RAD50 predicts the prognosis of hepatitis B virus-related hepatocellular carcinoma

doi:10.21203/rs.2.11886/v2

Background Increasing evidence indicates that RAD50, which is involved in the DNA double-strand break (DSB) repair process, is also involved in cancer outcomes. However, its role in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) remains unclear.Aim This study was designed to investigate the expression of RAD50 and its prognostic value in HCC patients.

Method A total of 207 patientswith HBV-associated HCCfrom two cohorts (107 and 100 patientsfrom the Affiliated Hospital of Youjiang Medical University of Nationalities and the Affiliated Hospital of Nantong University, respectively) were enrolled in the current study.The distribution of the categorical clinical-pathological data and the levels of RAD50 expression were compared with a χ 2 test. IHC staining of RAD50 was performed.A partial likelihood test based onunivariate and multivariate Cox regression analysis was developed to address the influence of independent factors on disease-free survival (DFS) and overall survival (OS). The Oncomine online database was used to analyse and validate the differential expression of RAD50. The Kaplan-Meier method and a log-rank test were performed to assess the influence of RAD50 on survival at different levels.

Results RAD50 was highly expressed in HCC tissues compared to normal tissues and was significantly correlated with OS in the TCGA cohort. The validation analysis indicated that significantly increased levels of RAD50 were expressed in HCC tissues in the two independent cohorts, AHYMUN and AHNTU. In addition, HCC patients with elevated RAD50 expression levels showed poor OS and DFSin the AHYMUN cohort and decreased OS and DF Sin the AHNTU cohort. Furthermore, four datasets obtained from the Oncomine database validated the analysis of the differential expression of RAD50 in HCC tumours and normal tissues.

Discussion In our study, we demonstrated that RAD50 was positively correlated with poor prognosis in HCC patients in the TCGA cohort. Our study also suggested that increased RAD50 expression in HBV-related HCC is a marker of poor prognosis. In this study, the analysis of the data form the two cohorts supported our hypothesis and clearly demonstrated thehigh expression of RAD50 in tumour tissues from HCC patients, which results inincreases in the HCC recurrence rate and poor overall survival.

Gastroenterology & Hepatology

RAD50

hepatitis B virus

hepatocellular carcinoma

In China, the incidence and mortality of primary hepatocellular carcinoma (HCC) is the second highest of all malignant cancers [1]. In addition, in China, because the incidence of hepatitis B virus (HBV) infection is high, HBV accounts for at least 80% of HCC cases. In the Guangxi Zhuang Autonomous Region [2] and Qidong in Nantong Prefecture [3], HBV infectionis very common, leading to a high incidence of HCC [4]. Although alpha fetoprotein (AFP) and other markers have been widely used in clinical practice, the accuracy and sensitivity of diagnosis and targeted therapy need to be improved. Therefore, to reduce the cost of treatment for patients with HCC and to improve their quality of life, it is importantto understand the underlying molecular mechanisms and apply them to the development of targeted treatments.

The DNA repair component protein Rad50 (RAD50) is a component of the MRN complex, which is a heterotrimer composed of RAD50, meiotic recombination 11 (MRE11), and Nijmegen fracture syndrome 1 (NBS1), that participates in the DNA damage response (DDR). Increased RAD50 expression is associated with multiple tumours, including breast cancer, ovarian cancer, lung cancer and rectal cancer [5]. For example, RAD50 increases in colorectal cancer patients have been shown to be related to tumour development and prognosis [6]. It is speculated thatthe high expression of RAD50 leads to cell instability, which increases the tendency toward DNA damage accumulation and malignant transformation. Following HBV infection, RAD50 expression in hepatocytes has been shown to be increased [7].

To investigate the expression and prognosticvalue of RAD50 in HCC, we recruited 207 HCC patients from two centreswith confirmed pathological evidence of HCC. The prognostic role of RAD50 in HCC patients was validated and functionally annotated in silico. We speculate that RAD50 is associated with poor prognosis and may serve as a potential therapeutic target for HCC.

2.1. Patients and Variables

In this study, a total of 107 HCC patients were recruitedfrom the Affiliated Hospital of Youjiang Medical University of Nationalities (AHYMUM) (Youjiang, China) between April 2008 and October 2018, and 100 HCC patients were also recruited from the Affiliated Hospital of Nantong University (AHNTU) (Nantong, China) for analyses between July 2013 and May 2017. The inclusion criteriaconsisted of the following: (1) HCC diagnosed by pathology (surgery or biopsy), typical dynamic imaging studies, and alpha fetoprotein serology according to the American Association for the Study of Liver Diseases (AASLD) guidelines [17]; (2) chronic viral hepatitis B infection [hepatitis B surface antigen (+)]; (3) no previous oncological treatment or liver resection; (4) no tumour invasionin thebranches ortrunk of the portal vein or hepatic veins according to MRI; (5) no other diffuse liver disease, including primarysclerosing cholangitis or primary biliary cirrhosis. The clinical and pathological parametersin the two cohorts, includingage at surgery, gender, tumour size, AJCC stage, primary tumour lesion, the degree of pathological nuclear differential expression, Okuda score, albumin level, bilirubin level, presence of liver cirrhosis, and the degrees of microvascular invasion and capsular invasion are summarized in Table 1. Tissue samples, including HCC and adjacent normal tissues, were collected during surgery and were available from the AHYMUM and AHNTU tissue banks. All study designs and test procedures were performed in accordance with the Helsinki Declaration II. Ethical approval and consent to participate in the current study were obtained from the ethics committee.

