co-expression matrix. The merging threshold of similar modules is set to 0.25 (mergeCutHeight = 0.25), the topological overlap matrix (TOM) is deepSplit = 1, and the minimum number of genes in each module is set to 30 (minModuleSize = 30). Other parameters are set according to the default setting. The samples in the data set were divided into control group and HBV-HCC group, and the modules with high correlation with tumors were screened out. Screening and verification of diagnostic markers The intersection genes of DEGS and WGCNA modules with high tumor correlation were screened out. Lasso regression, random forest and SVM-RF were used to screen the variables of the intersection genes, and the intersection of the variables screened by the three algorithms was used as a preliminary diagnostic biomarker. The relationship between biomarkers and the survival and prognosis of patients with liver cancer was analyzed in the GEPIA2 database. The 'RMS' package was used to construct a nomogram model for the diagnosis of HBV-HCC based on diagnostic biomarkers, and the clinical decision curve (DCA) was drawn. The ROC curve of diagnostic biomarkers was calculated to analyze the accuracy of prediction. Results Differential gene screening results Download gene array datasets GSE121248 and GSE55092 from the National Center for Biotechnology Information (NCBI) public Gene Expression Omnibus (GEO) platform ( https://www.ncbi.nlm.nih.gov/geo/ ). After merging the datasets, a total of 119 samples of HBV-HCC and 128 control samples were obtained. The merged dataset was then used as a training set for gene differential analysis, resulting in 133 downregulated genes and 64 upregulated genes. Subsequently, the dataset containing HBV-HCC-related data, GSE47197, was used as a validation set (Table 1 ). Table 1 GEO array data information Group Data Series Platforms Normal VS Tumor Training Data GSE121248 GSE55092 GPL570 128 VS 119 Validation Data GSE47197 GPL16699 63 VS 61 Table 2 List of differential genes Status Gene Symbol Up CAP2、RACGAP1、HMMR、TOP2A、NDC80、MELK、ASPM、ECT2、PRC1、ROBO1、FAM72A 、BUB1B、CDK1、CCNB1、BC017398、GPR158、FAM83D、KIF20A、RRM2、DNAJC6、PBKDTL、NCAPG、GINS1、NEK2、RBM24、E2F7、TTK、CDC20、DUXAP10、LOC344887、 ZIC2、NUF2、COL15A1、TRIM16、CR936796、CRNDE、SULT1C2、 GPC3、CD109、FAM133A、AK093362、SMPX、NRCAM、FGF13、 SSX1、LOC101930288、MAGEA1、CTNNA2、SPINK1、AKR1B10、 LCN2、GABBR1、REG3A、COX7B2、MAGEA12、DKK1、MAGEA3、 LOC100506403、MAGEA6、ALDH3A1、LINC01419、GAGE1、GAGE12B Down CLEC1B、FCN2、OIT3、CLEC4M、GPR128、CLEC4G、CXCL14、CYP26A1、CRHBP、LINC01093、RSPO3、FCN3 PLAC8、CDHR2、CCBE1、SLC25A47、CXCL12、FAM65C、LCAT MARCO、KCNN2、 HAMP、CETP、GPM6A、CNDP1、TTC36、NPY1R、CYP39A1、 RND3、CYP1A2、FOS、C8orf4、OLFML3、CD5L、HGF、GADD45B、IGFBP3、ESR1、IDO2、ZG16、FBP1、KMO、ASPA、 IGHM、CA2、GCH1、SRPX、FOSB、NAT2、TBX15、ID1、HAO2、 MT1F、CYP2B6、C7、LPA、SRD5A2、FREM2、BCO2、SPP2、DCN、IGJ、GRAMD1C、APOF、MT1H、MT1E、MT1G、IGF1、 IL13RA2、CYP4A11、GHR、PGLYRP2、SLC22A1、AKR1D1、 TMEM27、PLGLB1、IGH 、IGF2、TDO2、EGR1、ADH4、HGFAC、 ANXA10、CYP2C18、LOC101928916、COLEC11、MT1X、CLRN3、 ID4、VNN1、FOLH1B、MT1M、TMEM45A、IGHG1 、ALDOB、 PRG4、CYP2B6、IGLV1-44、IGLC1、HSD17B2、C9、ATF5、 FAM110C、ANK3、SLCO1B3、GBA3、GNMT、HAL、CYP2B7P、 BBOX1、RDH16、CYP2A6、PCK1、FGFR2、C6、CNTN3、ACOT12、 AFM、GYS2、CYP2C8、SLC51A、C3P1、SLC10A1、MBL2、ADH1A、CYP3A7、LECT2、H19、FABP1、LUM、EPCAM、HPGD、 CYP2E1 Co-expression gene identification results WGCNA was used to locate co-expressed genes in the HBV-HCC dataset. The WGCNA co-expression network was constructed after calculating the optimal soft threshold ( β = 8 ) using the WGCNA package ( Fig. 1 B ). By analyzing the correlation between genes and phenotypes, it was found that 518 genes in the black module and 102 genes in the cyan module were highly correlated with HBV-HCC. The correlation between the black module and HBV-HCC was 0.79 ( P < 0.001 ), and the correlation between the cyan module and HBV-HCC was 0.77 ( P < 0.001 ) ( Fig. 1 E, F ). Screening results of diagnostic biomarkers Core genes verification The overlapping genes selected by the three machine learning methods were RACGAP1, NDC80, and ECT2 (Fig. 4 A). In the validation set, the mRNA expression levels of RACGAP1, ECT2, and NDC80 were higher in liver cancer tissues compared to normal liver tissues. Analysis based on the TCGA database in the GEPIA2 database showed that the high expression of RACGAP1, ECT2, and NDC80 was associated with poor prognosis in HCC patients (Figs. 3 ). A clinical diagnostic model for diagnosing HBV-HCC was constructed based on RACGAP1, ECT2, and NDC80, showing good model calibration curves (Fig. 4 B). The decision curve analysis (DCA) demonstrated that patients could benefit from the clinical diagnostic model based on RACGAP1, ECT2, and NDC80 (Fig. 4 C). A nomogram model based on RACGAP1, ECT2, and NDC80 was constructed and displayed as a calibration plot (Fig. 4 D). In the training set, the AUC values for RACGAP1, ECT2, and NDC80 were 0.979, 0.969, and 0.976, respectively (Fig. 4 E); in the validation set, the expression levels of RACGAP1, ECT2, and NDC80 were higher in liver cancer tissues compared to normal liver tissues, with AUC values of 0.878, 0.731, and 0.915, respectively (Fig. 4 J), indicating that these diagnostic biomarkers have high predictive accuracy and diagnostic value. GO and KEGG enrichment analysis The results of KEGG and GO enrichment analysis showed that the main enriched BPs were: nuclear chromosome segregation, chromosome segregation and sister chromatid segregation (Fig. 5 A).S indle, chromosome, centromeric region and condensed chromosome were the main enriched CCs (Fig. 5 B). The main enriched pathways of MF were protein serine kinase activity, microtubule binding and protein tyrosine kinase activity (Fig. 5 C). The main enriched pathways were cell cycle, p53 signaling pathway, FoxO signaling pathway and Viral carcinogenesis pathway, and most of the genes in most of the pathways were up-regulated (Fig. 5 D, E). molecular docking By molecular docking of the core targets related to HBV-HCC with the commonly used drugs for anti-hepatocellular carcinoma, it was found that the docking energy of the docking binding configuration was less than-5kcal / mol, which proved that the binding configuration had good activity (Table 3 ). Except that the binding energy of NDC80 and Lenvatinib is greater than-7kcal / mol, the binding energy of other target proteins and drugs is less than-7kcal / mol, which proves that the binding configuration has strong activity (Fig. 6 ). Table 3 Parameters of molecular docking box and docking binding energy Target Ligand Grid Center NPTs Binding Energie ECT2 Lenvatinib 59 72 72 -31.42 -20.35 -48.11 -7.3(kcal/mol) ECT2 Regorafenib 59 72 72 -31.42 -20.35 -48.11 -8.0(kcal/mol) ECT2 Sorafenib 59 72 72 -31.42 -20.35 -48.11 -8.5(kcal/mol) NDC80 Lenvatinib 36 47 31 3.74 27.09 44.14 -6.7(kcal/mol) NDC80 Regorafenib 36 47 31 3.74 27.09 44.14 -7.9(kcal/mol) NDC80 Sorafenib 36 47 31 3.74 27.09 44.14 -7.6(kcal/mol) RACGAP1 Lenvatinib 35 39 36 13.48 1.87 12.16 -7.6(kcal/mol) RACGAP1 Regorafenib 35 39 36 13.48 1.87 12.16 -9.3(kcal/mol) RACGAP1 Sorafenib 35 39 36 13.48 1.87 12.16 -9.1(kcal/mol) Discussion Hepatocellular carcinoma (HCC) is the most common primary liver cancer and an important medical problem. The mortality rate has increased in recent years. (Vogel et al., 2022 )。