CYB5D2 inhibits the malignant progression of hepatocellular carcinoma by inhibiting TGF-β expression and epithelial-mesenchymal transition

CYB5D2 inhibits the malignant progression of hepatocellular carcinoma by inhibiting TGF-β expression and epithelial-mesenchymal transition | 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 CYB5D2 inhibits the malignant progression of hepatocellular carcinoma by inhibiting TGF-β expression and epithelial-mesenchymal transition Dong Jiang, Zhi Qi, Zhi-ying Xu, Yi-ran Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3899388/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 Aim Hepatocellular carcinoma (HCC) is a common liver malignancy. In this study, our goal was to investigate how TGF-β and CYB5D2 function in the etiology of HCC and their potential as prognostic biomarkers. Methods Gene co-expression network and prognostic analysis were executed on the GSE101685 dataset, and CYB5D2 was determined to be a hub gene. Then the expression of CYB5D2 and TGF-β in HCC and their correlation were detected. In vitro experiments analyzed the effects of CYB5D2 and TGF-β on the progression of HCC. Tumor xenograft experiments were performed to detect the regulation of CYB5D2 overexpression on tumor growth. Results Immunohistochemistry (IHC) and expression analysis results showed that CYB5D2 can serve as a tumor suppressor in HCC. In contrast, TGF-β , which is inversely correlated with CYB5D2 , was overexpressed in liver hepatocellular carcinoma (LIHC) and linked to poor patient prognosis. In vitro experiments confirmed that CYB5D2 expression was upregulated in HCC cell lines, while TGF-β expression was upregulated, and results from the Human Protein Atlas (HPA) database confirmed these findings. Functional analysis showed that CYB5D2 overexpression inhibited the proliferation, migration, and invasion of HCC cells and induced G1 phase arrest. Furthermore, TGF-β treatment counteracted CYB5D2 -mediated epithelial-mesenchymal transition (EMT) marker expression and tumor progression. Finally, in vivo studies showed that CYB5D2 overexpression significantly reduced tumor growth, suggesting its potential anticancer activity against HCC. Conclusion Overall, the tumor suppressor function of CYB5D2 in HCC and its interaction with TGF-β offer fresh information on the molecular pathophysiology of HCC and possible treatment avenues. Hepatocellular carcinoma CYB5D2 TGF-β Epithelial-mesenchymal transition Malignant progression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Roughly 5% of cancer patients have liver cancer, which is the sixth most common kind of cancer globally( 1 , 2 ). There are more than 700,000 newly diagnosed patients, and about 745,000 registered deaths from liver cancer each year, and among the most frequent reasons of death concerning cancer, it ranks second around the world( 3 , 4 ). One kind of liver cancer that occurs often is hepatocellular carcinoma (HCC). According to the research, hepatitis B, hepatitis C virus, alcohol consumption, aflatoxin B1 pollution, liver fibrosis, HBV infection, HCV virus infection, liver cirrhosis, etc. are also the main risk factors for HCC occurrence( 5 , 6 ). Although chemotherapy has greatly improved HCC treatment, the overall survival (OS) rate is still low and its prognosis is poor( 7 , 8 ). Consequently, the development of novel therapeutic approaches and the establishment of reliable early detection techniques are imperative for the clinical management of patients with HCC. CYB5D2 is a heme-binding protein that promotes neuronal but not astrocyte differentiation, enables heme-binding activity, is associated with nervous system development, and acts upstream or within the positive regulation of neuronal differentiation( 9 , 10 ). Researchers with relevant experience verified that CYB5D2 regulates several malignancies. Like, for example, Ojo D et al. demonstrated through cellular experiments and survival analysis that overexpression of the CYB5D2 induces apoptosis in breast cancer cells, and its down-regulation leads to decreased overall survival in breast cancer patients( 11 ). Other studies have also shown that CYB5D2 helps prevent the formation of cervical cancer, and up-regulating the expression of this gene can prevent the malignant progression of human cervical cancer HeLa cells( 12 ). It has been demonstrated that individuals with cervical cancer who express high levels of CYB5D2 mRNA have a better prognosis for their condition in the study of Li D et al., and this gene mainly reduced epithelial-mesenchymal transition (EMT) via decreasing the production of E-cadherin ( 13 ). Currently, the mechanistic role of CYB5D2 in HCC is not fully understood, necessitating further investigation to elucidate its potential function and impact on HCC progression. Members of the TGF-β (transforming growth factor) family, such as TGF-β1 , TGF-β2 , and TGF-β3 , have been shown to operate as tumor suppressors by decreasing cell proliferation and causing apoptosis, as well as by regulating EMT( 14 ). It influences tumor growth and metastasis and is essential in controlling several tumor cell activities( 15 ). TGF-β has been the subject of study into possible therapeutic approaches due to its dual involvement in cancer. The purpose of this study was to look at the role that TGF-β and CYB5D2 have in tumor growth and HCC. It is possible to provide unique and focused ways for HCC therapy by examining the function of CYB5D2 and the molecular mechanism of its interaction with TGF-β . Materials and methods Microarray data collection and analysis of genes with differentially expressed genes (DEGs) The GSE101685 microarray dataset was obtained from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ), which includes 24 groups of HCC tissues and 8 groups of normal liver tissues. Probes were then summarized via Affymetrix annotation files using the median polished probe set from the “Affy” R package. DEGs were screened out from the GSE101685 dataset through the GEO2R tool. Setting fold change (FC) > 2 or < 0.5 and p < 0.05 identified up-regulated DEGs and down-regulated DEGs. Creating the co-expression network for GSE101685-DEGs The co-expression network of DEGs in the GSE101685 dataset was established using the utilization of the Weighted gene co-expression network analysis (WGCNA) package in R. First, we determined the paired gene Pearson correlation matrix. The power function am=|cmn| was used to create the weighted adjacency matrix. With the soft threshold power β, the weak and strong correlations between penalizing genes may be displayed. A topological overlap matrix (TOM) was created from the adjacency. ALM-based average linkage hierarchical clustering was used to classify genes with comparable expression patterns into modules. Ultimately, the gene module with the highest correlation between GSE101685 samples and gene modules was chosen as the key module. Batch survival analysis RNAseq information was retrieved from 371 individuals with liver cancer (LIHC) using The Cancer Genome Atlas (TCGA; https://tcga-data.nci.nih.gov/tcga ) database. Next, the "forestplot" program was used to do a batch survival analysis for the genes in the important modules, and the effect of each gene on Disease-free survival (DFS) was visualized. Statistical significance, considered as p -values less than 0.05, along with hazard ratios (HR) and their 95% confidence intervals (CI), were determined using the log-rank test and univariate Cox regression. Construction of signature prognosis model of the turquoise module The "glmnet" tool in R software was utilized to investigate 56 genes in the turquoise module that had strong predictive significance for our investigation. The tuning parameters in the Least absolute shrinkage and selection operator (LASSO) model were identified using ten-fold cross-validation. Based on the minimum criteria for the tuning parameter (λ), the most predictive genes were determined. The chosen genes represent the most statistically significant predictors within our dataset and form the basis of our prognostic model. Next, the LIHC cohort from the TCGA database was divided into two groups (high-risk group and low-risk group) based on the expression pattern of relevant genes. Subsequently, a risk assessment was conducted for the two cohorts. For both sets of samples, a Kaplan-Meier (KM) analysis was used to calculate the DFS probability. Furthermore, the median survival time was calculated, and a log-rank test was used to establish the statistical significance of the survival differences between these two cohorts, producing a p -value. In order to offer further context for the comparative risk, the HR for the high-risk group was also calculated. Lastly, Area Under Curve (AUC) analysis was used to create the receiver operating characteristic (ROC) curve and assess the predictive ability of the risk model for patient 1-, 3-, and 5-year survival. This was done using the "timeROC" program. The larger the AUC value, the stronger the prognosis prediction ability. Construction of a prognostic nomogram and survival analysis After identifying nine prognostic genes from the risk model, uni/multivariate Cox regression analyses were carried out on these genes as well as clinical variables including age, pT stage, pTNM stage, and grade. These analyses were implemented using the “forestplot” package in R, which provides a powerful tool to visualize the resulting p -values and HRs with 95% CIs. A nomogram was then constructed for the key variables (variables with p-values less than 0.05) to analyze the impact of these key variables on patient survival. Differential expression and prognostic significance of CYB5D2 and TGF-β in LIHC TGF-β often acts as a tumor suppressor in early-stage HCC( 16 ). By curtailing cell proliferation and promoting apoptosis, TGF-β forestalls the initiation and progression of this malignancy( 17 ). First, the expression levels of CYB5D2 in both normal and TCGA-LIHC samples were determined using the Wilcoxon test in our investigation. Second, we studied the correlation between CYB5D2 and TGF-β . Furthermore, the Wilcoxon test was once again utilized to examine the TGF-β expression levels in TCGA-LIHC samples with the goal of clarifying TGF-β 's possible involvement in the pathophysiology of LIHC. Lastly, the effect of variations in TGF-β expression on the OS likelihood of patients was assessed using KM survival curves. Immunohistochemical analysis of CYB5D2 and TGF-β in the Human Protein Atlas (HPA) database The HPA ( https://www.proteinatlas.org/ ) database provides a large amount of protein immunohistochemical image data to explore the distribution and expression patterns of human proteins in various tissues and cells. In this database, we detected immunohistochemistry of the protein expression of CYB5D2 and TGF-β in normal liver tissue and HCC tissue, respectively. Cellular manipulation and transfection procedure The human HCC cell lines C3A (obtained from ATCC), HepG2, MHCC97H, and Hep3B, together with the human normal liver cell line QSG7701, were acquired from the Shanghai Institute of Cell Biology (Shanghai, China). These cells were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and maintained at 37°C in a humidified atmosphere with 5% CO 2 . The CYB5D2 overexpression plasmid was transfected into C3A and HepG2 cell lines for the transfection process, while the empty vector plasmid served as the control. Furthermore, 5 ng/mL TGF-β was added to C3A and HepG2 cell lines in order to examine the pertinent cellular reactions. Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) was used for transfections in accordance with the manufacturer's instructions. Real-time quantitative PCR (qRT-PCR) TRIzol reagent (Invitrogen) was used to extract total RNA from cells in accordance with the provided procedure. The concentration and purity of RNA were measured with a NanoDrop 2000 spectrophotometer. The PrimeScript RT Reagent Kit (Takara Bio, Otsu, Japan) was utilized to convert to cDNA in accordance with the manufacturer's instructions. For reverse transcription, the same kit was employed. qRT-PCR execution involved the SYBR Green PCR Master Mix (Takara) and particular primers for CYB5D2 , TGF-β , E-cadherin , N-cadherin , Snail , and Twist , with their exact sequences mentioned in Supplementary Table 2. The internal control was GAPDH , and expression levels were calculated using 2 −ΔΔCt . Western Blotting (WB) A protease inhibitor cocktail (Roche) and a phosphatase inhibitor cocktail (Roche) were used with RIPA lysis buffer (Beyotime, Shanghai, China) to generate protein lysates from cells. To quantify protein concentrations, Thermo Fisher Scientific's BCA protein assay kit was used. Proteins in equivalent amounts were separated on 10% SDS-PAGE gels and then transferred to PVDF membranes. After blocking for an hour at 5% nonfat milk in TBST, membranes were left overnight at 4°C to be incubated with primary antibodies ( CYB5D2 , TGF-β , E-cadherin, N-cadherin, Snail, and Twist ) diluted at a 1:1000 ratio. Horseradish peroxidase-conjugated secondary antibodies were incubated on TBST-washed membranes for one hour at room temperature. With the Millipore ECL Plus kit, protein bands were discovered and analyzed using ImageJ software. Immunohistochemistry (IHC) To evaluate the expression level of CYB5D2 , IHC analysis was done on tissue samples obtained from the Third Affiliated Hospital of Naval Medical University, including HCC and surrounding normal liver tissue. The sections were rehydrated in a graded ethanol series after being deparaffinized in xylene. Antigens were extracted from citrate buffer (pH 6.0) using microwave heating. 3% hydrogen peroxide and 5% bovine serum albumin were used to block nonspecific binding and endogenous peroxidase activity, respectively. Sections were treated with a 1:1000 solution of a primary antibody specific to CYB5D2 for an entire night at 4°C. Hematoxylin staining the slices allowed one to view the nuclei. In order to evaluate CYB5D2 expression between tumor and normal tissue, stained slices were finally imaged. Cell proliferation assay An evaluation of cellular proliferation was conducted using the cell counting kit (CCK-8) assay (Dojindo). In 96-well plates, transfected cells were cultivated for 24, 48, 72, and 96 hours. After that, each well received a 10 µL aliquot of CCK-8 solution, and the mixture was incubated at 37°C. After that, a microplate reader was used to measure the absorbance at 450 nm. Flow Cytometry For cell cycle analysis, cells were harvested, cleaned in cold phosphate-buffered saline (PBS), and preserved in 70% ethanol for an overnight stay at -20°C. After fixation, cells were once again cleaned with PBS and stained with propidium iodide (Sigma-Aldrich) for 30 minutes in the dark to highlight the DNA. After that, a BD Biosciences flow cytometer was used to determine the DNA content of the labeled cells. The FlowJo software was utilized to handle and analyze the data from the flow cytometer in order to evaluate the distribution of cells during the cell cycle. Cell proliferation and migration assays In transwell chambers with or without Matrigel (Corning Inc., Corning, NY, USA), cell migration and invasion were assessed. The trypsinized cells were resuspended in medium devoid of serum after being cleaned. 3×10 4 cells were injected into the upper chamber of each Transwell, followed by the addition of 200 µL of serum-free media and 600 µL of chemoattractant-containing medium with 10% FBS to the lower chamber. Following a 24-hour incubation period, cells passing through the bottom wells of the membrane were fixed with 4% paraformaldehyde and stained with crystal violet. Five randomly chosen areas inside each well were counted for the quantity of migrating and invasive cells under the microscope. Xenotransplantation experiments The Third Affiliated Hospital of Naval Medical University's Ethics Committee gave its clearance for all treatments involving animals. We got six-week-old male BALB/c nude mice from SLAC Laboratory in Shanghai, China. In their left hind leg, the mice received subcutaneous injections of either empty control C3A cells or C3A cells that overexpressed CYB5D2 . After 2–3 weeks, the size and weight of the mouse tumors were measured and compared before and after injection to assess how CYB5D2 overexpression affects the development of tumors. Statistical analysis Data were processed via through R language packages, with the application of either the student's t-test or one-way ANOVA for statistical scrutiny. A threshold of p < 0.05 was set for significance determination. The data was provided as mean ± SEM. Results Identification of a key module in the gene co-expression networks We identified 1033 DEGs from the GSE101685 dataset using the GEO2R tool, including 363 up-regulated DEGs and 670 down-regulated DEGs (Fig. 1 A). Next, we made use of the WGCNA package on R for putting the probes into the module through average linkage clustering (Figs. 1 B- 1 E). In the research, we chose a power of β = 14 (R 2 = 0.85 without scale) as a soft threshold, aiming to ensure a scale-free network, and identified 2 modules (except in the grey module). By comparing the correlation between the two modules and the two sets of samples in the GSE101685 dataset, it was found that the turquoise module (number of genes = 452) has the highest correlation with the two sets of samples, with a correlation coefficient of 0.801. Prognostic analysis of 9 signature genes in the key module After identifying the turquoise module as a key module, we performed DFS prognostic analysis on the genes within this module. As evident from the results in Supplementary Table 1, a total of 56 genes showed significant prognosis. Subsequently, we performed LASSO Cox regression analysis on these 56 genes and identified 9 signature prognostic genes according to the optimal lambda value (lambda.min = 0.0688) (Figs. 2 A and 2 B). The risk score for these genes was determined as follows: Riskscore=(-0.0212)* CFHR4 +(-0.0405)*SPP2+(-0.0021)*FBLN5+(-0.0157)*ANO1+(-0.0261)*STEAP4+(-0.0012)*GLUD1+(-0.0336) * CYB5D2 +(-0.0321)*LCAT+(-0.2391)*IL18RAP. According to the risk model study, the high-risk group outperformed the low-risk group in terms of survival and death (Fig. 2 C). The findings of the KM survival analysis, which are shown in Fig. 2 D, indicated that the high-risk group's median survival time was 1.2 years, while the low-risk group's was 3.9 years, with an HR of 2.287 (> 1). Moreover, the DFS probability was decreased in the high-risk group. The ROC curve results further highlighted that the predictive accuracy of the risk model was most potent at the five-year mark (AUC = 0.729). Our research concludes by highlighting the important prognostic potential of these nine genes. Nomogram analysis of key prognostic variables In the analysis of nine genes and four clinical variables in the risk model, we identified five variables with statistical significance ( p < 0.05), namely CFHR4 , CYB5D2 , IL18RAP , pT stage, and pTNM stage (Figs. 3 A and 3 B). The results of the nomogram analysis showed that these variables had significant predictive power on the 1-, 3-, and 5-year survival rates of patients, which was also confirmed by the calibration curve results (Figs. 3 C and 3 D). Inverse association between CYB5D2 and TGF-β expression in LIHC From the prognostic genes identified through nomogram analysis, CYB5D2 , CFHR4 , and IL18RAP have already been substantiated as participating in the pathogenesis of HCC. We chose CYB5D2 as the hub gene in this investigation to examine its relationship to HCC. Figure 4 A illustrated a noticeable underexpression of CYB5D2 in LIHC, hinting towards a potential role in disease manifestation. Immunohistochemistry results also confirmed this. Compared with normal liver tissue, the staining intensity of CYB5D2 in tumor tissue was weakened, indicating that its expression level in tumor tissue was low (Fig. 4 B). With a correlation coefficient (r) of -0.65, correlation analysis revealed a substantial negative association between CYB5D2 and TGF-β (Fig. 4 C). To further substantiate our findings, we employed the Wilcoxon test, which confirmed an overexpression of TGF-β in LIHC (Figs. 4 D and 4 E). Interestingly, TGF-β overexpression was related to a poorer OS prognosis, indicating that TGF-β may have a prognostic function in LIHC. Downregulation of CYB5D2 and upregulation of TGF-β may be associated with malignant progression of HCC By using the qRT-PCR and WB method in vitro experiments, we detected the CYB5D2 and TGF-β expression levels in HCC cells. In C3A and HepG2, CYB5D2 mRNA and protein levels were found to be much lower than in QSG7701 (Figs. 5 A and 5 B). In contrast, qRT-PCR and WB confirmed that C3A and HepG2 cells had elevated TGF-β expression levels (Figs. 5 C and 5 D). The HPA database also analyzed the levels of CYB5D2 and TGF-β in HCC tissues. Among them, no staining signal was detected for CYB5D2 in tumor tissue, and TGF-β showed high staining intensity in tumor tissue (Figs. 5 E and 5 F). This suggests that whilst TGF-β expression is markedly elevated in tumor tissues, CYB5D2 expression is low in malignant tissues. Therefore, we speculate that the downregulation of CYB5D2 and the upregulation of TGF-β may be related to the malignant progression of HCC. CYB5D2 overexpression inhibits HCC cell proliferation through the cell cycle We performed overexpression experiments by transfecting the CYB5D2 plasmid into C3A and HepG2 cells. The mRNA level of CYB5D2 was shown to be up-regulated using the qRT-PCR method (Fig. 6 A). After that, the CCK-8 test was employed to look at how CYB5D2 overexpression affected cell division. The overexpression of CYB5D2 was observed to drastically decrease the growth of C3A and HepG2 cells (Figs. 6 B and 6 C). Subsequently, as shown in Figs. 6 D- 6 G, in the G1 phase, overexpression of CYB5D2 caused cell cycle arrest (57.79–65.69%, 55.67–62.21%). These findings collectively imply that CYB5D2 overexpression inhibits the proliferation of C3A /HepG2 cells and induces cell cycle arrest, providing insight into the potential therapeutic implications of targeting CYB5D2 in HCC. Overexpression of CYB5D2 inhibits HCC cell migration and invasion Next, utilizing transwell studies, the impact of CYB5D2 overexpression on C3A and HepG2 cell migration and invasion was examined. The over- CYB5D2 group showed a substantial reduction in migrating and invasive cells as compared to the control group (Figs. 7 A- 7 D), demonstrating that CYB5D2 overexpression prevented the migration and invasion of C3A as well as HepG2 cells. TGF-β reverses the regulation of EMT markers and tumor progression by CYB5D2 overexpression in HCC The development and metastasis of HCC are significantly influenced by the EMT( 18 , 19 ). Key regulators of EMT, such as