The value of Nuclear UBTF expression for hepatocellular carcinoma

The value of Nuclear UBTF expression for 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The value of Nuclear UBTF expression for hepatocellular carcinoma Hao Yu, Peng-Fei Su, Hui-Wen Qiu, Jun-Feng Yang, Hong-Kun Zhou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4952392/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background This study aimed to investigate the value of nuclear UBTF for HCC. Methods The expression of UBTF was detected by western blot and immunohistochemistry. 289 HCC patients were included in this study. X-tile software was used to calculate the outcome-based cut-point of UBTF expression. Pearson’s χ2 test was used to analyze the association between UBTF expression and clinicopathologic parameters. Kaplan-Meier analysis and Cox regression analysis were used to evaluate prognostic factors. Results UBTF expression was significant higher in HCC nucleus than paired adjacent tissues ( p = 0.0247). Nuclear UBTF expression was associated with AFP, liver cirrhosis, and tumor size. For OS, tumor size, tumor number, nuclear UBTF/AFP combination were the independent risk factors (all P < 0.05). For TTR, liver cirrhosis, tumor size, tumor number, nuclear UBTF/AFP combination were the independent risk factors (all P < 0.05). Survival curves showed that OS ( P = 0.003) and TTR ( P = 0.003) with high nuclear UBTF were worse than those with low nuclear UBTF, especially when nuclear UBTF and AFP were considered simultaneously. UBTF expression was significantly higher in HCC than LC ( P = 0.0305), whereas no significant differences between LGDN and LC ( P = 0.0937), also HGDN and LC ( P = 0.4674). Discussion Our study confirms that nuclear UBTF is a valuable prognostic biomarker for HCC. Hepatocellular Carcinoma upstream binding transcription factor prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Primary liver carcinoma is a prevalent human lethal malignant disease, with a highly aggressive and intractable biological behaviour, causing the third largest cancer related deaths worldwide[ 1 ]. Hepatocellular carcinoma (HCC) is the major type of the primary liver carcinoma, approximately accounting for 90% of the total, and its morbidity and mortality keep increasing in recent years[ 2 ], while China has a large number of HCC patients. Treatment methods include surgical resection, topical ablation therapy, chemotherapy (either systemic, by hepatic artery or infusion-chemoembolization), immunotherapy, radiation therapy. Since more than half of HCC patients are advanced at the time of first diagnosis, the current treatment efficacy is usually not suboptimal[ 3 – 5 ]. Therefore, more effective therapeutic targets remain the focus direction for HCC. The upstream binding transcription factor (UBTF), located on chromosome 17, is considered as a major transcriptional regulator of RNA Polymerase I (RNA Pol I) dependent rRNA genes, as a member of the high mobility group (HMG)-box protein family, which is a group of ubiquitously expressed non-histone architectural proteins[ 6 ]. Past reports indicate that UBTF mediates the recruitment of RNA pol I to rDNA promoter regions through the assembly of the preinitiation complex[ 7 , 8 ] and the formation of nucleosome free regions[ 9 ]. Moreover, through different signalling pathways, UBTF can regulate differentiation, proliferation, and cell growth[ 10 ]. The expression of UBTF plays a crucial role in promoting hepatocarcinogenesis by HBx oncoprotein of hepatitis B virus[ 1 ]. Although it has been shown that UBTF was upregulated in HCC[ 11 ] and was associated with prognosis (Fig. 1), past studies have not revealed the important significance of nuclear UBTF expression for HCC. Our study suggests that nuclear UBTF expression has an extremely high prognostic value for HCC. Materials and methods Patients’ samples From January 2005 to December 2011, 289 HCC formalin-fixed paraffin-embedded (FFPE) samples were randomly collected at the Eastern Hepatobiliary Surgery Hospital (EHBH). We remained in contact with the patients for follow-up and observation until December 2014. A total of 45 cases of tumour with peritumoral tissue were used as the expression pattern cohort 1. 51 FFPE specimens were selected randomly and used as the expression pattern cohort 2. The 51 cases were analysed to identify the differences in nuclear UBTF expression patterns between normal and diseased tissues of HCC patients, which included 7 cases of liver cirrhosis (LC), 12 cases of low-grade dysplastic nodules (LGDNs), 13 cases of high-grade dysplastic nodules (HGDNs) and 19 cases of HCCs. All of these patients were treated at the EHBH. For Western blot analyses, 10 paired HCC tissues and peritumoral liver tissues were obtained at the same hospital between April 2017 and May 2017. This study got approved by the Institutional Review Committee (approval number: EHBHKY2014-03-006) and informed consent in writing for all patients. Similarly to a past study[ 12 ], our case-enrollment criterias for this study were: (1) the WHO histological diagnostic criteria as the basis for our diagnosis, (2) pathological diagnosis of HCC, (3) without preoperative anticancer therapy and extrahepatic metastasis, (4) complete follow-up data and clinical data. The Overall survival (OS) was defined as the interval between surgery and death or the final checking. The time-to-recurrence (TTR) was the interval between the date of tumor resection and tumor recurrence, mortality, or final checking. Patients were followed up every 3 months during the first year after surgery. Subsequently, they were followed up every 3–6 months until December 2014. We selected two physicians unrelated to this study for follow-up. For all the patients in this study, the abdominal ultrasound, chest radiography, and serum alpha-fetoprotein (AFP) concentrations were conducted every 1–6 months during the first year after surgery, and then every 3–6 months thereafter. Every 6 months or immediately when suspected recurrence, we examined abdominal CT scan or MRI. Hematatoxylin and eosin (HE) stained slides were generated from FFPE samples. Two senior liver pathologists examined the samples[ 13 ]. Western blot The tissues were lysed in RIPA buffer (P0013B, Beyotime Biotechnology, China). The lysate was centrifuged at 14,000 g at 4 ◦C for 10 min. Western blot was performed as described previously[ 14 ]. The membranes were blocked with 5% nonfat milk and then incubated with primary UBTF antibody (1:1000, sc13125, Sunta Cruz, USA) and secondary antibody (anti-rabbit, 1:5000, 170–6516, Bio-Rad, USA). Then, the ECL reagent (#34095, Thermo Scientific, USA) was used. GAPDH (AP0063, Bioworld, China) was used as an internal reference. Tissue microarrays and immunohistochemistry Tissue microarray construction, immunohistochemistry, and optical density (OD) measurements were performed according to the reported methods[ 15 ]. All the HE-stained glass slides were investigated and evaluated by two senior pathologists, then representative cores in the paraffin blocks were marked. The 1.0 mm diameter tissue cylinders were dashed out of the marked areas and then merged into the receiving paraffin block. Sections with a thickness of 4 µm were then placed on glass slides soaked with 3-aminopropyltriethyxysilane. We implemented the method of dewaxing the paraffin sections in xylene and reducing the ethanol concentrations (one hundred percent, ninety-five percent and eighty-five percent, 5 min each time). Tripsin was used to retrieve antigen at 37 ◦C for 10 min. To block endogenous peroxidase activity, 3% H2O2/PBS was used to incubate the slides, while nonspecific binding sites were blocked by goat serum. The kit (GK500710, Gene Tech, China) was used to detect the antigen. The average OD value (Avg OD) of the nucleus and cytoplasm in each sample was calculated by HALO Multiplex IHC v2.3.4. The optimal cutoff value was calculated by the X-Tile statistical package to divide the cohort into two groups. We used the rabbit polyclonal antibody against UBTF (sc13125, Sunta Cruz, USA; 1:400 dilution, cytoplasmic and/or nuclear staining) as the primary antibody. An EnVision Detection kit (GK500705: Gene Tech, Shanghai, China) was used for visualization. Haematoxylin was used to reverse staining of