As a major risk factor for the occurrence and progression of HCC, HBV infection poses a threat to human life and health. HBV infection can directly or indirectly promote hepatocellular carcinogenesis. At the genetic level, HBV can increase the instability of the host cell genome, cause epigenetic remodeling of the host DNA, and lead to chromosomal remodeling and abnormal expression of oncogenes and tumor suppressor genes by integrating or inducing host gene mutations. It can also activate various cancer-related signaling pathways, regulate cell metabolism and other mechanisms to cause malignant transformation of liver cells. It is of great significance to study the specific mechanism of the occurrence and progression of HBV-HCC for its prevention and treatment. In the liver microenvironment, chronic inflammation induced by HBV infection, changes in the interaction between the virus and innate immune cells and adaptive immune cells help the virus evade immune surveillance and promote the evolution of the disease from inflammation to tumor formation(Levrero and Zucman-Rossi, 2016 ). Further study of the mechanism of HBV infection-induced HCC can provide reliable new ideas and methods for the prevention, diagnosis and treatment of HBV-HCC. With the development of gene sequencing technology and various deep learning algorithms, it provides a method for identifying new biomarkers in diseases. In this study, three deep learning methods, random forest, Lasso regression and SVM-RF, were used to identify the key genes RACGAP1, ECT2 and NDC80 in HBV-HCC-related gene sequencing data. Through difference analysis, it was found that they were highly expressed in tumor tissues compared with normal tissues. Survival analysis showed that its high expression was associated with poor prognosis in patients with liver cancer. The selected three key genes were used to construct a clinical diagnostic model. In the training set, the key genes showed high accuracy in the diagnosis of HBV-HCC, and also had good accuracy in the validation set. It can be concluded from the DCA curve that patients can get better benefits from the model. Lenvatinib, Sorafenib and Regorafenib are clinically used drugs for the systematic treatment of hepatocellular carcinoma, which can improve the survival and prognosis of patients with hepatocellular carcinoma(Villanueva, 2019 ). Through molecular docking, it was found that the docking configurations of Lenvatinib, Sorafenib and Regorafenib with RACGAP1, ECT2 and NDC80 had strong activity, indicating that the target may be a potential therapeutic target for hepatocellular carcinoma. RACGAP1 is an important cellular protein. It is a GTPase-activating protein that acts on the Rho GTPase family. It belongs to the GTPase-activating protein family and participates in many cellular processes, including cell division, transformation, and invasive migration(Saigusa et al., 2015 ), Studies have found that RACGAP1 is highly expressed in a variety of cancers, such as the poor prognosis and adverse clinicopathological features of gastrointestinal stromal tumors with high expression of RACGAP1(Imaoka et al., 2015 ). RACGAP1 can drive breast cancer metastasis by regulating ECT2-dependent mitochondrial quality control(Yin et al., 2019 ). RACGAP1 is used as a biomarker for lymphatic metastasis and poor prognosis of colon cancer(Ren et al., 2021 ). In hepatocellular carcinoma, high expression of RACGAP1 promotes tumor progression, and studies have shown that up-regulation of RACGAP1 is significantly associated with early recurrence of hepatocellular carcinoma(Gu et al., 2022 ; Zabady et al., 2022 ). ECT2 is a guanine nucleotide dissociation exchange factor. It is a high incidence area of chromosomal abnormalities in malignant tumors. It is widely present in cells and tissues, and has the effects of regulating cell proliferation, apoptosis and DNA damage repair(Xu et al., 2021 ). ECT2 has been reported to be overexpressed in a variety of human tumors, such as hepatocellular carcinoma(J. Chen et al., 2015 )、prostatic cancer(Guo et al., 2017 )、ovary carcinoma(Huff et al., 2013 )、oral cancer(Iyoda et al., 2010 ) And gastric cancer(Wang et al., 2016 ). Promoting the expression of ECT2 will enhance the proliferation of HCC cells and enhance the metastasis of cancer cells. (He et al., 2022 ; Lv et al., 2023 ). NDC80 is a core component of the outer kinetochore and mitogen regulators and is involved in the migration, proliferation, invasion and apoptosis of various types of tumor cells(Ju et al., 2017 ; J. Chen and Ünal, 2021 ). High expression of NDC80 enhances cisplatin resistance in triple-negative breast cancer(Li et al., 2022 ). Overexpression of NDC80 can lead to decreased apoptosis of HCC cells and overcome cell cycle arrest to promote the development of HCC and is associated with poor prognosis in HCC patients(Xie et al., 2020 ; Ju et al., 2017 ). The p53 signaling pathway plays an important role in cell cycle regulation, metabolism, aging development, reproduction and inhibition of tumor expression(Hu et al., 2007 ; Kruiswijk et al., 2015 ; Tanikawa et al., 2017 ). It has been found that p53, as a tumor suppressor, mutates or loses in nearly half of cancers. In the other half of the tumor, although the p53 protein is normal, the upstream regulatory factors and downstream mediators are disordered, resulting in the destruction of the entire p53 pathway(Huang, 2021 ). Cell cycle is a highly regulated process that makes cell growth, genetic material replication and cell division possible. In the normal cell cycle, the expression of various cell cycle proteins is strictly regulated. However, in tumor cells, the mechanism of cell cycle regulation is disordered, resulting in abnormal activation of cyclins, which plays a pathogenic role in the development of most tumor types(Suski et al., 2021 ). The results of GO enrichment analysis showed that the genes interacting with key genes RACGAP1, NDC80 and ECT2 were mainly enriched in chromosome-related pathways. The p53 and cell cycle pathways mainly enriched by KEGG played an important role in the occurrence and progression of cancer. Previous studies have also shown that overexpression of RACGAP1, ECT2 and NDC80 is associated with malignant progression and poor prognosis of HCC. Previous studies have shown that HBV leads to chromosomal remodeling and abnormal expression of oncogenes and tumor suppressor genes by integrating or inducing host gene mutations. It can also activate various cancer-related signaling pathways to promote the occurrence and progression of cancer(Jiang et al., 2021 ; Jia et al., 2020 ). The clinical prediction model for the diagnosis of HBV-HCC based on RACGAP1, ECT2 and NDC80 also showed good accuracy. In summary, according to the results of machine learning and molecular docking, we speculate that HBV may induce gene mutations in RACGAP1, ECT2 and NDC80, affect the normal function of chromosomes, affect the normal regulation of p53 and cell cycle signaling pathways, and then lead to the occurrence and progression of HCC. Moreover, NDC80, RACGAP1 and ECT2 may be valuable diagnostic biomarkers for HBV-HCC and potential therapeutic targets. Declarations Data Availability Statement The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. Author Contributions Lianping Wu and Xulei Zhang conceived and designed the study and critically revised the manuscript. Anyin Yang, Jianping Liu, Mengru Li performed the experiments, analyzed the data, and drafted the manuscript, Yang Anyin, Liu Jianping, Li Mengru have a common contribution to the article. Hong Zhong participated in study design, study implementation and manuscript revision. Funding This work was no funding. Conflict of Interest The authors declare no conflict of interest, financial or otherwise. Acknowledgements Declared none. References Chen, C. J., Yang, H. I., Su, J., Jen, C. L., You, S. L., Lu, S. N., et al. (2006). Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. Jama, 295(1), 65–73. doi: 10.1001/jama.295.1.65 . Chen, J., & Ünal, E. (2021). Meiotic regulation of the Ndc80 complex composition and function. Curr Genet, 67(4), 511–518. doi: 10.1007/s00294-021-01174-3 . Chen, J., Xia, H., Zhang, X., Karthik, S., Pratap, S. V., Ooi, L. L., et al. (2015). ECT2 regulates the Rho/ERK signalling axis to promote early recurrence in human hepatocellular carcinoma. J Hepatol, 62(6), 1287–95. doi: 10.1016/j.jhep.2015.01.014 . Fitzmaurice, C., Allen, C., Barber, R. M., Barregard, L., Bhutta, Z. A., Brenner, H., et al. (2017). