The Tumor Suppressive and Potential Value of STEAP4 in Hepatocellular Carcinoma

The Tumor Suppressive and Potential Value of STEAP4 in Hepatocellular Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Tumor Suppressive and Potential Value of STEAP4 in Hepatocellular Carcinoma Yang Ting, Min-hong Zou, Yu-jie xie, Zhang Yong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3928009/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 The six-transmembrane epithelial antigen of prostate 4(STEAP4), is a complete membrane metal reductase. Abnormal STEAP4 expression is potentially associated with carcinogenesis. However, the biological role of STEAP4 in hepatocellular carcinoma (HCC) remains to be further determined. We analyzed STEAP4 expression level, prognosis, the correlations between STEAP4 and cancer immune infiltrates, differentially expressed genes (DEGs) between STEAP4 high- and low-expressed groups, and the functional networks in hepatocellular carcinoma using multiple databases. STEAP4 was found down-regulated in tumor tissues in multiple HCC cohorts. Low STEAP4 expression was associated with poorer overall (OS) and disease-free survival (DFS). Functional network analysis suggested that STEAP4 regulates cell cycle signaling and may be correlated with infiltrating levels of T follicular helper cells. These findings lay a foundation for further study of the cell cycle regulatory role of STEAP4 in HCC, and, indicate STEAP4 may be a promising prognostic biomarker and a novel therapeutic target for HCC patients. STEAP4 bioinformatics prognosis cell cycle immune HCC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide, leading to nearly half a million deaths annually 1,2 . The 5-year survival rate of advanced HCC is poor due to the high recurrence rate and metastasis rate 3 . At present, the molecular mechanism of HCC formation and progression is not entirely understood, which further complicates the effective treatment of HCC(4).In addition, the lack of tumor type - or stage-specific molecular markers is another critical gap in the understanding and treatment of HCC 4 . Hence, finding a novel biomarker with high accuracy for predicting the prognosis of HCC patients is urgently needed. The six-transmembrane epithelial antigen of the prostate (STEAP) is highly expressed in prostate cancer and is first isolated in 1999 by Hubert et al. from an advanced prostate cancer xenograft model 5 . Subsequently, the family members of STEAP are successively identified, and STEAP 4 is one of them 6-9 and is involved in the reduction and transport of iron and copper 10 . The STEAP4 is broadly expressed in various tissues, including the prostate, lung, pancreas, heart, bone marrow, liver, etc. 10-12 . It is associated with obesity, insulin resistance, inflammation, and cancer progression 13-16 . The expression of STEAP4 is upregulated in prostate cancer, and its role in the occurrence and development of prostate cancer has been confirmed by many studies 9,16 . In addition, some studies have shown that STEAP4 is involved in the occurrence and development of colon cancer 15,17 , breast cancer 18,19 , hepatocellular carcinoma 2,20 , and other tumors 21,22 . For example, Xue et al. have shown that STEAP4 was overexpressed in murine models of colitis-associated colon cancer (CAC) and rendered mice susceptible to chemically induced colitis and colon tumorigenesis suggesting a poor prognosis 17 . Liao et al. identified an axis, the IL-17-STEAP4-XIAP axis, and found that STEAP4-dependent cellular copper uptake is critical for developing colon cancer in a murine model 15 . Wu et al. showed that the expression of STEAP4 was down-regulated in breast cancer and was associated with the prognosis of breast cancer 18 . However, there are relatively few studies on liver cancer. In 2016, a Japanese study showed that STEAP4 was significantly down-regulated in HCC due to DNA hypermethylation, which may be related to the occurrence of HCC 2 . But the biological role of STEAP4 in HCC remains to be determined. Here, we explored the expression of STEAP4 in HCC patients, the relationship between its expression and the prognosis, and, the functional network of STEAP4. Results 2.1 Decreased STEAP4 expression in LIHC The GEPIA (http://gepia.cancer-pku.cn) database and Oncomine 4.5 (https://www.oncomine.org/) were used to obtain the expression level of the STEAP4 gene in HCC patients. STEAP4 was found significantly down-regulated in tumor tissues in multiple HCC cohorts ( Fig.1A-B ). To verify the STEAP4 expression in HCC tissues, we performed immunohistochemistry (IHC) staining of 20 HCC and para-tumor normal tissue specimens. The positive intensity and percentage were evaluated and recorded. The positive intensity was defined as 0 for negative staining, 1 for light yellow, 2 for light brown, and 3 for dark brown. As the positive percentage only has 0% or 100%, the positive grade was judged according to the positive intensity: (1) a score of 0-1 is negatively defined; (2) a score of 2-3 is positively defined. The representative immunohistochemical images and result of quantitative analysis are shown in Fig.1C-D , STEAP4 expression was significantly lower in HCC tissues than in adjacent normal tissues (p < 0.0001); the p-value was determined by Student’s t-test. We downloaded all the publicly available LIHC RNA-Seq data information from The Cancer Genome Atlas (TCGA) official website (https://cancergenome.Nih.gov/) before November 2022, through the GDC Data Transfer Tool. 371 primary tumor samples were divided into 185 low STEAP4 expression groups and 186 high STEAP4 expression groups based on the median values of STEAP4 mRNA level. Then the associations between STEAP4 mRNA expression and the clinicopathological features were investigated using the chi-squared test. As shown in Table 1. , the expression of STEAP4 was significantly correlated with tumor T stage ( p =0.024) and race( p =0.012) in LIHC patients. T1 stage and White patients have higher STEAP4 expression than others. There was no significant correlation between STEAP4 expression and other clinicopathological factors, including age ( p = 0.931), gender ( p = 0.210), lymph node metastasis ( p = 0.212), distant metastasis ( p = 0.079) and BMI level ( p =0.133). Table 1. Association between STEAP4 expression and clinicopathological characteristics in LIHC patients. 2.2 STEAP4 expression is survival-associated We assessed the association between STEAP4 expression and the survival outcomes of LIHC cohorts in the GEPIA database. The patients were separated into two groups according to the median value of STEAP4 expression level. A positive relationship between the STEAP4 gene level and the overall survival (OS) and disease-free survival (DFS) in liver cancer was found. That was the high STEAP4 expression group had significantly longer overall survival (OS) (log-rank test, p =0.024) and disease-free survival (DFS) (log-rank test, p =0.02), compared to the low expression group in the LIHC cohort ( Fig. 2A-B ). The same result was also obtained using the Kaplan-Meier plotter database with TCGA-LIHC data ( Fig. 2C ). To further analyze the correlation between STEAP4 expression with survival rates and clinical-pathological characteristics, R version 4.0.5 software was used, with the survival and survminer packages used appropriately. Multivariate Cox survival analysis revealed that STEAP4 expression was an independent predictor of unfavorable prognosis in LIHC patients ( p < 0.05; Fig. 2D ). Overall, our results suggest that STEAP4 is a good prognostic factor and an independent prognostic factor. 2.3 STEAP4 co-expression networks in HCC STEAP4 co-expression was analyzed statistically using Pearson’s correlation coefficient in the LinkedOmics database (http://www.linkedomics.org/login.php) 23 , presenting in volcano plots, heat maps, or scatter plots. As shown in Fig. 3A ,4433 genes (dark red dots) were shown significant positive correlations with STEAP4, whereas 4101 genes (dark green dots) were shown significant negative correlations ( P <0.05 and FDR<0.01). The top 50 significant genes positively and negatively correlated with STEAP4 were shown in the heat map. The function module of LinkedOmics performs analysis of the KEGG pathways enrichment by the gene set enrichment analysis (GSEA). Significant enrichment in the Chemical carcinogenesis and Cell cycle were shown ( Fig. 3B ). We further explored the regulators (kinases, mRNA and transcription factors (TF)) of STEAP4 in HCC( Fig. 3C ). The results ( Supplementary Table 1 ) showed that the significantly related kinases were Ataxia telangiectasia and Rad3-related (ATR), cyclin-dependent kinase 1 (CDK1), cyclin-dependent kinase 2 (CDK2), Ataxia telangiectasia mutated (ATM), polo-like kinase 1 (PLK1), checkpoint kinase 1 (CHEK1), Aurora kinase B (AURKB), checkpoint kinase 2 (CHEK2). In fact, among them, the transcriptional levels of AURKB, CDK1, CHEK1, and PLK1 were significantly elevated in HCC tissues. The significant enrichment of transcription factors for overlapped co-expression gene was mainly the E2F transcription factor family, including V$E2F_Q6, V$E2F_Q4, V$E2F4DP1_01, V$E2F1DP1RB_01 and V$E2F1_Q6. After differential expression analysis of top 10 co-expressed genes( Supplementary Fig. 1A ), one positively and seven negatively significant genes were identified, they are EMCN(r =0.576, p = 3.77E-34), CDT1(r =-0.524, p = 1.68E-27), CDCA3(r =-0.523, p = 1.92E-27), KIF2C(r =-0.515, p = 1.55E-26), BIRC5( r =-0.531, p = 2.14E-28), TROAP(r =-0.529, p = 3.61E-28),AURKB(r =-0.529, p = 4.19E-28)and MYBL2(r =-0.526, p = 9.06E-28).The correlation between STEAP4 and these genes was verified in TIMER ( Supplementary Fig. 1B ).In addition, we found that the high expression of the 7 negatively significant STEAP4-related genes was significantly associated with worse overall survival in LIHC patients, the high expression of the positively related gene EMCN was significantly associated with better prognosis( Supplementary Fig. 1C ). It is possible that STEAP4 and its co-expressed genes collectively contribute to liver carcinogenesis, resulting in poor survival in LIHC patients. 