Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma

Development and validation of an RNA binding protein-associated prognostic model 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 Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma Min wang, Shan Huang, Zefeng Chen, Zhiwei Han, Kezhi Li, Chuang Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-40802/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Nov, 2020 Read the published version in BMC Cancer → Version 3 posted 4 You are reading this latest preprint version Show more versions Abstract Background: Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods: Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Finally, find protein-coding genes (PCGs) related to hub RBPs were used to construct a hub RBP-PCG co-expression network. Results: In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS. Conclusion: We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes. Cancer Biology Oncology RNA binding protein hepatocellular carcinoma prognosis comprehensive bioinformatics analysis Figures Figure 1 Figure 1 Figure 2 Figure 2 Figure 3 Figure 3 Figure 4 Figure 4 Figure 5 Figure 5 Figure 6 Figure 6 Figure 7 Figure 7 Figure 8 Figure 8 Figure 9 Figure 9 Background Liver cancer is among the most common forms of cancer, and owing to its highly invasive nature it is the fourth leading cancer-related cause of death globally [1]. Hepatocellular carcinoma (HCC) accounts for approximately 80% of all liver cancer cases [2], and it can be difficult to reliably diagnose and treat in its early stages, as its detection is largely dependent upon imaging evaluations and biopsy. HCC treatments generally include hepatectomy, liver transplantation, radiofrequency ablation (RFA), and transcatheter arterial chemoembolization (TACE). As his disease is generally only detected when it is in an advanced stage, HCC patients generally have a poor overall prognosis [3,4]. The identification of novel diagnostic and prognostic biomarkers associated with HCC is thus very important. RNA binding proteins (RBPs) are a broad class of highly-conserved RNA-interacting proteins, of which roughly 60% are expressed in a tissue-specific manner [5]. Genome-wide screening analyses have detected over 1500 RBPs in the human genome. These proteins are capable of binding to diverse RNA types (including rRNAs, ncRNAs, snRNAs, miRNAs, mRNAs, tRNAs, and snoRNAs), and can serve as key post-transcriptional regulators of gene expression to maintain intracellular homeostasis [6,7]. RBP dysregulation has been shown to be associated with oncogenesis in multiple studies [8]. For example, Lin28 is an oncogenic RBP that has been found to promote the metastatic progression of diverse human cancers [9]. The RBP Musashi1 (Msi1) has been shown to promote glioma progression when its normal interactions with miR-137 are disrupted [10]. PUM2 is an RBP that is overexpressed in breast cancer and to be negatively correlated with OS and a lack of tumor recurrence in these patients [11]. The RBP insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) has similarly been found to be overexpressed in mixed-lineage leukemia–rearranged (MLL rearranged) B-acute lymphoblastic leukemia (B-ALL) and to be associated with poorer outcomes and higher recurrence risks in these patients [12]. There is also specific evidence linking certain RBPs to liver cancer. For example, Sorbin and SH3 domain containing 2 (RBPSORBS2) expression is reduced in HCC patients and associated with a poor prognosis. This RBP is believed to function via regulating RORA expression to control liver cancer onset and metastasis [13]. RBM3 is an RBP capable of promoting HCC cell proliferation owing to its ability to regulate SCD-CircRNA2 production, with RBM3 overexpression being linked to reduced OS and decreased recurrence-free survival (RFS) in HCC patients[14]. While these findings are informative, few studies to date have systematically evaluated RBP expression patterns in liver cancer. In the present study, we downloaded HCC patient gene expression and clinical data from The Cancer Genome Atlas (TCGA) database, after which we used these data to identify RBPs that were differentially expressed in HCC tumor tissues relative to healthy normal tissues. We further explored the functional roles of these RBPs through protein-protein interaction (PPI) network, gene ontology (GO) enrichment analyses, and Kyoto gene and genome encyclopedia (KEGG) pathway analyses. We also constructed a prognostic model based upon seven key hub RBPs, identifying them as potentially viable diagnostic and prognostic biomarkers of HCC. Methods Data collection We downloaded level 3 mRNA expression and clinical data from 374 HCC and 50 normal control samples from the TCGA – liver HCC dataset (TCGA-LIHC)(https://portal.gdc.cancer.gov/). Differentially expressed RBP identification Appropriate R packages were used to standardize data by excluding genes with an average count < 1. Differentially expressed RBPs were then identified using R (v3.6.0) with the following criteria: | log2FC | ≥ 1 and FDR < 0.05. Functional enrichment analyses In order to explore the functional roles of these differentially expressed RBPs, they were next separated into those that were upregulated and downregulated in HCC. The clusterProfiler R package [15] was then used to conduct GO and KEGG pathway enrichment [16] analyses on these two groups of RBPs, with P < 0.05 and FDR < 0.05 being used as significance thresholds. PPI network construction and analysis The STRING database (http://www.string-db.org/) [17] was used to assess interactions between proteins related to these differentially expressed RBPs, with Cytoscape v3.7.1 being used to construct a PPI network. The MCODE plugin was then used to identify key modules and hub genes within this network based on the following criteria: degree cutoff=5, node score cutoff=0.2, k-core=5, max.depth=100 truncation standard, and P < 0.05 was the significance threshold. Evaluation of hub gene prognostic