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol, 3(4), 524–548. doi: 10.1001/jamaoncol.2016.5688 . Gu, Y., Chen, B., Guo, D., Pan, L., Luo, X., Tang, J., et al. (2022). Up-Regulation of RACGAP1 Promotes Progressions of Hepatocellular Carcinoma Regulated by GABPA via PI3K/AKT Pathway. Oxid Med Cell Longev , 2022, 3034150. doi: 10.1155/2022/3034150 . Guo, Z., Chen, X., Du, T., Zhu, D., Lai, Y., Dong, W., et al. (2017). Elevated levels of epithelial cell transforming sequence 2 predicts poor prognosis for prostate cancer. Med Oncol, 34(1), 13. doi: 10.1007/s12032-016-0872-3 . He, C. K., Li, Z. B., Yi, D., Zhu, X. Y., Liu, R. R., Zhang, D. X., et al. (2022). LncRNA FGD5-AS1 enhances the proliferation and stemness of hepatocellular carcinoma cells through targeting miR-223 and regulating the expression of ECT2 and FAT1. Hepatol Res, 52(7), 614–629. doi: 10.1111/hepr.13767 . Hu, W., Feng, Z., Teresky, A. K., & Levine, A. J. (2007). p53 regulates maternal reproduction through LIF. Nature, 450(7170), 721–4. doi: 10.1038/nature05993 . Huang, J. (2021). Current developments of targeting the p53 signaling pathway for cancer treatment. Pharmacol Ther, 220, 107720. doi: 10.1016/j.pharmthera.2020.107720 . Huff, L. P., Decristo, M. J., Trembath, D., Kuan, P. F., Yim, M., Liu, J., et al. (2013). The Role of Ect2 Nuclear RhoGEF Activity in Ovarian Cancer Cell Transformation. Genes Cancer, 4(11–12), 460 – 75. doi: 10.1177/1947601913514851 . Imaoka, H., Toiyama, Y., Saigusa, S., Kawamura, M., Kawamoto, A., Okugawa, Y., et al. (2015). RacGAP1 expression, increasing tumor malignant potential, as a predictive biomarker for lymph node metastasis and poor prognosis in colorectal cancer. Carcinogenesis, 36(3), 346–54. doi: 10.1093/carcin/bgu327 . Iyoda, M., Kasamatsu, A., Ishigami, T., Nakashima, D., Endo-Sakamoto, Y., Ogawara, K., et al. (2010). Epithelial cell transforming sequence 2 in human oral cancer. PLoS One, 5(11), e14082. doi: 10.1371/journal.pone.0014082 . Jia, L., Gao, Y., He, Y., Hooper, J. D., & Yang, P. (2020). HBV induced hepatocellular carcinoma and related potential immunotherapy. Pharmacol Res, 159, 104992. doi: 10.1016/j.phrs.2020.104992 . Jiang, Y., Han, Q., Zhao, H., & Zhang, J. (2021). The Mechanisms of HBV-Induced Hepatocellular Carcinoma. J Hepatocell Carcinoma, 8, 435–450. doi: 10.2147/jhc.S307962 . Ju, L. L., Chen, L., Li, J. H., Wang, Y. F., Lu, R. J., Bian, Z. L., et al. (2017). Effect of NDC80 in human hepatocellular carcinoma. World J Gastroenterol, 23(20), 3675–3683. doi: 10.3748/wjg.v23.i20.3675 . Kleppe, A., Skrede, O. J., De Raedt, S., Liestøl, K., Kerr, D. J., & Danielsen, H. E. (2021). Designing deep learning studies in cancer diagnostics. Nat Rev Cancer, 21(3), 199–211. doi: 10.1038/s41568-020-00327-9 . Kruiswijk, F., Labuschagne, C. F., & Vousden, K. H. (2015). p53 in survival, death and metabolic health: a lifeguard with a licence to kill. Nat Rev Mol Cell Biol, 16(7), 393–405. doi: 10.1038/nrm4007 . LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–44. doi: 10.1038/nature14539 . Levrero, M., & Zucman-Rossi, J. (2016). Mechanisms of HBV-induced hepatocellular carcinoma. J Hepatol, 64(1 Suppl), S84-s101. doi: 10.1016/j.jhep.2016.02.021 . Li, J., Xu, X., & Peng, X. (2022). NDC80 Enhances Cisplatin-resistance in Triple-negative Breast Cancer. Arch Med Res, 53(4), 378–387. doi: 10.1016/j.arcmed.2022.03.003 . Lv, E., Sheng, J., Yu, C., Rao, D., & Huang, W. (2023). Long noncoding RNA MAPKAPK5-AS1 promotes metastasis through regulation miR-376b-5p/ECT2 axis in hepatocellular carcinoma. Dig Liver Dis, 55(7), 945–954. doi: 10.1016/j.dld.2022.11.024 . Ren, K., Zhou, D., Wang, M., Li, E., Hou, C., Su, Y., et al. (2021). RACGAP1 modulates ECT2-Dependent mitochondrial quality control to drive breast cancer metastasis. Exp Cell Res, 400(1), 112493. doi: 10.1016/j.yexcr.2021.112493 . Saigusa, S., Tanaka, K., Mohri, Y., Ohi, M., Shimura, T., Kitajima, T., et al. (2015). Clinical significance of RacGAP1 expression at the invasive front of gastric cancer. Gastric Cancer, 18(1), 84–92. doi: 10.1007/s10120-014-0355-1 . Suski, J. M., Braun, M., Strmiska, V., & Sicinski, P. (2021). Targeting cell-cycle machinery in cancer. Cancer Cell, 39(6), 759–778. doi: 10.1016/j.ccell.2021.03.010 . Tanikawa, C., Zhang, Y. Z., Yamamoto, R., Tsuda, Y., Tanaka, M., Funauchi, Y., et al. (2017). The Transcriptional Landscape of p53 Signalling Pathway. EBioMedicine , 20, 109–119. doi: 10.1016/j.ebiom.2017.05.017 . Villanueva, A. (2019). Hepatocellular Carcinoma. N Engl J Med, 380(15), 1450–1462. doi: 10.1056/NEJMra1713263 . Vogel, A., Meyer, T., Sapisochin, G., Salem, R., & Saborowski, A. (2022). Hepatocellular carcinoma. Lancet, 400(10360), 1345–1362. doi: 10.1016/s0140-6736(22)01200-4 . Wang, H. B., Yan, H. C., & Liu, Y. (2016). Clinical significance of ECT2 expression in tissue and serum of gastric cancer patients. Clin Transl Oncol, 18(7), 735–42. doi: 10.1007/s12094-015-1428-2 . Xie, W., Wang, B., Wang, X., Hou, D., Su, H., & Huang, H. (2020). Nine hub genes related to the prognosis of HBV-positive hepatocellular carcinoma identified by protein interaction analysis. Ann Transl Med, 8(7), 478. doi: 10.21037/atm.2020.03.94 . Xu, D., Wang, Y., Wu, J., Zhang, Z., Chen, J., Xie, M., et al. (2021). ECT2 overexpression promotes the polarization of tumor-associated macrophages in hepatocellular carcinoma via the ECT2/PLK1/PTEN pathway. Cell Death Dis, 12(2), 162. doi: 10.1038/s41419-021-03450-z . Yin, C., Toiyama, Y., Okugawa, Y., Shigemori, T., Yamamoto, A., Ide, S., et al. (2019). Rac GTPase-Activating Protein 1 (RACGAP1) as an Oncogenic Enhancer in Esophageal Carcinoma. Oncology, 97(3), 155–163. doi: 10.1159/000500592 . Zabady, S., Mahran, N., Soltan, M. A., Alaa Eldeen, M., Eid, R. A., Albogami, S., et al. (2022). Cyanidin-3-Glucoside Modulates hsa_circ_0001345/miRNA106b/ATG16L1 Axis Expression as a Potential Protective Mechanism against Hepatocellular Carcinoma. Curr Issues Mol Biol, 44(4), 1677–1687. doi: 10.3390/cimb44040115 . Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.zip 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 Advisory Board 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-4117465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282617703,"identity":"436bf304-18be-4a64-a9cc-a737a1792a22","order_by":0,"name":"Anyin Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACfv7mgw8SftjU8zMcPkCcFskZx5INPvakJUg2HksgTovBgRw1yRlshxMMms8YEOmyA2cYpHl40vIM2M58vPGGwU5Ot4GADsbm3gPGPBY2xeY8ZzdbzmFINjY7QEALM8O5hGSgLYw7Z5zdJs3DcCBxGyEtbAw5Bod52A4zbrj/5hlxWngYcgwbgd5P3HDgDBtxWiQkjiUzAAPZWLLhmLHlHAMi/GJ/vvn4D2BUygGj8uGNNxV2cgS1oFrJQ2zUIGkhVccoGAWjYBSMCAAAZv5JptXKPfoAAAAASUVORK5CYII=","orcid":"","institution":"Gaochun People 's Hospital, Gaochun Hospital Affiliated to Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Anyin","middleName":"","lastName":"Yang","suffix":""},{"id":282617705,"identity":"2512026b-b4a9-461f-8f76-ce11d97a87bc","order_by":1,"name":"Jianping Liu","email":"","orcid":"","institution":"Gaochun People 's Hospital, Gaochun Hospital Affiliated to Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Liu","suffix":""},{"id":282617706,"identity":"47ed6484-01f7-4a8d-b962-9d604c97ec0b","order_by":2,"name":"Mengru Li","email":"","orcid":"","institution":"Gaochun People 's Hospital, Gaochun Hospital Affiliated to Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Mengru","middleName":"","lastName":"Li","suffix":""},{"id":282617710,"identity":"abfa654c-2d7c-489b-a8b9-6f18f55a7ff4","order_by":3,"name":"Hong Zhang","email":"","orcid":"","institution":"Gaochun People 's Hospital, Gaochun Hospital Affiliated to Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhang","suffix":""},{"id":282617712,"identity":"ffdc5eac-ff10-4e1a-9e96-3af990ef43d8","order_by":4,"name":"Xulei Zhang","email":"","orcid":"","institution":"Gaochun People 's Hospital, Gaochun Hospital Affiliated to Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Xulei","middleName":"","lastName":"Zhang","suffix":""},{"id":282617713,"identity":"aab5ded1-5c74-4bd4-9049-f088ea419dd7","order_by":5,"name":"Lianping Wu","email":"","orcid":"","institution":"Gaochun People 's Hospital, Gaochun Hospital Affiliated to Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Lianping","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-03-17 14:29:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4117465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4117465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53591960,"identity":"37d18534-5845-476e-8c7f-d32825926a80","added_by":"auto","created_at":"2024-03-27 20:34:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":909998,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA analysis results\u003c/p\u003e","description":"","filename":"Fig1WGCNAanalysisresults.