2.4 Construction and validation of model-based STEAP4 for predicting the prognosis We combined 8 co-expressed genes (EMCN, CDT1, CDCA3, KIF2C, BIRC5, TROAP, AURKB, and MYBL2), 4 kinases regulators (AURKB, CDK1, CHEK1, and PLK1), and STEAP4 to construct a model for predicting prognosis in HCC. To avoid overfitting, we performed a multivariate Cox proportional hazards regression analysis and stepwise analysis to establish an optimal predictive model: risk score = CDCA3* 0.5289133 + STEAP4* -3.7328420. Based on the formula, the risk score of each patient in the training set was calculated. Patients were classified into the high- and low-risk group using the median risk score as the cutoff value. Patients in the high-risk group had a significantly poorer OS (P < 0.0001) ( Supplementary Fig. 2A ). The areas under the curves (AUCs) of signature were 0.783 for 1-year OS, 0.722 for 3-year OS, and 0.754 for 5-year OS, respectively ( Supplementary Fig. 2C ). We ranked patients’ risk scores, distributed survival status, and exhibited gene expression patterns of 2 genes ( Supplementary Fig. 2B ). Furthermore, the multivariate analysis demonstrated that the risk score is a prognostic factor independent of age, gender, and stage ( Supplementary Fig. 2D ). Besides, we performed subgroup analysis to further investigate the potential prognosis of the signature. As shown in Supplementary Fig. 2E, the ability of the signature to stratify patients was achieved in patients with early-stage, advanced stage, male, female, <60-year, =60-year. The predictive capability of this signature was further verified in the testing set. According to the same predict signature and same stratify method, each of the patients in the International Cancer Genome Consortium (ICGC) dataset was divided into the high- and low-risk group. Survival analysis showed that patients with low-risk scores had longer OS (p = 0.0088) ( Fig. 4A ); the distribution of risk score, patients’ survival status, and gene expression pattern was presented by scatter plots and heatmaps ( Fig. 4B ); the AUCs of signature were 0.560 for 1-year OS, 0.684 for 3-year OS, and 0.951 for 5-year OS, respectively ( Fig. 4C ). Besides, the signature was verified to be an independent prognostic factor ( Fig. 4D ). For easy to use in clinical, we combined the independent prognostic factors in the TCGA cohort to build a nomogram ( Fig. 4 E ). The calibration plots of 1-year, 3-year, and 5-year survival rates were shown in Fig. 4 F . 2.5 Associations Between Genome-wide Expression Profiles and STEAP4 Expression By using R version 4.0.5 software, we screened out the differentially expressed genes in the high and low expression groups of the STEAP gene in all tumor samples.142 upregulated and 250 down-regulated genes were identified as being significantly associated with STEAP4 expression (FDR-adjusted P <0.05, FDR<0.05, and |FC|≥2, Fig. 5A ). Further, we performed a functional enrichment analysis of these genes based on the David database to study the enrichment pathways of these differentially expressed genes. As shown in Fig. 5B, the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in the down-regulated gene set is the cell cycle. Then, we further investigated the correlation between STEAP4 and the major cell cycle-related genes (Fig. 5C and Supplementary Table 2) . The results showed that the expression of STEAP4 was significantly correlated with 14 cell cycle-related genes.The top 5 negative cell cycle-related genes were CDC20, CDC25C,CCNB1,CCNB2 and PLK1 ( p values are all < 0.05, correlation coefficients are -0.45, -0.41, -0.41, -0.40, and -0.38, respectively).However, since this is only a correlation analysis, the exact way in which these genes interact with STEAP4 is unclear. Discussion Hepatocellular carcinoma (HCC) accounts for 90% of all primary liver tumors and is the second leading cause of cancer-related death 1 . Advanced HCC has a very poor prognosis, with a median overall survival of only about 8 months if left untreated 24 . Several clinically relevant risk factors have been reported, including hepatitis virus B and C infection, aflatoxin intake, alcohol consumption, etc. However, the molecular pathogenesis of HCC is not fully understood 2 . Hence, finding a novel biomarker with high accuracy for predicting the prognosis of HCC patients is urgently needed. The abnormal gene expression may be related to tumorigenesis and the prognosis 25 . In this study, we first confirmed that the expression of STEAP4 was significantly decreased in HCC, and the lower expression of STEAP4 was significantly associated with poor prognosis and overall survival in HCC patients. These results suggest that STEAP4 may serve as a tumor suppressor gene and the molecular marker predicting the prognosis of HCC patients. The STEAP gene was first isolated in 1999 by Hubert et al. from an advanced prostate cancer xenograft model 5 . This gene was highly expressed in prostate cancer and was named STEAP, for it was a 6-transmembrane epithelial antigen of the prostate 5 . Subsequently, the family members of STEAP were successively identified, and STEAP 4 was one of them 6-9 . Except for expression in the prostate, STEAP4 was also found in various tissues, such as adipose tissue, placenta, bone marrow, lung, pancreas, heart, liver, skeletal muscle, pancreas, testicles, small intestine, and thymus. STEAP4 has been widely reported to be associated with obesity; its expression was downregulated in obese patients, mainly in those with type 2 diabetes 26 . Several studies have confirmed that STEAP4 regulates adipocyte sensitivity to insulin, which was related to insulin resistance 14,27,28 . Some studies showed that the decreased expression of STEAP4 in vitro caused the occurrence of inflammation in adipose tissue 28,29 and was associated with the progression of prostate cancer 9,30 , colon cancer 15,17 , breast cancer 18,31 and bladder cancer 21 . However, few articles about the relationship between STEAP4 and hepatocellular carcinoma were reported 2,20 . Therefore, this study explored the potential molecular functions of STEAP4 in HCC carcinogenesis and its regulatory network. To gain the relevant information, we used bioinformatics methods to analyze the function and the regulatory network of STEAP4 in HCC to guide future studies on HCC and identify its possible novel biomarkers. In our study, we found that STEAP4 was significantly down-regulated in human HCC, and the lower expression was significantly related to poorer prognosis, lower overall survival (OS), and (DFS) in TCGA-LIHC cohorts. This conclusion may suggest that STEAP4 acts as a tumor suppressor and it deserved further in-depth study as a potential diagnostic and prognostic marker. Further, through the subgroup analysis, we could see that STEAP4 expression level was significantly correlated with tumor T stage and patients' race. In the group with high expression of STEAP4, the proportion of the T1 stage was higher than that of the low expression group, which also suggested that STEAP4 played a tumor-suppressive role in HCC. At the same time, in the ethnic, we found that there were more white patients in the high expression group, meanwhile, more Asian patients in the low expression group. However, there was no significant correlation between STEAP4 expression and gender, age, lymph node metastasis, and other clinical characteristics. Multivariate Cox survival analysis revealed that STEAP4 was an independent predictor of unfavorable prognosis in LIHC patients ( p < 0.05). By using the LinkedOmics database, 9839 co-expressed genes were identified. STEAP4 co-expressed genes participate primarily in protein activation cascade, acute inflammatory response, small molecule catabolic process, fatty acid metabolic process, and multiple metabolic processes, while the activities like chromosome segregation, ribonucleoprotein complex biogenesis, spindle organization, DNA recombination, and rRNA metabolic process were inhibited. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed enrichment in the Complement and coagulation cascades, Chemical carcinogenesis, Fatty acid degradation, Spliceosome, Ribosome, Cell cycle, etc. We also found that STEAP4 in HCC was associated with a network of kinases including ATR, CDK1, CDK2, ATM, PLK1, CHEK1, AURKB, and CHEK2. These kinases regulated DNA damage and replication, mitosis, and the cell cycle. Among them, cancer-related kinases AURKB, CDK1, CHEK1, and PLK1 were significantly highly expressed in tumor tissues and were significantly associated with the OS of HCC. Polo-like kinase 1 (PLK1) was highly expressed during mitosis, mainly involved in the control of the G2/M phase, and elevated levels were found in many different types of cancer 32 . Some PLK1-related pathways were aberrantly activated in human HCC 33,34 . And Longerich’s study showed that PLK1 played oncogenic functions in HCC 35 . Ataxia-telangiectasia and Rad3-related (ATR)-checkpoint kinase 1 (CHEK1) signaling was critical for genomic stability by regulating DNA damage response 36,37 , and many studies have shown that the ATR-CHEK1 pathway was upregulated in various types of cancer and was involved in tumor progression 38,39 . Bao et al. investigated the role of this pathway in HCC and found a positive correlation between PLK-4 expression and ATR/CHEK1 pathway activation which was critical for tumorigenesis and progression of HCC 40 . The overexpression of AURKA can enhance tumor proliferation and promote HCC metastasis 41 . Next, we found the E2F family, including $E2F_Q6, V$E2F_Q4, V$E2F4DP1_01, V$E2F1DP1RB_01, and $E2F1_Q6 were the main negatively enriched transcription factors for STEAP4 dysregulation. The E2F family of transcription factors activated many genes involved in the cell cycle, DNA repair, and apoptosis 42 . The previous study has confirmed that E2F-driven transcription was associated with the development and progression of HCC 43 . Studies showed that E2F1 was the key to the cell cycle regulatory network. Its abnormal expression would cause the occurrence of HCC, and its elevated expression was associated with poor prognosis in HCC patients 44 . Among the top 10 significantly positively or negatively correlated genes, we screened one positively and 7 negatively correlated genes that were differentially expressed in LIHC: EMCN, CDT1, CDCA3, KIF2C BIRC5, TROAP, AURKB, and MYBL2. The correlation between STEAP4 and these genes was verified in TIMER. In addition, we found that the high expression of the 7 negatively significant STEAP4-related genes was significantly associated with lower OS in LIHC patients, the high expression of the positively related gene EMCN was significantly associated with higher OS. It was possible that STEAP4 and its co-expressed genes collectively contributed to liver carcinogenesis and progression. An overview of the prognostic role, regulatory networks, and functional analysis suggested that the STEAP4 played an important role in the development of HCC. Given these facets, we further designed the identification and development of a gene expression signature associated with the co-expressed genes and regulators of STEAP4, which provided a more individualized risk assessment in HCC patients. We constructed a 2-gene (STEAP4 and CDCA3) signature, which was successfully validated in publicly available datasets from the TCGA cohort and followed by an independent validation of this panel in the ICGC cohort. Furthermore, the prognostic performance of the signature was significantly exhibited in subgroup analysis. To offer an easy-to-use clinical assay, we combined the TNM stage and the signature to build a risk-assessment nomogram, of which discrimination and calibration were well. This could be attributed to the improvement of prognostic stratification of resectable HCC patients to optimize the selection of patients to be offered adjuvant treatments. Through R language analysis, we found that 250 genes in the high expression group of STEAP4 were significantly downregulated compared to the low expression group. Further enrichment pathway analysis of downregulated genes using the DAVID database revealed that they were significantly enriched in the cell cycle. Among them, STEAP4 was significantly correlated with 14 cell cycle related genes, and the top five genes were CDC20, CDC25C, CCNB1, CCNB2,and, PLK1. We envision STEAP4 inhibiting tumor cell proliferation by affecting the cell cycle, but further research is needed. Conclusion In conclusion, our study provided multidimensional evidence for the importance of STEAP4 in hepatocarcinogenesis and its potential as a biomarker in HCC. The results suggested that STEAP4 down-regulation in HCC may likely have far-reaching effects in the cell cycle of HCC through acting on E2F transcription factors and cell cycle- and cancer-associated kinases. These efforts may provide new research directions to identify prognostic biomarkers and also provide an important theoretical basis and clinical guidance for the development of therapeutic targets for HCC. But large-scale subsequent functional studies are still needed. Materials And Methods 5.1 Differential expression analysis of STEAP4 The GEPIA (http://gepia.cancer-pku.cn) database and Oncomine 4.5 (https://www.oncomine.org/) were used to obtain the expression level of the STEAP4 gene in normal tissues and HCC tumors. For subgroup analysis, we downloaded all the publicly available LIHC RNA-Seq data information from The Cancer Genome Atlas (TCGA) official website (https://cancergenome.nih.gov/) before november 14, 2022, through the GDC Data Transfer Tool. For subgroup analysis, the frozen tissue samples and normal tissue samples of primary or recurrent HCC were screened and based on the expression of STEAP4, and tumor samples were divided into high and low STEAP4 expression groups according to the median, the relationship between STEAP4 expression and multiple clinic-pathological parameters was analyzed using the chi-squared test. 5.2 The relationship between STEAP4 expression and prognosis We use the GEPIA database to generate survival curves, including overall survival (OS) and recurrence-free survival (RFS), based on gene expression with the log-rank test and the Mantel-Cox test. To further analyze the correlation between STEAP4 expression with survival rates and clinical-pathological characteristics, R version 4.0.5 software was used, with the survival and survminer packages used appropriately. Multivariate Cox regression analyses identified independent prognostic factors. 5.3 LinkedOmics Database Analysis STEAP4 co-expression was analyzed statistically using Pearson’s correlation coefficient in the LinkedOmics database (http://www.linkedomics.org/login.php), presenting in volcano plots, heat maps, or scatter plots. Function module of LinkedOmics performs analysis of Gene Ontology biological process (GO_BP), KEGG pathways, kinase-target enrichment, and transcription factor-target enrichment by the gene set enrichment analysis (GSEA). The top 10 positively and negatively significant genes correlated with STEAP4 were analyzed through the GEPIA database. Those genes differentially expressed in LIHC patients were further screened, and the TIME database further verified their correlation with STEAP4. Moreover, by using the GEPIA database, we identified and explored the prognostic significance of these genes of STEAP4 in HCC. 5.4 Establishment and Validation of the prognostic model Combining co-expression genes and regulators of STEAP4 to construct a prognostic model, we applied stepwise analysis of the multivariate Cox model based on the Akaike information criterion to avoid overfitting and select candidate’s genes. The following formula calculated the risk score of each LUAD patient: risk score = gene a * coefficient a + gene b * coefficient b + gene c * coefficient c + …… + gene n * coefficient n. The median value of risk scores stratified high- and low-risk groups. Kaplan–Meier survival curves were applied for survival comparison between low- and high-risk groups, and log-rank was used to test statistical significance (p-value < 0.05). The performance of the signature was evaluated and validated in the TCGA-LIHC and ICGC- LIHC cohorts, respectively. Visualization of the model was achieved by the nomogram; calibration plots were used to assess the predicting accuracy of the model. 5.5 Associations Between Genome-wide Expression Profiles and STEAP4 Expression To investigate the biological role of STEAP4 in hepatocellular carcinoma (HCC), the differentially expressed genes in the high and low expression groups of STEAP4 gene in hepatocellular carcinoma tumor samples were analyzed using R version 4.0.5 software. Further, we performed functional enrichment analysis of these aberrant genes based on the David database and found the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Then, the association between the STEAP4 gene and the path was further explored. 