relevance Follow-up analyses incorporated all HCC patients surviving for at least 30 days. Hub RBPs associated with patient prognosis were identified through univariate Cox regression analyses, with patients being randomly separated into training and test cohorts. RBPs identified in these initial analyses were then assessed via a multivariate stepwise Cox regression approach to identify hub RBPs individually associated with HCC patient OS. Prognostic risk score model construction and analysis A prognostic risk score model was constructed using patients in the training cohort (n=172) based upon multivariate stepwise Cox regression model coefficient (β) values for selected hub RBPs. Risk scores for n hub genes were computed as follows: risk score = (β-mRNA1 * expression mRNA1) + (β-mRNA2 * expression mRNA2) + (β-mRNA3 * expressionmRNA3) + (β-mRNA n * expression mRNA n ). The R survival and Survminer packages were used to select the optimal risk score cutoff values [18]. HCC patients were then separated into low- and high-risk groups based upon median risk score values. The OS of patients in these two risk groups was then compared using Kaplan-Meier survival curves and log-rank sum tests with the R survival package. The Survival ROC package was additionally utilized for time-related ROC analyses assessing the value of individual hub RBPs as predictors of patient OS. These analyses were then repeated in the test group of patients. Nomogram construction Nomograms have been used to predict outcomes in patients with a range of cancer types [19]. In order to construct a nomogram in the present study, the multivariate Cox analysis results pertaining to hub RBPs were used to construct line diagrams. Total nomogram scores were then used to predict 1-, 3-, and 5-year OS in HCC patients in both the training and test cohorts. Assessment of the correlation between risk scores and clinical characteristics Logistic regression analyses of the entire TCGA-LIHC cohort were used to analyze the relationship between risk scores and HCC clinical characteristics. These clinical parameters included age, fender, AFP, Hepatitis B or C status, and alcohol consumption. P<0.05 was the significance threshold. Assessment of the independent prognostic relevance of risk scores The independent prognostic relevance of hub RBP risk scores, age, sex, tumor grade, tumor stage, and TNM stage was analyzed through univariate and multivariate Cox regression analysis. TCGA entries with incomplete data were omitted from these analyses. P < 0.05 was the significance threshold. Prognostic RBP validation To analyze the prognostic relevance of identified hub RBPs in HCC patients, we utilized Kaplan-Meier curves. The survival R package was used to compute P-values corresponding to these curves via the log-rank test, with P < 0.05 as the significance threshold. Hub RBP-PCG co-expression network construction A co-expression network of hub RBPs and protein-coding genes (PCGs) was additionally constructed in order to further explore the potential mechanisms whereby hub RBPs influence tumor development. Pearson correlation coefficients between RBP and PCG expression levels were calculated, and when these coefficients were > 0.5 or < -0.5 with a p-value < 0.01, this was indicative of a significant correlation. An RBP-PCG co-expression network was constructed using these values, and GO and KEGG enrichment analyses of PCGs were performed. Results Differentially expressed RBP identification In total, we evaluated the expression of 1542 different RBPs in 374 HCC tumors and 50 normal tissue samples [6]. Of these, we identified 81 differentially expressed RBPs, including 54 and 27 that were upregulated and downregulated, respectively (|log2FC| > 1.0 and P < 0.05) (Figure 1). Functional enrichment analyses GO and KEGG analyses were next used to assess the potential functional roles of up- and down-regulated RBPs in HCC patients. GO analyses revealed upregulated RBPs to be enriched for roles in mRNA metabolic processes, RNA catabolic processes, DNA methylation or demethylation, DNA modification, and mRNA catabolic processes (Figure 2A). In contrast, downregulated RBPs were enriched for roles in RNA catabolic processes, intracellular mRNA localization, translational regulation, 3'−UTR−mediated mRNA destabilization, and RNA phosphodiester bond hydrolysis (Figure 2B). With respect to molecular functions, upregulated RBPs were enriched in mRNA 3'−UTR binding, catalytic activity, acting on RNA, translation regulator activity, poly(U) RNA binding, and poly−pyrimidine tract binding (Figure 2A), whereas downregulated RBPs were enriched in mRNA 3'−UTR AU−rich region binding, AU−rich element binding, mRNA 3'−UTR binding, ribonuclease activity and double−stranded RNA binding (Figure 2B). Upregulated RBPs were additionally enriched in the cytoplasmic ribonucleoprotein granule, ribonucleoprotein granule, cytoplasmic stress granule, telomerase holoenzyme complex, and cytosolic large ribosomal subunit compartments (Figure 2A), while downregulated RBPs were primarily enriched in mRNA cap-binding complex, RNA cap-binding complex, endolysosome membrane, and apical dendrite compartments (Figure 2B). Upregulated RBPs were additionally enriched in the mRNA surveillance pathway, microRNAs in cancer, RNA transport, RNA degradation, DNA replication, and cysteine and methionine metabolism KEGG pathways (Table 1), whereas downregulated RBPs were enriched in the influenza A, mRNA surveillance, and Hepatitis C pathways (Table 1). PPI network construction and analysis We next utilized Cytoscape (3.7.1) to construct a PPI network based on the STRING database. The resultant network incorporated 66 nodes and 127 edges (Figure 3A). Key co-expressed modules within this network were then identified using the MCODE plugin (Figure 3B). Functional enrichment analyses revealed that hub RBPs within this network were enriched in mRNA catabolic processes, RNA catabolic processes, mRNA surveillance pathways, and ribosome pathways. Identification of hub