E-cadherin , N-cadherin , etc., are typically associated with tumor progression( 20 , 21 ). To investigate the regulatory roles of CYB5D2 and TGF-β on these EMT-associated factors, we segregated C3A and HepG2 cells into three groups: control (over-NC), CYB5D2 overexpression (over- CYB5D2 ), and CYB5D2 overexpression with TGF-β (over- CYB5D2 + TGF-β ). qRT-PCR analysis revealed a significant increase in e-cadherin expression in the over- CYB5D2 group as compared to the control. However, with the addition of TGF-β , its expression level decreased. Conversely, CYB5D2 overexpression downregulated the expression of N-cadherin , Snail , and Twist . Following the administration of TGF-β , these three EMT-related regulatory factors demonstrated an upregulation in their expression levels (Figs. 8 A and 8 B). These observations were further validated by WB analysis (Fig. 8 C). Based on these findings, we conducted Transwell rescue experiments and discovered that overexpression of CYB5D2 markedly curtailed the migratory and invasive behavior of C3A and HepG2 cells relative to the control group. The addition of TGF-β partially counteracted this inhibitory effect (Figs. 8 D- 8 G). Thus, our results suggest that CYB5D2 inhibits both the migration and invasion of HCC cells and modifies the expression of EMT-related proteins. TGF-β might function a role in reversing the inhibitory effects of CYB5D2 on C3A and HepG2. Overexpression of CYB5D2 exhibits anticancer activity in vivo In this study, the in vivo effects of overexpressing CYB5D2 were assessed using a xenograft model of HCC in nude mice. After CYB5D2 overexpression, a significant reduction in tumor size and weight was observed in the nude mouse model (Figs. 9 A and 9 B). This outcome underscores the impact of CYB5D2 modulation on tumorigenesis in HCC. Discussion The onset of liver cancer is a complex multi-factor process( 22 ). Excessive drinking, viral infections (hepatitis B or C), and worsening of liver cirrhosis can all lead to HCC( 23 , 24 ). Under normal circumstances, the ratio of HCC to men and women is 7–10:1. That means, the number of male HCC patients is 7–10 times that of females. More than 90% of HCC cases are related to hepatic inflammation and damage, and chronic unresolved inflammation is linked to persistent liver injury and concurrent regeneration, which results in fibrosis, cirrhosis, and eventually HCC( 25 , 26 ). Currently, people can diagnose HCC by non-invasive imaging, which is inclusive of magnetic resonance imaging and computed tomography( 27 , 28 ). But despite this, the diagnostic effect of HCC is still not optimistic, and more in-depth research and discovery of new treatment methods are needed. Currently, prognostic and target analysis of HCC based on WGCNA and LASSO regression algorithms has become more and more common. Yang Z et al. performed bioinformatics analysis of HCC-related data in TCGA and GEO based on the above two datasets, and found 4 macrophage-related genes ( CDCA8 , CBX2 , UCK2 and SOCS2 ) with good prognostic independence in HCC, which are expected to be the potential prognostic target( 29 ). Shan Y et al. performed WGCNA on the DEGs in the GSE22058 and GSE54238 datasets, screened key modules, and constructed a prognostic model through the LASSO regression algorithm, from which they analyzed UBA1 as a characteristic marker for liver cancer diagnosis and prognosis( 30 ). Based on the WGCNA algorithm and the LASSO algorithm, Ding H et al. found that MMP9 is highly correlated with the prognosis of HCC patients. They also found that the dual immunological signal of MMP9 and CD8 + T cells can increase the survival rate of HCC patients( 31 ). In summary, research on the main goals and processes of HCC using the WGCNA and LASSO algorithms is persuasive. Based on this, within our research, we first built a network of gene co-expression for DEGs in the GSE101685 dataset by WGCNA, and identified the turquoise as a key gene module. Subsequently, survival analysis and LASSO Cox prognostic model and nomogram analysis were performed on the genes in these two modules, and three significant prognostic genes were identified, namely CFHR4 , CYB5D2 , and IL18RAP . This analysis highlights their potential role in HCC progression and patient prognostic outcomes. Research by Ding Q et al. pointed out that CFHR4 is significantly low-expressed in HCC, which can lead to poor patient prognosis, and its level of expression and the extent of immune cell infiltration are also correlated( 32 ). The study by Yu H et al. further showed the substantial predictive usefulness of CFHR4 in HCC by demonstrating how the expression of this gene and several clinicopathological factors are strongly correlated with immune cell infiltration( 33 ). CFHR4 has also been identified as one of the hepatocyte subtypes in extrahepatic metastasis of advanced HCC and may serve as a predictive target for resistance to the combination therapy of lenvatinib, FOLFOX , and toripalimab( 34 ). Li R et al. identified five genes, including IL18RAP , that are associated with the prognosis of HCC and verified that there is a relationship between immune cell infiltration and immunotherapy sensitivity and the expression of these genes( 35 ). By performing LASSO and multivariable Cox regression analysis on HCC-related data sets in public databases, CYB5D2 was also identified as a key prognostic gene for HCC by Ren Z et al( 36 ). However, the precise mechanism of action of CYB5D2 in HCC is still unknown and requires additional research. Our study shows that CYB5D2 is underexpressed in LIHC and negatively correlated with TGF-β . TGF-β has been confirmed to be highly expressed in LIHC and can lead to poor prognosis in HCC patients. Research has indicated that TGF-β is one of the main cytokines known to cause EMT and that it plays a significant role in the process( 37 ). Through the activation of certain transcription factors that downregulate epithelial indicators and upregulate mesenchymal markers, TGF-β can contribute to tumor growth, metastasis, and resistance to clinical therapy( 38 ). One study found that STAT3 induces EMT and metastasis of liver cancer by positively regulating TGF-β1 , and STAT3 cooperates with the Snail-Smad3/ TGF-β1 signaling pathway to promote cancer progression( 39 ). Through the PI3K/Akt/Rac1 pathway, MMP-8 and TGF-β1 mutual activation induces EMT, which in turn promotes the development of HCC( 40 ). In addition, another study also pointed out that the FCN2 / TGF-β /EMT axis is an important mechanism affecting HCC metastasis and affects the metastasis of HCC( 41 ). Our goal was to investigate the precise mechanism of action of TGF-β and CYB5D2 in HCC using in vitro experimental study. The results demonstrate that CYB5D2 is downregulated in HCC cells, and overexpression of CYB5D2 inhibits HCC cell growth and induces G1 arrest. CYB5D2 and TGF-β jointly regulate the expression of EMT-related factors and the progression of HCC. Specifically, CYB5D2 overexpression leads to upregulation of E-cadherin and downregulation of N-cadherin , Snail , and Twist , indicating that CYB5D2 may function as an EMT inhibitor. This modulation of EMT-related factors corresponds to a significant inhibition of HCC migration and invasion capabilities, supporting the theory that CYB5D2 could have tumor-suppressive effects in HCC. These effects were partially reversed upon the addition of TGF-β , suggesting a potential interaction between TGF-β and CYB5D2 . In addition, tumor xenograft experiments also confirmed that CYB5D2 overexpression significantly inhibited tumor growth in mice. Overall, CYB5D2 is important for the formation and progression of HCC and may have a significant impact on preventing EMT and tumor growth of HCC by regulating key EMT indicators and counteracting the tumorigenic effects of TGF-β . Conclusion To sum up, our study analyzed DEGs in GSE101685 based on bioinformatics methods and identified the hub gene CYB5D2 associated with HCC prognosis. CYB5D2 is significantly low-expressed in HCC and negatively correlated with TGF-β . TGF-β is a key factor promoting tumor progression and metastasis through EMT. TGF-β can counteract the inhibition of HCC cell proliferation, migration, and invasion as well as the regulation of EMT markers by CYB5D2 overexpression to a certain extent, indicating that there is a complex interaction between CYB5D2 and TGF-β in regulating HCC progression. In vivo experiments further confirmed that CYB5D2 overexpression can significantly reduce tumor growth, indicating its potential application value as a therapeutic target for HCC. Consequently, our research underscores the possibility of CYB5D2 -targeted therapies as innovative therapeutic approaches for HCC, offering fresh therapeutic directions for patient treatment. Declarations Ethics approval and consent to participate This study was conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All animal experiments were approved by the Ethics Committee of the Third Affiliated Hospital of Naval Medical University(approval number: EHBHKY2014-03-006). Regarding human samples, the study was approved by the Institutional Review Board of Ethics Committee of the Third Affiliated Hospital of Naval Medical University (approval number: EHBHKY2023-KO39-P001). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. All samples were anonymized to respect the privacy of participants. Author Contribution Guarantors of integrity of entire study, D.J.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, D.J., Z.Q.; experimental studies, D.J.; statistical analysis, D.J., Z.Q., Z.X., Y.L.; and manuscript editing, D.J., Z.Q., Z.X., Y.L. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Front Mol Biosci 9:917839 Ren Z, Gao D, Luo Y, Song Z, Wu G, Qi N et al (2023) Identification of fatty acid metabolism-related clusters and immune infiltration features in hepatocellular carcinoma. Aging 15(5):1496 Dash S, Sarashetti PM, Rajashekar B, Chowdhury R, Mukherjee S (2018) TGF-β2-induced EMT is dampened by inhibition of autophagy and TNF-α treatment. Oncotarget 9(5):6433 Prieto-García E, Díaz-García CV, García-Ruiz I, Agulló-Ortuño MT (2017) Epithelial-to-mesenchymal transition in tumor progression. Med Oncol 34:1–10 Wang B, Liu T, Wu J-C, Luo S-Z, Chen R, Lu L-G et al (2018) STAT3 aggravates TGF-β1-induced hepatic epithelial-to-mesenchymal transition and migration. Biomed Pharmacother 98:214–221 Scheau C, Badarau IA, Costache R, Caruntu C, Mihai GL, Didilescu AC et al (2019) The role of matrix metalloproteinases in the epithelial-mesenchymal transition of hepatocellular carcinoma. Analytical cellular pathology. ;2019 Yang G, Liang Y, Zheng T, Song R, Wang J, Shi H et al (2016) FCN2 inhibits epithelial–mesenchymal transition-induced metastasis of hepatocellular carcinoma via TGF-β/Smad signaling. Cancer Lett 378(2):80–86 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx 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. 