5.6 Statistical analysis SPSS 25.0 and R version 4.0.5 software was used for statistical analysis. The measured data were presented as mean ± SD. Independent samples t-test and paired sample t-test were used to analyze the differential expression of STEAP4 mRNA between the HCC tissues and the adjacent normal tissues from the TCGA databases. The Pearson chi-squared test was used to analyze the association between STEAP4 and clinical characteristic variables. Multivariate analyses were performed using Cox proportional hazards regression model. Statistically significant differences were considered when P < 0.05. Abbreviations Declarations Acknowledgments Not applicable. Authors' contributions Yang Ting and Zou Min-hong designed the article, analyzed data, and write the manuscript. Xie Yu-jie performed the histological examination. Zhang Yong revised the manuscript. All authors reviewed and approved the final manuscript. Conflicts of interest The authors declare no potential conflicts of interests. 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Cell. 2007;129(3):451-452. Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW. Obesity is associated with macrophage accumulation in adipose tissue. Journal of Clinical Investigation. 2003;112(12):1796-1808. Jiang C, Wu B, Xue M, et al. Inflammation accelerates copper‐mediated cytotoxicity through induction of six‐transmembrane epithelial antigens of prostate 4 expression. Immunology & Cell Biology. 2020;99(4):392-402. Pihlstrom N, Jin Y, Nenseth Z, Kuzu OF, Saatcioglu F. STAMP2 Expression Mediated by Cytokines Attenuates Their Growth-Limiting Effects in Prostate Cancer Cells. Cancers (Basel). 2021;13(7). Orfanou I-M, Argyros O, Papapetropoulos A, Tseleni-Balafouta S, Vougas K, Tamvakopoulos C. Discovery and Pharmacological Evaluation of STEAP4 as a Novel Target for HER2 Overexpressing Breast Cancer. Frontiers in oncology. 2021;11. Barr FA, Sillje HH, Nigg EA. Polo-like kinases and the orchestration of cell division. Nat Rev Mol Cell Biol. 2004;5(6):429-440. 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Mol Oncol. 2015;9(3):569-585. Parikh RA, Appleman LJ, Bauman JE, et al. Upregulation of the ATR-CHEK1 pathway in oral squamous cell carcinomas. Genes Chromosomes Cancer. 2014;53(1):25-37. Bao J, Yu Y, Chen J, et al. MiR-126 negatively regulates PLK-4 to impact the development of hepatocellular carcinoma via ATR/CHEK1 pathway. Cell Death Dis. 2018;9(10):1045. Chen C, Song G, Xiang J, Zhang H, Zhao S, Zhan Y. AURKA promotes cancer metastasis by regulating epithelial-mesenchymal transition and cancer stem cell properties in hepatocellular carcinoma. Biochem Biophys Res Commun. 2017;486(2):514-520. Moreno E, Pandit SK, Toussaint MJM, et al. Atypical E2Fs either Counteract or Cooperate with RB during Tumorigenesis Depending on Tissue Context. Cancers (Basel). 2021;13(9). Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893-2917. Emanuele MJ, Enrico TP, Mouery RD, Wasserman D, Nachum S, Tzur A. Complex Cartography: Regulation of E2F Transcription Factors by Cyclin F and Ubiquitin. Trends Cell Biol. 2020;30(8):640-652. Additional Declarations No competing interests reported. Supplementary Files supplementaryfigure1.tif Supplementaryfigure2.tif 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 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-3928009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":273468422,"identity":"45f332b4-bc36-484f-9c7c-3823fda4a23c","order_by":0,"name":"Yang Ting","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Ting","suffix":""},{"id":273468423,"identity":"cafca9ca-70ec-4994-9bd3-43c14d7b063b","order_by":1,"name":"Min-hong Zou","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min-hong","middleName":"","lastName":"Zou","suffix":""},{"id":273468424,"identity":"d805c69e-2304-4284-9ee7-9d3fae1cfecd","order_by":2,"name":"Yu-jie xie","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu-jie","middleName":"","lastName":"xie","suffix":""},{"id":273468425,"identity":"c186c01b-01c6-47e9-b123-5953dd42908e","order_by":3,"name":"Zhang Yong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYDACCQglxw8kDjyAifIQocVYsgGoJYEULYkbDgBJorTIz25+9vBr253EzdcOPwTaUpc4f0YC44O3bQzy5ji0MM45Zm4s2/bMeNvtNAOglsOJG24kMBvObWMw3NmAXQuzRIKZtGTbYdlttxNAWg4kbpBIYJPmbWMAcrFrYZNI/wbSwrh5dvoHmMPYf+PTwiORYyb5se2w4gbpHJAtzIkNNxLYmPFpkZDIKZNmOHfYWOJ2TsGBBIPDxhvOPGyWnHNOwnADDi3yM9K3Sf4oOyzHPzt984cPFXWy89uTD354U2Yjj8sWcBDwssGYBgyODQyMDQzw+MIBGH/8QXDs8SodBaNgFIyCEQkAAYFhqBdY62kAAAAASUVORK5CYII=","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Yong","suffix":""}],"badges":[],"createdAt":"2024-02-04 15:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3928009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3928009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51393441,"identity":"b39614f4-7be7-4e3c-8cef-2587f94ac41c","added_by":"auto","created_at":"2024-02-20 18:54:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":542680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSTEAP4 transcription level in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Box plots showing the expression of STEAP4 in tumor tissues and para-carcinoma tissues, according to the t-test in the GEPIA database (*, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003e(B) Scatter plots showing the mRNA expression levels of STEAP4 between tumor and non-tumor samples in LIHC patients in the Oncomine database, series including the Chen Liver, Roessler Liver, Wurmbach Liver, Roessler Liver 2 and Mas Liver datasets, respectively (*, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ****, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003e(C) Representative IHC images (10X and 40X) for STEAP4 expression in HCC tissue and para-carcinoma tissue. The scale bar was 50 µm for 40X.\u003c/p\u003e\n\u003cp\u003e(D) Statistical analysis of STEAP4 expression in LIHC patients through IHC staining (****, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/c82eaa60a475d35c12e5d845.png"},{"id":51393849,"identity":"a50b0874-0551-47e2-a38c-08506b815f1e","added_by":"auto","created_at":"2024-02-20 19:02:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":341570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prognostic evaluation of STEAP4 in HCC patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) Overall survival (OS) and Disease-free survival (DFS)of high and low expression of STEAP4 in LIHC patients were analyzed in the GEPIA database.\u003c/p\u003e\n\u003cp\u003e(C) Overall survival of high and low expression of STEAP4 was analyzed in the Kaplan-Meier plotter database with the TCGA-LIHC samples.\u003c/p\u003e\n\u003cp\u003e(D) Multivariate Cox proportional hazards analysis of OS was visualized by forest plot.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/fd92cb2ef1e278b6547c4a49.png"},{"id":51393443,"identity":"801b0ca8-2fae-4d53-b980-a218d06479ef","added_by":"auto","created_at":"2024-02-20 18:54:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1262196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSTEAP4 co-expression genes and function analysis in HCC(LinkedOmics)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The global STEAP4 highly correlated genes identified by Pearson test in LIHC cohort and Heat maps showing the top 50 genes positively and negatively correlated with STEAP4 in LIHC.\u003c/p\u003e\n\u003cp\u003e(B) Significantly enriched KEGG pathways of STEAP4 in LIHC cohort.\u003c/p\u003e\n\u003cp\u003e(C) The kinases,transcription factors and mRNA-target networks of STEAP4 in LIHC.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/96a87a570553310bb012986a.png"},{"id":51393446,"identity":"863fbdb1-262a-4ec5-8667-6a461d6cf911","added_by":"auto","created_at":"2024-02-20 18:54:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1120488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the signature in ICGC-LIHC and construction of the nomogram.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Kaplan-Meier curves and log-rank test of overall survival based low- and high-risk groups in the ICGC-LIHC.\u003c/p\u003e\n\u003cp\u003e(B) Scattered plots of genes in the signature.\u003c/p\u003e\n\u003cp\u003e(C) ROC curves for evaluating the prediction performance of signature in the ICGC- LIHC.\u003c/p\u003e\n\u003cp\u003e(D) Identification of the independent factor of the signature.\u003c/p\u003e\n\u003cp\u003e(E) The nomogram enrolled the TNM stage and the signature.\u003c/p\u003e\n\u003cp\u003e(F) Calibration plots of the nomogram.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/a93b1a3b4f90386be38bb54a.png"},{"id":51393445,"identity":"872a3b23-a4eb-48c6-834b-a4fed9d9e86f","added_by":"auto","created_at":"2024-02-20 18:54:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":865956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDEGs in the high and low expression group of STEAP in LIHC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Differentially expressed genes are displayed on volcano plots.