RBPs associated with HCC patient prognosis We next randomly separated 343 total HCC patients in the TCGA-LIHC dataset that had survived for a minimum of 30 days into a training cohort (n = 172) and a test cohort (n = 171). These two patient cohorts were then used to conduct survival analyses, leading us to identify 22 hub RBPs that were associated with patient OS (Figure 4A). A further multivariate Cox regression analysis determined that seven of these hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, NR0B1) were independently associated with HCC patient OS (Figure 4B). Construction and validation of a hub RBP-based prognostic model We next utilized these seven independent prognostic hub RBPs to construct a prognostic risk score model as follows: risk score =0.7291*ExpressionSMG5+0.4424*ExpressionBOP1+0.0610*ExpressionLIN28B+0.0936*ExpressionRNF17+(0.2779)*ExpressionANG+0.6005*ExpressionLARP1B+0.0731*ExpressionNR0B1. Risk scores for each patient in the training set were then calculated, and the Survminer R package was used to calculate the median risk score in this patient cohort. This median value was used to stratify patients into low- and high-risk groups, and survival outcomes between these groups were then compared via Kaplan-Meier survival and time-dependent ROC analyses. This analysis confirmed that the OS of HCC patients in the high-risk group was significantly reduced relative to that of patients in the low-risk group (Figure 5A), with an area under the ROC curve value of 0.801 for this seven RBP risk score model (Figure 5B), consistent with its moderate diagnostic performance. In Figure 5C, mRNA expression levels, survival status, and risk score values for patients in the low- and high-risk groups are shown. We then utilized this same risk score formula to analyze patients in the test cohort (n=171) (Figure 6A-C). Consistent with the above results, HCC patients in the low-risk group exhibited an OS that was significantly longer than that of patients in the high-risk group, with an area under the ROC curve of 0.676. This thus indicates that our prognostic model was able to successfully predict HCC patient survival outcomes. Construction of a hub RBP-based prognostic nomogram A nomogram incorporating the results of the above multivariate Cox regression analysis pertaining to the seven hub RBPs was next constructed and used to predict 1-, 3-, and 5-year HCC patient OS (Figure 7) in our training dataset. This analysis revealed that patient 1-, 3-, and 5-year OS declined as risk scores increased, consistent with our above results, confirming the prognostic value of this risk nomogram. The relationship between risk scores and clinical parameters Logistic regression analyses were used to assess the relationship between risk scores and HCC clinical characteristics, revealing that high risk scores were associated with low histological grade (G3-4 vs G1-2, OR=2.060) and high AFP levels (>20 ng/mL vs <=20 ng/mL, OR=1.986) (P<0.05). In contrast, these scores were unrelated to hepatitis status, vascular invasion, or alcohol intake (Table 2). RBP risk scores independently predict HCC patient prognosis We next conducted univariate Cox analyses or factors associated with prognosis in 226 patients that survived for a minimum of 30 days and for whom complete clinical data were available. These analyses revealed that cancer tissue stage, T stage, and risk scores were all associated with HCC patient OS (P < 0.001) (Figure 8A). Subsequent multivariate Cox analysis confirmed that the RBP risk score was an independent predictor of HCC patient OS, with a hazard ratio (HR) of 1.160 and a 95% confidence interval of 1.095-1.229 (P=4.305E-07)(Figure 8B). Validation of hub RBP prognostic value Lastly, the relationship between identified hub RBPs and HCC patient OS was evaluated using the Kaplan-Meier plotter database. This analysis confirmed that 4/7 hub RBPs (ANG, LIN28B, SMG5, and NR0B1) were significantly associated with HCC patient OS, with respective P-values of 0.017, 0.013, 0.002, and 0.003 (Figure 9A-D). SMG5-PCG co-expression network analysis A correlation analysis of SMG5 and PCGs revealed that there were 3756 total PCGs correlated with SMG5, of which 7 were negatively correlated and 3749 were positively correlated. The top five PCGs positively correlated with SMG5 expression were ISG20L2, DENND4B, UBQLN4, PI4KB, and SLC39A1 (Table 3), while the top five PCGs negatively correlated with SMG5 expression were TTC36, CLEC4M, FCN2, MFSD2, and MT1X (Table 3). GO and KEGG analyses of these SMG5-related PCGs revealed them to be primarily enriched in the mTOR, AMPK, VEGF, and hepatitis B signaling pathways, indicating that they are closely related to tumor development. Discussion While available treatments for HCC have improved significantly in recent years [20], it remains a condition associated with high rates of morbidity and mortality [21]. As such, it is essential that novel diagnostic and prognostic biomarkers of HCC be identified in order to improve patient outcomes. RBP dysregulation has been shown to be a hallmark of many tumor types [8]. In gliomas [10], breast cancer [11], and B-ALL [12], RBPs have been found to be directly related to tumor development and patient prognosis. In the present study, we identified 81 RBPs that were differentially expressed in HCC tissues relative to healthy control tissues in the TCGA-LIHC dataset. We analyzed the biological roles of these RBPs through functional enrichment analyses and by constructing a PPI network, after which we employed Cox regression analyses, survival analyses, and time-dependent ROC analyses of key hub RBPs within this network to construct a prognostic risk model. This model was capable of predicting HCC patient OS based upon the intratumoral expression of seven key RBPs. As such, our results highlight these RBPs as novel prognostic biomarkers of HCC, and additionally identify these genes as potential diagnostic or therapeutic targets. These differentially expressed RBPs were found to be