tissue sections for five minutes. Negative control slides without the primary antibody had been established for all tests. The OD measurements were performed by an imaging system that included a high-speed scanner connected to the nano-oZoomerS60 (Hamamatsu, Japan). With the high-speed scanning, high-resolution digital slice images of specimens on slides could be obtained. The HALO software (Indica Lab, New Mexico, USA) was used to count and measure the ODs of each image. Statistical Analysis Statistical analyses were performed by the SPSS statistical software package (SPSS Standard version 22.0; SPSS, Chicago, Illinois, USA) and Graph Pad Prism 8.01. X-tile software version 3.6.1 (Yale University School of Medicine, New Haven, Connecticut, USA) was used to obtain the most suitable critical point of UBTF expression for survival analysis[ 16 ]. Miller–Siegmund P value provides a corrected P value based on the model proposed by R Miller and D. Siegmund. Pearson’s χ2 test was performed to determine the relationship between nuclear UBTF expression and clinicopathological features. Mantel-Cox logarithmic rank examine was used to evaluate the statistical significance of the correlation between UBTF expression and survival rate of patients. The prognostic factors for OS and TTR were examined by both univariate and multivariate analyses (Cox’s proportional hazards model) by SPSS. Survival curves were plotted by Kaplan-Meier analysis and by a log rank test. A P value < 0.05 (two-sided) was considered statistically significant. Results 1. UBTF expression in HCC and paired adjacent tissues By Western blot, we found that the UBTF protein expression was signifcantly upregulated in HCC tissues than paired adjacent tissues ( p = 0.033, Fig. 2A, B). Immunohistochemistry detection showed that UBTF was expressed in both HCC and adjacent tissues, either in cytoplasm or nucleus (Fig. 2C, D). Statistical analysis showed that UBTF expression level was significantly higher in HCC nucleus than in adjacent tissues nucleus ( p = 0.0247, Fig. 2E), but no difference in cytoplasmic expression ( p = 0.1251, Fig. 2F). 2.Association between nuclear UBTF expression and clinicopathologic features As Table 1 shown, nuclear UBTF expression was associated with serum alpha-fetoprotein (AFP), liver cirrhosis, and tumor size (all P < 0.05). There was no significant correlation with gender, age, surface antigen, TNM stage, Child stage, tumor number, tumor differentiation, and vascular invasion. Table 1 Association of nuclear UBTF expression with clinicopathological features in 289 HCC patients HBs Ag, hepatitis B surface antigen; AFP, alpha-fetoprotein. Variable Nuclear UBTF Low High P value Sex 0.420 Male Female 134 21 120 14 Age 0.727 ≤ 50 > 50 72 83 65 69 HBsAg Negative Positive 30 125 15 119 0.056 Serum AFP (ng/ml) ≤ 20 > 20 62 93 38 96 0.038 Liver cirrhosis No Yse 64 91 31 103 0.001 TNM 0.450 I II III 54 83 18 41 71 22 Child-pugh 0.397 A B 139 16 124 10 Tumor size (cm) 0.026 ≤ 5 > 5 84 71 55 79 Tumor number 0.426 Single Multiple 124 31 102 32 Tumor differentiation 0.406 Well Moderate Poor 15 138 2 12 122 0 Vascular invasion 0.331 No Yes 63 92 47 97 HBs Ag, hepatitis B surface antigen; AFP, alpha-fetoprotein. Table 2. Univariate and multivariate analyses of factors associated with OS in 289 HCC patients Factors OS TTR Univariate p Multivariate Multivariate HR 95% Cl P value Univariate p HR 95% Cl P value Sex: Male vs Female 0.409 0.851 Age: ≤50 vs >50 0.930 0.964 HBsAg: positive vs negative 0.088 0.067 Serum AFP (ng/ml): ≤20 vs >20 <0.0001 0.001 Liver cirrhosis: yes vs no 0.029 0.001 1.645 1.149-2.356 0.007 TNM: I vs II vs III-IV <0.0001 5 <0.0001 2.144 1.475-3.117 <0.0001 <0.0001 1.779 1.296-2.443 <0.0001 Tumor number: single vs multiple <0.0001 2.082 1.447-2.995 <0.0001 <0.0001 2.2.3 1.584-3.066 <0.0001 Tumor differentiation: well vs moderate vs poor 0.055 0.018 Vascular invasion: no vs yes 0.025 0.012 Nucler UBTF: low vs high 0.004 0.002 Nuclear UBTF/AFP combination N-UBTF low and AFP - vs N-UBTF low and AFP + or N-UBTF high and AFP - vs N-UBTF high and AFP + <0.0001 1.511 1.181-1.933 0.01 <0.0001 1.339 1.070-1.675 0.011 Table 2. Univariate and multivariate analyses of factors associated with OS in 289 HCC patients HBs Ag, hepatitis B surface antigen; AFP, alpha-fetoprotein; N-UBTF, Nuclear UBT; A - , AFP negative (serum AFP ≤20ng/ml); A + , AFP positive (serum AFP >20ng/ml); OS, overall survival; TTR, time to recurrence; HR, hazard ratio; CI, confidence interval. 3. Prognostic analysis The X-tile program was used to gain the optimal critical point of UBTF expression for prognostic cohort survival analysis. The optimal critical points with minimum P values can be gained from OS lookup table, by using a standard logarithmic rank method. For OS on univariate analysis, serum AFP, liver cirrhosis, TNM stage, tumor size, tumor number, vascular invasion, nuclear UBTF level, nuclear UBTF/AFP combination were statistically significant (all P < 0.05, Table 2 ). When adjusted by multivariate analysis by Cox’s proportional hazard model, tumor size, tumor number, nuclear UBTF/AFP combination were considered to be the independent risk factors for OS (all P < 0.05, Table 2 ). We also analyzed the risk factors for TTR (Table 2 ). The result of univariate analysis showed serum AFP, liver cirrhosis, TNM stage, tumor size, tumor number, tumor differentiation, vascular invasion, nuclear UBTF level, nuclear UBTF/AFP combination were statistically significant (all P < 0.05, Table 2 ). After adjustment, multivariate analysis revealed liver cirrhosis, tumor size, tumor number, nuclear UBTF/AFP combination were the independent risk factors for TTR (all P < 0.05, Table 2 ). Survival curves plotted according to different expression levels of UBTF are shown in Figs. 3 and 4. Significantly, UBTF-high patients had worse OS ( P = 0.015 in cytoplasm, P = 0.003 in nuclear, Fig. 3A, C) and TTR ( P = 0.010 in cytoplasm, P = 0.003 in nuclear, Fig. 3B, D) than the UBTF-low patients, evaluated either in cytoplasm or in nucleus. It is worth mentioning that nuclear expression seemed to have a better value, as the curves corresponding to nuclear expression showed large differences and smaller P -values. Furthermore, when nuclear UBTF and serum AFP were taken into consideration together, a significantly worse prognosis was found in the nuclear UBTF-high and AFP-positive patients compared with the ones of nuclear UBTF-low and AFP-negative for both OS ( P = 0.000025, Fig. 4A) and TTR ( P = 0.000019, Fig. 4B). The outcomes analysis indicated that the average OS time of HCC patients with low nuclear UBTF and low nuclear UBTF combined with AFP negative were 57.1 months and 64.4 months respectively, while the corresponding time with high nuclear UBTF and high nuclear UBTF combined with AFP positive were 50.3 months and 40.2 months respectively. The average TTR for HCC patients with low nuclear UBTF and low nuclear UBTF combined with AFP negative were 39.7 months and 52.6 months respectively, while the corresponding time with high nuclear UBTF and high nuclear UBTF combined with AFP positive were 32.3 months and 27.4 months respectively. 