\u003c/p\u003e\n\u003cp\u003e(B) KEGG pathway enrichment analysis of DEGs based on DAVID displayed on bubble plots. The y‐axis shows significantly enriched KEGG pathways, and the x‐axis shows different gene ratios. DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e\n\u003cp\u003e(C) Top 5 negative cell cycle-related genes with STEAP4 analyzed by TIMER.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/60e44ec28acaf606bf7fc2f6.png"},{"id":51758729,"identity":"9748f32d-a3ee-460d-95de-54f7d5b18e2f","added_by":"auto","created_at":"2024-02-28 15:21:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1123108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/e6bd1b31-3923-4c9c-810c-705e83359f60.pdf"},{"id":51393449,"identity":"6bc73f08-9334-45fa-ae46-eda035cf1f9d","added_by":"auto","created_at":"2024-02-20 18:54:19","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10266996,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/0e739b154e3e4c60d0ace261.tif"},{"id":51393448,"identity":"4198010c-021c-4409-9d2d-e24693a049b9","added_by":"auto","created_at":"2024-02-20 18:54:19","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8561044,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/ae6f62f27a2414b246d6b9d7.tif"},{"id":51393447,"identity":"a0a0c4be-e54e-4363-8fe7-ad9fabe58b8d","added_by":"auto","created_at":"2024-02-20 18:54:19","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14601,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/84c3e0648471a61505eb607e.docx"},{"id":51393444,"identity":"966681ba-5cfa-4eeb-b6a0-8ace2325dec2","added_by":"auto","created_at":"2024-02-20 18:54:18","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13922,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3928009/v1/83a01fcde0aa2c986b81567b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Tumor Suppressive and Potential Value of STEAP4 in Hepatocellular Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide, leading to nearly half a million deaths annually\u003csup\u003e1,2\u003c/sup\u003e. The 5-year survival rate of advanced HCC is poor due to the high recurrence rate and metastasis rate\u003csup\u003e3\u003c/sup\u003e. At present, the molecular mechanism of HCC formation and progression is not entirely understood, which further complicates the effective treatment of HCC(4).In addition, the lack of tumor type - or stage-specific molecular markers is another critical gap in the understanding and treatment of HCC\u003csup\u003e4\u003c/sup\u003e. Hence, finding a novel biomarker with high accuracy for predicting the prognosis of HCC patients is urgently needed.\u003c/p\u003e\n\u003cp\u003eThe six-transmembrane epithelial antigen of the prostate (STEAP) is highly expressed in prostate cancer and is first isolated in 1999 by Hubert et al. from an advanced prostate cancer xenograft model\u003csup\u003e5\u003c/sup\u003e. Subsequently, the family members of STEAP are successively identified, and STEAP 4 is one of them\u003csup\u003e6-9\u003c/sup\u003e and is involved in the reduction and transport of iron and copper\u003csup\u003e10\u003c/sup\u003e. The STEAP4 is broadly expressed in various tissues, including the prostate, lung, pancreas, heart, bone marrow, liver, etc.\u0026nbsp;\u003csup\u003e10-12\u003c/sup\u003e. It is associated with obesity, insulin resistance, inflammation, and cancer progression\u0026nbsp;\u003csup\u003e13-16\u003c/sup\u003e. The expression of STEAP4 is upregulated in prostate cancer, and its role in the occurrence and development of prostate cancer has been confirmed by many studies\u003csup\u003e9,16\u003c/sup\u003e. In addition, some studies have shown that STEAP4 is involved in the occurrence and development of colon cancer\u003csup\u003e15,17\u003c/sup\u003e, breast cancer\u003csup\u003e18,19\u003c/sup\u003e, hepatocellular carcinoma\u003csup\u003e2,20\u003c/sup\u003e, and other tumors\u003csup\u003e21,22\u003c/sup\u003e. For example, Xue et al. have shown that STEAP4 was overexpressed in murine models of colitis-associated colon cancer (CAC) and rendered mice susceptible to chemically induced colitis and colon tumorigenesis suggesting a poor prognosis\u003csup\u003e17\u003c/sup\u003e. Liao et al. identified an axis, the IL-17-STEAP4-XIAP axis, and found that STEAP4-dependent cellular copper uptake is critical for developing colon cancer in a murine model\u003csup\u003e15\u003c/sup\u003e. Wu et al. showed that the expression of STEAP4 was down-regulated in breast cancer and was associated with the prognosis of breast cancer\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, there are relatively few studies on liver cancer. In 2016, a Japanese study showed that STEAP4 was significantly down-regulated in HCC due to DNA hypermethylation, which may be related to the occurrence of HCC\u003csup\u003e2\u003c/sup\u003e. But the biological role of STEAP4 in HCC remains to be determined. Here, we explored the expression of STEAP4 in HCC patients, the relationship between its expression and the prognosis, and, the functional network of STEAP4.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Decreased STEAP4 expression in LIHC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GEPIA (http://gepia.cancer-pku.cn) database and Oncomine 4.5 (https://www.oncomine.org/) were used to obtain the expression level of the STEAP4 gene in HCC patients. STEAP4 was found significantly down-regulated in tumor tissues\u0026nbsp;in multiple HCC cohorts\u0026nbsp;(\u003cstrong\u003eFig.1A-B\u003c/strong\u003e). To verify the STEAP4 expression in HCC tissues, we performed immunohistochemistry (IHC) staining of 20 HCC and para-tumor normal tissue specimens. The positive intensity and percentage were evaluated and recorded. The positive intensity was defined as 0 for negative staining, 1 for light yellow, 2 for light brown, and 3 for dark brown. As the positive percentage only has 0% or 100%, the positive grade was judged according to the positive intensity: (1) a score of 0-1 is negatively defined; (2) a score of 2-3 is positively defined. The representative immunohistochemical images and result of quantitative analysis are shown in \u003cstrong\u003eFig.1C-D\u003c/strong\u003e, STEAP4 expression was significantly lower in HCC tissues than in adjacent normal tissues (p \u0026lt; 0.0001); the p-value was determined by Student\u0026rsquo;s t-test. We downloaded all the publicly available LIHC RNA-Seq data information from The Cancer Genome Atlas (TCGA) official website (https://cancergenome.Nih.gov/) before November 2022, through the GDC Data Transfer Tool. 371 primary tumor samples were divided into 185 low STEAP4 expression groups and 186 high STEAP4 expression groups based on the median values of STEAP4 mRNA level. Then the associations between STEAP4 mRNA expression and the clinicopathological features were investigated using the chi-squared test. As shown in\u003cstrong\u003e\u0026nbsp;Table 1.\u003c/strong\u003e,\u0026nbsp;the expression of STEAP4 was significantly correlated with tumor T stage (\u003cem\u003ep\u003c/em\u003e=0.024) and race(\u003cem\u003ep\u003c/em\u003e=0.012) in LIHC patients. T1 stage and White patients have higher STEAP4 expression than others. There was no significant correlation between STEAP4 expression and other clinicopathological factors, including age (\u003cem\u003ep\u003c/em\u003e = 0.931), gender (\u003cem\u003ep\u003c/em\u003e = 0.210), lymph node metastasis (\u003cem\u003ep\u003c/em\u003e = 0.212), distant metastasis (\u003cem\u003ep\u003c/em\u003e = 0.079) and BMI level (\u003cem\u003ep\u003c/em\u003e =0.133).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Association between STEAP4 expression and clinicopathological characteristics in LIHC patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1708329912.png\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 STEAP4 expression is survival-associated\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the association between STEAP4 expression and the survival outcomes of LIHC cohorts in the GEPIA database. The patients were separated into two groups according to the median value of STEAP4 expression level. A positive relationship between the STEAP4 gene level and the overall survival (OS) and disease-free survival (DFS) in liver cancer was found. That was the high STEAP4 expression group had significantly longer overall survival (OS) (log-rank test, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e=0.024) and disease-free survival (DFS) (log-rank test, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e=0.02), compared to the low expression group in the LIHC cohort (\u003cstrong\u003eFig. 2A-B\u003c/strong\u003e). The same result was also obtained using the Kaplan-Meier plotter database with TCGA-LIHC data (\u003cstrong\u003eFig. 2C\u003c/strong\u003e).