functionally enriched in pathways relating to the regulation of mRNA metabolism, RNA catabolism, DNA methylation or demethylation, DNA modification, translation regulation, mRNA3'-UTR binding, ribonuclease activity, , ribonucleoprotein granule, telomerase holoenzyme complex, and dsRNA binding. It has been reported that human ribosomal protein S3 (RPS3) regulates the expression of silent information regulator 1 (SIRT1) after transcription to promote liver cancer [22]. IGF2 mRNA-binding proteins (IGF2BPs) can specifically bind to the lncRNA HULC (Highly Up-regulated in Liver Cancer) HULC, thereby controlling its expression [23]. Polypyrimidine tract-binding protein 1 (PTBP1) is highly expressed in hepatocellular carcinoma and promotes the translation of cyclin D3 (CCND3) via interacting with the 5'-untranslated region (5'-UTR) of its mRNA, thereby playing a role in the development of hepatocellular carcinoma [24].RBPs are capable of specifically binding to conserved 3’-UTR sequences in target mRNAs, thereby modulating their stability and subsequent translation [25,26]. Appropriate regulation of DNA modification is essential to ensure that chromosomes replicate correctly, and that genes are expressed or silenced in a context-appropriate manner [27]. Promoter or gene body hypermethylation can lead to the inactivation of key tumor suppressor genes, and methylation-based epigenetic silencing of specific genes is a hallmark of many forms of cancer [28]. There are also many studies that show that telomerase plays an important role in the development of liver cirrhosis and liver cancer [29]. Our KEGG pathway analyses further suggested that these dysregulated RBPs may be linked to HCC onset and progression owing to their ability to influence mRNA monitoring pathway, microRNA, RNA transport, RNA degradation, and DNA replication pathway. For example, microRNAs have been shown to play an important role in post-transcriptional regulation of gene expression. Indeed, microRNA dysregulation is thought to be associated with tumor suppressor gene inactivation and oncogene activation in liver cancer [30]. The mRNA monitoring pathway is essential for maintaining homeostasis such that when this regulation is disrupted it can facilitate tumor pathogenesis [31]. As such, these mechansims may explain how differentially expressed RBPs are associated with the development of liver cancer. Through Cox regression analyses, we detected seven key RBPs that were associated with HCC patient prognosis, including SMG5, BOP1, LIN28B, RNF17, ANG, and LARP1B. These seven hub RBPs exhibit telomerase RNA binding, ribonucleoprotein complex binding, DNA binding, ribonuclease, DNA-binding transcription factor, and RNA polymerase II-specific functions [32-34]. They are additionally involved in the regulation of telomere maintenance, the cell cycle, RNA 3'-end processing, cell migration, and in the negative regulation of transcription [33] [35-38]. These genes are closely linked to tumor development. In prior studies, BOP1 has been shown to promote liver cancer development via driving epithelial to mesenchymal transition [39]. Lin28b is a miR-125a target gene that, when downregulated, can inhibit liver cancer cell proliferation [40], NR0B1 (also called DAX-1) can inhibit the proliferation of liver cancer cells by suppressing the transcriptional activity of β-catenin [41]. In HCC patients, plasma samples contain high levels of angiopoietin-1 (Ang-1), and patients with low angiopoietin-2 (Ang-2) levels exhibit better OS [42].We then employed a multivariate stepwise Cox regression analysis to establish a risk model incorporating these seven hub RBPs that can be used to predict HCC patient prognosis. Time-dependent ROC curve analyses revealed that these seven genes offered good diagnostic ability, and that our risk model could be readily used to identify HCC patients with a poor prognosis. However, few studies to date have explored the molecular mechanisms whereby these hub RBPs influence HCC pathogenesis, and as such, further research is essential. We additionally constructed a nomogram capable of predicting the 1-, 3-, and 5-year HCC patient OS. In addition, we utilized Kaplan-Meier curves to assess the prognostic value of these seven hub RBPs, with four of them being found to be associated with patient outcomes. We also constructed a co-expression network of SMG5 and correlated PCGs in order to discover its potential downstream target genes and to explore the possible regulation of RBPs involved in the development of liver cancer. We found that PCGs associated with SMG5 were related to tumorigenesis. Closely-related genes such as UBQLN4 are upregulated in aggressive tumors and promote non-homologous end binding (NHEJ) during DSB repair, resulting in DNA mismatches [43]. HCC patients exhibiting CLEC4M overexpression have a better OS, and CLEC4M overexpression inhibits the proliferation of liver cancer cells and promotes their apoptotic death [44]. KEGG pathway enrichment analyses revealed that these PCGs were enriched in the mTOR, AMPK, and VEGF signaling pathways, all of which are closely linked to cancer development and progression [45-47]. These pathways may thus be one mechanism whereby these RBPs participate in the occurrence and development of liver cancer and other malignancies. As such, these differentially expressed hub RBPs offer clear value in the assessment of HCC patients and may represent viable therapeutic targets. Despite the lack of effective adjuvant therapy for liver cancer, the application of targeted drugs provides a promising opportunity for imporiving the prognosis of those affected by this disease [48]. Conclusions In summary, in the present study we developed a predictive model of HCC patient survival based upon the expression of seven key RBPs within tumor tissues. While this model exhibited significant prognostic value, this study is limited by the fact that it is solely based upon data within the TCGA database and lacks any external validation. In addition, we have not explored the functional roles of these RBPs in the context of HCC, and as such, future in vitro and in vivo analyses will be necessary to confirm and expand upon our findings. In addition, candidate RBPs may provide insight into the regulation of HCC while offering value as prognostic biomarkers. Abbreviations HCC: hepatocellular carcinoma; RBPs: RNA-binding proteins; TCGA-LIHC: the Cancer Genome Atlas - liver HCC; OS: overall survival; PCGs: protein-coding genes; RFA: recurrence-free survival; TACE: transcatheter arterial chemoembolization; RFS: recurrence-free survival; TCGA: The Cancer Genome Atlas; PPI: protein-protein interaction; GO: gene ontology; KEGG: Kyoto gene and genome encyclopedia. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets analysed during the current study are available in the The Cancer Genome Atlas database（ https://portal.gdc.cancer.gov/ ）. Competing interests The authors declare that there are no conflicts of interest. Funding This work was funded by Guangxi colleges and universities young and middle-aged teachers basic ability improvement project, Grant/Award Number: 2018KY0126; The central government guides local science and technology development projects (local professional technology innovation platforms), Grant/Award Number: GUIKE ZY1949017 The funding agency did not have any role in design of the study, data collection, analysis, interpretation of the results and writing of the manuscript. Authors' contributions MW conceived the study and performed the bioinformatics analyses. MW and ZC downloaded and organized the clinical and gene expression data. MW, ZC and ZH performed the statistical analyses. MW and SH wrote the manuscript. KL,CC,GW and YZ critically revised the article for essential intellectual content and administrative support. All authors read and approved the final. Acknowledgements The authors thank you for sharing the data in the Cancer Genome Atlas (TCGA) database. Authors' Information ORCID: Min Wang, https://orcid.org/0000-0002-5474-449x https://orcid.org/0000-0002-5474-449x Shan Huang, https://orcid.org/0000-0001-8581-3649 https://orcid.org/ 0000-0001-8581-3649 Zefeng Chen, https://orcid.org/0000-0002-8446-4878 https://orcid.org/ 0000-0002-8446-4878 Zhiwei Han, https://orcid.org/0000-0002-2669-2354 Kezhi Li, https://orcid.org/0000-0002-4205-4088 Chuang Chen, https://orcid.org/0000-0001-5367-4349 Guobin Wu, https://orcid.org/0000-0003-0505-9637 Yinnong Zhao, https://orcid.org/0000-0002-3080-1912 References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6):394-424. El-Serag HB, Rudolph KL. Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology 2007; 132(7):2557-2576. Bruix J, Reig M, Sherman M. 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Nat Rev Clin Oncol 2018, 15(10):599-616. Tables Table1: analysis of KEGG pathway of aberrantly expressed RBPs term count p-value Up-regulated RBPs mRNA surveillance pathway 5 1.71E-06 MicroRNAs in cancer 5 0.00061399 RNA transport 4 0.00072374 RNA degradation 3 0.000789978 DNA replication 2 0.00317763 Cysteine and methionine metabolism 2 0.005824133 Down-regulated RBPs Influenza A 4 0.000219498 mRNA surveillance pathway 3 0.000578381 Hepatitis C 3 0.002696851 Table 2: The relationship between risk scores and HCC clinical characteristics parameter count (N) Odds ratio in riskScore p-Value age(>60vs20 ng/mLvs<=20 ng/mL) 260 1.986 (1.215 -3.270) 0.006 Hepatitis B or C (Yes vs No) 315 0.670 (0.428 -1.046) 0.079 Alcohol consumption (Yes vs No) 315 1.161 (0.731 -1.846) 0.528 Cirrhosis status (Yes vs No) 199 0.612 (0.334- 1.110) 0.108 Vascular invasion (Yes vs No) 289 1.042(0.642- 1.692) 0.868 Grade (G3-4 vs G1-2) 338 2.060 (1.316 -3.249) 0.002 Stage (Stage III-IV vs Stage I-II) 321 1.649 (0.997 -2.751) 0.053 T (T3-4 vs T1-2) 340 1.541 (0.946- 2.527) 0.084 N (N1 vs N0) 242 2.017 (0.191 -43.740) 0.569 M (M1 vs M0) 248 2.016 (0.191- 43.723) 0.569 Bold values indicate P<0.05. Table 3: The top 10 PCGs correlated with SMG5 expression Correlated PCG Spearman's Correlation p-value ISG20L2 0.8 9.48E-96 DENND4B 0.8 8.02E-96 UBQLN4 0.801 4.57E-96 PI4KB 0.805 1.19E-97 SLC39A1 0.808 5.05E-99 TTC36 -0.563 7.86E-37 CLEC4M -0.534 1.07E-32 FCN2 -0.514 5.75E-30 MFSD2A -0.511 1.53E-29 MT1X -0.508 3.48E-29 Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2020 Read the published version in BMC Cancer → Version 3 posted Editorial decision: Accept 09 Nov, 2020 Editor assigned by journal 07 Nov, 2020 Submission checks completed at journal 07 Nov, 2020 Editor invited by journal 07 Nov, 2020 You are reading this latest preprint version Show more versions 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-40802","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":4540461,"identity":"e602c270-882c-4db0-ac94-907d7c6f7cb6","order_by":0,"name":"Min wang","email":"","orcid":"https://orcid.org/0000-0002-5474-449X","institution":"Guangxi Medical University cancr hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"wang","suffix":""},{"id":4540462,"identity":"ab22accc-d3c1-46a3-a60d-7418796bc89a","order_by":1,"name":"Shan Huang","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Huang","suffix":""},{"id":4540463,"identity":"4d16be2e-5406-422f-afec-8374cf4964af","order_by":2,"name":"Zefeng Chen","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Chen","suffix":""},{"id":4540464,"identity":"0f8f0f3d-5786-4bf5-ae00-50b5c61d1b47","order_by":3,"name":"Zhiwei Han","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Han","suffix":""},{"id":4540465,"identity":"8d8b7fc8-f7bb-473c-a1fb-25d7d299f10a","order_by":4,"name":"Kezhi Li","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kezhi","middleName":"","lastName":"Li","suffix":""},{"id":4540466,"identity":"704e55ab-4fb9-480c-895b-474f5ccf6edb","order_by":5,"name":"Chuang Chen","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Chen","suffix":""},{"id":4540467,"identity":"9f3744bc-8f9d-4c8e-94f0-708134e96801","order_by":6,"name":"Guobin