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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-3899388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269533044,"identity":"86461a3d-b170-4721-8f6a-153b47e2ec8d","order_by":0,"name":"Dong Jiang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Jiang","suffix":""},{"id":269533045,"identity":"c2d56a8b-2f52-4971-85bc-7b3c20e19eef","order_by":1,"name":"Zhi Qi","email":"","orcid":"","institution":"The Third Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Qi","suffix":""},{"id":269533046,"identity":"622eb07a-5fa4-4488-95de-3e001ce08646","order_by":2,"name":"Zhi-ying Xu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-ying","middleName":"","lastName":"Xu","suffix":""},{"id":269533047,"identity":"bdc24c9d-0bb6-4484-acef-f0cc933ce0da","order_by":3,"name":"Yi-ran Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACPmYGhgMghgEDD+MDIEkYsCFpYTYgTguMAdTCJkGUw9jYeQwPV9TckTPnP3us8kfBHXkG9sNHN+B3GI/BwTPHnhlbzshLu81j8MywgSct7QZBLQ1shxM33OAxu81gcJixQYLHjAgt/w7Xbzh/xqzwh8Fhe+K0NLYdTjA4kGPGwGNwOJEILWwFBxv7DhtuuJFjLA3UktxGyC/8/Ic3f2z4dlje4PwZw48//hy27Wc/fAyvFgYGDrToY8OuDBmwPyCsZhSMglEwCkY2AACzvUnk0DNO0QAAAABJRU5ErkJggg==","orcid":"","institution":"The Third Affiliated Hospital of Naval Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yi-ran","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-01-26 08:16:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3899388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3899388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50390146,"identity":"2d611973-6a29-4f62-ad49-7f05fafc1c9c","added_by":"auto","created_at":"2024-01-30 18:40:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1260661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of gene co-expression network.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot, statistical significance of difference (negative log10-adjusted \u003cem\u003ep\u003c/em\u003e-value; y-axis), and amount of change (log2 fold change; x-axis) for DEGs in the GSE101685 dataset. Each dot represents an individual gene. Orange dots indicate DEGs that are up-regulated, whereas green dots indicate DEGs that are down-regulated.\u003c/p\u003e\n\u003cp\u003e(B) Graphical representation of the scale-free fit exponential analysis for various soft threshold powers (β). The scale-free fit index (y-axis) is shown against the soft-thresholding power (x-axis) in the above graph, which helps in choosing the optimal soft threshold. The mean connection (y-axis) for different soft-threshold powers (x-axis) is displayed in the picture below.\u003c/p\u003e\n\u003cp\u003e(C) Trait heatmap of the dendrogram of 32 samples in the GSE101685 dataset. The heatmap provides a visual summary of the sample traits, with clustering patterns revealing relationships among samples.\u003c/p\u003e\n\u003cp\u003e(D) Cluster dendrogram of gene modules derived from hierarchical clustering. Each color represents a different module, and the branches of the dendrogram group genes with similar expression profiles.\u003c/p\u003e\n\u003cp\u003e(E) Heatmap of correlations between gene modules and clinical features of HCC, with numbers in modules showing correlation coefficients and\u003cem\u003e p\u003c/em\u003e-values.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/6adadda1d27257e699b88f33.png"},{"id":50391372,"identity":"5302d51d-e3af-4e0d-ba88-acd8ce9424a7","added_by":"auto","created_at":"2024-01-30 18:56:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":884027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignature prognostic model for the turquoise module.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Distribution of LASSO coefficients for genes with significant survival analysis results in the turquoise module. Each line corresponds to an individual gene, with the path of the coefficient profile depicted against the log(λ) sequence.\u003c/p\u003e\n\u003cp\u003e(B) Partial likelihood deviation plot in a LASSO Cox regression model vs log(λ). The plot is utilized to select the optimal parameter (λ), where the model achieves the smallest cross-validated error. The ideal values according to the 1-SE and minimum criterion are indicated by the two vertical lines.\u003c/p\u003e\n\u003cp\u003e(C) The distribution of risk scores, survival time, and survival status are displayed in the riskscore analysis for the chosen dataset samples.\u003c/p\u003e\n\u003cp\u003e(D) KM survival curve analysis using the median risk score as a stratification tool to identify high- and low-risk groups. The log-rank test is used to determine the difference in survival probability between the two groups.\u003c/p\u003e\n\u003cp\u003e(E) The 1-year, 3-year, and 5-year OS prediction accuracy is shown by the AUC values on the ROC curve of the landmark prognostic model.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/7220158b1c2e51294bfd6052.png"},{"id":50391213,"identity":"38c6fade-279a-4465-8b8d-be136fc13495","added_by":"auto","created_at":"2024-01-30 18:48:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":510884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram analysis of key prognostic variables.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Univariate and (B) multivariate Cox regression analysis of nine genes and four clinical variables in the risk model yielded five significant variables: \u003cem\u003eCFHR4\u003c/em\u003e, \u003cem\u003eCYB5D2\u003c/em\u003e, \u003cem\u003eIL18RAP\u003c/em\u003e, pT-stage, and pTNM-stage.\u003c/p\u003e\n\u003cp\u003e(C) Nomogram analysis showing the significant predictive power of the identified variables on patient survival at 1, 3, and 5 years, with the length of the line segments reflecting the magnitude of the effect of the variables on patient survival.\u003c/p\u003e\n\u003cp\u003e(D) Calibration curves that validate the predictive accuracy of the nomograms, with the middle dashed line being the ideal calibration curve.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/cc171ddadfd74147460b7dee.png"},{"id":50390156,"identity":"6a17fdda-0157-4b2d-9167-f84464a38e1f","added_by":"auto","created_at":"2024-01-30 18:40:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5787303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression levels and correlation analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCYB5D2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e TGF-β \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ein LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Boxplot, Wilcoxon detection of \u003cem\u003eCYB5D2\u003c/em\u003e expression in TCGA-LIHC samples. ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(B) Immunohistochemistry of \u003cem\u003eCYB5D2 \u003c/em\u003ein HCC tumor samples and normal samples.\u003c/p\u003e\n\u003cp\u003e(C) Scatter plot, correlation analysis between \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003eexpression levels, the correlation coefficient (r) is -0.65.\u003c/p\u003e\n\u003cp\u003e(D) Boxplot, Wilcoxon detection of \u003cem\u003eTGF-β\u003c/em\u003e expression in TCGA-LIHC samples. ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(E) The correlation between \u003cem\u003eTGF-β\u003c/em\u003e differential expression and LIHC OS prognosis is represented by the KM survival curve. OS probability is the vertical axis, while survival duration is the horizontal axis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/2d326185e10a5c5f6031b194.png"},{"id":50391214,"identity":"871e86a0-5363-4f35-8b01-21c21077367d","added_by":"auto","created_at":"2024-01-30 18:48:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2012594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCYB5D2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTGF-β\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in HCC was detected\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e in vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A and B) qRT-PCR and WB were used to determine the \u003cem\u003eCYB5D2\u003c/em\u003e expression levels in human normal liver cells (QSG7701) and HCC cell lines (C3A, HepG2, Hep3B, and MHCC97).\u003c/p\u003e\n\u003cp\u003e(C and D) qRT-PCR and WB were used to determine the \u003cem\u003eTGF-β\u003c/em\u003e expression levels in human normal liver cells (QSG7701) and HCC cell lines (C3A, HepG2, Hep3B, and MHCC97).\u003c/p\u003e\n\u003cp\u003e(E and F) Immunohistochemical staining of \u003cem\u003eCYB5D2\u003c/em\u003e/\u003cem\u003eTGF-β\u003c/em\u003e expression in clinical samples of liver cancer. Images demonstrating the expression of \u003cem\u003eCYB5D2\u003c/em\u003e/\u003cem\u003eTGF-β\u003c/em\u003e in liver cancer tissues and nearby normal tissues are displayed. The brown color indicates positive staining.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/5a246be190759fbdf04c5a57.png"},{"id":50390150,"identity":"c6026755-43b6-46ae-9cad-65f3407fb376","added_by":"auto","created_at":"2024-01-30 18:40:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":425955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e CYB5D2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e inhibits proliferation and induces cell cycle arrest in C3A and HepG2 cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) \u003cem\u003eCYB5D2\u003c/em\u003e mRNA expression in C3A and HepG2 cells following transfection with \u003cem\u003eCYB5D2\u003c/em\u003e plasmid or empty vector control was examined using qRT-PCR.\u003c/p\u003e\n\u003cp\u003e(B and C) The CCK-8 experiment demonstrates the growth of HepG2 and C3A cells following transfection with either the \u003cem\u003eCYB5D2\u003c/em\u003e plasmid or an empty vector control.\u003c/p\u003e\n\u003cp\u003e(D-G) Cell cycle distribution in C3A and HepG2 cells following transfection with either the \u003cem\u003eCYB5D2\u003c/em\u003e plasmid or an empty vector control was examined using flow cytometry.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/c89beeb8d1c4620d75ec5715.png"},{"id":50390154,"identity":"601ab8ea-ece4-43f6-9102-1f685895be75","added_by":"auto","created_at":"2024-01-30 18:40:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3567882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCYB5D2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e regulates the migration and invasion of C3A and HepG2 cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-D) Transwell tests for C3A and HepG2 cells transfected with the \u003cem\u003eCYB5D2\u003c/em\u003eplasmid or empty vector control to measure invasion and migration. The left panel displays representative microscopic pictures of the Transwell test findings, while the right panel displays bars that represent the quantification of migrating and invading cells.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/3d1cab43b807f39af9f5b7a0.png"},{"id":50391215,"identity":"7da7ef65-0bdc-451d-ba56-38b7b278566e","added_by":"auto","created_at":"2024-01-30 18:48:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":7593996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCYB5D2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTGF-β\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e on the expression of EMT-related factors and the migration, and invasion ability of C3A and HepG2 cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A and B) EMT-related variables were analyzed by qRT-PCR in three groups: control, over-\u003cem\u003eCYB5D2\u003c/em\u003e, and over-\u003cem\u003eCYB5D2\u003c/em\u003e+\u003cem\u003eTGF-β\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(C) WB analysis of EMT\u003cstrong\u003e-\u003c/strong\u003erelated factors in control, over-\u003cem\u003eCYB5D2\u003c/em\u003e, and over-\u003cem\u003eCYB5D2\u003c/em\u003e+\u003cem\u003eTGF-β\u003c/em\u003e groups.