\u0026nbsp;To further analyze the correlation between STEAP4 expression with survival rates and clinical-pathological characteristics, R version 4.0.5 software was used, with the survival and survminer packages used appropriately. Multivariate Cox survival analysis revealed that STEAP4 expression was an independent predictor of unfavorable prognosis in LIHC patients (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; \u003cstrong\u003eFig. 2D\u003c/strong\u003e). Overall, our results suggest that STEAP4 is a good prognostic factor and an independent prognostic factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 STEAP4 co-expression networks in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTEAP4 co-expression was analyzed statistically using Pearson\u0026rsquo;s correlation coefficient in the LinkedOmics database (http://www.linkedomics.org/login.php) \u003csup\u003e23\u003c/sup\u003e, presenting in volcano plots, heat maps, or scatter plots. As shown in \u003cstrong\u003eFig. 3A\u003c/strong\u003e,4433 genes (dark red dots) were shown significant positive correlations with STEAP4, whereas 4101 genes (dark green dots) were shown significant negative correlations (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.05 and FDR\u0026lt;0.01).\u0026nbsp;The top 50 significant genes positively and negatively correlated with STEAP4 were shown in the heat map.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The function module of LinkedOmics performs analysis of the KEGG pathways enrichment by the gene set enrichment analysis (GSEA). Significant enrichment in the Chemical carcinogenesis and Cell cycle were shown (\u003cstrong\u003eFig. 3B\u003c/strong\u003e). We further explored the regulators (kinases, mRNA and transcription factors (TF)) of STEAP4 in HCC(\u003cstrong\u003eFig. 3C\u003c/strong\u003e). The results (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e) showed that the significantly related kinases were Ataxia telangiectasia and Rad3-related (ATR), cyclin-dependent kinase 1 (CDK1), cyclin-dependent kinase 2 (CDK2), Ataxia telangiectasia mutated (ATM), polo-like kinase 1 (PLK1), checkpoint kinase 1 (CHEK1), Aurora kinase B (AURKB), checkpoint kinase 2 (CHEK2). In fact, among them, the transcriptional levels of AURKB, CDK1, CHEK1, and PLK1 were significantly elevated in HCC tissues. The significant enrichment of transcription factors for overlapped co-expression gene was mainly the E2F transcription factor family, including V$E2F_Q6, V$E2F_Q4, V$E2F4DP1_01, V$E2F1DP1RB_01 and V$E2F1_Q6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter differential expression analysis of top 10 co-expressed genes(\u003cstrong\u003eSupplementary Fig. 1A\u003c/strong\u003e), one positively and seven negatively significant genes were identified, they are EMCN(r =0.576, p = 3.77E-34), CDT1(r =-0.524, p = 1.68E-27), CDCA3(r =-0.523, p = 1.92E-27), KIF2C(r =-0.515, p = 1.55E-26), BIRC5( r =-0.531, p = 2.14E-28), TROAP(r =-0.529, p = 3.61E-28),AURKB(r =-0.529, p = 4.19E-28)and MYBL2(r =-0.526, p = 9.06E-28).The correlation between STEAP4 and these genes was verified in TIMER (\u003cstrong\u003eSupplementary Fig. 1B\u003c/strong\u003e).In addition, we found that the high expression of the 7 negatively significant STEAP4-related genes was significantly associated with worse overall survival in LIHC patients, the high expression of the positively related gene EMCN was significantly associated with better prognosis(\u003cstrong\u003eSupplementary Fig. 1C\u003c/strong\u003e). It is possible that STEAP4 and its co-expressed genes collectively contribute to liver carcinogenesis, resulting in poor survival in LIHC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Construction and validation of model-based STEAP4 for predicting the prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe combined 8\u0026nbsp;co-expressed genes (EMCN, CDT1, CDCA3, KIF2C, BIRC5, TROAP, AURKB, and MYBL2), 4\u0026nbsp;kinases regulators (AURKB, CDK1, CHEK1, and PLK1), and STEAP4 to construct a model for predicting prognosis in HCC. To avoid overfitting, we performed a multivariate Cox proportional hazards regression analysis and stepwise analysis to establish an optimal predictive model: risk score = CDCA3*\u0026nbsp;0.5289133 + STEAP4*\u0026nbsp;-3.7328420. Based on the formula, the risk score of each patient in the training set was calculated. Patients were classified into the high- and low-risk group using the median risk score as the cutoff value. Patients in the high-risk group had a significantly poorer OS (P \u0026lt; 0.0001) (\u003cstrong\u003eSupplementary Fig. 2A\u003c/strong\u003e). The areas under the curves (AUCs) of signature were 0.783 for 1-year OS, 0.722 for 3-year OS, and 0.754 for 5-year OS, respectively (\u003cstrong\u003eSupplementary Fig. 2C\u003c/strong\u003e). We ranked patients\u0026rsquo; risk scores, distributed survival status, and exhibited gene expression patterns of 2 genes (\u003cstrong\u003eSupplementary Fig. 2B\u003c/strong\u003e). Furthermore, the multivariate analysis demonstrated that the risk score is a prognostic factor independent of age, gender, and stage (\u003cstrong\u003eSupplementary Fig. 2D\u003c/strong\u003e). Besides, we performed subgroup analysis to further investigate the potential prognosis of the signature. As shown in \u003cstrong\u003eSupplementary Fig. 2E,\u003c/strong\u003e the ability of the signature to stratify patients was achieved in patients with early-stage, advanced stage, male, female,\u0026nbsp;\u0026lt;60-year, \u0026lt;60-year, and \u0026gt;=60-year.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe predictive capability of this signature was further verified in the testing set. According to the same predict signature and same stratify method, each of the patients in the International Cancer Genome Consortium (ICGC) dataset was divided into the high- and low-risk group. Survival analysis showed that patients with low-risk scores had longer OS (p = 0.0088) (\u003cstrong\u003eFig. 4A\u003c/strong\u003e); the distribution of risk score, patients\u0026rsquo; survival status, and gene expression pattern was presented by scatter plots and heatmaps (\u003cstrong\u003eFig. 4B\u003c/strong\u003e); the AUCs of signature were 0.560 for 1-year OS, 0.684 for 3-year OS, and 0.951 for 5-year OS, respectively (\u003cstrong\u003eFig. 4C\u003c/strong\u003e). Besides, the signature was verified to be an independent prognostic factor (\u003cstrong\u003eFig. 4D\u003c/strong\u003e). For easy to use in clinical, we combined the independent prognostic factors in the TCGA cohort to build a nomogram (\u003cstrong\u003eFig.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003eE\u003c/strong\u003e). The calibration plots of 1-year, 3-year, and 5-year survival rates were shown in \u003cstrong\u003eFig.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Associations Between Genome-wide Expression Profiles and STEAP4 Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy using R version 4.0.5 software, we screened out the differentially expressed genes in the high and low expression groups of the STEAP gene in all tumor samples.142 upregulated and 250 down-regulated genes were identified as being significantly associated with STEAP4 expression (FDR-adjusted \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.05, FDR\u0026lt;0.05, and |FC|\u0026ge;2, \u003cstrong\u003eFig. 5A\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eFurther, we performed a functional enrichment analysis of these genes based on the David database to study the enrichment pathways of these differentially expressed genes.\u0026nbsp;As shown in \u003cstrong\u003eFig. 5B,\u003c/strong\u003e the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in the down-regulated gene set is the cell cycle. Then, we further investigated the correlation between STEAP4 and the major cell cycle-related genes \u003cstrong\u003e(Fig. 5C and Supplementary Table 2)\u003c/strong\u003e. The results showed that the expression of STEAP4 was significantly correlated with 14 cell cycle-related genes.The top 5 negative cell cycle-related genes were CDC20, CDC25C,CCNB1,CCNB2 and PLK1 (\u003cem\u003ep\u003c/em\u003e values are all \u0026lt; 0.05, correlation coefficients are -0.45, -0.41, -0.41, -0.40, and -0.38, respectively).However, since this is only a correlation analysis, the exact way in which these genes interact with STEAP4 is unclear.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHepatocellular carcinoma (HCC) accounts for 90% of all primary liver tumors and is the second leading cause of cancer-related death\u003csup\u003e1\u003c/sup\u003e. Advanced HCC has a very poor prognosis, with a median overall survival of only about 8 months if left untreated\u003csup\u003e24\u003c/sup\u003e. Several clinically relevant risk factors have been reported, including hepatitis virus B and C infection, aflatoxin intake, alcohol consumption, etc. However, the molecular pathogenesis of HCC is not fully understood\u003csup\u003e2\u003c/sup\u003e. Hence, finding a novel biomarker with high accuracy for predicting the prognosis of HCC patients is urgently needed. The abnormal gene expression may be related to tumorigenesis and the prognosis\u003csup\u003e25\u003c/sup\u003e.\u0026nbsp;In this study, we first confirmed that the expression of STEAP4 was significantly decreased in HCC, and the lower expression of STEAP4 was significantly associated with poor prognosis and overall survival in HCC patients. These results suggest that STEAP4 may serve as a tumor suppressor gene and the molecular marker predicting the prognosis of HCC patients.