Wu","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guobin","middleName":"","lastName":"Wu","suffix":""},{"id":4540468,"identity":"ed68f8ce-51b2-4cc4-9780-caa957a93129","order_by":7,"name":"Yinnong Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCRBhAGYyPoCKGRCthRmmlBgtEMAGY+PXIj+7+eFjnoI7efzS7dcqv7ZtS2xgb94mwVBzB6cWxjnHjI15DJ4VS845U3Zb5sztxAaeY2USDMee4dTCLJFgJs1jcDhxw42ctNsSFUAtEjlmEowNh3FqYZNI/wbXUixhANQi/wa/Fh6gmVAt6ccYP4Bt4cGvRUIip9hwDlDLzBk5zNIMZ24bt/GkFVskHMOtRX5G+sYHb/4cTuyXSH/48Wfbbdl+9sMbb3yowa0FBJh4IG40YAYx2EDsBLwagAH9A0yxP4AyRsEoGAWjYBSgAgD+8FbYWMBPogAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3080-1912","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yinnong","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2020-07-09 12:32:06","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-40802/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-40802/v3","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-020-07625-3","type":"published","date":"2020-11-23T15:00:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":3577055,"identity":"34393d42-e572-4b8a-9e7e-162dd59b6279","added_by":"auto","created_at":"2020-11-13 19:59:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354843,"visible":true,"origin":"","legend":"Volcano plots and Heat maps of differentially expressed RBPs. (A) Heat map; (B) Volcano plot.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/18ee7cfcd2c07281ebdb997b.jpg"},{"id":3577045,"identity":"292fca6b-7367-4d93-8d0c-3003138ff6f1","added_by":"auto","created_at":"2020-11-13 19:59:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354843,"visible":true,"origin":"","legend":"Volcano plots and Heat maps of differentially expressed RBPs. (A) Heat map; (B) Volcano plot.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/a8a6a78d9a91b60b088a917a.jpg"},{"id":3577056,"identity":"7d50a04d-6bcf-4256-b5af-dc4a0820984e","added_by":"auto","created_at":"2020-11-13 19:59:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241999,"visible":true,"origin":"","legend":"The top 5 significantly enriched GO annotations associated with differentially expressed RBPs. (A) Up-regulated RBPs; (B) down-regulated RBPs. Where CC stands for cellular component, BP for the biological process, and MF for molecular function.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/9edcb93b78beab5ad77394f6.jpg"},{"id":3577046,"identity":"c17703a2-d922-4ff0-8c94-c431d14532ff","added_by":"auto","created_at":"2020-11-13 19:59:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241999,"visible":true,"origin":"","legend":"The top 5 significantly enriched GO annotations associated with differentially expressed RBPs. (A) Up-regulated RBPs; (B) down-regulated RBPs. Where CC stands for cellular component, BP for the biological process, and MF for molecular function.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/fa1905fff04f609698fd5efc.jpg"},{"id":3577057,"identity":"01c99663-341d-4254-aefb-ade3e88bad62","added_by":"auto","created_at":"2020-11-13 19:59:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":384579,"visible":true,"origin":"","legend":"Analysis of modules and network of protein-protein interaction. (A) The network of protein-protein interaction of differentially expressed RBPs; (B) A critical module from the network of PPI. Red circles: \u003e 2-fold up-regulation Green circles: \u003e 2-fold down-regulation","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/8f7b5b220305ff4e3fafd2fe.jpg"},{"id":3577047,"identity":"1a42671c-5c6f-4565-808b-84128ed6f151","added_by":"auto","created_at":"2020-11-13 19:59:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":384579,"visible":true,"origin":"","legend":"Analysis of modules and network of protein-protein interaction. (A) The network of protein-protein interaction of differentially expressed RBPs; (B) A critical module from the network of PPI. Red circles: \u003e 2-fold up-regulation Green circles: \u003e 2-fold down-regulation","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/46be170dee53f0d8b208a0cb.jpg"},{"id":3577058,"identity":"f75db824-ea79-430d-a2bd-a99e4ab91d0e","added_by":"auto","created_at":"2020-11-13 19:59:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":525268,"visible":true,"origin":"","legend":"Forest plot for univariate and multivariate Cox regression analyses of HCC patients. (A) Univariate Cox regression analysis for the hub RBPs identification in the TCGA patient cohort; (B) Multivariate Cox regression analysis for the identification of hub RBPs related to patient prognosis in the training set (n=172)","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/2143a12e3bafe2161f334fd1.jpg"},{"id":3577048,"identity":"480236d2-9049-4718-b8e4-7e63b74a644f","added_by":"auto","created_at":"2020-11-13 19:59:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":525268,"visible":true,"origin":"","legend":"Forest plot for univariate and multivariate Cox regression analyses of HCC patients. (A) Univariate Cox regression analysis for the hub RBPs identification in the TCGA patient cohort; (B) Multivariate Cox regression analysis for the identification of hub RBPs related to patient prognosis in the training set (n=172)","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/d3a0b98997cd50f1f104b47f.jpg"},{"id":3577059,"identity":"25bcbd56-69c2-4f1f-849e-b4a79af2aad7","added_by":"auto","created_at":"2020-11-13 19:59:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":581522,"visible":true,"origin":"","legend":"Risk score analysis of a seven hub RBP-based prognostic model in the training set (n=172). (A) Survival curves for high- and low-risk patient groups; (B) ROC curves used to predict OS on the basis of risk score; (C) Expression survival status, distribution of risk score, and heat map.","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/b90a50791b687350e63ecede.jpg"},{"id":3577049,"identity":"919f34cb-1ec2-4e67-a87d-714bd4a25680","added_by":"auto","created_at":"2020-11-13 19:59:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":581522,"visible":true,"origin":"","legend":"Risk score analysis of a seven hub