4.Expression of UBTF in liver cirrhosis (LC), low-grade dysplastic nodules (LGDNs), high-grade dysplastic nodules (HGDNs) and HCC Typical immunostaining of LC, LGDNs, HGDNs, and HCC was shown in Fig. 5A. To compare the expression levels of UBTF at different stages of hepatocyte aberrant transformations, 51 cases in total were evaluated (LC = 7, LGDNs = 12, HGDNs = 13, and HCC = 19). Then the OD values representing nuclear UBTF expression were inputted into Graph Pad Prism software. As shown in Fig. 5B, UBTF expression was significantly higher in HCC tissues than LC ( P = 0.0305), whereas no significant difference between LGDN tissues and LC ( P = 0.0937), also HGDN tissues and LC ( P = 0.4674). Discussion Hepatocellular carcinoma (HCC), as the main form of primary liver carcinoma, has high morbidity and mortality[ 17 ], which seriously endangers the health and survival of patients. In recent years, with the advent of targeted drugs and immune checkpoint inhibitors (ICIs)[ 18 ], the therapeutic efficacy of HCC has been greatly improved. However, some HCC patients, in either early or late stage, still have unsatisfactory curative effect and poor prognosis. Therefore, we have been working to identify innovative biomarkers to predict the prognosis of HCC and to provide novel therapeutic targets. UBTF is an HMG-box DNA-binding protein, an essential RNA Pol I / II basal transcription factor[ 19 , 20 ], that plays a critical role in cellular translation and cell proliferation in ribosomal organisms[ 21 – 23 ]. In some previous reports, as a regulator of gene expression in the ribosomal DNA promoter region, UBTF is involved in the carcinogenesis and progression of some cancers, such as colorectal, breast, lung, and cervical cancers[ 24 – 26 ]. Zhang et al showed that UBTF stimulated the proliferation of tumor cells by promoting the G1-S phase transition in melanoma[ 27 ]. Although it has been demonstrated that UBTF is highly expressed in HCC and has a prognostic value[ 11 , 25 ], previous studies have not distinguished the different significance of UBTF expression in the cytoplasm or nucleus. Our results showed that UBTF was overexpressed in both the cytoplasm and nucleus of HCC tissues, with significantly higher expression levels compared with the corresponding adjacent tissues. HCC patients with higher UBTF in cancer tissues had a worse prognosis with a statistically significance. In particularly, differential expression level in the nucleus has a better value in predicting prognosis for both OS ( P = 0.004) and TTR ( P = 0.002), especially when evaluated in combination with AFP (for OS p < 0.0001, for TTR P < 0.0001). Multivariate analysis showed that the combined assessment of nuclear UBTF levels and AFP levels was an independent risk factor for both OS ( P = 0.01) and TTR ( P = 0.011) in HCC patients. Tumor size and tumor number were also independent prognostic factors for OS and TTR, and liver cirrhosis was also an independent prognostic factor for TTR but not for OS. Furthermore, our study showed that UBTF expression was correlated with AFP, liver cirrhosis and Tumor size, implying that increased UBTF expression levels predicted increased invasiveness in HCC. In this study, our results revealed that UBTF expression varied in LC, LGDNs, HGDNs and HCC, suggesting that UBTF expression has a predictive role for the progression of hepatocyte to HCC transformation. In conclusion, our study demonstrates that UBTF acts as an oncogene marker in HCC. Our study confirms that UBTF is overexpressed in HCC and is associated with both the clinicopathological features of HCC patients and the malignant progression of hepatocytes, especially evaluated by nuclear expression. Nuclear UBTF expression has an extremely high prognostic value for HCC, which can be used as a good indicator to evaluate the prognosis of HCC patients and may be a potential therapeutic target for the treatment of HCC patients, and its mechanism of action needs further investigation. Abbreviations HCC: Hepatocellular Carcinoma; UBTF: upstream binding transcription factor; HMG: high mobility group; OS: Overall survival; TTR: Time to recurrence; RNA Pol I: RNA Polymerase I; FFPE: formalin-fixed paraffin-embedded; EHBH : Eastern Hepatobiliary Surgery Hospital; LC: liver cirrhosis; LGDNs: low-grade dysplastic nodules; HGDNs: high-grade dysplastic nodules; OD: optical density; ICIs: immune checkpoint inhibitors. Declarations Competing Interests The authors have declared that no competing interest exists. Authors' contributions All authors contributed to the design, analysis, and writing of this manuscript. Acknowledgement This study was supported by grants from the National Natural Science Foundation of China (81972574 Guang-Zhi Jin) and Medical-health science and technology plan of Zhejiang Province (2024KY434, Hong-Kun Zhou). Ethics approval and consent to participate Each patient or their guardians provided informed consent and the Ethics Committee of Eastern Hepatobiliary Surgery Hospital Research Ethics Committee approved the study. Data Availability The data used to support the findings of the present study are available from the corresponding author upon request. References Rajput, P., S. Shukla, and V. Kumar, The HBx oncoprotein of hepatitis B virus potentiates cell transformation by inducing c-Myc-dependent expression of the RNA polymerase I transcription factor UBF. Virology journal, 2015. 12 : p. 62. Llovet, J., et al., Hepatocellular carcinoma. Nature reviews. Disease primers, 2021. 7 (1): p. 6. Llovet, J., et al., Locoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma. Nature reviews. Gastroenterology & hepatology, 2021. 18 (5): p. 293-313. Qin, S., et al., Randomized, multicenter, open-label study of oxaliplatin plus fluorouracil/leucovorin versus doxorubicin as palliative chemotherapy in patients with advanced hepatocellular carcinoma from Asia. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2013. 31 (28): p. 3501-8. Ladd, A., et al., Mechanisms of drug resistance in HCC. Hepatology (Baltimore, Md.), 2023. McStay, B., Nucleolar organizer regions: genomic 'dark matter' requiring illumination. Genes & development, 2016. 30 (14): p. 1598-610. Aufiero, B. and R. Schneider, The hepatitis B virus X-gene product trans-activates both RNA polymerase II and III promoters. The EMBO journal, 1990. 9 (2): p. 497-504. Wang, H., et al., The hepatitis B virus X protein increases the cellular level of TATA-binding protein, which mediates transactivation of RNA polymerase III genes. Molecular and cellular biology, 1995. 15 (12): p. 6720-8. Kim, C., et al., HBx gene of hepatitis B virus induces liver cancer in transgenic mice. Nature, 1991. 351 (6324): p. 317-20. Stefanovsky, V. and T. Moss, The splice variants of UBF differentially regulate RNA polymerase I transcription elongation in response to ERK phosphorylation. Nucleic acids research, 2008. 36 (15): p. 5093-101. Huang, R., et al., Upstream binding factor up-regulated in hepatocellular carcinoma is related to the survival and cisplatin-sensitivity of cancer cells. FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 2002. 16 (3): p. 293-301. Chen, K., et al., The prognostic value of Niemann-Pick C1-like protein 1 and Niemann-Pick disease type C2 in hepatocellular carcinoma. Journal of Cancer, 2018. 9 (3): p. 556-563. Tan, N., et al., Low expression of B-cell-associated protein 31 in human primary hepatocellular carcinoma correlates with poor prognosis. Histopathology, 2016. 68 (2): p. 221-9. Jin, H., et al., Regulator of Calcineurin 1 Gene Isoform 4, Down-regulated in Hepatocellular Carcinoma, Prevents Proliferation, Migration, and Invasive Activity of Cancer Cells and Metastasis of Orthotopic Tumors by Inhibiting Nuclear Translocation of NFAT1. Gastroenterology, 2017. 153 (3): p. 799-811.e33. Yu, H., et al., The diagnostic and prognostic value of UBE2T in intrahepatic cholangiocarcinoma. PeerJ, 2020. 8 : p. e8454. Camp, R., M. Dolled-Filhart, and D. Rimm, X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clinical cancer research : an official journal of the American Association for Cancer Research, 2004. 10 (21): p. 7252-9. Fang, C., et al., Ferroptosis-related lncRNA signature predicts the prognosis and immune microenvironment of hepatocellular carcinoma. Scientific reports, 2022. 12 (1): p. 6642. Yang, H. and Q. Jiang, A multi-omics-based investigation of the immunological and prognostic impact of necroptosis-related genes in patients with hepatocellular carcinoma. Journal of clinical laboratory analysis, 2022. 36 (4): p. e24346. Moss, T., et al., A housekeeper with power of attorney: the rRNA genes in ribosome biogenesis. Cellular and molecular life sciences : CMLS, 2007. 64 (1): p. 29-49. Sanij, E., et al., UBF levels determine the number of active ribosomal RNA genes in mammals. The Journal of cell biology, 2008. 183 (7): p. 1259-74. Drygin, D., W. Rice, and I. Grummt, The RNA polymerase I transcription machinery: an emerging target for the treatment of cancer. Annual review of pharmacology and toxicology, 2010. 50 : p. 131-56. Sanij, E. and R. Hannan, The role of UBF in regulating the structure and dynamics of transcriptionally active rDNA chromatin. Epigenetics, 2009. 4 (6): p. 374-82. Panov, K., et al., UBF activates RNA polymerase I transcription by stimulating promoter escape. The EMBO journal, 2006. 25 (14): p. 3310-22. Tsoi, H., et al., Pre-45s rRNA promotes colon cancer and is associated with poor survival of CRC patients. Oncogene, 2017. 