2.2. Immunohistochemistry (IHC)

Immunostaining of RAD50 was performed using a mouse monoclonal anti-RAD50 antibody (1:1000, #Ab89, Abcam, USA). Positive or negative staining of a specific protein on one FFPE slide was independently assessed by two experienced pathologists and determined according to the following description. The staining intensity level was graded from 0 to 3. Samples with no staining or weak, median orstrong staining were graded atthe level of 0, 1, 2 or 3, respectively. The staining extent ranged from 0 to 4 based on the percentage of coverage of the immunoreactive tumour cells (0%, 1-25%, 26-50%, 51-75%, or 76-100%, respectively). The overall IHC score, which was graded on a scale from 0 to 12, was generated by multiplying the staining intensity and extent scores. Negative staining was indicatedby a grade from 0 to 3 and positive staining was indicatedby a grade from 4 to 12 for each sample. All samples were classified into the tumour or normal group to confirm the differential expression of RAD50.

2.3. Statistical analysis

To determine the association of different RAD50 mRNA expression levels with clinicopathological characteristics, a χ²-test was performed to compare the distribution of the categorical data between groups. A scatter plot was utilized to represent the differential expression of RAD50 in normal and HCC tissues. The primary end point was overall survival (OS) for patients who survived for a specificperiod of time, which was determinedaccording to the length of time from the date of surgery to the date of death or date of last follow-up. Disease-free survival (DFS), as the secondary end point, was defined as the length of time from the initiation of curative treatment when no disease could be detected until the date of progression, the initiation of second-line treatment or the date of death, which ever occurred first. The follow-up duration was estimated using the Kaplan-Meier method with a 95% CI and a log-rank test with separate curves. The hazard ratios were derived from Cox proportional hazard regression models based on a high-versus-low comparison to identify the independent predictors. Univariate and multivariate Cox regression models were independently analysed to evaluate the influence of the confounding covariates, including age at surgery, gender, tumour size, AJCC stage, primary tumour site, the degree of pathological nuclear differential expression, Okuda score, albumin level, bilirubin level, presence of liver cirrhosis, presence of microvascular invasion, presence of capsular invasion, and RAD50 expression, on survival. Statistical analyses were performed with SPSS software (version 23.0, SPSS Inc., Chicago, IL, USA). All hypothetical tests were two-sided, and P-values less than 0.05 were considered significant forall tests.

2.4. Determination of differential RAD50 expression based onOncomine datasets

RAD50 expression profiles in hepatocellular tumour and normal tissue were analysed and displayed using the Oncomine online database (http://www.oncomine.com). A dataset including the known gene expression patterns in human HCC were included to validate the mRNA expression of RAD50 in the two groups. An unpaired t test was used to analysethe significant differences.

2.5. Data processing of Gene Set Enrichment Analysis (GSEA)

The Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org) (version 10.0) online database was used to predict the PPI network forRAD50. Any interaction with a combined score > 0.4 was considered statistically significant. The significant genes were included in functional and pathway enrichment analyses in a bubble chart using DAVID bioinformatics resources, which indicated the most significant signaling pathways according to GO and KEGG analysis. Datasets from the Cancer Genome Atlas (TCGA) database were analysed with the GSEA method using the category version 2.10.1 package in R. For each separate analysis, the Student’s t-test statistical score was obtained for each of theconsistent pathways, and the mean differential expression of each gene was calculated. A permutation test was performed 1000 times to identify the pathways with significant changes. The adjusted P values (adj. P), which were obtainedby default using the Benjamini and Hochberg (BH) false discovery rate (FDR) method, were utilizedto correct for the occurrence of false positive results. Significantly related genes were defined byan adj. P less than 0.01 and an FDR less than 0.25.

In this study, the research was conducted in three steps. In the first step, the differential expression of RAD50 in normal and prostate tissues was analysed; in the second step, RAD50 expression in HCC patients was assessed as an indicator of progression and prognosis; in the third step, the differential expression of RAD50 was validated in the Oncomine datasets, and GSEA was used to identify the relationships between significant genes and the involved signaling pathways.