RBP-based prognostic model in the training set (n=172). (A) Survival curves for high- and low-risk patient groups; (B) ROC curves used to predict OS on the basis of risk score; (C) Expression survival status, distribution of risk score, and heat map.","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/20ceb8d7c721954f0ae96536.jpg"},{"id":3577060,"identity":"7adb16ec-135f-4d1e-930b-85b7c909dcc3","added_by":"auto","created_at":"2020-11-13 19:59:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":591447,"visible":true,"origin":"","legend":"Analysis of risk score of a seven hub RBP-based prognostic model in the testing set (n=171). (A) Survival curves for high- and low-risk patient groups; (B) ROC curves used to predict OS on the basis of risk score; (C) Expression survival status, distribution of risk score, and heat map.","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/7da79b94587362fc26765edd.jpg"},{"id":3577050,"identity":"5c180cbf-f57e-4d90-a798-de367830cc20","added_by":"auto","created_at":"2020-11-13 19:59:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":591447,"visible":true,"origin":"","legend":"Analysis of risk score of a seven hub RBP-based prognostic model in the testing set (n=171). (A) Survival curves for high- and low-risk patient groups; (B) ROC curves used to predict OS on the basis of risk score; (C) Expression survival status, distribution of risk score, and heat map.","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/f2e06f9bd0aa33929d140070.jpg"},{"id":3577061,"identity":"9b614696-acef-4250-af76-e0cf50c24b0e","added_by":"auto","created_at":"2020-11-13 19:59:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":900684,"visible":true,"origin":"","legend":"Nomogram for the prediction of OS in LIHC patients at 1, 3, and 5 years in the training set (n=172)","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/a0cc07f92427ea93d381248d.jpg"},{"id":3577051,"identity":"c48cbdf5-356f-4356-bd82-e76494272ea1","added_by":"auto","created_at":"2020-11-13 19:59:23","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":900684,"visible":true,"origin":"","legend":"Nomogram for the prediction of OS in LIHC patients at 1, 3, and 5 years in the training set (n=172)","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/4573cbf5ab5f410737c8405f.jpg"},{"id":3577062,"identity":"eea53583-f3c0-4f57-b4ce-c205884b8346","added_by":"auto","created_at":"2020-11-13 19:59:30","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":131884,"visible":true,"origin":"","legend":"Univariate and multivariate analyses of the correlation between \nrisk score and OS. (A) Univariate Cox analyses; (B) multivariate Cox analysis.\n","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/6a23f3e0f30716036140e309.jpg"},{"id":3577052,"identity":"991c3f91-089b-4698-9a96-8327f65c31c9","added_by":"auto","created_at":"2020-11-13 19:59:23","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":131884,"visible":true,"origin":"","legend":"Univariate and multivariate analyses of the correlation between \nrisk score and OS. (A) Univariate Cox analyses; (B) multivariate Cox analysis.\n","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/dbea0c57d31612d0f6be8ff4.jpg"},{"id":3577063,"identity":"6bf36f08-ebc4-4002-89c9-cb7f97cabe27","added_by":"auto","created_at":"2020-11-13 19:59:30","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":934885,"visible":true,"origin":"","legend":"Validation of the hub RBPs prognostic value in HCC patients in the TCGA cohort.","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/009700982abfd4b93ce7cb83.jpg"},{"id":3577053,"identity":"5dfc1415-fe1e-46be-8692-c53a992fbce3","added_by":"auto","created_at":"2020-11-13 19:59:23","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":934885,"visible":true,"origin":"","legend":"Validation of the hub RBPs prognostic value in HCC patients in the TCGA cohort.","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/0013f488fa7ce3c76b145ac7.jpg"},{"id":13615020,"identity":"58a9b2b2-3414-4605-998a-b14b0906f37c","added_by":"auto","created_at":"2021-09-17 06:43:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2066185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-40802/v3/380bee7b-de56-4aa0-9cd4-2eadbe24cb5f.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDevelopment and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eLiver cancer is among the most common forms of cancer, and owing to its highly invasive nature it is the fourth leading cancer-related cause of death globally [1]. Hepatocellular carcinoma (HCC) accounts for approximately 80% of all liver cancer cases [2], and it can be difficult to reliably diagnose and treat in its early stages, as its detection is largely dependent upon imaging evaluations and biopsy. HCC treatments generally include hepatectomy, liver transplantation, radiofrequency ablation (RFA), and transcatheter arterial chemoembolization (TACE). As his disease is generally only detected when it is in an advanced stage, HCC patients generally have a poor overall prognosis [3,4]. The identification of novel diagnostic and prognostic biomarkers associated with HCC is thus very important.