36 (44): p. 6109-6118. Yu, F., et al., Analysis of histone modifications at human ribosomal DNA in liver cancer cell. Scientific reports, 2015. 5 : p. 18100. Dichamp, I., et al., Human papillomavirus 16 oncoprotein E7 stimulates UBF1-mediated rDNA gene transcription, inhibiting a p53-independent activity of p14ARF. PloS one, 2014. 9 (5): p. e96136. Zhang, J., et al., UBTF facilitates melanoma progression via modulating MEK1/2-ERK1/2 signalling pathways by promoting GIT1 transcription. Cancer cell international, 2021. 21 (1): p. 543. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 01 Sep, 2024 Submission checks completed at journal 23 Aug, 2024 First submitted to journal 21 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4952392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352865852,"identity":"9f9a8742-435c-48cd-b39c-c5631a30dc57","order_by":0,"name":"Hao Yu","email":"","orcid":"","institution":"The First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Yu","suffix":""},{"id":352865853,"identity":"18f860f7-bcbf-473d-b6ef-cf93b748f172","order_by":1,"name":"Peng-Fei Su","email":"","orcid":"","institution":"Central Hospital of Liaoyang","correspondingAuthor":false,"prefix":"","firstName":"Peng-Fei","middleName":"","lastName":"Su","suffix":""},{"id":352865854,"identity":"a541ba02-309e-40d6-aefd-0a0dbebf394d","order_by":2,"name":"Hui-Wen Qiu","email":"","orcid":"","institution":"The First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Hui-Wen","middleName":"","lastName":"Qiu","suffix":""},{"id":352865855,"identity":"9460e704-91c6-45be-8de2-42dcab962c66","order_by":3,"name":"Jun-Feng Yang","email":"","orcid":"","institution":"The First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Jun-Feng","middleName":"","lastName":"Yang","suffix":""},{"id":352865856,"identity":"bcab0a8f-e057-4577-9ed6-d9dd1ba35dba","order_by":4,"name":"Hong-Kun Zhou","email":"","orcid":"","institution":"The First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Hong-Kun","middleName":"","lastName":"Zhou","suffix":""},{"id":352865857,"identity":"6c4812f0-9e3e-47fb-9d89-427b6448fec9","order_by":5,"name":"Wei Dong","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Dong","suffix":""},{"id":352865859,"identity":"84510a6e-e39e-4bd1-a873-429c7b815e72","order_by":6,"name":"Guang-Zhi Jin","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Guang-Zhi","middleName":"","lastName":"Jin","suffix":""},{"id":352865860,"identity":"ba8dac5a-79c6-4324-a732-882bcbf042e1","order_by":7,"name":"Wen-Tao Huang","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Wen-Tao","middleName":"","lastName":"Huang","suffix":""},{"id":352865862,"identity":"5b1eaac3-ce1b-4a91-932d-10014be16db8","order_by":8,"name":"Jing Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACfvbGxsd//9nIybM3EKlFsudwswEPW5qxYc8BIrUY3Ehvk+BhO5zYcCOBWJfdSGyTkOBhTmyc+XjjDYYam2iCOhh7HjZbGEiwGbdLpxVbMBxLy20gpIWZPbHxRoIBj2zj7BwzCcaGw4S1sDEkNkgcSAAqvnmGSC08HIlNkg0HDBQbbvAQqUWC52CzMWNDAjCQgX5JIMYv9sfbHz5mbPgPjMrDG298qLEhrAUZGEgkkKIcooVUHaNgFIyCUTAyAACDLEFjjuU1/gAAAABJRU5ErkJggg==","orcid":"","institution":"Wuxi Traditional Chinese Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-08-21 14:40:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4952392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4952392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66760309,"identity":"9f1a3735-118b-40c4-aeec-057ccdea7944","added_by":"auto","created_at":"2024-10-16 08:42:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":345457,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4952392/v1/db75167acd135476c8ab3696.png"},{"id":66758461,"identity":"f32ecbe2-cb14-4d54-8ee0-3a2694f3cd85","added_by":"auto","created_at":"2024-10-16 08:34:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":722072,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4952392/v1/5f30d522a1e6c1f2ae1b18ce.png"},{"id":66758462,"identity":"17445595-1379-4c38-a52e-4f88bc2b454f","added_by":"auto","created_at":"2024-10-16 08:34:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372890,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4952392/v1/8ad93e569a4d924b34719dea.png"},{"id":66758460,"identity":"6ef47450-b24c-4a17-a025-c67a80a1d411","added_by":"auto","created_at":"2024-10-16 08:34:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257974,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4952392/v1/8cda7b20f332d5b369341592.png"},{"id":66758464,"identity":"7970b1e2-d5f2-41cf-b582-8791c53d9a23","added_by":"auto","created_at":"2024-10-16 08:34:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":444062,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4952392/v1/1f8b3ff747d5ab21d28dc0ba.png"},{"id":66760317,"identity":"8c0f6c50-bfd5-45ac-8f96-ce60d3eae0d2","added_by":"auto","created_at":"2024-10-16 08:42:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2550517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4952392/v1/e3dcacfb-42ed-45b8-9296-5417b9354ffa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The value of Nuclear UBTF expression for hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary liver carcinoma is a prevalent human lethal malignant disease, with a highly aggressive and intractable biological behaviour, causing the third largest cancer related deaths worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hepatocellular carcinoma (HCC) is the major type of the primary liver carcinoma, approximately accounting for 90% of the total, and its morbidity and mortality keep increasing in recent years[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], while China has a large number of HCC patients. Treatment methods include surgical resection, topical ablation therapy, chemotherapy (either systemic, by hepatic artery or infusion-chemoembolization), immunotherapy, radiation therapy. Since more than half of HCC patients are advanced at the time of first diagnosis, the current treatment efficacy is usually not suboptimal[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, more effective therapeutic targets remain the focus direction for HCC.\u003c/p\u003e \u003cp\u003eThe upstream binding transcription factor (UBTF), located on chromosome 17, is considered as a major transcriptional regulator of RNA Polymerase I (RNA Pol I) dependent rRNA genes, as a member of the high mobility group (HMG)-box protein family, which is a group of ubiquitously expressed non-histone architectural proteins[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Past reports indicate that UBTF mediates the recruitment of RNA pol I to rDNA promoter regions through the assembly of the preinitiation complex[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and the formation of nucleosome free regions[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, through different signalling pathways, UBTF can regulate differentiation, proliferation, and cell growth[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The expression of UBTF plays a crucial role in promoting hepatocarcinogenesis by HBx oncoprotein of hepatitis B virus[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although it has been shown that UBTF was upregulated in HCC[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and was associated with prognosis (Fig.\u0026nbsp;1), past studies have not revealed the important significance of nuclear UBTF expression for HCC.\u003c/p\u003e \u003cp\u003eOur study suggests that nuclear UBTF expression has an extremely high prognostic value for HCC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePatients\u0026rsquo; samples\u003c/p\u003e \u003cp\u003eFrom January 2005 to December 2011, 289 HCC formalin-fixed paraffin-embedded (FFPE) samples were randomly collected at the Eastern Hepatobiliary Surgery Hospital (EHBH). We remained in contact with the patients for follow-up and observation until December 2014. A total of 45 cases of tumour with peritumoral tissue were used as the expression pattern cohort 1. 