Prognostic Values of Stabilin-2 in Hepatocellular Carcinoma Zhicheng Du Zhengzhou University First Aliated Hospital https://orcid.org/0000-0002-1257-6619 Pengfei Zhu Zhengzhou University First Aliated Hospital Long Yu Zhengzhou University First Aliated Hospital Kunlun Chen Zhengzhou University First Aliated Hospital Janwen Ye Zhengzhou University First Aliated Hospital Wenlong Zhai ( [email protected] ) https://orcid.org/0000-0003-2559-2105 Primary research Keywords: Hepatocellular carcinoma, Stabilin-2, biomarker, prognostic Posted Date: May 11th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-27280/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/20 Abstract Background: Hepatocellular carcinoma (HCC) is the primary malignancy of the liver. However, biomarkers for early HCC diagnosis are not available. Stabilin (STAB) proteins are scavenger receptors involved in apoptosis and clearance of hyaluronic acid .The role of STAB in HCC has not been previously explored; therefore, the aim of this study was to assess whether STAB gene expression can be used as a novel HCC biomarker. Materials and Methods: Data on 370 HCC patients in the Cancer Genome Atlas database and 221 patients in the Gene Expression Comprehensive Database were retrieved and analyzed. Kaplan–Meier analysis and Cox regression model were used to calculate median survival time using hazard ratio (HR) and 95% condence interval (CI). Results: The Gene Expression Omnibus dataset showed that high Stabilin-2STAB2 expression implies longer overall survival (HR after correction = 0.541; 95% CI, 0.339–0.865; p = 0.0182, after correction p = 0.010) and longer recurrence-free survival time (adjusted HR = 0.554; 95% CI, 0.376-0.816; p = 0.0085, adjusted p = 0.003). Conclusions: STAB2 is a potential biomarker for the diagnosis and prognosis of HCC. Background Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor worldwide, and the third leading cause of cancer-related deaths accounting for 8.2% of all cancer deaths. China contributes to about half of the new liver cancer cases and deaths recorded worldwide every year[1, 2]. Hepatocellular carcinoma accounts for about 90% of liver tumors and causes more than 700,000 deaths worldwide each year due to poor prognosis [3, 4]. Cirrhosis is a main hepatocellular carcinoma risk factor and about 70- 90% of hepatocellular carcinoma patients have a cirrhosis history[5]. Chronic hepatitis B virus (HBV) infection is one of the high risk factors for human HCC, accounting for 50% to 80% of the HCC cases worldwide[6]. In addition, alcoholism, aatoxin B1 intake, non-alcoholic fatty liver and hepatitis C virus are also risk factors for hepatocellular carcinoma[7-9] . Although surgical resection and liver transplantation can effectively treat HCC, the 5-year overall survival rate remains at 7% [10]. Early screening for liver cancer relies on liver ultrasound and alpha-fetoprotein (AFP) [11]. However, ultrasound monitoring is limited by low detection sensitivity, which often leads to misdiagnosis of malignant nodules[12]. AFP is also reported to have low sensitivity and specicity [13]. Recent studies have reported potential HCC diagnostic markers such as protein induced by vitamin K absence or antagonist-II (PIVKA-II), Lens culinaris-agglutinin-reactive fraction of AFP (AFP-L3), MicroRNA-4651 (miR-4651) and MicroRNA-125b (miR-125b) [14-17]. HCC diagnosis using these markers requires invasive procedures, therefore, there is need to identify non-invasive biomarkers. Stabilin-1(STAB1) and Stabilin-2(STAB2) also known as FEEL-1and FEEL-2, are structurally highly conserved type I transmembrane proteins and members of the scavenger receptor family [18]. Previous Page 2/20 studies report that STAB is implicated in the proliferation and distant metastasis of melanoma cells, lymph node metastasis of prostate cancer and tongue cancer[19-21]. However, few studies have explored STAB expression in HCC. Therefore, our study utilized multiple datasets to explore the relationship between STAB expression levels and HCC. Materials And Methods Patient information We retrieved STAB1 and STAB2 mRNA expression levels data for HCC patients from The Cancer Genome Atlas (TCGA) database. We normalized and transformed the raw data using R software (RStudio 1.2.5001 Inc., Boston, MA, USA). The expression levels were classied into high and low groups using a 50% cutoff value. Processed data for 370 patients including race, gender, age, TNM stage, body mass index (BMI), family history, survival status and time were also retrieved. Further, the GSE14520 dataset consisting of [HT_HG-U133A] Affymetrix HT human genome U133A array (445 samples) and [HT_HG-U133A_2] Affymetrix HT human genome U133A genes was downloaded from the Gene Expression Synthesis (GEO) database. The expression data consisted of a 2.0 array (57 