\u003c/p\u003e\n\u003cp\u003eThe STEAP gene\u0026nbsp;was\u0026nbsp;first isolated in 1999 by Hubert et al. from an advanced prostate cancer xenograft model\u003csup\u003e5\u003c/sup\u003e. This gene\u0026nbsp;was\u0026nbsp;highly expressed in prostate cancer and\u0026nbsp;was\u0026nbsp;named STEAP, for it\u0026nbsp;was\u0026nbsp;a 6-transmembrane epithelial antigen of the prostate\u003csup\u003e5\u003c/sup\u003e. Subsequently, the family members of STEAP\u0026nbsp;were\u0026nbsp;successively identified, and STEAP 4\u0026nbsp;was\u0026nbsp;one of them\u003csup\u003e6-9\u003c/sup\u003e.\u0026nbsp;Except for expression in the prostate, STEAP4\u0026nbsp;was\u0026nbsp;also found in various tissues, such as adipose tissue, placenta, bone marrow, lung, pancreas, heart, liver, skeletal muscle, pancreas, testicles, small intestine, and thymus. STEAP4 has been widely reported to be associated with obesity; its expression\u0026nbsp;was\u0026nbsp;downregulated in obese patients, mainly in those with type 2 diabetes\u003csup\u003e26\u003c/sup\u003e.\u0026nbsp;Several studies have confirmed that STEAP4 regulates adipocyte sensitivity to insulin, which\u0026nbsp;was\u0026nbsp;related to insulin resistance\u003csup\u003e14,27,28\u003c/sup\u003e. Some studies showed that the decreased expression of STEAP4 in vitro caused the occurrence of inflammation in adipose tissue\u003csup\u003e28,29\u003c/sup\u003e and\u0026nbsp;was\u0026nbsp;associated with the progression of prostate cancer\u003csup\u003e9,30\u003c/sup\u003e, colon cancer\u003csup\u003e15,17\u003c/sup\u003e, breast cancer\u003csup\u003e18,31\u003c/sup\u003e and bladder cancer\u003csup\u003e21\u003c/sup\u003e.\u0026nbsp;However, few articles about the relationship between STEAP4 and hepatocellular carcinoma\u0026nbsp;were\u0026nbsp;reported\u003csup\u003e2,20\u003c/sup\u003e. Therefore, this study explored\u0026nbsp;the potential\u0026nbsp;molecular\u0026nbsp;functions of \u003cem\u003eSTEAP4\u0026nbsp;\u003c/em\u003ein\u0026nbsp;HCC carcinogenesis\u0026nbsp;and its regulatory network.\u0026nbsp;To gain the relevant information, we used\u0026nbsp;bioinformatics methods to analyze the function and the regulatory network of STEAP4 in HCC to guide future studies on HCC and identify its possible novel biomarkers.\u003c/p\u003e\n\u003cp\u003eIn our study, we found\u0026nbsp;that STEAP4\u0026nbsp;was\u0026nbsp;significantly down-regulated in human HCC, and the lower expression\u0026nbsp;was\u0026nbsp;significantly related to poorer prognosis, lower overall survival (OS), and (DFS) in TCGA-LIHC cohorts. This conclusion may suggest that STEAP4\u0026nbsp;acts as a tumor suppressor and it deserved\u0026nbsp;further in-depth study as a potential diagnostic and prognostic marker.\u0026nbsp;Further, through the subgroup analysis, we could\u0026nbsp;see that\u0026nbsp;STEAP4 expression level\u0026nbsp;was\u0026nbsp;significantly correlated with tumor T stage and patients\u0026apos; race. In the group with high expression of STEAP4, the proportion of the T1 stage\u0026nbsp;was\u0026nbsp;higher than that of the low expression group, which also suggested\u0026nbsp;that STEAP4 played\u0026nbsp;a tumor-suppressive role in HCC. At the same time, in the ethnic, we found\u0026nbsp;that there\u0026nbsp;were\u0026nbsp;more white patients in the high expression group, meanwhile, more Asian patients in the low expression group. However, there\u0026nbsp;was\u0026nbsp;no significant correlation between STEAP4 expression and gender, age, lymph node metastasis, and other clinical characteristics. Multivariate Cox survival analysis revealed\u0026nbsp;that STEAP4\u0026nbsp;was\u0026nbsp;an independent predictor of unfavorable prognosis in LIHC patients (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eBy using the LinkedOmics database, 9839 co-expressed genes\u0026nbsp;were\u0026nbsp;identified.\u0026nbsp;STEAP4 co-expressed genes participate primarily in protein activation cascade, acute inflammatory response, small molecule catabolic process, fatty acid metabolic process, and multiple metabolic processes, while the activities like chromosome segregation, ribonucleoprotein complex biogenesis, spindle organization, DNA recombination, and rRNA metabolic process\u0026nbsp;were\u0026nbsp;inhibited. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed\u0026nbsp;enrichment in the Complement and coagulation cascades, Chemical carcinogenesis, Fatty acid degradation, Spliceosome, Ribosome, Cell cycle, etc.\u0026nbsp;We also\u0026nbsp;found\u0026nbsp;that STEAP4 in HCC\u0026nbsp;was\u0026nbsp;associated with a network of kinases including ATR, CDK1, CDK2, ATM, PLK1, CHEK1, AURKB, and CHEK2. These kinases regulated\u0026nbsp;DNA damage and replication, mitosis, and the cell cycle.\u0026nbsp;Among them,\u0026nbsp;cancer-related kinases\u0026nbsp;AURKB, CDK1, CHEK1, and PLK1\u0026nbsp;were\u0026nbsp;significantly highly expressed in tumor tissues and\u0026nbsp;were\u0026nbsp;significantly associated with the OS of HCC. Polo-like kinase 1 (PLK1)\u0026nbsp;was\u0026nbsp;highly expressed during mitosis, mainly involved in the control of the G2/M phase, and elevated levels\u0026nbsp;were\u0026nbsp;found in many different types of cancer\u003csup\u003e32\u003c/sup\u003e.\u0026nbsp;Some\u0026nbsp;PLK1-related\u0026nbsp;pathways\u0026nbsp;were\u0026nbsp;aberrantly activated in human HCC\u003csup\u003e33,34\u003c/sup\u003e.\u0026nbsp;And Longerich\u0026rsquo;s study showed that PLK1 played oncogenic functions in HCC\u003csup\u003e35\u003c/sup\u003e. Ataxia-telangiectasia and Rad3-related (ATR)-checkpoint kinase 1 (CHEK1) signaling\u0026nbsp;was\u0026nbsp;critical for genomic stability by regulating DNA damage response\u003csup\u003e36,37\u003c/sup\u003e, and many studies have shown that the ATR-CHEK1 pathway\u0026nbsp;was\u0026nbsp;upregulated in various types of cancer and\u0026nbsp;was\u0026nbsp;involved in tumor progression\u003csup\u003e38,39\u003c/sup\u003e.\u0026nbsp;Bao et al. investigated the role of this pathway in HCC and found a positive correlation between PLK-4 expression and ATR/CHEK1 pathway activation which was critical for tumorigenesis and progression of HCC\u003csup\u003e40\u003c/sup\u003e.\u0026nbsp;The overexpression of AURKA can enhance tumor proliferation and promote HCC metastasis\u003csup\u003e41\u003c/sup\u003e. Next, we found\u0026nbsp;the E2F family, including $E2F_Q6, V$E2F_Q4, V$E2F4DP1_01, V$E2F1DP1RB_01, and $E2F1_Q6\u0026nbsp;were\u0026nbsp;the main negatively enriched transcription factors for STEAP4 dysregulation. The E2F family of transcription factors activated\u0026nbsp;many genes involved in the cell cycle, DNA repair, and apoptosis\u003csup\u003e42\u003c/sup\u003e.\u0026nbsp;The previous study has confirmed that E2F-driven transcription was associated with the development and progression of HCC\u003csup\u003e43\u003c/sup\u003e.\u0026nbsp;Studies showed that\u0026nbsp;E2F1 was the key to the cell cycle regulatory network. Its abnormal expression would cause the occurrence of HCC, and its elevated expression was associated with poor prognosis in HCC patients\u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAmong the top 10 significantly positively or negatively correlated genes, we screened\u0026nbsp;one positively and 7 negatively correlated genes that\u0026nbsp;were\u0026nbsp;differentially expressed in LIHC: EMCN, CDT1, CDCA3, KIF2C BIRC5, TROAP, AURKB, and MYBL2. The correlation between STEAP4 and these genes\u0026nbsp;was\u0026nbsp;verified in TIMER. In addition, we found\u0026nbsp;that the high expression of the 7 negatively significant STEAP4-related genes\u0026nbsp;was\u0026nbsp;significantly associated with lower OS in LIHC patients, the high expression of the positively related gene EMCN\u0026nbsp;was\u0026nbsp;significantly associated with higher OS. It\u0026nbsp;was\u0026nbsp;possible that STEAP4 and its co-expressed genes collectively contributed\u0026nbsp;to liver carcinogenesis and progression. An overview of the prognostic role, regulatory networks, and functional analysis suggested that the STEAP4 played an important role in the development of HCC. Given these facets, we further designed the identification and development of a gene expression signature associated with the co-expressed genes and regulators of STEAP4, which provided a more individualized risk assessment in HCC patients. We constructed a 2-gene (STEAP4 and CDCA3) signature, which was successfully validated in publicly available datasets from the TCGA cohort and followed by an independent validation of this panel in the ICGC cohort. Furthermore, the prognostic performance of the signature was significantly exhibited in subgroup analysis. To offer an easy-to-use clinical assay, we combined the TNM stage and the signature to build a risk-assessment nomogram, of which discrimination and calibration were well. This could be attributed to the improvement of prognostic stratification of resectable HCC patients to optimize the selection of patients to be offered adjuvant treatments.