51 FFPE specimens were selected randomly and used as the expression pattern cohort 2. The 51 cases were analysed to identify the differences in nuclear UBTF expression patterns between normal and diseased tissues of HCC patients, which included 7 cases of liver cirrhosis (LC), 12 cases of low-grade dysplastic nodules (LGDNs), 13 cases of high-grade dysplastic nodules (HGDNs) and 19 cases of HCCs. All of these patients were treated at the EHBH. For Western blot analyses, 10 paired HCC tissues and peritumoral liver tissues were obtained at the same hospital between April 2017 and May 2017. This study got approved by the Institutional Review Committee (approval number: EHBHKY2014-03-006) and informed consent in writing for all patients.\u003c/p\u003e \u003cp\u003eSimilarly to a past study[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], our case-enrollment criterias for this study were: (1) the WHO histological diagnostic criteria as the basis for our diagnosis, (2) pathological diagnosis of HCC, (3) without preoperative anticancer therapy and extrahepatic metastasis, (4) complete follow-up data and clinical data.\u003c/p\u003e \u003cp\u003eThe Overall survival (OS) was defined as the interval between surgery and death or the final checking. The time-to-recurrence (TTR) was the interval between the date of tumor resection and tumor recurrence, mortality, or final checking. Patients were followed up every 3 months during the first year after surgery. Subsequently, they were followed up every 3\u0026ndash;6 months until December 2014. We selected two physicians unrelated to this study for follow-up. For all the patients in this study, the abdominal ultrasound, chest radiography, and serum alpha-fetoprotein (AFP) concentrations were conducted every 1\u0026ndash;6 months during the first year after surgery, and then every 3\u0026ndash;6 months thereafter. Every 6 months or immediately when suspected recurrence, we examined abdominal CT scan or MRI. Hematatoxylin and eosin (HE) stained slides were generated from FFPE samples. Two senior liver pathologists examined the samples[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWestern blot\u003c/p\u003e \u003cp\u003eThe tissues were lysed in RIPA buffer (P0013B, Beyotime Biotechnology, China). The lysate was centrifuged at 14,000 g at 4 ◦C for 10 min. Western blot was performed as described previously[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The membranes were blocked with 5% nonfat milk and then incubated with primary UBTF antibody (1:1000, sc13125, Sunta Cruz, USA) and secondary antibody (anti-rabbit, 1:5000, 170\u0026ndash;6516, Bio-Rad, USA). Then, the ECL reagent (#34095, Thermo Scientific, USA) was used. GAPDH (AP0063, Bioworld, China) was used as an internal reference.\u003c/p\u003e \u003cp\u003eTissue microarrays and immunohistochemistry\u003c/p\u003e \u003cp\u003eTissue microarray construction, immunohistochemistry, and optical density (OD) measurements were performed according to the reported methods[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. All the HE-stained glass slides were investigated and evaluated by two senior pathologists, then representative cores in the paraffin blocks were marked. The 1.0 mm diameter tissue cylinders were dashed out of the marked areas and then merged into the receiving paraffin block. Sections with a thickness of 4 \u0026micro;m were then placed on glass slides soaked with 3-aminopropyltriethyxysilane. We implemented the method of dewaxing the paraffin sections in xylene and reducing the ethanol concentrations (one hundred percent, ninety-five percent and eighty-five percent, 5 min each time). Tripsin was used to retrieve antigen at 37 ◦C for 10 min. To block endogenous peroxidase activity, 3% H2O2/PBS was used to incubate the slides, while nonspecific binding sites were blocked by goat serum.\u003c/p\u003e \u003cp\u003eThe kit (GK500710, Gene Tech, China) was used to detect the antigen. The average OD value (Avg OD) of the nucleus and cytoplasm in each sample was calculated by HALO Multiplex IHC v2.3.4. The optimal cutoff value was calculated by the X-Tile statistical package to divide the cohort into two groups.\u003c/p\u003e \u003cp\u003eWe used the rabbit polyclonal antibody against UBTF (sc13125, Sunta Cruz, USA; 1:400 dilution, cytoplasmic and/or nuclear staining) as the primary antibody. An EnVision Detection kit (GK500705: Gene Tech, Shanghai, China) was used for visualization. Haematoxylin was used to reverse staining of tissue sections for five minutes. Negative control slides without the primary antibody had been established for all tests. The OD measurements were performed by an imaging system that included a high-speed scanner connected to the nano-oZoomerS60 (Hamamatsu, Japan). With the high-speed scanning, high-resolution digital slice images of specimens on slides could be obtained. The HALO software (Indica Lab, New Mexico, USA) was used to count and measure the ODs of each image.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed by the SPSS statistical software package (SPSS Standard version 22.0; SPSS, Chicago, Illinois, USA) and Graph Pad Prism 8.01. X-tile software version 3.6.1 (Yale University School of Medicine, New Haven, Connecticut, USA) was used to obtain the most suitable critical point of UBTF expression for survival analysis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Miller\u0026ndash;Siegmund \u003cem\u003eP\u003c/em\u003e value provides a corrected \u003cem\u003eP\u003c/em\u003e value based on the model proposed by R Miller and D. Siegmund. Pearson\u0026rsquo;s χ2 test was performed to determine the relationship between nuclear UBTF expression and clinicopathological features. Mantel-Cox logarithmic rank examine was used to evaluate the statistical significance of the correlation between UBTF expression and survival rate of patients. The prognostic factors for OS and TTR were examined by both univariate and multivariate analyses (Cox\u0026rsquo;s proportional hazards model) by SPSS. Survival curves were plotted by Kaplan-Meier analysis and by a log rank test. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e1. UBTF expression in HCC and paired adjacent tissues\u003c/p\u003e\n\u003cp\u003eBy Western blot, we found that the UBTF protein expression was signifcantly upregulated in HCC tissues than paired adjacent tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033, Fig.\u0026nbsp;2A, B). Immunohistochemistry detection showed that UBTF was expressed in both HCC and adjacent tissues, either in cytoplasm or nucleus (Fig.\u0026nbsp;2C, D). Statistical analysis showed that UBTF expression level was significantly higher in HCC nucleus than in adjacent tissues nucleus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0247, Fig.\u0026nbsp;2E), but no difference in cytoplasmic expression (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1251, Fig.\u0026nbsp;2F).\u003c/p\u003e\n\u003cp\u003e2.Association between nuclear UBTF expression and clinicopathologic features\u003c/p\u003e\n\u003cp\u003eAs Table \u003cspan\u003e1\u003c/span\u003e shown, nuclear UBTF expression was associated with serum alpha-fetoprotein (AFP), liver cirrhosis, and tumor size (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no significant correlation with gender, age, surface antigen, TNM stage, Child stage, tumor number, tumor differentiation, and vascular invasion.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation of nuclear UBTF expression with clinicopathological features in 289 HCC patients HBs Ag, hepatitis B surface antigen; AFP, alpha-fetoprotein.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNuclear UBTF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBsAg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum AFP (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;20\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild-pugh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle Multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHBs Ag, hepatitis B surface antigen; AFP, alpha-fetoprotein.