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/815d9f8edd52aaac4cb43528.jpg"},{"id":53591958,"identity":"31e34fb5-fc14-43b1-9776-a676c8f9b1e4","added_by":"auto","created_at":"2024-03-27 20:34:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":663542,"visible":true,"origin":"","legend":"\u003cp\u003eVariable screening and feature importance ranking\u003c/p\u003e","description":"","filename":"Fig.2Variablescreeningandfeatureimportanceranking.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/f4f283d409a99f287f7fdf6e.jpg"},{"id":53591959,"identity":"2666bfd4-6b81-4651-a62f-2f635578b113","added_by":"auto","created_at":"2024-03-27 20:34:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1130313,"visible":true,"origin":"","legend":"\u003cp\u003eCore target protein expression and survival analysis\u003c/p\u003e","description":"","filename":"Fig.3Coretargetproteinexpressionandsurvivalanalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/184078865b76ebb69f148c7f.jpg"},{"id":53592205,"identity":"6d693022-da15-4265-a2a2-c33ccb16a860","added_by":"auto","created_at":"2024-03-27 20:42:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":867204,"visible":true,"origin":"","legend":"\u003cp\u003eCore target screening and verification\u003c/p\u003e","description":"","filename":"ig.4Coretargetscreeningandverification.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/cd259e6473ad2a65d8169ed9.jpg"},{"id":53591963,"identity":"124c718f-66f6-4b1d-bfe6-cf03929bf5df","added_by":"auto","created_at":"2024-03-27 20:34:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1458384,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG and GO enrichment analysis\u003c/p\u003e","description":"","filename":"Fig.5KEGGandGOenrichmentanalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/002931ab2f7488f0e470754d.jpg"},{"id":53591964,"identity":"b9ff064c-a70c-4cb9-b194-de883fddf3e8","added_by":"auto","created_at":"2024-03-27 20:34:18","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2158886,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results\u003c/p\u003e","description":"","filename":"Fig.6Moleculardockingresults.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/3c8e699c45976562921bbb56.jpg"},{"id":58485662,"identity":"9c0801d2-f572-4b2b-8a30-0e0a37c11700","added_by":"auto","created_at":"2024-06-17 09:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7673496,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/7fbae633-893b-4cbb-a0b9-d8f0e9e595e1.pdf"},{"id":53591965,"identity":"598745a9-a002-4127-9ce5-9c7f2fb1ee17","added_by":"auto","created_at":"2024-03-27 20:34:21","extension":"zip","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":60368266,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-4117465/v1/ac3610536d6f421c1325b4a2.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced HCC","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma is a malignant tumor, and significant progress has been made in the epidemiology, risk factors, and molecular characteristics of HCC in many countries around the world over the past few decades. However, in many countries worldwide, the incidence and cancer-specific mortality rate of HCC continue to increase(Fitzmaurice et al., 2017). The main risk factors for hepatocellular carcinoma include chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), aflatoxin-contaminated food, heavy alcohol consumption, and type 2 diabetes. Chronic infection with hepatitis B virus (HBV) is considered a major risk factor for the occurrence and progression of HCC, accounting for more than half of global HCC cases(Jia et al., 2020). In patients with hepatitis B, the incidence of hepatocellular carcinoma increases with viral load, duration of infection, and severity of liver disease(C. J. Chen et al., 2006). Numerous studies have shown that the presence of other pathogenic factors in conjunction with HBV can increase the incidence of hepatocellular carcinoma(Jiang et al., 2021).\u003c/p\u003e\n\u003cp\u003eHBV can increase the genomic instability of host cells, leading to epigenetic reshaping of host DNA, chromosomal reshaping, and abnormal expression of oncogenes and tumor suppressor genes through integration or induction of host gene mutations. It can also induce malignant transformation of liver cells by activating various cancer-related signaling pathways, regulating cell metabolism, and other mechanisms. The liver microenvironment undergoes changes induced by chronic inflammation and interactions between the virus and innate immune cells, adaptive immune cells, helping the virus evade immune surveillance and promoting the progression of the disease from inflammation to tumor formation(Jiang et al., 2021). As a high-risk factor for inducing HCC, HBV influences the occurrence and progression of tumors. Therefore, further research on the molecular mechanisms of HBV infection-induced HCC can help improve the prevention, diagnosis, and treatment of HCC.\u003c/p\u003e\n\u003cp\u003eWith the continuous development of computer technology, artificial intelligence (AI) is also becoming increasingly mature. Machine learning, as a branch of AI, focuses on using mathematical algorithms to identify patterns in data for prediction. Deep learning, as a subfield of machine learning, specifically utilizes multi-layer neural network algorithms inspired by the structure of the brain for prediction(LeCun et al., 2015). Due to the increasing availability and integration of various types of data such as genomics, transcriptomics, and pathology, cancer treatment is shifting towards precision medicine. Deep learning models have the potential to identify relevant granular features from multiple data types. Deep learning is being applied in the diagnosis, prognosis, and treatment of tumors, providing meaningful insights(Kleppe et al., 2021).\u003c/p\u003e\n\u003cp\u003eIn this study, we aim to analyze the pathogenic mechanisms of HBV-induced HCC using a combination of bioinformatics and deep learning methods. We seek to identify valuable diagnostic biomarkers and hope to provide new insights for the diagnosis and treatment of HBV-induced HCC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially Expressed Gene Screening\u003c/h2\u003e \u003cp\u003eRetrieve gene chip data with the keyword \"HBV-HCC\" from the National Center for Biotechnology Information (NCBI) public Gene Expression Omnibus (GEO) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), download data with a sample size greater than 100, filter differentially expressed genes using the R software by reading the downloaded matrix file, analyze the tumor group and control group using the \"limma\" package, obtain differentially expressed genes (DEGs), and screen DEGs with criteria: |logFC|\u0026gt;2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGO function enrichment analysis and KEGG pathway enrichment analysis were performed using the ' clusterProfiler ' package of R language to discover the biological functions and pathways that DEGS may be involved in. Both of them used P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the screening index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA co-expression analysis\u003c/h2\u003e \u003cp\u003eThe 'WGCNA 'R package in R language was used to locate the co-expressed genes in the HBV-HCC dataset.The sample clustering tree algorithm is used to eliminate outlier samples, and the pick Soft Threshold function is used to select the best soft threshold β to ensure the construction of the scale-free network. The blockwiseModules function in the WGCNA package is used to construct the co-expression matrix. The merging threshold of similar modules is set to 0.25 (mergeCutHeight\u0026thinsp;=\u0026thinsp;0.25), the topological overlap matrix (TOM) is deepSplit\u0026thinsp;=\u0026thinsp;1, and the minimum number of genes in each module is set to 30 (minModuleSize\u0026thinsp;=\u0026thinsp;30). Other parameters are set according to the default setting. The samples in the data set were divided into control group and HBV-HCC group, and the modules with high correlation with tumors were screened out.