3.1. Prognostic role of RAD50 in HCC based on TCGA

As shown in Table 1, in the AHYMUM cohort, increased RAD50 expression was significantly correlated with decreased age (p=0.021), advanced AJCC stage (p = 0.001), the presence of multiple primary tumour lesions (p = 0.001), the presence of micro vascular invasion (p = 0.028), the presence of capsular invasion (p = 0.015), higher HBV DNA level (p = 0.004) , increased Okuda score (p = 0.009) and increased Child-Pugh Stage (p = 0.025). In the AHNTU cohort, increased RAD50 expression was significantly associated with larger tumour size (p < 0.001), advanced AJCC stage (p = 0.003), the presence of multiple primary tumour lesions (p = 0.010), the degree of pathological nuclear differential expression (p = 0.005), the presence of microvascular invasion (p = 0.001), the presence of capsular invasion (p = 0.003), and higher Okuda score (p = 0.008). A chi-square test showed that the baseline data were balanced in terms of the distribution of the categorical data, including gender, albumin level, bilirubin level and the presence of liver cirrhosis (p > 0.05).

As shown in Fig. 1A, the Kaplan-Meier survival analyses of different RAD50 expression groups based on OS in 207 patients with hepatocellular carcinoma from the TCGA database, We can see that patients with high RAD50 expression have significantly lower OS than patients with low expression . And in the TCGA cohorts, the expression of RAD50 in tumor tissues is significantly higher than that in normal tissues (Fig. 1B).

3.2. Expression of RAD50 in the AHYMUN and AHNTU cohorts

To analyse the RAD50 expression profile in HCC tissue, IHC was performed to reveal the staining distribution intumour and normal tissues (Fig. 2A). The scatter plot of the IHC scores revealed that RAD50 expression was significantly elevatedin HCC tissues in the AHYMUM (p < 0.001) and AHNTU (p < 0.001) cohorts (Fig. 2B).

3.3. Cox regression analyses

The univariate Cox regression analysis, as shown in the forest plots,revealedthat age, gender, albumin level, bilirubin level and liver cirrhosis were not significantly associated with DFS in both AHYMUN (Fig. 3A) and AHNTU (Fig. 3B) patients. Meanwhile, tumour size was not a significant independent predictor of DFS in the AHYMUN cohort. In the multivariate models, the Okuda score (ref. I) was significantly associated with DFS in HCC patients, indicating the good representativeness of the population in the AHYMUN (HR: 1.914, p = 0.045) and AHNTU (HR: 3.084, p = 0.001) cohorts. More importantly, the subgroup analysis of RAD50expression showed that RAD50 amplification was significantly correlated with DFS in the AHYMUN (HR: 4.612, p < 0.001) and AHNTU (HR: 2.660, p = 0.006) cohorts. Additionally, in the AHNTU cohort, a number ofprimary lesionswassignificantly correlated with poor DFS (HR: 2.563; p = 0.007)according tothe multivariate model used for theCox regression analyses (Table 2).

In Fig. 3C and 3D, several parameters, including age, gender, albumin level, bilirubin level and the presence of liver cirrhosis, were not significantly associated with OS in the two cohorts. As shown in Table 3, the multivariate Cox analysis indicated that the Okuda score (ref. I) was significantly associated with OS in both the AHYMUN (HR: 2.329, p = 0.031) and AHNTU (HR: 2.819, p = 0.030) cohorts. RAD50 expression (ref. low) was significantly associated with OS in AHYMUM patients (HR: 6.807; p < 0.001) but was not markedly correlated with OS in HCC patients from the AHNTU (HR: 1.735; p = 0.195) cohort. The other factors, including tumour size (ref.＜5 cm), the presence of multiple primary lesions (ref. single), the degree of pathological nuclear differential expression (ref. low), the presence of microvascular invasion (ref. absent) and the presence of capsular invasion (ref. absent), were not identifiedas prognostic indicators of OS in our study (p > 0.05).

The survival curves suggested that HCC patients in the AHYMUN cohort with elevated RAD50 expression levels showed poor OS (p < 0.001) and poor DFS (p < 0.001) (Fig. 4A and 4B). In addition, in the AHNTU cohort, increased RAD50 expression was significantly associated with decreased OS (p = 0.021) and DFS (p < 0.001) (Fig. 4C and 4D).

3.4. Expression validation and GSEA

Four datasets (Rossler Liver 2 [8], Guichard Liver [9], Rossler Liver [8], and Chen Liver [10]) from the Oncomine database wereanalysed to validate the differential expression of RAD50 in HCC tumour and normal tissues (Fig. 5). A total of 11 significant genes, including RAD51, ATM, XRCC6, RAD50, XRCC5, TERF2, ATR, MRE11A, BRAC1, NBN and TERF2IP, were included in the molecular model shown in Fig. 6A. Functional and pathway enrichment analyses, including analyses of biological processes, cellular components, molecular functions and KEGG pathways, were performed using DAVID and are represented inthe bubble chart in Supplementary Fig. 1. The significant and potentially related IncRNAs, targeted miRNAs and protein-protein interaction nodes areshownin Fig. 6B. Significant differences were found among the four validation datasets (p < 0.05). A total of 100 significant genes were obtained from GSEA, and the genes with positive correlations were plotted. In addition, RAD50 was found to be involved in pathways from most functional annotation categories, including the nuclear body, ubiquitin ligase complex, G2M checkpoint and mitotic spindle signaling pathways. The details are shown in Fig. 6.