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2. Univariate and multivariate analyses of factors associated with OS in 289 HCC patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"962\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.425752855659397%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.12564901349948%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.322949117341641%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.12564901349948%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.08955223880597%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnivariate \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.52238805970149%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.776119402985074%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.582089552238806%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"34.02985074626866%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.525423728813559%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.76271186440678%\" valign=\"top\"\u003e\n \u003cp\u003e95% Cl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.05084745762712%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.423728813559322%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003eUnivariate \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.35593220338983%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.10169491525424%\" valign=\"top\"\u003e\n \u003cp\u003e95% Cl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.35593220338983%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eSex: Male vs Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eAge: \u0026le;50 vs \u0026gt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eHBsAg: positive vs negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eSerum AFP (ng/ml): \u0026le;20 vs \u0026gt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eLiver cirrhosis: yes vs no\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e1.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\n \u003cp\u003e1.149-2.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eTNM: I vs II vs III-IV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eChild-pugh: A vs B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eTumor size (cm): \u0026le;5 vs \u0026gt;5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\n \u003cp\u003e2.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\n \u003cp\u003e1.475-3.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e1.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\n \u003cp\u003e1.296-2.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eTumor number: single vs multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\n \u003cp\u003e2.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\n \u003cp\u003e1.447-2.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e2.2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\n \u003cp\u003e1.584-3.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eTumor differentiation: well vs moderate vs poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eVascular invasion: no vs yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eNucler UBTF: low vs high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eNuclear UBTF/AFP combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.394190871369293%\" valign=\"top\"\u003e\n \u003cp\u003eN-UBTF low and AFP\u003csup\u003e-\u003c/sup\u003e vs N-UBTF low and AFP\u003csup\u003e+\u003c/sup\u003e or N-UBTF high and AFP\u003csup\u003e-\u003c/sup\u003e vs N-UBTF high and AFP\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.402489626556017%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.053941908713693%\" valign=\"top\"\u003e\n \u003cp\u003e1.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.647302904564315%\" valign=\"top\"\u003e\n \u003cp\u003e1.181-1.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.987551867219917%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.319502074688797%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.439834024896266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e1.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.854771784232366%\" valign=\"top\"\u003e\n \u003cp\u003e1.070-1.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.950207468879668%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Univariate and multivariate analyses of factors associated with OS in 289 HCC patients\u003c/p\u003e\n\u003cp\u003eHBs Ag, hepatitis B surface antigen; AFP, alpha-fetoprotein; N-UBTF, Nuclear UBT; A\u003csup\u003e-\u0026nbsp;\u003c/sup\u003e, AFP negative (serum AFP \u0026le;20ng/ml); A\u003csup\u003e+\u003c/sup\u003e, AFP positive (serum AFP \u0026gt;20ng/ml); OS, overall survival; TTR, time to recurrence; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e3. Prognostic analysis\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe X-tile program was used to gain the optimal critical point of UBTF expression for prognostic cohort survival analysis. The optimal critical points with minimum \u003cem\u003eP\u003c/em\u003e values can be gained from OS lookup table, by using a standard logarithmic rank method.\u003c/p\u003e\n\u003cp\u003eFor OS on univariate analysis, serum AFP, liver cirrhosis, TNM stage, tumor size, tumor number, vascular invasion, nuclear UBTF level, nuclear UBTF/AFP combination were statistically significant (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan\u003e2\u003c/span\u003e). When adjusted by multivariate analysis by Cox\u0026rsquo;s proportional hazard model, tumor size, tumor number, nuclear UBTF/AFP combination were considered to be the independent risk factors for OS (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003eWe also analyzed the risk factors for TTR (Table \u003cspan\u003e2\u003c/span\u003e). The result of univariate analysis showed serum AFP, liver cirrhosis, TNM stage, tumor size, tumor number, tumor differentiation, vascular invasion, nuclear UBTF level, nuclear UBTF/AFP combination were statistically significant (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan\u003e2\u003c/span\u003e). After adjustment, multivariate analysis revealed liver cirrhosis, tumor size, tumor number, nuclear UBTF/AFP combination were the independent risk factors for TTR (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan\u003e2\u003c/span\u003e).\u003c/div\u003e\n\u003cp\u003eSurvival curves plotted according to different expression levels of UBTF are shown in Figs.\u0026nbsp;3 and 4. Significantly, UBTF-high patients had worse OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015 in cytoplasm, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003 in nuclear, Fig.\u0026nbsp;3A, C) and TTR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010 in cytoplasm, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003 in nuclear, Fig. 3B, D) than the UBTF-low patients, evaluated either in cytoplasm or in nucleus. It is worth mentioning that nuclear expression seemed to have a better value, as the curves corresponding to nuclear expression showed large differences and smaller \u003cem\u003eP\u003c/em\u003e-values. Furthermore, when nuclear UBTF and serum AFP were taken into consideration together, a significantly worse prognosis was found in the nuclear UBTF-high and AFP-positive patients compared with the ones of nuclear UBTF-low and AFP-negative for both OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000025, Fig.\u0026nbsp;4A) and TTR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000019, Fig.\u0026nbsp;4B). The outcomes analysis indicated that the average OS time of HCC patients with low nuclear UBTF and low nuclear UBTF combined with AFP negative were 57.1 months and 64.4 months respectively, while the corresponding time with high nuclear UBTF and high nuclear UBTF combined with AFP positive were 50.3 months and 40.2 months respectively. The average TTR for HCC patients with low nuclear UBTF and low nuclear UBTF combined with AFP negative were 39.7 months and 52.6 months respectively, while the corresponding time with high nuclear UBTF and high nuclear UBTF combined with AFP positive were 32.3 months and 27.4 months respectively.\u003c/p\u003e\n\u003cp\u003e4.Expression of UBTF in liver cirrhosis (LC), low-grade dysplastic nodules (LGDNs), high-grade dysplastic nodules (HGDNs) and HCC\u003c/p\u003e\n\u003cp\u003eTypical immunostaining of LC, LGDNs, HGDNs, and HCC was shown in Fig.\u0026nbsp;5A. To compare the expression levels of UBTF at different stages of hepatocyte aberrant transformations, 51 cases in total were evaluated (LC\u0026thinsp;=\u0026thinsp;7, LGDNs\u0026thinsp;=\u0026thinsp;12, HGDNs\u0026thinsp;=\u0026thinsp;13, and HCC\u0026thinsp;=\u0026thinsp;19). Then the OD values representing nuclear UBTF expression were inputted into Graph Pad Prism software. As shown in Fig.