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eScreening and verification of diagnostic markers\u003c/h2\u003e \u003cp\u003eThe intersection genes of DEGS and WGCNA modules with high tumor correlation were screened out. Lasso regression, random forest and SVM-RF were used to screen the variables of the intersection genes, and the intersection of the variables screened by the three algorithms was used as a preliminary diagnostic biomarker. The relationship between biomarkers and the survival and prognosis of patients with liver cancer was analyzed in the GEPIA2 database. The 'RMS' package was used to construct a nomogram model for the diagnosis of HBV-HCC based on diagnostic biomarkers, and the clinical decision curve (DCA) was drawn. The ROC curve of diagnostic biomarkers was calculated to analyze the accuracy of prediction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene screening results\u003c/h2\u003e \u003cp\u003eDownload gene array datasets GSE121248 and GSE55092 from the National Center for Biotechnology Information (NCBI) public Gene Expression Omnibus (GEO) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After merging the datasets, a total of 119 samples of HBV-HCC and 128 control samples were obtained. The merged dataset was then used as a training set for gene differential analysis, resulting in 133 downregulated genes and 64 upregulated genes. Subsequently, the dataset containing HBV-HCC-related data, GSE47197, was used as a validation set (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGEO array data information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Series\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatforms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal VS Tumor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE121248\u003c/p\u003e \u003cp\u003eGSE55092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 VS 119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE47197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL16699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 VS 61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of differential genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAP2、RACGAP1、HMMR、TOP2A、NDC80、MELK、ASPM、ECT2、PRC1、ROBO1、FAM72A 、BUB1B、CDK1、CCNB1、BC017398、GPR158、FAM83D、KIF20A、RRM2、DNAJC6、PBKDTL、NCAPG、GINS1、NEK2、RBM24、E2F7、TTK、CDC20、DUXAP10、LOC344887、\u003c/p\u003e \u003cp\u003eZIC2、NUF2、COL15A1、TRIM16、CR936796、CRNDE、SULT1C2、\u003c/p\u003e \u003cp\u003eGPC3、CD109、FAM133A、AK093362、SMPX、NRCAM、FGF13、\u003c/p\u003e \u003cp\u003eSSX1、LOC101930288、MAGEA1、CTNNA2、SPINK1、AKR1B10、\u003c/p\u003e \u003cp\u003eLCN2、GABBR1、REG3A、COX7B2、MAGEA12、DKK1、MAGEA3、\u003c/p\u003e \u003cp\u003eLOC100506403、MAGEA6、ALDH3A1、LINC01419、GAGE1、GAGE12B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCLEC1B、FCN2、OIT3、CLEC4M、GPR128、CLEC4G、CXCL14、CYP26A1、CRHBP、LINC01093、RSPO3、FCN3 PLAC8、CDHR2、CCBE1、SLC25A47、CXCL12、FAM65C、LCAT MARCO、KCNN2、 HAMP、CETP、GPM6A、CNDP1、TTC36、NPY1R、CYP39A1、\u003c/p\u003e \u003cp\u003eRND3、CYP1A2、FOS、C8orf4、OLFML3、CD5L、HGF、GADD45B、IGFBP3、ESR1、IDO2、ZG16、FBP1、KMO、ASPA、\u003c/p\u003e \u003cp\u003eIGHM、CA2、GCH1、SRPX、FOSB、NAT2、TBX15、ID1、HAO2、\u003c/p\u003e \u003cp\u003eMT1F、CYP2B6、C7、LPA、SRD5A2、FREM2、BCO2、SPP2、DCN、IGJ、GRAMD1C、APOF、MT1H、MT1E、MT1G、IGF1、\u003c/p\u003e \u003cp\u003eIL13RA2、CYP4A11、GHR、PGLYRP2、SLC22A1、AKR1D1、\u003c/p\u003e \u003cp\u003eTMEM27、PLGLB1、IGH 、IGF2、TDO2、EGR1、ADH4、HGFAC、\u003c/p\u003e \u003cp\u003eANXA10、CYP2C18、LOC101928916、COLEC11、MT1X、CLRN3、\u003c/p\u003e \u003cp\u003eID4、VNN1、FOLH1B、MT1M、TMEM45A、IGHG1 、ALDOB、\u003c/p\u003e \u003cp\u003ePRG4、CYP2B6、IGLV1-44、IGLC1、HSD17B2、C9、ATF5、\u003c/p\u003e \u003cp\u003eFAM110C、ANK3、SLCO1B3、GBA3、GNMT、HAL、CYP2B7P、\u003c/p\u003e \u003cp\u003eBBOX1、RDH16、CYP2A6、PCK1、FGFR2、C6、CNTN3、ACOT12、\u003c/p\u003e \u003cp\u003eAFM、GYS2、CYP2C8、SLC51A、C3P1、SLC10A1、MBL2、ADH1A、CYP3A7、LECT2、H19、FABP1、LUM、EPCAM、HPGD、\u003c/p\u003e \u003cp\u003eCYP2E1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCo-expression gene identification results\u003c/h2\u003e \u003cp\u003eWGCNA was used to locate co-expressed genes in the HBV-HCC dataset. The WGCNA co-expression network was constructed after calculating the optimal soft threshold ( β\u0026thinsp;=\u0026thinsp;8 ) using the WGCNA package ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB ). By analyzing the correlation between genes and phenotypes, it was found that 518 genes in the black module and 102 genes in the cyan module were highly correlated with HBV-HCC. The correlation between the black module and HBV-HCC was 0.79 ( P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ), and the correlation between the cyan module and HBV-HCC was 0.77 ( P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ) ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eScreening results of diagnostic biomarkers\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eCore genes verification\u003c/h2\u003e \u003cp\u003eThe overlapping genes selected by the three machine learning methods were RACGAP1, NDC80, and ECT2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the validation set, the mRNA expression levels of RACGAP1, ECT2, and NDC80 were higher in liver cancer tissues compared to normal liver tissues. Analysis based on the TCGA database in the GEPIA2 database showed that the high expression of RACGAP1, ECT2, and NDC80 was associated with poor prognosis in HCC patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A clinical diagnostic model for diagnosing HBV-HCC was constructed based on RACGAP1, ECT2, and NDC80, showing good model calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The decision curve analysis (DCA) demonstrated that patients could benefit from the clinical diagnostic model based on RACGAP1, ECT2, and NDC80 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). A nomogram model based on RACGAP1, ECT2, and NDC80 was constructed and displayed as a calibration plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In the training set, the AUC values for RACGAP1, ECT2, and NDC80 were 0.979, 0.969, and 0.976, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE); in the validation set, the expression levels of RACGAP1, ECT2, and NDC80 were higher in liver cancer tissues compared to normal liver tissues, with AUC values of 0.878, 0.731, and 0.915, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ), indicating that these diagnostic biomarkers have high predictive accuracy and diagnostic value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG enrichment analysis\u003c/h2\u003e \u003cp\u003eThe results of KEGG and GO enrichment analysis showed that the main enriched BPs were: nuclear chromosome segregation, chromosome segregation and sister chromatid segregation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).S indle, chromosome, centromeric region and condensed chromosome were the main enriched CCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The main enriched pathways of MF were protein serine kinase activity, microtubule binding and protein tyrosine kinase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The main enriched pathways were cell cycle, p53 signaling pathway, FoxO signaling pathway and Viral carcinogenesis pathway, and most of the genes in most of the pathways were up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003emolecular docking\u003c/h2\u003e \u003cp\u003eBy molecular docking of the core targets related to HBV-HCC with the commonly used drugs for anti-hepatocellular carcinoma, it was found that the docking energy of the docking binding configuration was less than-5kcal / mol, which proved that the binding configuration had good activity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Except that the binding energy of NDC80 and Lenvatinib is greater than-7kcal / mol, the binding energy of other target proteins and drugs is less than-7kcal / mol, which proves that the binding configuration has strong activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of molecular docking box and docking binding energy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLigand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eGrid Center\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNPTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBinding Energie\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLenvatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-20.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-48.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-7.3(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-20.