In our study, we demonstrated that RAD50 was positively correlated with poor prognosis inHCC patients in the TCGA cohort. In the two validated cohorts, increased RAD50 expression was observed in cancertissues compared to that in para-cancer tissues, which was shown be associated withdecreased OS and DFS. Four validation cohorts from the Oncomine database also showed elevated RAD50 expression in HCC tissues compared with that in adjacent normal tissues. Additionally, the functional annotation bioinformatics analysis identified significant related hub genes and signaling pathways.

According tothe IntAct molecular interaction database, human RAD50 potentially interacts with the transcription factor c-Jun/activator protein 1 (AP-1) (website: https://www.ebi.ac.uk/intact/interactors/id:Q92878*). C-Jun induces HBV-related liver tumourigenesis in mice via upregulating the transcription of its target geneosteopontin (OPN) [11]. OPN is overexpressed in several human carcinomas and contributes to inflammation, tumour progression, and metastasis [12]. Therefore, we speculated that the interaction of RAD50 with c-Jun activated the transcription activity of c-Jun, upregulated OPN expression, facilitated the development of HBV-related HCC, and contributed to poor prognosis.

Our study suggested that increased RAD50 expression in HBV-related HCC is a marker of poor prognosis. AudeDupré and colleagues designeda chemical forward genetic screen to identify a small-molecule inhibitor of the MRN complex, named Z-5-(4-hydroxybenzylidene)-2-imino-1, 3-thiazolidin-4-one (mirin) [13]. In 2009, other researchers confirmed the structure of mirin [14]. However, there have beenno reports about the use of mirin in cellular, animal and clinical applications. We questioned whether mirin could be used in an HBV-related HCC animal model to determine the therapeutic effect of the inhibition of the MRN complex.

The predictive power of RAD50 may be as strongas that of the classical factors commonly used in clinical practice.Serum tumour markers are the most commonly used markers for the monitoring and early diagnosis of HBV-related HCC because they are non-invasive, objective, and reproducible. The most common serological test utilizes AFP. Disappointingly, in a retrospective case-control study, the sensitivity of AFP test ingat the most effective cut-off value (10-20 ng/ml) in the diagnosis of HCC was approximately 60%, and its specificity was 80% [15]. These values are much lower than those required for the treatment and prediction of prognosis of HCC patients. However, RAD50 was shown to be a significant and independent biomarker [16]. This means that it may be used to shed further light on the mechanisms underlying HCC progression, to facilitate the discovery of new therapies, and to open up new avenues for the personalized treatment of HCC patients.

In this study, the analysis of the data form the two cohorts supported our hypothesis and clearly demonstrated thehigh expression of RAD50 in tumour tissues fromHCC patients, which results inincreases in the HCC recurrence rate and poor overall survival. At the same time, GSEA indicated that RAD50 is involved in some of the most important pathways, including the mitotic spindle, UV response and transforming growth factor beta (TGF-β) signalingpathways, thatare enriched in HCC specimens. In addition to participating in the DSB repair pathway, RAD50 also interacts with MMAP, which is expressed by the mitotic-specific MRN complex to maintain optimal genomic stability during mitosis [17].

The strength of our study is that we first attempted to study the role of RAD50 as a prognostic factor for HCC. The relationship between RAD50 and HCC hasrarely been investigated [18]. However, it is worth noting that RAD50 is a repair factor that has been identified inthe DSB reaction and is highly expressed in many cancers. With this in mind, to further explain the role of RAD50 in the invasion and metastasis of HCC, we used GSEA to analyse data from public databases to identify important genes and pathways that may be involved in therelevance of RAD50 to the initiation of carcinogenesis.

The limitations of this study are described as follows. First, the retrospective nature of the data set is obvious. Although data was obtained fromtwo institutesatthe Nantong University Affiliated Hospital and the Youjiang Nationalities Medical College Affiliated Hospital, the sample size is relatively small, and the patients in the two Chinese centresexhibit economic and geographical differences, which may lead differences in therapeuticstrategies and overall prognoses. Second, while our research involved a thorough functional annotation of RAD50 in HCC, we failed to confirmthe underlying mechanisms. In the future, we will conduct multi-center research in Europe and the United States to further explore the role of RAD50 in the development and prognosis of HCC.