\u003c/p\u003e\n\u003cp\u003e(D-G) Transwell detection of the effects of \u003cem\u003eTGF-β\u003c/em\u003e and over-\u003cem\u003eCYB5D2\u003c/em\u003e on C3A and HepG2 cell invasion and migration. The left panel displays representative microscopic pictures of the Transwell test findings, while the right panel displays bars that represent the quantification of migrating and invading cells.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/942f95853fdad699ab98d5b6.png"},{"id":50390152,"identity":"a2edc683-2192-489e-9901-2da4067ea7db","added_by":"auto","created_at":"2024-01-30 18:40:02","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":449684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCYB5D2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e overexpression on tumor growth in C3A cell xenografts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Measurement of tumor size changes in nude mice transfected with C3A cells after \u003cem\u003eCYB5D2 \u003c/em\u003eoverexpression.\u003c/p\u003e\n\u003cp\u003e(B) Observation of changes in tumor weight with or without overexpression of \u003cem\u003eCYB5D2.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/015715ce8b33671f681a9f87.png"},{"id":50965560,"identity":"1c963c3e-a5db-45d1-8273-23047b2f45ec","added_by":"auto","created_at":"2024-02-11 11:13:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4785198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/20feacc5-45f9-4ac6-a379-fe9c32577218.pdf"},{"id":50390147,"identity":"da0cd762-5f91-48e4-a481-9f3b47280701","added_by":"auto","created_at":"2024-01-30 18:40:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21435,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/dd62d634f26444c8b2aa345d.docx"},{"id":50390144,"identity":"3aa6bcac-65c3-4534-af69-63820705bd75","added_by":"auto","created_at":"2024-01-30 18:40:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16650,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3899388/v1/6e104ad4e45317c574bbb398.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"CYB5D2 inhibits the malignant progression of hepatocellular carcinoma by inhibiting TGF-β expression and epithelial-mesenchymal transition","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRoughly 5% of cancer patients have liver cancer, which is the sixth most common kind of cancer globally(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). There are more than 700,000 newly diagnosed patients, and about 745,000 registered deaths from liver cancer each year, and among the most frequent reasons of death concerning cancer, it ranks second around the world(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). One kind of liver cancer that occurs often is hepatocellular carcinoma (HCC). According to the research, hepatitis B, hepatitis C virus, alcohol consumption, aflatoxin B1 pollution, liver fibrosis, HBV infection, HCV virus infection, liver cirrhosis, etc. are also the main risk factors for HCC occurrence(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Although chemotherapy has greatly improved HCC treatment, the overall survival (OS) rate is still low and its prognosis is poor(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Consequently, the development of novel therapeutic approaches and the establishment of reliable early detection techniques are imperative for the clinical management of patients with HCC.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCYB5D2\u003c/em\u003e is a heme-binding protein that promotes neuronal but not astrocyte differentiation, enables heme-binding activity, is associated with nervous system development, and acts upstream or within the positive regulation of neuronal differentiation(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Researchers with relevant experience verified that \u003cem\u003eCYB5D2\u003c/em\u003e regulates several malignancies. Like, for example, Ojo D et al. demonstrated through cellular experiments and survival analysis that overexpression of the \u003cem\u003eCYB5D2\u003c/em\u003e induces apoptosis in breast cancer cells, and its down-regulation leads to decreased overall survival in breast cancer patients(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Other studies have also shown that \u003cem\u003eCYB5D2\u003c/em\u003e helps prevent the formation of cervical cancer, and up-regulating the expression of this gene can prevent the malignant progression of human cervical cancer HeLa cells(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). It has been demonstrated that individuals with cervical cancer who express high levels of \u003cem\u003eCYB5D2\u003c/em\u003e mRNA have a better prognosis for their condition in the study of Li D et al., and this gene mainly reduced epithelial-mesenchymal transition (EMT) via decreasing the production of \u003cem\u003eE-cadherin\u003c/em\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Currently, the mechanistic role of \u003cem\u003eCYB5D2\u003c/em\u003e in HCC is not fully understood, necessitating further investigation to elucidate its potential function and impact on HCC progression.\u003c/p\u003e \u003cp\u003eMembers of the \u003cem\u003eTGF-β\u003c/em\u003e (transforming growth factor) family, such as \u003cem\u003eTGF-β1\u003c/em\u003e, \u003cem\u003eTGF-β2\u003c/em\u003e, and \u003cem\u003eTGF-β3\u003c/em\u003e, have been shown to operate as tumor suppressors by decreasing cell proliferation and causing apoptosis, as well as by regulating EMT(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It influences tumor growth and metastasis and is essential in controlling several tumor cell activities(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). \u003cem\u003eTGF-β\u003c/em\u003e has been the subject of study into possible therapeutic approaches due to its dual involvement in cancer. The purpose of this study was to look at the role that \u003cem\u003eTGF-β\u003c/em\u003e and \u003cem\u003eCYB5D2\u003c/em\u003e have in tumor growth and HCC. It is possible to provide unique and focused ways for HCC therapy by examining the function of \u003cem\u003eCYB5D2\u003c/em\u003e and the molecular mechanism of its interaction with \u003cem\u003eTGF-β\u003c/em\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray data collection and analysis of genes with differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eThe GSE101685 microarray dataset was obtained from the Gene Expression Omnibus (GEO; \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), which includes 24 groups of HCC tissues and 8 groups of normal liver tissues. Probes were then summarized via Affymetrix annotation files using the median polished probe set from the \u0026ldquo;Affy\u0026rdquo; R package. DEGs were screened out from the GSE101685 dataset through the GEO2R tool. Setting fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;0.5 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 identified up-regulated DEGs and down-regulated DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCreating the co-expression network for GSE101685-DEGs\u003c/h2\u003e \u003cp\u003eThe co-expression network of DEGs in the GSE101685 dataset was established using the utilization of the Weighted gene co-expression network analysis (WGCNA) package in R. First, we determined the paired gene Pearson correlation matrix. The power function am=|cmn| was used to create the weighted adjacency matrix. With the soft threshold power β, the weak and strong correlations between penalizing genes may be displayed. A topological overlap matrix (TOM) was created from the adjacency. ALM-based average linkage hierarchical clustering was used to classify genes with comparable expression patterns into modules. Ultimately, the gene module with the highest correlation between GSE101685 samples and gene modules was chosen as the key module.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBatch survival analysis\u003c/h2\u003e \u003cp\u003eRNAseq information was retrieved from 371 individuals with liver cancer (LIHC) using The Cancer Genome Atlas (TCGA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/tcga\u003c/span\u003e\u003cspan address=\"https://tcga-data.nci.nih.gov/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. Next, the \"forestplot\" program was used to do a batch survival analysis for the genes in the important modules, and the effect of each gene on Disease-free survival (DFS) was visualized. Statistical significance, considered as \u003cem\u003ep\u003c/em\u003e-values less than 0.05, along with hazard ratios (HR) and their 95% confidence intervals (CI), were determined using the log-rank test and univariate Cox regression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of signature prognosis model of the turquoise module\u003c/h2\u003e \u003cp\u003eThe \"glmnet\" tool in R software was utilized to investigate 56 genes in the turquoise module that had strong predictive significance for our investigation. The tuning parameters in the Least absolute shrinkage and selection operator (LASSO) model were identified using ten-fold cross-validation. Based on the minimum criteria for the tuning parameter (λ), the most predictive genes were determined. The chosen genes represent the most statistically significant predictors within our dataset and form the basis of our prognostic model. Next, the LIHC cohort from the TCGA database was divided into two groups (high-risk group and low-risk group) based on the expression pattern of relevant genes. Subsequently, a risk assessment was conducted for the two cohorts. For both sets of samples, a Kaplan-Meier (KM) analysis was used to calculate the DFS probability. Furthermore, the median survival time was calculated, and a log-rank test was used to establish the statistical significance of the survival differences between these two cohorts, producing a \u003cem\u003ep\u003c/em\u003e-value. In order to offer further context for the comparative risk, the HR for the high-risk group was also calculated. Lastly, Area Under Curve (AUC) analysis was used to create the receiver operating characteristic (ROC) curve and assess the predictive ability of the risk model for patient 1-, 3-, and 5-year survival. This was done using the \"timeROC\" program. The larger the AUC value, the stronger the prognosis prediction ability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a prognostic nomogram and survival analysis\u003c/h2\u003e \u003cp\u003eAfter identifying nine prognostic genes from the risk model, uni/multivariate Cox regression analyses were carried out on these genes as well as clinical variables including age, pT stage, pTNM stage, and grade. These analyses were implemented using the \u0026ldquo;forestplot\u0026rdquo; package in R, which provides a powerful tool to visualize the resulting \u003cem\u003ep\u003c/em\u003e-values and HRs with 95% CIs. A nomogram was then constructed for the key variables (variables with \u003cem\u003ep-values\u003c/em\u003e less than 0.05) to analyze the impact of these key variables on patient survival.