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-48.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-8.0(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-20.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-48.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-8.5(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDC80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLenvatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-6.7(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDC80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-7.9(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDC80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-7.6(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRACGAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLenvatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-7.6(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRACGAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-9.3(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRACGAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-9.1(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the most common primary liver cancer and an important medical problem. The mortality rate has increased in recent years. (Vogel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)。As a major risk factor for the occurrence and progression of HCC, HBV infection poses a threat to human life and health. HBV infection can directly or indirectly promote hepatocellular carcinogenesis. At the genetic level, HBV can increase the instability of the host cell genome, cause epigenetic remodeling of the host DNA, and lead to chromosomal remodeling and abnormal expression of oncogenes and tumor suppressor genes by integrating or inducing host gene mutations. It can also activate various cancer-related signaling pathways, regulate cell metabolism and other mechanisms to cause malignant transformation of liver cells. It is of great significance to study the specific mechanism of the occurrence and progression of HBV-HCC for its prevention and treatment. In the liver microenvironment, chronic inflammation induced by HBV infection, changes in the interaction between the virus and innate immune cells and adaptive immune cells help the virus evade immune surveillance and promote the evolution of the disease from inflammation to tumor formation(Levrero and Zucman-Rossi, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Further study of the mechanism of HBV infection-induced HCC can provide reliable new ideas and methods for the prevention, diagnosis and treatment of HBV-HCC.\u003c/p\u003e \u003cp\u003eWith the development of gene sequencing technology and various deep learning algorithms, it provides a method for identifying new biomarkers in diseases. In this study, three deep learning methods, random forest, Lasso regression and SVM-RF, were used to identify the key genes RACGAP1, ECT2 and NDC80 in HBV-HCC-related gene sequencing data. Through difference analysis, it was found that they were highly expressed in tumor tissues compared with normal tissues. Survival analysis showed that its high expression was associated with poor prognosis in patients with liver cancer. The selected three key genes were used to construct a clinical diagnostic model. In the training set, the key genes showed high accuracy in the diagnosis of HBV-HCC, and also had good accuracy in the validation set. It can be concluded from the DCA curve that patients can get better benefits from the model. Lenvatinib, Sorafenib and Regorafenib are clinically used drugs for the systematic treatment of hepatocellular carcinoma, which can improve the survival and prognosis of patients with hepatocellular carcinoma(Villanueva, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Through molecular docking, it was found that the docking configurations of Lenvatinib, Sorafenib and Regorafenib with RACGAP1, ECT2 and NDC80 had strong activity, indicating that the target may be a potential therapeutic target for hepatocellular carcinoma.\u003c/p\u003e \u003cp\u003eRACGAP1 is an important cellular protein. It is a GTPase-activating protein that acts on the Rho GTPase family. It belongs to the GTPase-activating protein family and participates in many cellular processes, including cell division, transformation, and invasive migration(Saigusa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Studies have found that RACGAP1 is highly expressed in a variety of cancers, such as the poor prognosis and adverse clinicopathological features of gastrointestinal stromal tumors with high expression of RACGAP1(Imaoka et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). RACGAP1 can drive breast cancer metastasis by regulating ECT2-dependent mitochondrial quality control(Yin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). RACGAP1 is used as a biomarker for lymphatic metastasis and poor prognosis of colon cancer(Ren et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In hepatocellular carcinoma, high expression of RACGAP1 promotes tumor progression, and studies have shown that up-regulation of RACGAP1 is significantly associated with early recurrence of hepatocellular carcinoma(Gu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zabady et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ECT2 is a guanine nucleotide dissociation exchange factor. It is a high incidence area of chromosomal abnormalities in malignant tumors. It is widely present in cells and tissues, and has the effects of regulating cell proliferation, apoptosis and DNA damage repair(Xu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ECT2 has been reported to be overexpressed in a variety of human tumors, such as hepatocellular carcinoma(J. Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)、prostatic cancer(Guo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)、ovary carcinoma(Huff et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)、oral cancer(Iyoda et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) And gastric cancer(Wang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Promoting the expression of ECT2 will enhance the proliferation of HCC cells and enhance the metastasis of cancer cells. (He et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). NDC80 is a core component of the outer kinetochore and mitogen regulators and is involved in the migration, proliferation, invasion and apoptosis of various types of tumor cells(Ju et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; J. Chen and \u0026Uuml;nal, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). High expression of NDC80 enhances cisplatin resistance in triple-negative breast cancer(Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overexpression of NDC80 can lead to decreased apoptosis of HCC cells and overcome cell cycle arrest to promote the development of HCC and is associated with poor prognosis in HCC patients(Xie et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ju et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe p53 signaling pathway plays an important role in cell cycle regulation, metabolism, aging development, reproduction and inhibition of tumor expression(Hu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kruiswijk et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tanikawa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It has been found that p53, as a tumor suppressor, mutates or loses in nearly half of cancers. In the other half of the tumor, although the p53 protein is normal, the upstream regulatory factors and downstream mediators are disordered, resulting in the destruction of the entire p53 pathway(Huang, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cell cycle is a highly regulated process that makes cell growth, genetic material replication and cell division possible. In the normal cell cycle, the expression of various cell cycle proteins is strictly regulated. However, in tumor cells, the mechanism of cell cycle regulation is disordered, resulting in abnormal activation of cyclins, which plays a pathogenic role in the development of most tumor types(Suski et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of GO enrichment analysis showed that the genes interacting with key genes RACGAP1, NDC80 and ECT2 were mainly enriched in chromosome-related pathways. The p53 and cell cycle pathways mainly enriched by KEGG played an important role in the occurrence and progression of cancer. Previous studies have also shown that overexpression of RACGAP1, ECT2 and NDC80 is associated with malignant progression and poor prognosis of HCC. Previous studies have shown that HBV leads to chromosomal remodeling and abnormal expression of oncogenes and tumor suppressor genes by integrating or inducing host gene mutations. It can also activate various cancer-related signaling pathways to promote the occurrence and progression of cancer(Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jia et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The clinical prediction model for the diagnosis of HBV-HCC based on RACGAP1, ECT2 and NDC80 also showed good accuracy.