\u0026nbsp;5B, UBTF expression was significantly higher in HCC tissues than LC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0305), whereas no significant difference between LGDN tissues and LC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0937), also HGDN tissues and LC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4674).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHepatocellular carcinoma (HCC), as the main form of primary liver carcinoma, has high morbidity and mortality[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which seriously endangers the health and survival of patients. In recent years, with the advent of targeted drugs and immune checkpoint inhibitors (ICIs)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the therapeutic efficacy of HCC has been greatly improved. However, some HCC patients, in either early or late stage, still have unsatisfactory curative effect and poor prognosis. Therefore, we have been working to identify innovative biomarkers to predict the prognosis of HCC and to provide novel therapeutic targets.\u003c/p\u003e \u003cp\u003eUBTF is an HMG-box DNA-binding protein, an essential RNA Pol I / II basal transcription factor[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], that plays a critical role in cellular translation and cell proliferation in ribosomal organisms[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In some previous reports, as a regulator of gene expression in the ribosomal DNA promoter region, UBTF is involved in the carcinogenesis and progression of some cancers, such as colorectal, breast, lung, and cervical cancers[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Zhang et al showed that UBTF stimulated the proliferation of tumor cells by promoting the G1-S phase transition in melanoma[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although it has been demonstrated that UBTF is highly expressed in HCC and has a prognostic value[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], previous studies have not distinguished the different significance of UBTF expression in the cytoplasm or nucleus.\u003c/p\u003e \u003cp\u003eOur results showed that UBTF was overexpressed in both the cytoplasm and nucleus of HCC tissues, with significantly higher expression levels compared with the corresponding adjacent tissues. HCC patients with higher UBTF in cancer tissues had a worse prognosis with a statistically significance. In particularly, differential expression level in the nucleus has a better value in predicting prognosis for both OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and TTR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), especially when evaluated in combination with AFP (for OS \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, for TTR \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Multivariate analysis showed that the combined assessment of nuclear UBTF levels and AFP levels was an independent risk factor for both OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and TTR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) in HCC patients. Tumor size and tumor number were also independent prognostic factors for OS and TTR, and liver cirrhosis was also an independent prognostic factor for TTR but not for OS. Furthermore, our study showed that UBTF expression was correlated with AFP, liver cirrhosis and Tumor size, implying that increased UBTF expression levels predicted increased invasiveness in HCC. In this study, our results revealed that UBTF expression varied in LC, LGDNs, HGDNs and HCC, suggesting that UBTF expression has a predictive role for the progression of hepatocyte to HCC transformation.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates that UBTF acts as an oncogene marker in HCC. Our study confirms that UBTF is overexpressed in HCC and is associated with both the clinicopathological features of HCC patients and the malignant progression of hepatocytes, especially evaluated by nuclear expression. Nuclear UBTF expression has an extremely high prognostic value for HCC, which can be used as a good indicator to evaluate the prognosis of HCC patients and may be a potential therapeutic target for the treatment of HCC patients, and its mechanism of action needs further investigation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHCC: Hepatocellular Carcinoma; UBTF: upstream binding transcription factor; HMG: high mobility group; OS: Overall survival; TTR: Time to recurrence; RNA Pol I: RNA Polymerase I; FFPE: formalin-fixed paraffin-embedded; EHBH : Eastern Hepatobiliary Surgery Hospital; LC: liver cirrhosis; LGDNs: low-grade dysplastic nodules; HGDNs: high-grade dysplastic nodules; OD: optical density; ICIs: immune checkpoint inhibitors.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eCompeting Interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interest exists.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the design, analysis, and writing of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China (81972574 Guang-Zhi Jin) and Medical-health science and technology plan of Zhejiang Province (2024KY434, Hong-Kun Zhou).\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEach patient or their guardians provided informed consent and the Ethics Committee of Eastern Hepatobiliary Surgery Hospital Research Ethics Committee approved the study.\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of the present study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRajput, P., S. Shukla, and V. Kumar, \u003cem\u003eThe HBx oncoprotein of hepatitis B virus potentiates cell transformation by inducing c-Myc-dependent expression of the RNA polymerase I transcription factor UBF.\u003c/em\u003e Virology journal, 2015. \u003cstrong\u003e12\u003c/strong\u003e: p. 62.\u003c/li\u003e\n\u003cli\u003eLlovet, J., et al., \u003cem\u003eHepatocellular carcinoma.\u003c/em\u003e Nature reviews. Disease primers, 2021. \u003cstrong\u003e7\u003c/strong\u003e(1): p. 6.\u003c/li\u003e\n\u003cli\u003eLlovet, J., et al., \u003cem\u003eLocoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma.\u003c/em\u003e Nature reviews. Gastroenterology \u0026amp; hepatology, 2021. \u003cstrong\u003e18\u003c/strong\u003e(5): p. 293-313.\u003c/li\u003e\n\u003cli\u003eQin, S., et al., \u003cem\u003eRandomized, multicenter, open-label study of oxaliplatin plus fluorouracil/leucovorin versus doxorubicin as palliative chemotherapy in patients with advanced hepatocellular carcinoma from Asia.\u003c/em\u003e Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2013. \u003cstrong\u003e31\u003c/strong\u003e(28): p. 3501-8.\u003c/li\u003e\n\u003cli\u003eLadd, A., et al., \u003cem\u003eMechanisms of drug resistance in HCC.\u003c/em\u003e Hepatology (Baltimore, Md.), 2023.\u003c/li\u003e\n\u003cli\u003eMcStay, B., \u003cem\u003eNucleolar organizer regions: genomic \u0026apos;dark matter\u0026apos; requiring illumination.\u003c/em\u003e Genes \u0026amp; development, 2016. \u003cstrong\u003e30\u003c/strong\u003e(14): p. 1598-610.\u003c/li\u003e\n\u003cli\u003eAufiero, B. and R. Schneider, \u003cem\u003eThe hepatitis B virus X-gene product trans-activates both RNA polymerase II and III promoters.\u003c/em\u003e The EMBO journal, 1990. \u003cstrong\u003e9\u003c/strong\u003e(2): p. 497-504.\u003c/li\u003e\n\u003cli\u003eWang, H., et al., \u003cem\u003eThe hepatitis B virus X protein increases the cellular level of TATA-binding protein, which mediates transactivation of RNA polymerase III genes.\u003c/em\u003e Molecular and cellular biology, 1995. \u003cstrong\u003e15\u003c/strong\u003e(12): p. 6720-8.\u003c/li\u003e\n\u003cli\u003eKim, C., et al., \u003cem\u003eHBx gene of hepatitis B virus induces liver cancer in transgenic mice.\u003c/em\u003e Nature, 1991. \u003cstrong\u003e351\u003c/strong\u003e(6324): p. 317-20.\u003c/li\u003e\n\u003cli\u003eStefanovsky, V. and T. Moss, \u003cem\u003eThe splice variants of UBF differentially regulate RNA polymerase I transcription elongation in response to ERK phosphorylation.\u003c/em\u003e Nucleic acids research, 2008. \u003cstrong\u003e36\u003c/strong\u003e(15): p. 5093-101.\u003c/li\u003e\n\u003cli\u003eHuang, R., et al., \u003cem\u003eUpstream binding factor up-regulated in hepatocellular carcinoma is related to the survival and cisplatin-sensitivity of cancer cells.