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential expression and prognostic significance of\u003c/b\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eTGF-β\u003c/b\u003e \u003cb\u003ein LIHC\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eTGF-β\u003c/em\u003e often acts as a tumor suppressor in early-stage HCC(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). By curtailing cell proliferation and promoting apoptosis, \u003cem\u003eTGF-β\u003c/em\u003e forestalls the initiation and progression of this malignancy(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). First, the expression levels of \u003cem\u003eCYB5D2\u003c/em\u003e in both normal and TCGA-LIHC samples were determined using the Wilcoxon test in our investigation. Second, we studied the correlation between \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e. Furthermore, the Wilcoxon test was once again utilized to examine the \u003cem\u003eTGF-β\u003c/em\u003e expression levels in TCGA-LIHC samples with the goal of clarifying \u003cem\u003eTGF-β\u003c/em\u003e's possible involvement in the pathophysiology of LIHC. Lastly, the effect of variations in \u003cem\u003eTGF-β\u003c/em\u003e expression on the OS likelihood of patients was assessed using KM survival curves.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemical analysis of\u003c/b\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eTGF-β\u003c/b\u003e \u003cb\u003ein the Human Protein Atlas (HPA) database\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe HPA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database provides a large amount of protein immunohistochemical image data to explore the distribution and expression patterns of human proteins in various tissues and cells. In this database, we detected immunohistochemistry of the protein expression of \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e in normal liver tissue and HCC tissue, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCellular manipulation and transfection procedure\u003c/h2\u003e \u003cp\u003eThe human HCC cell lines C3A (obtained from ATCC), HepG2, MHCC97H, and Hep3B, together with the human normal liver cell line QSG7701, were acquired from the Shanghai Institute of Cell Biology (Shanghai, China). These cells were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and maintained at 37\u0026deg;C in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e. The \u003cem\u003eCYB5D2\u003c/em\u003e overexpression plasmid was transfected into C3A and HepG2 cell lines for the transfection process, while the empty vector plasmid served as the control. Furthermore, 5 ng/mL \u003cem\u003eTGF-β\u003c/em\u003e was added to C3A and HepG2 cell lines in order to examine the pertinent cellular reactions. Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) was used for transfections in accordance with the manufacturer's instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eReal-time quantitative PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTRIzol reagent (Invitrogen) was used to extract total RNA from cells in accordance with the provided procedure. The concentration and purity of RNA were measured with a NanoDrop 2000 spectrophotometer. The PrimeScript RT Reagent Kit (Takara Bio, Otsu, Japan) was utilized to convert to cDNA in accordance with the manufacturer's instructions. For reverse transcription, the same kit was employed. qRT-PCR execution involved the SYBR Green PCR Master Mix (Takara) and particular primers for \u003cem\u003eCYB5D2\u003c/em\u003e, \u003cem\u003eTGF-β\u003c/em\u003e, \u003cem\u003eE-cadherin\u003c/em\u003e, \u003cem\u003eN-cadherin\u003c/em\u003e, \u003cem\u003eSnail\u003c/em\u003e, and \u003cem\u003eTwist\u003c/em\u003e, with their exact sequences mentioned in Supplementary Table\u0026nbsp;2. The internal control was \u003cem\u003eGAPDH\u003c/em\u003e, and expression levels were calculated using 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blotting (WB)\u003c/h2\u003e \u003cp\u003eA protease inhibitor cocktail (Roche) and a phosphatase inhibitor cocktail (Roche) were used with RIPA lysis buffer (Beyotime, Shanghai, China) to generate protein lysates from cells. To quantify protein concentrations, Thermo Fisher Scientific's BCA protein assay kit was used. Proteins in equivalent amounts were separated on 10% SDS-PAGE gels and then transferred to PVDF membranes. After blocking for an hour at 5% nonfat milk in TBST, membranes were left overnight at 4\u0026deg;C to be incubated with primary antibodies (\u003cem\u003eCYB5D2\u003c/em\u003e, \u003cem\u003eTGF-β\u003c/em\u003e, \u003cem\u003eE-cadherin, N-cadherin, Snail, and Twist\u003c/em\u003e) diluted at a 1:1000 ratio. Horseradish peroxidase-conjugated secondary antibodies were incubated on TBST-washed membranes for one hour at room temperature. With the Millipore ECL Plus kit, protein bands were discovered and analyzed using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eTo evaluate the expression level of \u003cem\u003eCYB5D2\u003c/em\u003e, IHC analysis was done on tissue samples obtained from the Third Affiliated Hospital of Naval Medical University, including HCC and surrounding normal liver tissue. The sections were rehydrated in a graded ethanol series after being deparaffinized in xylene. Antigens were extracted from citrate buffer (pH 6.0) using microwave heating. 3% hydrogen peroxide and 5% bovine serum albumin were used to block nonspecific binding and endogenous peroxidase activity, respectively. Sections were treated with a 1:1000 solution of a primary antibody specific to \u003cem\u003eCYB5D2\u003c/em\u003e for an entire night at 4\u0026deg;C. Hematoxylin staining the slices allowed one to view the nuclei. In order to evaluate \u003cem\u003eCYB5D2\u003c/em\u003e expression between tumor and normal tissue, stained slices were finally imaged.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation assay\u003c/h2\u003e \u003cp\u003eAn evaluation of cellular proliferation was conducted using the cell counting kit (CCK-8) assay (Dojindo). In 96-well plates, transfected cells were cultivated for 24, 48, 72, and 96 hours. After that, each well received a 10 \u0026micro;L aliquot of CCK-8 solution, and the mixture was incubated at 37\u0026deg;C. After that, a microplate reader was used to measure the absorbance at 450 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFlow Cytometry\u003c/h2\u003e \u003cp\u003eFor cell cycle analysis, cells were harvested, cleaned in cold phosphate-buffered saline (PBS), and preserved in 70% ethanol for an overnight stay at -20\u0026deg;C. After fixation, cells were once again cleaned with PBS and stained with propidium iodide (Sigma-Aldrich) for 30 minutes in the dark to highlight the DNA. After that, a BD Biosciences flow cytometer was used to determine the DNA content of the labeled cells. The FlowJo software was utilized to handle and analyze the data from the flow cytometer in order to evaluate the distribution of cells during the cell cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation and migration assays\u003c/h2\u003e \u003cp\u003eIn transwell chambers with or without Matrigel (Corning Inc., Corning, NY, USA), cell migration and invasion were assessed. The trypsinized cells were resuspended in medium devoid of serum after being cleaned. 3\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells were injected into the upper chamber of each Transwell, followed by the addition of 200 \u0026micro;L of serum-free media and 600 \u0026micro;L of chemoattractant-containing medium with 10% FBS to the lower chamber. Following a 24-hour incubation period, cells passing through the bottom wells of the membrane were fixed with 4% paraformaldehyde and stained with crystal violet. Five randomly chosen areas inside each well were counted for the quantity of migrating and invasive cells under the microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eXenotransplantation experiments\u003c/h2\u003e \u003cp\u003e The Third Affiliated Hospital of Naval Medical University's Ethics Committee gave its clearance for all treatments involving animals. We got six-week-old male BALB/c nude mice from SLAC Laboratory in Shanghai, China. In their left hind leg, the mice received subcutaneous injections of either empty control C3A cells or C3A cells that overexpressed \u003cem\u003eCYB5D2\u003c/em\u003e. After 2\u0026ndash;3 weeks, the size and weight of the mouse tumors were measured and compared before and after injection to assess how \u003cem\u003eCYB5D2\u003c/em\u003e overexpression affects the development of tumors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were processed via through R language packages, with the application of either the student's t-test or one-way ANOVA for statistical scrutiny. A threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set for significance determination. The data was provided as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of a key module in the gene co-expression networks\u003c/h2\u003e \u003cp\u003eWe identified 1033 DEGs from the GSE101685 dataset using the GEO2R tool, including 363 up-regulated DEGs and 670 down-regulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Next, we made use of the WGCNA package on R for putting the probes into the module through average linkage clustering (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). In the research, we chose a power of β\u0026thinsp;=\u0026thinsp;14 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85 without scale) as a soft threshold, aiming to ensure a scale-free network, and identified 2 modules (except in the grey module). By comparing the correlation between the two modules and the two sets of samples in the GSE101685 dataset, it was found that the turquoise module (number of genes\u0026thinsp;=\u0026thinsp;452) has the highest correlation with the two sets of samples, with a correlation coefficient of 0.801.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic analysis of 9 signature genes in the key module\u003c/h2\u003e \u003cp\u003eAfter identifying the turquoise module as a key module, we performed DFS prognostic analysis on the genes within this module. As evident from the results in Supplementary Table\u0026nbsp;1, a total of 56 genes showed significant prognosis. Subsequently, we performed LASSO Cox regression analysis on these 56 genes and identified 9 signature prognostic genes according to the optimal lambda value (lambda.min\u0026thinsp;=\u0026thinsp;0.0688) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The risk score for these genes was determined as follows: Riskscore=(-0.0212)*\u003cem\u003eCFHR4\u003c/em\u003e+(-0.0405)*SPP2+(-0.0021)*FBLN5+(-0.0157)*ANO1+(-0.0261)*STEAP4+(-0.0012)*GLUD1+(-0.0336) *\u003cem\u003eCYB5D2\u003c/em\u003e+(-0.0321)*LCAT+(-0.2391)*IL18RAP. According to the risk model study, the high-risk group outperformed the low-risk group in terms of survival and death (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The findings of the KM survival analysis, which are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, indicated that the high-risk group's median survival time was 1.2 years, while the low-risk group's was 3.9 years, with an HR of 2.287 (\u0026gt;\u0026thinsp;1). Moreover, the DFS probability was decreased in the high-risk group. The ROC curve results further highlighted that the predictive accuracy of the risk model was most potent at the five-year mark (AUC\u0026thinsp;=\u0026thinsp;0.729). Our research concludes by highlighting the important prognostic potential of these nine genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eNomogram analysis of key prognostic variables\u003c/h2\u003e \u003cp\u003eIn the analysis of nine genes and four clinical variables in the risk model, we identified five variables with statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), namely \u003cem\u003eCFHR4\u003c/em\u003e, \u003cem\u003eCYB5D2\u003c/em\u003e, \u003cem\u003eIL18RAP\u003c/em\u003e, pT stage, and pTNM stage (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results of the nomogram analysis showed that these variables had significant predictive power on the 1-, 3-, and 5-year survival rates of patients, which was also confirmed by the calibration curve results (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInverse association between\u003c/b\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eTGF-β\u003c/b\u003e \u003cb\u003eexpression in LIHC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFrom the prognostic genes identified through nomogram analysis, \u003cem\u003eCYB5D2\u003c/em\u003e, \u003cem\u003eCFHR4\u003c/em\u003e, and \u003cem\u003eIL18RAP\u003c/em\u003e have already been substantiated as participating in the pathogenesis of HCC. We chose \u003cem\u003eCYB5D2\u003c/em\u003e as the hub gene in this investigation to examine its relationship to HCC. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA illustrated a noticeable underexpression of \u003cem\u003eCYB5D2\u003c/em\u003e in LIHC, hinting towards a potential role in disease manifestation. Immunohistochemistry results also confirmed this. Compared with normal liver tissue, the staining intensity of \u003cem\u003eCYB5D2\u003c/em\u003e in tumor tissue was weakened, indicating that its expression level in tumor tissue was low (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). With a correlation coefficient (r) of -0.65, correlation analysis revealed a substantial negative association between \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). To further substantiate our findings, we employed the Wilcoxon test, which confirmed an overexpression of \u003cem\u003eTGF-β\u003c/em\u003e in LIHC (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Interestingly, \u003cem\u003eTGF-β\u003c/em\u003e overexpression was related to a poorer OS prognosis, indicating that \u003cem\u003eTGF-β\u003c/em\u003e may have a prognostic function in LIHC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDownregulation of\u003c/b\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003eand upregulation of\u003c/b\u003e \u003cb\u003eTGF-β\u003c/b\u003e \u003cb\u003emay be associated with malignant progression of HCC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBy using the qRT-PCR and WB method \u003cem\u003ein vitro\u003c/em\u003e experiments, we detected the \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e expression levels in HCC cells. In C3A and HepG2, \u003cem\u003eCYB5D2\u003c/em\u003e mRNA and protein levels were found to be much lower than in QSG7701 (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In contrast, qRT-PCR and WB confirmed that C3A and HepG2 cells had elevated \u003cem\u003eTGF-β\u003c/em\u003e expression levels (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The HPA database also analyzed the levels of \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e in HCC tissues. Among them, no staining signal was detected for \u003cem\u003eCYB5D2\u003c/em\u003e in tumor tissue, and \u003cem\u003eTGF-β\u003c/em\u003e showed high staining intensity in tumor tissue (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). This suggests that whilst \u003cem\u003eTGF-β\u003c/em\u003e expression is markedly elevated in tumor tissues, \u003cem\u003eCYB5D2\u003c/em\u003e expression is low in malignant tissues. Therefore, we speculate that the downregulation of \u003cem\u003eCYB5D2\u003c/em\u003e and the upregulation of \u003cem\u003eTGF-β\u003c/em\u003e may be related to the malignant progression of HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003eoverexpression inhibits HCC cell proliferation through the cell cycle\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe performed overexpression experiments by transfecting the \u003cem\u003eCYB5D2\u003c/em\u003e plasmid into C3A and HepG2 cells. The mRNA level of \u003cem\u003eCYB5D2\u003c/em\u003e was shown to be up-regulated using the qRT-PCR method (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). After that, the CCK-8 test was employed to look at how \u003cem\u003eCYB5D2\u003c/em\u003e overexpression affected cell division. The overexpression of \u003cem\u003eCYB5D2\u003c/em\u003e was observed to drastically decrease the growth of C3A and HepG2 cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Subsequently, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, in the G1 phase, overexpression of \u003cem\u003eCYB5D2\u003c/em\u003e caused cell cycle arrest (57.79\u0026ndash;65.69%, 55.67\u0026ndash;62.21%). These findings collectively imply that \u003cem\u003eCYB5D2\u003c/em\u003e overexpression inhibits the proliferation of C3A /HepG2 cells and induces cell cycle arrest, providing insight into the potential therapeutic implications of targeting \u003cem\u003eCYB5D2\u003c/em\u003e in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOverexpression of\u003c/b\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003einhibits HCC cell migration and invasion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNext, utilizing transwell studies, the impact of \u003cem\u003eCYB5D2\u003c/em\u003e overexpression on C3A and HepG2 cell migration and invasion was examined. The over-\u003cem\u003eCYB5D2\u003c/em\u003e group showed a substantial reduction in migrating and invasive cells as compared to the control group (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), demonstrating that \u003cem\u003eCYB5D2\u003c/em\u003e overexpression prevented the migration and invasion of C3A as well as HepG2 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTGF-β\u003c/b\u003e \u003cb\u003ereverses the regulation of EMT markers and tumor progression by\u003c/b\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003eoverexpression in HCC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe development and metastasis of HCC are significantly influenced by the EMT(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Key regulators of EMT, such as \u003cem\u003eE-cadherin\u003c/em\u003e, \u003cem\u003eN-cadherin\u003c/em\u003e, etc., are typically associated with tumor progression(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). To investigate the regulatory roles of \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e on these EMT-associated factors, we segregated C3A and HepG2 cells into three groups: control (over-NC), \u003cem\u003eCYB5D2\u003c/em\u003e overexpression (over-\u003cem\u003eCYB5D2\u003c/em\u003e), and \u003cem\u003eCYB5D2\u003c/em\u003e overexpression with \u003cem\u003eTGF-β\u003c/em\u003e (over-\u003cem\u003eCYB5D2\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eTGF-β\u003c/em\u003e). qRT-PCR analysis revealed a significant increase in e-cadherin expression in the over-\u003cem\u003eCYB5D2\u003c/em\u003e group as compared to the control. However, with the addition of \u003cem\u003eTGF-β\u003c/em\u003e, its expression level decreased. Conversely, \u003cem\u003eCYB5D2\u003c/em\u003e overexpression downregulated the expression of \u003cem\u003eN-cadherin\u003c/em\u003e, \u003cem\u003eSnail\u003c/em\u003e, and \u003cem\u003eTwist\u003c/em\u003e. Following the administration of \u003cem\u003eTGF-β\u003c/em\u003e, these three EMT-related regulatory factors demonstrated an upregulation in their expression levels (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). These observations were further validated by WB analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Based on these findings, we conducted Transwell rescue experiments and discovered that overexpression of \u003cem\u003eCYB5D2\u003c/em\u003e markedly curtailed the migratory and invasive behavior of C3A and HepG2 cells relative to the control group. The addition of \u003cem\u003eTGF-β\u003c/em\u003e partially counteracted this inhibitory effect (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Thus, our results suggest that \u003cem\u003eCYB5D2\u003c/em\u003e inhibits both the migration and invasion of HCC cells and modifies the expression of EMT-related proteins. \u003cem\u003eTGF-β\u003c/em\u003e might function a role in reversing the inhibitory effects of \u003cem\u003eCYB5D2\u003c/em\u003e on C3A and HepG2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOverexpression of\u003c/b\u003e \u003cb\u003eCYB5D2\u003c/b\u003e \u003cb\u003eexhibits anticancer activity\u003c/b\u003e \u003cb\u003ein vivo\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, the \u003cem\u003ein vivo\u003c/em\u003e effects of overexpressing \u003cem\u003eCYB5D2\u003c/em\u003e were assessed using a xenograft model of HCC in nude mice. After \u003cem\u003eCYB5D2\u003c/em\u003e overexpression, a significant reduction in tumor size and weight was observed in the nude mouse model (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). This outcome underscores the impact of \u003cem\u003eCYB5D2\u003c/em\u003e modulation on tumorigenesis in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe onset of liver cancer is a complex multi-factor process(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Excessive drinking, viral infections (hepatitis B or C), and worsening of liver cirrhosis can all lead to HCC(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Under normal circumstances, the ratio of HCC to men and women is 7\u0026ndash;10:1. That means, the number of male HCC patients is 7\u0026ndash;10 times that of females. More than 90% of HCC cases are related to hepatic inflammation and damage, and chronic unresolved inflammation is linked to persistent liver injury and concurrent regeneration, which results in fibrosis, cirrhosis, and eventually HCC(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Currently, people can diagnose HCC by non-invasive imaging, which is inclusive of magnetic resonance imaging and computed tomography(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). But despite this, the diagnostic effect of HCC is still not optimistic, and more in-depth research and discovery of new treatment methods are needed.\u003c/p\u003e \u003cp\u003eCurrently, prognostic and target analysis of HCC based on WGCNA and LASSO regression algorithms has become more and more common. Yang Z et al. performed bioinformatics analysis of HCC-related data in TCGA and GEO based on the above two datasets, and found 4 macrophage-related genes (\u003cem\u003eCDCA8\u003c/em\u003e, \u003cem\u003eCBX2\u003c/em\u003e, \u003cem\u003eUCK2\u003c/em\u003e and \u003cem\u003eSOCS2\u003c/em\u003e) with good prognostic independence in HCC, which are expected to be the potential prognostic target(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Shan Y et al. performed WGCNA on the DEGs in the GSE22058 and GSE54238 datasets, screened key modules, and constructed a prognostic model through the LASSO regression algorithm, from which they analyzed \u003cem\u003eUBA1\u003c/em\u003e as a characteristic marker for liver cancer diagnosis and prognosis(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Based on the WGCNA algorithm and the LASSO algorithm, Ding H et al. found that MMP9 is highly correlated with the prognosis of HCC patients. They also found that the dual immunological signal of MMP9 and CD8\u003csup\u003e+\u003c/sup\u003e T cells can increase the survival rate of HCC patients(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In summary, research on the main goals and processes of HCC using the WGCNA and LASSO algorithms is persuasive.