In conclusion, our study is the first to reveal that elevated RAD50 expression is significantly correlated with cancer progression and poor survival in HBV-related HCC patients. These data suggest that RAD50, which is a potential therapeutic target, may act as an oncoprotein and could serve as a promising prognostic marker inHCC patients. In this regard, randomized clinical trials and further investigationare required to identify the true value of RAD50 in terms of its clinical application in HCC patients.

HBV hepatitis B virus

HCC Hepatocellular carcinoma

DDR DNA damage response

IHC immunohistochemistry

AHYMUM Affiliated Hospital of YouJiangMedical University forNationalities

AHNTU Affiliated Hospital of Nantong University

TCGA the Cancer Genome Atlas

DFS disease-free survival

OS overall survival

HR hazard ratio

CI confidence interval

Ethics approval and consent to participate:The ethics approval and consent to participate inthe current study wereobtained from the ethics committees of AHYMUM and AHNTU.

Consent for publication: Not applicable.

Availability of data and materials: The datasets and/or analysis results used during the current study are available from the corresponding author upon reasonable request.

Competing interests: The authors declare no competing interests.

Funding: This work is supported by grants from the National Natural Science Foundation of China (No. 81202004, 81802525, 81472377) and the Shanghai Natural Science Foundation of China (No. 16ZR1406400).This study was partly supported by the major project (No. MS22018009) and the general project (No. JC2018087) of Nantong city.

Acknowledgments: We especially appreciate the Affiliated Hospital of Youjiang Medical University of Nationalities and the Affiliated Hospital of Nantong University for the clinical sample collection.

Authors' contributions: The work presented here was carried out withthe collaboration of all authors. DW Ye and HL Zhang defined the research topicand discussed the analyses and their interpretation, and presentation. HN Huangand XJ Liu developed the algorithm, provided pathological samples, provided clinicopathological data and performed the statistical analysis. WH Xu, WR Liuand YY Chen drafted the manuscript, recorded the clinical data, analysed the IHC data and interpreted the results. LGGu and LQ He collected clinical and pathological records and helped to analyse the IHC data. HK Wang and YY Qu participated in reviewing all clinical records and performed the associated data collection. All authors read and approved the final manuscript.

1 Chen JG, Zhang SW. Liver cancer epidemic in China: past, present and future Seminars in cancer biology. 2011;21:59-69.

2 Zheng D, Deng W, Huang T, Li X, Li Z. [Relationship between hepatitis B virus genotype, BCP/Pre-C region mutations and risk of hepatocellular carcinoma in Guangxi Zhuang Autonomous Region] Zhonghua Liu Xing Bing Xue Za Zhi. 2015;36:725-729.

3 Chen T, Qian G, Fan Cet al. . Qidong hepatitis B virus infection cohort: a 25-year prospective study in high risk area of primary liver cancer Hepatoma Res. 2018;4.

4 Bei C, Tan C, Zhu X, Wang Z, Tan S. Association Between Polymorphisms in CMTM Family Genes and Hepatocellular Carcinoma in Guangxi of China DNA Cell Biol. 2018;37:691-696; Fan C, Li M, Gan Yet al. . A simple AGED score for risk classification of primary liver cancer: development and validation with long-term prospective HBsAg-positive cohorts in Qidong, China Gut. 2019;68:948-949.

5 Tommiska J, Seal S, Renwick Aet al. . Evaluation of RAD50 in familial breast cancer predisposition Int J Cancer. 2006;118:2911-2916; Zhang M, Liu G, Xue Fet al. . Copy number deletion of RAD50 as predictive marker of BRCAness and PARP inhibitor response in BRCA wild type ovarian cancer Gynecol Oncol. 2016;141:57-64; Adams DL, Adams DK, He Jet al. . Sequential Tracking of PD-L1 Expression and RAD50 Induction in Circulating Tumor and Stromal Cells of Lung Cancer Patients Undergoing Radiotherapy Clin Cancer Res. 2017;23:5948-5958; Ho V, Chung L, Singh Aet al. . Overexpression of the MRE11-RAD50-NBS1 (MRN) complex in rectal cancer correlates with poor response to neoadjuvant radiotherapy and prognosis BMC Cancer. 2018;18:869.

6 Gao J, Zhang H, Arbman G, Sun XF. RAD50/MRE11/NBS1 proteins in relation to tumour development and prognosis in patients with microsatellite stable colorectal cancer Histol Histopathol. 2008;23:1495-1502.

7 Ryu HM, Park SG, Yea SS, Jang WH, Yang YI, Jung G. Gene expression analysis of primary normal human hepatocytes infected with human hepatitis B virus World J Gastroenterol. 2006;12:4986-4995.

8 Roessler S, Jia HL, Budhu Aet al. . A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients Cancer research. 2010;70:10202-10212.

9 Guichard C, Amaddeo G, Imbeaud Set al. . Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma Nature genetics. 2012;44:694-698.