samples) [22, 23]. To avoid batch effects, we obtained mRNA expression data from 221 HCC patients selected from the TCGA dataset . The mRNA expression levels in 221 HCC patients were also divided into two groups using a 50% cutoff value . GraphPad Prism version 7.0 (GraphPad Software, La Jolla, CA, USA) for Windows was used to assess differential gene expression between primary liver tumors and normal liver tissues. Mutations in the STAB gene family were veried using cBioPortal webserver for Cancer Genomics. Survival analysis We evaluated the prognosis of HCC patients from the TCGA database using median survival time (MST) and we adjusted for race, gender, age, BMI, TNM staging, and family history using the Cox regression model. We assessed the prognosis of HCC patients in the GEO database using the overall survival (OS) and recurrence-free survival (RFS). Further, we adjusted for gender, age, hepatitis B virus (HBV) infection status, alanine aminotransferase (ALT) status, main tumor size, multinodule status, cirrhosis, alphafetoprotein (AFP) level, and Barcelona Clinic Liver Cancer (BCLC) stage using the Cox proportional hazards regression model in the GEO database. Stratied chi-square test We stratied the data and calculated the MST, hazard ratio (HR) at 95% condence interval (CI). These data revealed differences in the different layers. Functional enrichment analysis Page 3/20 We performed a gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using Metascape (a gene annotation and analysis tool) and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.8[24-26]. In GO analysis, we assessed biological processes, cellular components, and molecular functions. Further, we used the STRING (Search Interaction Gene / Protein Search Tool) database to predict protein-protein interactions between STAB gene family members and other proteins [27]. Statistical Analysis We used GraphPad Prism version 7.0 for Windows to generate survival curves and forest plots. All other statistical analyses were performed using SPSS version 22 (SPSS Inc., Chicago, IL, USA). P<0.05 was considered statistically signicant. Result Gene expression in tumors and normal tissues Presentation of STAB1 and STAB2 expression data from the TCGA dataset on a scatter plot showed that these two factors have different expressions levels (Fig. 1a, b). However, STAB1 and STAB2 were not differentially expressed (Fig. 1c, d) in tumor tissues and normal tissues of the GEO dataset. In addition, 7% of cancer samples showed STAB1 mutations, including missense mutation, truncating mutation, amplication, and deep deletion. Further, 5% of cancer samples showed mutations in the STAB2 gene including missense mutation, truncating mutation, amplication (Fig. 1e). Basic patient information Detailed information on the 370 HCC patients in TCGA database is shown in Table 1. BMI and TNM staging showed a signicant correlation with MST (p = 0.021 and <0.001, respectively). Information on patients evaluated in the GEO database is shown in Table 2. Major tumor size, TNM stage, cirrhosis, BCLC stage, and alpha-fetoprotein levels were signicantly correlated with OS (p = 0.003, 0.019, <0.001, <0.001, <0.001, 0.032, 0.019, and 0.026, respectively). Further, Gender, TNM stage, and BCLC stage were signicantly correlated with RFS (p = 0.016, <0.001, and <0.001, respectively). Table 1 Basic characteristics of HCC patients in the TCGA database Page 4/20 Variables Patients No. of events MST HR Log-rank P (n=370) (%) months (95%CI) Race 0.127 Asian 157 44(28.0%) 82 Ref White+others 203 81(40.1%) 68 1.42(0.98-2.05) Missing 10 Gender 0.488 Male 249 79(31.7%) 57 Ref Female 121 51(42.1%) 59 1.07(0.89-1.28) Age 0.248 ≤60 177 55(31.1%) 71 Ref >60 193 75(38.9%) 52 1.23(0.87-1.75) BMI 0.021 ≤25 177 61(34.5%) 68 Ref >25 157 51(32.5%) 66 0.82(0.64-1.04) Missing 36 TNM staging <0.001 + 274 76(27.7%) 84 Ref + 94 54(57.4%) 26 2.40(1.69-3.41) Missing 2 Family history 0.192 Yes 112 49(44.1%) 40 Ref No 207 69(33.3%) 62 0.78(0.54-1.13) Missing 51 MST, median survival time; HR, hazard ratio; 95%CI, 95% confidence interval; Ref, reference; BMI, body mass index; TNM stage, tumor, node and metastasis stage. Survival analysis The TCGA data were analyzed using a multivariate Cox regression model with adjustment for race, gender, age, BMI, TNM stage, and family history, which showed no signicant correlation between STAB1 and STAB2 with prognosis (adjusted p=0.300 and 0.865 respectively) (Fig. 2a, b, g).GEO data were evaluated by a multiple Cox regression model, with adjustment for race, age, HBV, ALT, major tumor size, multiple nodule status, liver cirrhosis, TNM staging, BCLC staging, and AFP, showing that STAB2 was signicantly correlated with OS (HR = 0.541; 95% CI, 0.339–0.865; adjusted p = 0.010) and RFS (HR = 0.554; 95% CI, 0.376-0.816; adjusted p = 0.003) after adjustment (Fig.