\u003c/p\u003e \u003cp\u003eBased on this, within our research, we first built a network of gene co-expression for DEGs in the GSE101685 dataset by WGCNA, and identified the turquoise as a key gene module. Subsequently, survival analysis and LASSO Cox prognostic model and nomogram analysis were performed on the genes in these two modules, and three significant prognostic genes were identified, namely \u003cem\u003eCFHR4\u003c/em\u003e, \u003cem\u003eCYB5D2\u003c/em\u003e, and \u003cem\u003eIL18RAP\u003c/em\u003e. This analysis highlights their potential role in HCC progression and patient prognostic outcomes. Research by Ding Q et al. pointed out that \u003cem\u003eCFHR4\u003c/em\u003e is significantly low-expressed in HCC, which can lead to poor patient prognosis, and its level of expression and the extent of immune cell infiltration are also correlated(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The study by Yu H et al. further showed the substantial predictive usefulness of \u003cem\u003eCFHR4\u003c/em\u003e in HCC by demonstrating how the expression of this gene and several clinicopathological factors are strongly correlated with immune cell infiltration(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). \u003cem\u003eCFHR4\u003c/em\u003e has also been identified as one of the hepatocyte subtypes in extrahepatic metastasis of advanced HCC and may serve as a predictive target for resistance to the combination therapy of lenvatinib, \u003cem\u003eFOLFOX\u003c/em\u003e, and toripalimab(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Li R et al. identified five genes, including \u003cem\u003eIL18RAP\u003c/em\u003e, that are associated with the prognosis of HCC and verified that there is a relationship between immune cell infiltration and immunotherapy sensitivity and the expression of these genes(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). By performing LASSO and multivariable Cox regression analysis on HCC-related data sets in public databases, \u003cem\u003eCYB5D2\u003c/em\u003e was also identified as a key prognostic gene for HCC by Ren Z et al(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, the precise mechanism of action of \u003cem\u003eCYB5D2\u003c/em\u003e in HCC is still unknown and requires additional research.\u003c/p\u003e \u003cp\u003eOur study shows that \u003cem\u003eCYB5D2\u003c/em\u003e is underexpressed in LIHC and negatively correlated with \u003cem\u003eTGF-β\u003c/em\u003e. \u003cem\u003eTGF-β\u003c/em\u003e has been confirmed to be highly expressed in LIHC and can lead to poor prognosis in HCC patients. Research has indicated that \u003cem\u003eTGF-β\u003c/em\u003e is one of the main cytokines known to cause EMT and that it plays a significant role in the process(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Through the activation of certain transcription factors that downregulate epithelial indicators and upregulate mesenchymal markers, \u003cem\u003eTGF-β\u003c/em\u003e can contribute to tumor growth, metastasis, and resistance to clinical therapy(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). One study found that \u003cem\u003eSTAT3\u003c/em\u003e induces EMT and metastasis of liver cancer by positively regulating \u003cem\u003eTGF-β1\u003c/em\u003e, and \u003cem\u003eSTAT3\u003c/em\u003e cooperates with the Snail-Smad3/\u003cem\u003eTGF-β1\u003c/em\u003e signaling pathway to promote cancer progression(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Through the PI3K/Akt/Rac1 pathway, \u003cem\u003eMMP-8\u003c/em\u003e and \u003cem\u003eTGF-β1\u003c/em\u003e mutual activation induces EMT, which in turn promotes the development of HCC(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In addition, another study also pointed out that the \u003cem\u003eFCN2\u003c/em\u003e/\u003cem\u003eTGF-β\u003c/em\u003e/EMT axis is an important mechanism affecting HCC metastasis and affects the metastasis of HCC(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Our goal was to investigate the precise mechanism of action of \u003cem\u003eTGF-β\u003c/em\u003e and \u003cem\u003eCYB5D2\u003c/em\u003e in HCC using in vitro experimental study. The results demonstrate that \u003cem\u003eCYB5D2\u003c/em\u003e is downregulated in HCC cells, and overexpression of \u003cem\u003eCYB5D2\u003c/em\u003e inhibits HCC cell growth and induces G1 arrest. \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e jointly regulate the expression of EMT-related factors and the progression of HCC. Specifically, \u003cem\u003eCYB5D2\u003c/em\u003e overexpression leads to upregulation of \u003cem\u003eE-cadherin\u003c/em\u003e and downregulation of \u003cem\u003eN-cadherin\u003c/em\u003e, \u003cem\u003eSnail\u003c/em\u003e, and \u003cem\u003eTwist\u003c/em\u003e, indicating that \u003cem\u003eCYB5D2\u003c/em\u003e may function as an EMT inhibitor. This modulation of EMT-related factors corresponds to a significant inhibition of HCC migration and invasion capabilities, supporting the theory that \u003cem\u003eCYB5D2\u003c/em\u003e could have tumor-suppressive effects in HCC. These effects were partially reversed upon the addition of \u003cem\u003eTGF-β\u003c/em\u003e, suggesting a potential interaction between \u003cem\u003eTGF-β\u003c/em\u003e and \u003cem\u003eCYB5D2\u003c/em\u003e. In addition, tumor xenograft experiments also confirmed that \u003cem\u003eCYB5D2\u003c/em\u003e overexpression significantly inhibited tumor growth in mice. Overall, \u003cem\u003eCYB5D2\u003c/em\u003e is important for the formation and progression of HCC and may have a significant impact on preventing EMT and tumor growth of HCC by regulating key EMT indicators and counteracting the tumorigenic effects of \u003cem\u003eTGF-β\u003c/em\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo sum up, our study analyzed DEGs in GSE101685 based on bioinformatics methods and identified the hub gene \u003cem\u003eCYB5D2\u003c/em\u003e associated with HCC prognosis. \u003cem\u003eCYB5D2\u003c/em\u003e is significantly low-expressed in HCC and negatively correlated with \u003cem\u003eTGF-β\u003c/em\u003e. \u003cem\u003eTGF-β\u003c/em\u003e is a key factor promoting tumor progression and metastasis through EMT. \u003cem\u003eTGF-β\u003c/em\u003e can counteract the inhibition of HCC cell proliferation, migration, and invasion as well as the regulation of EMT markers by \u003cem\u003eCYB5D2\u003c/em\u003e overexpression to a certain extent, indicating that there is a complex interaction between \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e in regulating HCC progression. \u003cem\u003eIn vivo\u003c/em\u003e experiments further confirmed that \u003cem\u003eCYB5D2\u003c/em\u003e overexpression can significantly reduce tumor growth, indicating its potential application value as a therapeutic target for HCC. Consequently, our research underscores the possibility of \u003cem\u003eCYB5D2\u003c/em\u003e-targeted therapies as innovative therapeutic approaches for HCC, offering fresh therapeutic directions for patient treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All animal experiments were approved by the Ethics Committee of the Third Affiliated Hospital of Naval Medical University(approval number: EHBHKY2014-03-006).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding human samples, the study was approved by the Institutional Review Board of Ethics Committee of the Third Affiliated Hospital of Naval Medical University (approval number: EHBHKY2023-KO39-P001). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. All samples were anonymized to respect the privacy of participants.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGuarantors of integrity of entire study, D.J.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, D.J., Z.Q.; experimental studies, D.J.; statistical analysis, D.J., Z.Q., Z.X., Y.L.; and manuscript editing, D.J., Z.Q., Z.X., Y.L.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Cancer Lett 378(2):80\u0026ndash;86\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":"Hepatocellular carcinoma, CYB5D2, TGF-β, Epithelial-mesenchymal transition, Malignant progression","lastPublishedDoi":"10.21203/rs.3.rs-3899388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3899388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) is a common liver malignancy. In this study, our goal was to investigate how \u003cem\u003eTGF-β\u003c/em\u003e and \u003cem\u003eCYB5D2\u003c/em\u003e function in the etiology of HCC and their potential as prognostic biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGene co-expression network and prognostic analysis were executed on the GSE101685 dataset, and \u003cem\u003eCYB5D2\u003c/em\u003e was determined to be a hub gene. Then the expression of \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e in HCC and their correlation were detected. \u003cem\u003eIn vitro\u003c/em\u003e experiments analyzed the effects of \u003cem\u003eCYB5D2\u003c/em\u003e and \u003cem\u003eTGF-β\u003c/em\u003e on the progression of HCC. Tumor xenograft experiments were performed to detect the regulation of \u003cem\u003eCYB5D2\u003c/em\u003e overexpression on tumor growth.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eImmunohistochemistry (IHC) and expression analysis results showed that \u003cem\u003eCYB5D2\u003c/em\u003e can serve as a tumor suppressor in HCC. In contrast, \u003cem\u003eTGF-β\u003c/em\u003e, which is inversely correlated with \u003cem\u003eCYB5D2\u003c/em\u003e, was overexpressed in liver hepatocellular carcinoma (LIHC) and linked to poor patient prognosis. \u003cem\u003eIn vitro\u003c/em\u003e experiments confirmed that \u003cem\u003eCYB5D2\u003c/em\u003e expression was upregulated in HCC cell lines, while \u003cem\u003eTGF-β\u003c/em\u003e expression was upregulated, and results from the Human Protein Atlas (HPA) database confirmed these findings. Functional analysis showed that \u003cem\u003eCYB5D2\u003c/em\u003e overexpression inhibited the proliferation, migration, and invasion of HCC cells and induced G1 phase arrest. Furthermore, \u003cem\u003eTGF-β\u003c/em\u003e treatment counteracted \u003cem\u003eCYB5D2\u003c/em\u003e-mediated epithelial-mesenchymal transition (EMT) marker expression and tumor progression. Finally, \u003cem\u003ein vivo\u003c/em\u003e studies showed that \u003cem\u003eCYB5D2\u003c/em\u003e overexpression significantly reduced tumor growth, suggesting its potential anticancer activity against HCC.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOverall, the tumor suppressor function of \u003cem\u003eCYB5D2\u003c/em\u003e in HCC and its interaction with \u003cem\u003eTGF-β\u003c/em\u003e offer fresh information on the molecular pathophysiology of HCC and possible treatment avenues.\u003c/p\u003e","manuscriptTitle":"CYB5D2 inhibits the malignant progression of hepatocellular carcinoma by inhibiting TGF-β expression and epithelial-mesenchymal transition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 18:39:57","doi":"10.21203/rs.3.rs-3899388/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":"7b64dc62-a68c-400f-b10d-b668cc502af1","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-11T11:05:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 18:39:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3899388","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3899388","identity":"rs-3899388","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}