10 Chen X, Cheung ST, So Set al. . Gene expression patterns in human liver cancers Molecular biology of the cell. 2002;13:1929-1939.

11 Trierweiler C, Hockenjos B, Zatloukal Ket al. . The transcription factor c-JUN/AP-1 promotes HBV-related liver tumorigenesis in mice Cell Death Differ. 2016;23:576-582.

12 Weber CE, Kothari AN, Wai PYet al. . Osteopontin mediates an MZF1-TGF-beta1-dependent transformation of mesenchymal stem cells into cancer-associated fibroblasts in breast cancer Oncogene. 2015;34:4821-4833; Qin X, Yan M, Wang Xet al. . Cancer-associated Fibroblast-derived IL-6 Promotes Head and Neck Cancer Progression via the Osteopontin-NF-kappa B Signaling Pathway Theranostics. 2018;8:921-940.

13 Dupre A, Boyer-Chatenet L, Sattler RMet al. . A forward chemical genetic screen reveals an inhibitor of the Mre11-Rad50-Nbs1 complex Nat Chem Biol. 2008;4:119-125.

14 Garner KM, Pletnev AA, Eastman A. Corrected structure of mirin, a small-molecule inhibitor of the Mre11-Rad50-Nbs1 complex Nat Chem Biol. 2009;5:129-130; author reply 130.

15 Marrero JA, Feng Z, Wang Yet al. . Alpha-fetoprotein, des-gamma carboxyprothrombin, and lectin-bound alpha-fetoprotein in early hepatocellular carcinoma Gastroenterology. 2009;137:110-118; Lok AS, Sterling RK, Everhart JEet al. . Des-gamma-carboxy prothrombin and alpha-fetoprotein as biomarkers for the early detection of hepatocellular carcinoma Gastroenterology. 2010;138:493-502.

16 Chen C, Wang Y, Mei JFet al. . Targeting RAD50 increases sensitivity to radiotherapy in colorectal cancer cells Neoplasma. 2018;65:75-80.

17 Xu R, Xu Y, Huo Wet al. . Mitosis-specific MRN complex promotes a mitotic signaling cascade to regulate spindle dynamics and chromosome segregation Proceedings of the National Academy of Sciences of the United States of America. 2018;115:E10079-e10088.

18 Jackson SP, Bartek J. The DNA-damage response in human biology and disease Nature. 2009;461:1071-1078.

Table 1. Clinicopathological characteristics in relation to RAD50 expression status in two cohorts