\u003c/em\u003e FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 2002. \u003cstrong\u003e16\u003c/strong\u003e(3): p. 293-301.\u003c/li\u003e\n\u003cli\u003eChen, K., et al., \u003cem\u003eThe prognostic value of Niemann-Pick C1-like protein 1 and Niemann-Pick disease type C2 in hepatocellular carcinoma.\u003c/em\u003e Journal of Cancer, 2018. \u003cstrong\u003e9\u003c/strong\u003e(3): p. 556-563.\u003c/li\u003e\n\u003cli\u003eTan, N., et al., \u003cem\u003eLow expression of B-cell-associated protein 31 in human primary hepatocellular carcinoma correlates with poor prognosis.\u003c/em\u003e Histopathology, 2016. \u003cstrong\u003e68\u003c/strong\u003e(2): p. 221-9.\u003c/li\u003e\n\u003cli\u003eJin, H., et al., \u003cem\u003eRegulator of Calcineurin 1 Gene Isoform 4, Down-regulated in Hepatocellular Carcinoma, Prevents Proliferation, Migration, and Invasive Activity of Cancer Cells and Metastasis of Orthotopic Tumors by Inhibiting Nuclear Translocation of NFAT1.\u003c/em\u003e Gastroenterology, 2017. \u003cstrong\u003e153\u003c/strong\u003e(3): p. 799-811.e33.\u003c/li\u003e\n\u003cli\u003eYu, H., et al., \u003cem\u003eThe diagnostic and prognostic value of UBE2T in intrahepatic cholangiocarcinoma.\u003c/em\u003e PeerJ, 2020. \u003cstrong\u003e8\u003c/strong\u003e: p. e8454.\u003c/li\u003e\n\u003cli\u003eCamp, R., M. Dolled-Filhart, and D. Rimm, \u003cem\u003eX-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization.\u003c/em\u003e Clinical cancer research : an official journal of the American Association for Cancer Research, 2004. \u003cstrong\u003e10\u003c/strong\u003e(21): p. 7252-9.\u003c/li\u003e\n\u003cli\u003eFang, C., et al., \u003cem\u003eFerroptosis-related lncRNA signature predicts the prognosis and immune microenvironment of hepatocellular carcinoma.\u003c/em\u003e Scientific reports, 2022. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 6642.\u003c/li\u003e\n\u003cli\u003eYang, H. and Q. Jiang, \u003cem\u003eA multi-omics-based investigation of the immunological and prognostic impact of necroptosis-related genes in patients with hepatocellular carcinoma.\u003c/em\u003e Journal of clinical laboratory analysis, 2022. \u003cstrong\u003e36\u003c/strong\u003e(4): p. e24346.\u003c/li\u003e\n\u003cli\u003eMoss, T., et al., \u003cem\u003eA housekeeper with power of attorney: the rRNA genes in ribosome biogenesis.\u003c/em\u003e Cellular and molecular life sciences : CMLS, 2007. \u003cstrong\u003e64\u003c/strong\u003e(1): p. 29-49.\u003c/li\u003e\n\u003cli\u003eSanij, E., et al., \u003cem\u003eUBF levels determine the number of active ribosomal RNA genes in mammals.\u003c/em\u003e The Journal of cell biology, 2008. \u003cstrong\u003e183\u003c/strong\u003e(7): p. 1259-74.\u003c/li\u003e\n\u003cli\u003eDrygin, D., W. Rice, and I. Grummt, \u003cem\u003eThe RNA polymerase I transcription machinery: an emerging target for the treatment of cancer.\u003c/em\u003e Annual review of pharmacology and toxicology, 2010. \u003cstrong\u003e50\u003c/strong\u003e: p. 131-56.\u003c/li\u003e\n\u003cli\u003eSanij, E. and R. Hannan, \u003cem\u003eThe role of UBF in regulating the structure and dynamics of transcriptionally active rDNA chromatin.\u003c/em\u003e Epigenetics, 2009. \u003cstrong\u003e4\u003c/strong\u003e(6): p. 374-82.\u003c/li\u003e\n\u003cli\u003ePanov, K., et al., \u003cem\u003eUBF activates RNA polymerase I transcription by stimulating promoter escape.\u003c/em\u003e The EMBO journal, 2006. \u003cstrong\u003e25\u003c/strong\u003e(14): p. 3310-22.\u003c/li\u003e\n\u003cli\u003eTsoi, H., et al., \u003cem\u003ePre-45s rRNA promotes colon cancer and is associated with poor survival of CRC patients.\u003c/em\u003e Oncogene, 2017. \u003cstrong\u003e36\u003c/strong\u003e(44): p. 6109-6118.\u003c/li\u003e\n\u003cli\u003eYu, F., et al., \u003cem\u003eAnalysis of histone modifications at human ribosomal DNA in liver cancer cell.\u003c/em\u003e Scientific reports, 2015. \u003cstrong\u003e5\u003c/strong\u003e: p. 18100.\u003c/li\u003e\n\u003cli\u003eDichamp, I., et al., \u003cem\u003eHuman papillomavirus 16 oncoprotein E7 stimulates UBF1-mediated rDNA gene transcription, inhibiting a p53-independent activity of p14ARF.\u003c/em\u003e PloS one, 2014. \u003cstrong\u003e9\u003c/strong\u003e(5): p. e96136.\u003c/li\u003e\n\u003cli\u003eZhang, J., et al., \u003cem\u003eUBTF facilitates melanoma progression via modulating MEK1/2-ERK1/2 signalling pathways by promoting GIT1 transcription.\u003c/em\u003e Cancer cell international, 2021. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 543.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-peptide-research-and-therapeutics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijpr","sideBox":"Learn more about [International Journal of Peptide Research and Therapeutics](http://link.springer.com/journal/10989)","snPcode":"10989","submissionUrl":"https://submission.nature.com/new-submission/10989/3","title":"International Journal of Peptide Research and Therapeutics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hepatocellular Carcinoma, upstream binding transcription factor, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4952392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4952392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the value of nuclear UBTF for HCC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe expression of UBTF was detected by western blot and immunohistochemistry. 289 HCC patients were included in this study. X-tile software was used to calculate the outcome-based cut-point of UBTF expression. Pearson\u0026rsquo;s χ2 test was used to analyze the association between UBTF expression and clinicopathologic parameters. Kaplan-Meier analysis and Cox regression analysis were used to evaluate prognostic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUBTF expression was significant higher in HCC nucleus than paired adjacent tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0247). Nuclear UBTF expression was associated with AFP, liver cirrhosis, and tumor size. For OS, tumor size, tumor number, nuclear UBTF/AFP combination were the independent risk factors (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For TTR, liver cirrhosis, tumor size, tumor number, nuclear UBTF/AFP combination were the independent risk factors (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Survival curves showed that OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and TTR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) with high nuclear UBTF were worse than those with low nuclear UBTF, especially when nuclear UBTF and AFP were considered simultaneously. UBTF expression was significantly higher in HCC than LC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0305), whereas no significant differences between LGDN and LC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0937), also HGDN and LC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4674).\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eOur study confirms that nuclear UBTF is a valuable prognostic biomarker for HCC.\u003c/p\u003e","manuscriptTitle":"The value of Nuclear UBTF expression for hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 08:34:11","doi":"10.21203/rs.3.rs-4952392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-09-01T08:34:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-23T09:18:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Peptide Research and Therapeutics","date":"2024-08-21T14:39:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-peptide-research-and-therapeutics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijpr","sideBox":"Learn more about [International Journal of Peptide Research and Therapeutics](http://link.springer.com/journal/10989)","snPcode":"10989","submissionUrl":"https://submission.nature.com/new-submission/10989/3","title":"International Journal of Peptide Research and Therapeutics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"30f54590-64f2-4dbf-8420-ff637828ba56","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-16T08:34:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-16 08:34:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4952392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4952392","identity":"rs-4952392","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}