\u003c/p\u003e \u003cp\u003eIn summary, according to the results of machine learning and molecular docking, we speculate that HBV may induce gene mutations in RACGAP1, ECT2 and NDC80, affect the normal function of chromosomes, affect the normal regulation of p53 and cell cycle signaling pathways, and then lead to the occurrence and progression of HCC. Moreover, NDC80, RACGAP1 and ECT2 may be valuable diagnostic biomarkers for HBV-HCC and potential therapeutic targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLianping Wu and Xulei Zhang conceived and designed the study and critically revised the manuscript. Anyin Yang, Jianping Liu, Mengru Li performed the experiments, analyzed the data, and drafted the manuscript, Yang Anyin, Liu Jianping, Li Mengru have a common contribution to the article. Hong Zhong participated in study design, study implementation and manuscript revision.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was no funding.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eDeclared none.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen, C. J., Yang, H. I., Su, J., Jen, C. L., You, S. L., Lu, S. N., et al. (2006). Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. Jama, 295(1), 65\u0026ndash;73. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.295.1.65\u003c/span\u003e\u003cspan address=\"10.1001/jama.295.1.65\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J., \u0026amp; \u0026Uuml;nal, E. (2021). Meiotic regulation of the Ndc80 complex composition and function. Curr Genet, 67(4), 511\u0026ndash;518. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00294-021-01174-3\u003c/span\u003e\u003cspan address=\"10.1007/s00294-021-01174-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J., Xia, H., Zhang, X., Karthik, S., Pratap, S. V., Ooi, L. L., et al. (2015). ECT2 regulates the Rho/ERK signalling axis to promote early recurrence in human hepatocellular carcinoma. J Hepatol, 62(6), 1287\u0026ndash;95. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2015.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2015.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzmaurice, C., Allen, C., Barber, R. M., Barregard, L., Bhutta, Z. A., Brenner, H., et al. (2017). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol, 3(4), 524\u0026ndash;548. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2016.5688\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2016.5688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu, Y., Chen, B., Guo, D., Pan, L., Luo, X., Tang, J., et al. (2022). Up-Regulation of RACGAP1 Promotes Progressions of Hepatocellular Carcinoma Regulated by GABPA via PI3K/AKT Pathway. \u003cem\u003eOxid Med Cell Longev\u003c/em\u003e, 2022, 3034150. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2022/3034150\u003c/span\u003e\u003cspan address=\"10.1155/2022/3034150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, Z., Chen, X., Du, T., Zhu, D., Lai, Y., Dong, W., et al. (2017). Elevated levels of epithelial cell transforming sequence 2 predicts poor prognosis for prostate cancer. Med Oncol, 34(1), 13. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12032-016-0872-3\u003c/span\u003e\u003cspan address=\"10.1007/s12032-016-0872-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, C. K., Li, Z. B., Yi, D., Zhu, X. Y., Liu, R. R., Zhang, D. X., et al. (2022). LncRNA FGD5-AS1 enhances the proliferation and stemness of hepatocellular carcinoma cells through targeting miR-223 and regulating the expression of ECT2 and FAT1. Hepatol Res, 52(7), 614\u0026ndash;629. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/hepr.13767\u003c/span\u003e\u003cspan address=\"10.1111/hepr.13767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, W., Feng, Z., Teresky, A. K., \u0026amp; Levine, A. J. (2007). p53 regulates maternal reproduction through LIF. Nature, 450(7170), 721\u0026ndash;4. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature05993\u003c/span\u003e\u003cspan address=\"10.1038/nature05993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, J. (2021). Current developments of targeting the p53 signaling pathway for cancer treatment. Pharmacol Ther, 220, 107720. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pharmthera.2020.107720\u003c/span\u003e\u003cspan address=\"10.1016/j.pharmthera.2020.107720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuff, L. P., Decristo, M. J., Trembath, D., Kuan, P. F., Yim, M., Liu, J., et al. (2013). The Role of Ect2 Nuclear RhoGEF Activity in Ovarian Cancer Cell Transformation. Genes Cancer, 4(11\u0026ndash;12), 460\u0026thinsp;\u0026ndash;\u0026thinsp;75. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1947601913514851\u003c/span\u003e\u003cspan address=\"10.1177/1947601913514851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImaoka, H., Toiyama, Y., Saigusa, S., Kawamura, M., Kawamoto, A., Okugawa, Y., et al. (2015). RacGAP1 expression, increasing tumor malignant potential, as a predictive biomarker for lymph node metastasis and poor prognosis in colorectal cancer. Carcinogenesis, 36(3), 346\u0026ndash;54. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/carcin/bgu327\u003c/span\u003e\u003cspan address=\"10.1093/carcin/bgu327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIyoda, M., Kasamatsu, A., Ishigami, T., Nakashima, D., Endo-Sakamoto, Y., Ogawara, K., et al. (2010). Epithelial cell transforming sequence 2 in human oral cancer. PLoS One, 5(11), e14082. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0014082\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0014082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia, L., Gao, Y., He, Y., Hooper, J. D., \u0026amp; Yang, P. (2020). HBV induced hepatocellular carcinoma and related potential immunotherapy. Pharmacol Res, 159, 104992. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.phrs.2020.104992\u003c/span\u003e\u003cspan address=\"10.1016/j.phrs.2020.104992\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, Y., Han, Q., Zhao, H., \u0026amp; Zhang, J. (2021). The Mechanisms of HBV-Induced Hepatocellular Carcinoma. J Hepatocell Carcinoma, 8, 435\u0026ndash;450. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/jhc.S307962\u003c/span\u003e\u003cspan address=\"10.2147/jhc.S307962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJu, L. L., Chen, L., Li, J. H., Wang, Y. F., Lu, R. J., Bian, Z. L., et al. (2017). Effect of NDC80 in human hepatocellular carcinoma. World J Gastroenterol, 23(20), 3675\u0026ndash;3683. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v23.i20.3675\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v23.i20.3675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKleppe, A., Skrede, O. J., De Raedt, S., Liest\u0026oslash;l, K., Kerr, D. J., \u0026amp; Danielsen, H. E. (2021). Designing deep learning studies in cancer diagnostics. Nat Rev Cancer, 21(3), 199\u0026ndash;211. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41568-020-00327-9\u003c/span\u003e\u003cspan address=\"10.1038/s41568-020-00327-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKruiswijk, F., Labuschagne, C. F., \u0026amp; Vousden, K. H. (2015). p53 in survival, death and metabolic health: a lifeguard with a licence to kill. Nat Rev Mol Cell Biol, 16(7), 393\u0026ndash;405. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrm4007\u003c/span\u003e\u003cspan address=\"10.1038/nrm4007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeCun, Y., Bengio, Y., \u0026amp; Hinton, G. (2015). Deep learning. Nature, 521(7553), 436\u0026ndash;44. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature14539\u003c/span\u003e\u003cspan address=\"10.1038/nature14539\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevrero, M., \u0026amp; Zucman-Rossi, J. (2016). Mechanisms of HBV-induced hepatocellular carcinoma. J Hepatol, 64(1 Suppl), S84-s101. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2016.02.021\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2016.02.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., Xu, X., \u0026amp; Peng, X. (2022). NDC80 Enhances Cisplatin-resistance in Triple-negative Breast Cancer. Arch Med Res, 53(4), 378\u0026ndash;387. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arcmed.2022.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.arcmed.2022.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv, E., Sheng, J., Yu, C., Rao, D., \u0026amp; Huang, W. (2023). Long noncoding RNA MAPKAPK5-AS1 promotes metastasis through regulation miR-376b-5p/ECT2 axis in hepatocellular carcinoma. Dig Liver Dis, 55(7), 945\u0026ndash;954. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dld.2022.11.024\u003c/span\u003e\u003cspan address=\"10.1016/j.dld.2022.11.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen, K., Zhou, D., Wang, M., Li, E., Hou, C., Su, Y., et al. (2021). RACGAP1 modulates ECT2-Dependent mitochondrial quality control to drive breast cancer metastasis. Exp Cell Res, 400(1), 112493. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.yexcr.2021.112493\u003c/span\u003e\u003cspan address=\"10.1016/j.yexcr.2021.112493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaigusa, S., Tanaka, K., Mohri, Y., Ohi, M., Shimura, T., Kitajima, T., et al. (2015). Clinical significance of RacGAP1 expression at the invasive front of gastric cancer. Gastric Cancer, 18(1), 84\u0026ndash;92. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10120-014-0355-1\u003c/span\u003e\u003cspan address=\"10.1007/s10120-014-0355-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuski, J. M., Braun, M., Strmiska, V., \u0026amp; Sicinski, P. (2021). Targeting cell-cycle machinery in cancer. Cancer Cell, 39(6), 759\u0026ndash;778. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccell.2021.03.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2021.03.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanikawa, C., Zhang, Y. Z., Yamamoto, R., Tsuda, Y., Tanaka, M., Funauchi, Y., et al. (2017). The Transcriptional Landscape of p53 Signalling Pathway. \u003cem\u003eEBioMedicine\u003c/em\u003e, 20, 109\u0026ndash;119. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2017.05.017\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2017.05.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillanueva, A. (2019). Hepatocellular Carcinoma. N Engl J Med, 380(15), 1450\u0026ndash;1462. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMra1713263\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra1713263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogel, A., Meyer, T., Sapisochin, G., Salem, R., \u0026amp; Saborowski, A. (2022). Hepatocellular carcinoma. Lancet, 400(10360), 1345\u0026ndash;1362. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(22)01200-4\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(22)01200-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, H. B., Yan, H. C., \u0026amp; Liu, Y. (2016). Clinical significance of ECT2 expression in tissue and serum of gastric cancer patients. Clin Transl Oncol, 18(7), 735\u0026ndash;42. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12094-015-1428-2\u003c/span\u003e\u003cspan address=\"10.1007/s12094-015-1428-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, W., Wang, B., Wang, X., Hou, D., Su, H., \u0026amp; Huang, H. (2020). Nine hub genes related to the prognosis of HBV-positive hepatocellular carcinoma identified by protein interaction analysis. Ann Transl Med, 8(7), 478. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/atm.2020.03.94\u003c/span\u003e\u003cspan address=\"10.21037/atm.2020.03.94\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, D., Wang, Y., Wu, J., Zhang, Z., Chen, J., Xie, M., et al. (2021). ECT2 overexpression promotes the polarization of tumor-associated macrophages in hepatocellular carcinoma via the ECT2/PLK1/PTEN pathway. Cell Death Dis, 12(2), 162. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41419-021-03450-z\u003c/span\u003e\u003cspan address=\"10.1038/s41419-021-03450-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, C., Toiyama, Y., Okugawa, Y., Shigemori, T., Yamamoto, A., Ide, S., et al. (2019). Rac GTPase-Activating Protein 1 (RACGAP1) as an Oncogenic Enhancer in Esophageal Carcinoma. Oncology, 97(3), 155\u0026ndash;163. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000500592\u003c/span\u003e\u003cspan address=\"10.1159/000500592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZabady, S., Mahran, N., Soltan, M. A., Alaa Eldeen, M., Eid, R. A., Albogami, S., et al. (2022). Cyanidin-3-Glucoside Modulates hsa_circ_0001345/miRNA106b/ATG16L1 Axis Expression as a Potential Protective Mechanism against Hepatocellular Carcinoma. Curr Issues Mol Biol, 44(4), 1677\u0026ndash;1687. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cimb44040115\u003c/span\u003e\u003cspan address=\"10.3390/cimb44040115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4117465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma (HCC), as a malignant tumor, is expected to become the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths globally by 2018. It is estimated that approximately 50–80% of HCC cases worldwide are caused by hepatitis b virus (HBV) infection, and other pathogenic factors have been shown to promote the development of HCC when coexisting with HBV. Understanding the molecular mechanisms of HBV-induced hepatocellular carcinoma (HBV-HCC) is crucial for the prevention, diagnosis, and treatment of the disease. In this study, we analyzed the molecular mechanisms of HBV-induced HCC by combining bioinformatics and deep learning methods. Firstly, we collected a gene set related to HBV-HCC from the GEO database, performed differential analysis and WGCNA analysis to identify genes with abnormal expression in tumors and high relevance to tumors. We used three deep learning methods, Lasso, random forest, and SVM, to identify key genes RACGAP1, ECT2, and NDC80. By establishing a diagnostic model, we determined the accuracy of key genes in diagnosing HBV-HCC. In the training set, RACGAP1 (AUC: 0.976), ECT2 (AUC: 0.969), and NDC80 (AUC: 0.976) showed high accuracy. They also exhibited good accuracy in the validation set: RACGAP1 (AUC: 0.878), ECT2 (AUC: 0.731), and NDC80 (AUC: 0.915). The key genes were found to be highly expressed in liver cancer tissues compared to normal liver tissues, and survival analysis indicated that high expression of key genes was associated with poor prognosis in liver cancer patients. This suggests a close relationship between key genes RACGAP1, ECT2, and NDC80 and the occurrence and progression of HBV-HCC. Molecular docking results showed that the key genes could spontaneously bind to the anti-hepatocellular carcinoma drugs Lenvatinib, Regorafenib, and Sorafenib with strong binding activity. Therefore, ECT2, NDC80, and RACGAP1 may serve as potential biomarkers for the diagnosis of HBV-HCC and as targets for the development of targeted therapeutic drugs.\u003c/p\u003e","manuscriptTitle":"Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced HCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 20:34:12","doi":"10.21203/rs.3.rs-4117465/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":"487e6ae8-4795-417f-b2b7-3da0228a789c","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-26T03:44:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 20:34:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4117465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4117465","identity":"rs-4117465","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}