Characteristics	AHYMUM cohort (N=107)	RAD50 expression		c²	P	AHNTU cohort (N=100)	RAD50 expression		c²	P
Characteristics	AHYMUM cohort (N=107)	Low IHC score (N=53)	High IHC score (N=54)	c²	P	AHNTU cohort (N=100)	Low IHC score (N=52)	High IHC score (N=48)	c²	P
N (%)
Age*				5.287	0.021				0.038	0.844
<60 years	85 (79.4)	40 (75.5)	45 (83.3)			72 (72.0)	37 (71.2)	35 (72.9)
≥60 years	22 (20.6)	13 (24.5)	9 (16.7)			28 (28.0)	15 (28.2)	13 (27.1)
Gender				0.698	0.403				1.512	0.219
Male	90 (84.1)	43 (81.1)	47 (87.0)			82 (82.0)	45 (86.5)	37 (77.1)
Female	17 (15.9)	10 (18.9)	7 (13.0)			18 (18.0)	7 (13.5)	11 (22.9)
Tumor size				3.447	0.063				13.117	<0.001
<5cm	59 (55.1)	34 (64.2)	25 (46.3)			48 (48.0)	34 (65.4)	14 (29.2)
≥5cm	48 (44.9)	19 (35.8)	29 (53.7)			52 (52.0)	18 (34.6)	34 (70.8)
AJCC stage †				10.363	0.001				9.324	0.003
I – II	94 (87.9)	52 (98.1)	42 (77.8)			66 (66.0)	42 (80.8)	14 (29.2)
III – IV	13 (12.1)	1 (1.9)	12 (22.2)			34 (34.0)	10 (19.2)	24 (70.8)
Primary lesion				10.194	0.001				6.732	0.010
Single	83 (77.8)	48 (90.6)	35 (64.8)			79 (79.0)	46 (88.5)	33 (68.8)
≥2	24 (22.4)	5 (9.4)	19 (35.2)			21 (21.0)	6 (11.5)	15 (31.2)
Albumin level				0.291	0.590				0.496	0.503
<35g/L	37 (34.6)	17 (32.1)	20 (37.0)			51 (51.0)	23 (44.2)	28 (58.3)
≥35g/L	70 (65.4)	36 (67.9)	34 (63.0)			49 (49.0)	29 (55.8)	20 (41.7)
Bilirubin level				0.641	0.423				1.803	0.179
<20.4umol/L	77 (72.0)	40 (75.5)	37 (68.5)			65 (65.0)	37 (71.2)	28 (58.3)
≥20.4umol /L	30 (28.0)	13 (24.5)	17 (31.5)			35 (35.0)	15 (28.8)	20 (41.7)
Liver cirrhosis				2.905	0.088				0.550	0.458
Absent	6 (5.6)	5 (9.4)	1 (1.9)			6 (6.0)	4 (7.7)	2 (4.2)
Present	101 (94.4)	48 (90.6)	53 (98.1)			94 (94.0)	48 (92.3)	46 (95.8)
Differential degree				0.849	0.357				7.771	0.005
Low	30 (28.0)	17 (32.1)	13 (24.1)			62 (62.0)	39 (75.0)	23 (47.9)
High	77 (72.0)	36 (67.9)	41 (75.9)			38 (38.0)	13 (25.0)	25 (52.1)
Microvascular invasion				4.821	0.028				10.359	0.001
Absent	96 (89.7)	51 (96.2)	45 (83.3)			62 (62.0)	40 (88.9)	22 (45.8)
Present	11 (10.3)	2 (3.8)	9 (16.7)			38 (38.0)	12 (11.1)	26 (54.2)
Capsular invasion				5.922	0.015				9.324	0.003
Absent	87 (81.3)	48 (90.6)	39 (72.2)			67 (67.0)	42 (80.8)	25 (52.1)
Present	20 (18.7)	5 (9.4)	15 (27.8)			33 (33.0)	10 (19.2)	23 (47.9)
PT INR ≤1.2	77(71.9)	40(55.8)		0.016	0.690	47(47.0)	27(57.4)	20(42.6)	1.837	0.178
＞1.2	30(28.1)	10(33.3)				53(53.0)	23(43.4)	30(56.6)
HBV DNA level				8.682	0.004				0.746	0.390
≤1000cps/ml	78(72.9)	42(53.8)	36(46.2)			30(30.0)	16(53.3)	14(46.7)
＞1000cps/ml	29(27.1)	11(37.9)	18(62.1)			70(70.0)	34(48.6)	36(51.4)
Antiviral therapy history				1.994	0.161				1.320	0.253
Had	78(72.9)	37(47.4)	41(52.6)			86(86.0)	44(51.2)	42(48.8)
Never before	29(27.1)	16(55.2)	13(44.8)			14(14.0)	6(42.9)	8(57.1)
Okuda score				6.805	0.009				7.015	0.008
I	51 (47.7)	32 (60.4)	19 (35.2)			69 (69.0)	42 (80.8)	27 (56.3)
II	56 (52.3)	21 (39.6)	35 (64.8)			31 (31.0)	10 (19.2)	21 (43.8)
Child-Pugh Stage				5.137	0.025				0.606	0.438
A	45(42.1)	17(37.8)	28(62.2)			93(93.0)	46(49.5)	47(50.5)
B	62(57.9)	36(58.1)	26(41.9)			7(7.0)	4(57.1)	3(42.9)
Meld Stage				1.387	0.242				0.508	0.478
＜10	50(46.7)	28(56.0)	22(44.0)			54(54.0)	26(48.1)	28(51.9)
≥10	57(53.3)	25(43.9)	32(56.1)			46(46.0)	24(52.2)	22(47.8)

† The AJCC staging system is a classification system developed by the American Joint Committee on Cancer for describing the extent of disease progression in cancer patients. It utilizes in part the TNM scoring system: Tumor size, Lymph Nodes affected, Metastases.

Table 2. Multivariate Cox regression analysis of DFS in AHYMUM and AHNTU cohorts (DFS: disease-free survival)

Covariates	AHYMUM			AHNTU
Covariates	HR	95% CI	P value	HR	95% CI	P value
Tumor size (ref. <5cm)	-	-	-	0.839	0.412-1.707	0.627
AJCC stage (ref. II – IV)	0.660	0.267-1.631	0.269	1.796	0.912-3.538	0.090
Primary lesion (ref. Single)	1.156	0.706-2.349	0.155	2.563	1.286-5.110	0.007
Differential degree (ref. Low)	2.052	0.964-4.371	0.062	1.404	0.633-3.110	0.404
Microvascular invasion (ref. Absent)	1.020	0.374-2.780	0.969	1.317	0.626-2.769	0.468
Capsular invasion (ref. Absent)	1.281	0.672-2.441	1.281	0.899	0.418-1.933	0.784
Okuda score (ref. I)	1.914	1.014-3.611	0.045	3.084	1.632-5.828	0.001
RAD50 expression (ref. low)	4.612	2.336-9.106	<0.001	2.660	1.331-5.317	0.006

Table 3. Multivariate Cox regression analysis of OS in AHYMUM and AHNTU cohorts (OS: overall survival)