\u003c/p\u003e\n\u003cp\u003eThrough R language analysis, we found that 250 genes in the high expression group of STEAP4 were significantly downregulated compared to the low expression group. Further enrichment pathway analysis of downregulated genes using the DAVID database revealed that they were significantly enriched in the cell cycle. Among them, STEAP4 was significantly correlated with 14 cell cycle related genes, and the top five genes were CDC20, CDC25C, CCNB1, CCNB2,and, PLK1. We envision STEAP4 inhibiting tumor cell proliferation by affecting the cell cycle, but further research is needed.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provided multidimensional evidence for the importance of STEAP4 in hepatocarcinogenesis and its potential as a biomarker in HCC. The results suggested that STEAP4 down-regulation in HCC may likely have far-reaching effects in the cell cycle of HCC through acting on E2F transcription factors and cell cycle- and cancer-associated kinases. These efforts may provide new research directions to identify prognostic biomarkers and also provide an important theoretical basis and clinical guidance for the development of therapeutic targets for HCC. But large-scale subsequent functional studies are still needed.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e5.1 Differential expression analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of STEAP4\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GEPIA (http://gepia.cancer-pku.cn) database and Oncomine 4.5 (https://www.oncomine.org/) were used to obtain the expression level of the STEAP4 gene in normal tissues and HCC tumors. For subgroup analysis, we downloaded all the publicly available LIHC RNA-Seq data information from The Cancer Genome Atlas (TCGA) official website (https://cancergenome.nih.gov/) before november 14, 2022, through the GDC Data Transfer Tool. For subgroup analysis, the frozen tissue samples and normal tissue samples of primary or recurrent HCC were screened and based on the expression of STEAP4, and tumor samples were divided into high and low STEAP4 expression groups according to the median, the relationship between STEAP4 expression and multiple clinic-pathological parameters was analyzed using the chi-squared test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 The relationship between STEAP4 expression and prognosis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use the GEPIA database to generate survival curves, including overall survival (OS) and recurrence-free survival (RFS), based on gene expression with the log-rank test and the Mantel-Cox test. To further analyze the correlation between STEAP4 expression with survival rates and clinical-pathological characteristics, R version 4.0.5 software was used, with the survival and survminer packages used appropriately. Multivariate Cox regression analyses identified independent prognostic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 LinkedOmics Database Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTEAP4 co-expression was analyzed statistically using Pearson\u0026rsquo;s correlation coefficient in the LinkedOmics database (http://www.linkedomics.org/login.php), presenting in volcano plots, heat maps, or scatter plots. Function module of LinkedOmics performs analysis of Gene Ontology biological process (GO_BP), KEGG pathways, kinase-target enrichment, and transcription factor-target enrichment by the gene set enrichment analysis (GSEA). The top 10 positively and negatively significant genes correlated with STEAP4 were analyzed through the GEPIA database. Those genes differentially expressed in LIHC patients were further screened, and the TIME database further verified their correlation with STEAP4. Moreover, by using the GEPIA database, we identified and explored the prognostic significance of these genes of STEAP4 in HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Establishment and Validation of the prognostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eCombining co-expression genes and regulators\u003cstrong\u003e\u0026nbsp;of\u0026nbsp;\u003c/strong\u003eSTEAP4 to construct a prognostic model, we applied stepwise analysis of the multivariate Cox model\u0026nbsp;based on the Akaike information criterion\u0026nbsp;to avoid overfitting and select candidate\u0026rsquo;s genes.\u0026nbsp;The following formula calculated the risk score of each LUAD patient: risk score = gene a *\u0026nbsp;coefficient a + gene b * coefficient b + gene c * coefficient c + \u0026hellip;\u0026hellip; + gene n * coefficient n.\u0026nbsp;The median value of risk scores stratified high- and low-risk groups. Kaplan\u0026ndash;Meier survival curves were applied for survival comparison between low- and high-risk groups, and log-rank was used to test statistical significance (p-value \u0026lt; 0.05). The performance of the signature was evaluated and validated in the TCGA-LIHC and ICGC- LIHC cohorts, respectively. Visualization of the model was achieved by the nomogram; calibration plots were used to assess the predicting accuracy of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Associations Between Genome-wide Expression Profiles and STEAP4 Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the biological role of STEAP4 in hepatocellular carcinoma (HCC), the differentially expressed genes in the high and low expression groups of STEAP4 gene in hepatocellular carcinoma tumor samples were analyzed using R version 4.0.5 software. Further, we performed functional enrichment analysis of these aberrant genes based on the David database and found the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Then, the association between the STEAP4 gene and the path was further explored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.6 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 25.0 and R version 4.0.5 software was used for statistical analysis. The measured data were presented as mean \u0026plusmn; SD. Independent samples t-test and paired sample t-test were used to analyze the differential expression of STEAP4 mRNA between the HCC tissues and the adjacent normal tissues from the TCGA databases. The Pearson chi-squared test was used to analyze the association between STEAP4 and clinical characteristic variables. Multivariate analyses were performed using Cox proportional hazards regression model. Statistically significant differences were considered when \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1708329831.png\"\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Ting and Zou Min-hong designed the article, analyzed data, and write the manuscript.\u003c/p\u003e\n\u003cp\u003eXie Yu-jie\u0026nbsp;performed the histological examination.\u003c/p\u003e\n\u003cp\u003eZhang Yong revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors\u0026nbsp;reviewed\u0026nbsp;and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData accessibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are available in the GEPIA (http://gepia.cancer-pku.cn) database, Oncomine 4.5 (https://www.oncomine.org/), the Cancer Genome Atlas (TCGA) official website (https://cancergenome.nih.gov/), and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSingh A, Beechinor RJ, Huynh JC, et al. 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Abnormal STEAP4 expression is potentially associated with carcinogenesis. However, the biological role of STEAP4 in hepatocellular carcinoma (HCC) remains to be further determined. We analyzed STEAP4 expression level, prognosis, the correlations between STEAP4 and cancer immune infiltrates, differentially expressed genes (DEGs) between STEAP4 high- and low-expressed groups, and the functional networks in hepatocellular carcinoma using multiple databases. STEAP4 was found down-regulated in tumor tissues in multiple HCC cohorts. Low STEAP4 expression was associated with poorer overall (OS) and disease-free survival (DFS). Functional network analysis suggested that STEAP4 regulates cell cycle signaling and may be correlated with infiltrating levels of T follicular helper cells. These findings lay a foundation for further study of the cell cycle regulatory role of STEAP4 in HCC, and, indicate STEAP4 may be a promising prognostic biomarker and a novel therapeutic target for HCC patients.","manuscriptTitle":"The Tumor Suppressive and Potential Value of STEAP4 in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 18:54:14","doi":"10.21203/rs.3.rs-3928009/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":"2e5d7b8c-1506-483d-942a-0ab3496edf68","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-28T15:21:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-20 18:54:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3928009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3928009","identity":"rs-3928009","version":["v1"]},"buildId":"CiT4i_kKBbxQbnFL0ufpk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}