Covariates	AHYMUM			AHNTU
Covariates	HR	95% CI	P value	HR	95% CI	P value
Tumor size (ref. <5cm)	1.199	0.463-3.104	0.708	0.905	0.358-2.283	0.832
AJCC stage (ref. II – IV)	0.763	0.256-2.277	0.628	2.826	1.150-6.947	0.024
Primary lesion (ref. Single)	1.151	0.514-0.576	0.733	2.151	0.898-5.154	0.086
Differential degree (ref. Low)	2.019	0.792-5.147	0.141	0.925	0.377-2.269	0.865
Microvascular invasion (ref. Absent)	1.488	0.719-3.081	0.284	2.023	0.783-5.228	0.146
Capsular invasion (ref. Absent)	0.984	0.326-2.973	0.978	1.363	0.518-3.585	0.530
Okuda score (ref. I)	2.329	1.079-5.027	0.031	2.819	1.105-7.194	0.030
RAD50 expression (ref. low)	6.807	2.598-17.839	<0.001	1.735	0.753-3.996	0.195

Supplementary Table 1. Univariate Cox regression analysis of DFS in AHYMUM and AHNTU cohorts (DFS: disease-free survival)

Covariates	AHYMUM			AHNTU
Covariates	HR	95% CI	P value	HR	95% CI	P value
Age	0.885	0.453-1.730	0.721	0.988	0.960-1.017	0.397
Gender (ref. Male)	0.873	0.410-1.860	0.725	0.975	0.436-2.183	0.951
Tumor size (ref. ＜5cm)	1.618	0.933-2.804	0.087	1.974	1.103-3.534	0.022
AJCC stage (ref. II – IV)	2.263	1.172-4.368	0.015	2.157	1.617-2.878	<0.001
Primary site (ref. Single)	2.026	1.147-3.579	0.015	2.909	1.539-5.496	0.001
ALB (ref. ＜35g/L)	0.964	0.551-1.686	0.897	1.339	0.757-2.369	0.316
TBIL (ref. ＜20.4umol/L)	1.283	0.717-2.296	0.401	1.543	0.872-2.731	0.137
Liver cirrhosis (ref. Absent)	4.456	0.598-33.23	0.145	0.786	0.311-1.988	0.611
Differential degree (ref. Low)	2.973	1.436-6.156	0.003	2.467	1.396-4.362	0.002
Microvascular invasion (ref. Absent)	3.305	1.607-6.796	0.001	2.157	1.219-3.816	0.008
Capsular invasion (ref. Absent)	2.573	1.430-4.631	0.002	1.920	1.086-3.396	0.025
Okuda score (ref. I)	2.869	1.570-5.245	0.001	4.564	2.636-7.902	<0.001
RAD50 expression (ref. low)	5.354	2.737-10.476	<0.001	3.438	1.855-6.371	<0.001

Supplementary Table 2. Univariate Cox regression analysis of OS in AHYMUM and AHNTU cohorts (OS: overall survival)

Covariates	AHYMUM			AHNTU
Covariates	HR	95% CI	P value	HR	95% CI	P value
Age	0.746	0.325-1.712	0.489	0.981	0.944-1.019	0.320
Gender (ref. Male)	0.930	0.386-2.237	0.871	0.670	0.202-2.224	0.513
Tumor size (ref. ＜5cm)	1.984	1.019-3.862	0.044	2.228	1.024-4.844	0.043
AJCC stage (ref. II – IV)	1.884	1.376-2.580	<0.001	5.900	2.672-13.15	<0.001
Primary site (ref. Single)	2.467	1.285-4.739	0.007	2.737	1.200-6.243	0.017
ALB (ref. ＜35g/L)	0.883	0.460-1.698	0.710	1.464	0.686-3.132	0.324
TBIL (ref. ＜20.4umol/L)	1.702	0.885-3.273	0.111	1.796	0.853-3.780	0.123
Liver cirrhosis (ref. Absent)	27.045	0.109-678.9	0.242	0.793	0.286-5.140	0.793
Differential degree (ref. Low)	3.227	1.332-7.821	0.009	2.396	1.137-5.046	0.022
Microvascular invasion (ref. Absent)	4.026	1.899-8.537	<0.001	3.381	1.762-8.332	0.001
Capsular invasion (ref. Absent)	3.050	1.568-5.931	0.001	3.175	1.435-7.026	0.004
Okuda score (ref. I)	3.242	1.524-6.900	0.002	3.737	1.820-7.673	<0.001
RAD50 expression (ref. low)	7.962	3.078-20.59	<0.001	2.403	1.117-5.168	0.021

Sup.Figure.png

RAD50 predicts the prognosis of hepatitis B virus-related hepatocellular carcinoma

Status:

Version 2

Abstract

Figures

1. Introduction

2. Materials And Methods

3. Results

4. Discussion

5. Conclusion

List Of Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Version 2