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRNA binding proteins (RBPs) are a broad class of highly-conserved RNA-interacting proteins, of which roughly 60% are expressed in a tissue-specific manner [5]. Genome-wide screening analyses have detected over 1500 RBPs in the human genome. These proteins are capable of binding to diverse RNA types (including rRNAs, ncRNAs, snRNAs, miRNAs, mRNAs, tRNAs, and snoRNAs), and can serve as key post-transcriptional regulators of gene expression to maintain intracellular homeostasis [6,7]. RBP dysregulation has been shown to be associated with oncogenesis in multiple studies [8]. For example, Lin28 is an oncogenic RBP that has been found to promote the metastatic progression of diverse human cancers [9]. The RBP Musashi1 (Msi1) has been shown to promote glioma progression when its normal interactions with miR-137 are disrupted [10]. PUM2 is an RBP that is overexpressed in breast cancer and to be negatively correlated with OS and a lack of tumor recurrence in these patients [11]. The RBP insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) has similarly been found to be overexpressed in mixed-lineage leukemia\u0026ndash;rearranged (MLL rearranged) B-acute lymphoblastic leukemia (B-ALL) and to be associated with poorer outcomes and higher recurrence risks in these patients [12]. There is also specific evidence linking certain RBPs to liver cancer. For example, Sorbin and SH3 domain containing 2 (RBPSORBS2) expression is reduced in HCC patients and associated with a poor prognosis. This RBP is believed to function via regulating RORA expression to control liver cancer onset and metastasis [13]. RBM3 is an RBP capable of promoting HCC cell proliferation owing to its ability to regulate SCD-CircRNA2 production, with RBM3 overexpression being linked to reduced OS and decreased recurrence-free survival (RFS) in HCC patients[14].\u0026nbsp;While these findings are informative, few studies to date have systematically evaluated RBP expression patterns in liver cancer.\u003c/p\u003e\n\u003cp\u003eIn the present study, we downloaded HCC patient gene expression and clinical data from The Cancer Genome Atlas (TCGA) database, after which we used these data to identify RBPs that were differentially expressed in HCC tumor tissues relative to healthy normal tissues. We further explored the functional roles of these RBPs through protein-protein interaction (PPI) network, gene ontology (GO) enrichment analyses, and Kyoto gene and genome encyclopedia (KEGG) pathway analyses. We also constructed a prognostic model based upon seven key hub RBPs, identifying them as potentially viable diagnostic and prognostic biomarkers of HCC.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData collection\u003c/p\u003e\n\u003cp\u003eWe downloaded level 3 mRNA expression and clinical data from 374 HCC and 50 normal control samples from the TCGA \u0026ndash; liver HCC dataset (TCGA-LIHC)(https://portal.gdc.cancer.gov/).\u003c/p\u003e\n\u003cp\u003eDifferentially expressed RBP identification\u003c/p\u003e\n\u003cp\u003eAppropriate R packages were used to standardize data by excluding genes with an average count \u0026lt; 1. Differentially expressed RBPs were then identified using R (v3.6.0) with the following criteria: | log2FC |\u0026nbsp;\u0026ge;\u0026nbsp;1 and FDR \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analyses\u003c/p\u003e\n\u003cp\u003eIn order to explore the functional roles of these differentially expressed RBPs, they were next separated into those that were upregulated and downregulated in HCC. The clusterProfiler R package [15] was then used to conduct GO and KEGG pathway enrichment\u0026nbsp;[16] analyses on these two groups of RBPs, with P \u0026lt; 0.05 and FDR \u0026lt; 0.05 being used as significance thresholds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPI network construction and analysis\u003c/p\u003e\n\u003cp\u003eThe STRING database (http://www.string-db.org/) [17] was used to assess interactions between proteins related to these differentially expressed RBPs, with Cytoscape v3.7.1 being used to construct a PPI network. The MCODE plugin was then used to identify key modules and hub genes within this network based on the following criteria: degree cutoff=5, node score cutoff=0.2, k-core=5, max.depth=100 truncation standard, and P \u0026lt; 0.05 was the significance threshold.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvaluation of hub gene prognostic relevance\u003c/p\u003e\n\u003cp\u003eFollow-up analyses incorporated all HCC patients surviving for at least 30 days. Hub RBPs associated with patient prognosis were identified through univariate Cox regression analyses, with patients being randomly separated into training and test cohorts. RBPs identified in these initial analyses were then assessed via a multivariate stepwise Cox regression approach to identify hub RBPs individually associated with HCC patient OS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrognostic risk score model construction and analysis\u003c/p\u003e\n\u003cp\u003eA prognostic risk score model was constructed using patients in the training cohort (n=172) based upon multivariate stepwise Cox regression model coefficient (\u0026beta;) values for selected hub RBPs. Risk scores for \u003cem\u003en\u0026nbsp;\u003c/em\u003ehub genes were computed as follows: \u0026nbsp;risk score = (\u0026beta;-mRNA1 * expression mRNA1) + (\u0026beta;-mRNA2 * expression mRNA2) + (\u0026beta;-mRNA3 * expressionmRNA3) + (\u0026beta;-mRNA\u003cem\u003en\u003c/em\u003e * expression mRNA\u003cem\u003en\u003c/em\u003e). The R \u003cem\u003esurvival\u003c/em\u003e and \u003cem\u003eSurvminer\u003c/em\u003e packages were used to select the optimal risk score cutoff values [18]. HCC patients were then separated into low- and high-risk groups based upon median risk score values. The OS of patients in these two risk groups was then compared using Kaplan-Meier survival curves and log-rank sum tests with the R \u003cem\u003esurvival\u003c/em\u003e package. The \u003cem\u003eSurvival ROC\u003c/em\u003e package was additionally utilized for time-related ROC analyses assessing the value of individual hub RBPs as predictors of patient OS. These analyses were then repeated in the test group of patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNomogram construction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNomograms have been used to predict outcomes in patients with a range of cancer types [19]. In order to construct a nomogram in the present study, the multivariate Cox analysis results pertaining to hub RBPs were used to construct line diagrams. Total nomogram scores were then used to predict 1-, 3-, and 5-year OS in HCC patients in both the training and test cohorts.\u003c/p\u003e\n\u003cp\u003eAssessment of the correlation between risk scores and clinical characteristics\u003c/p\u003e\n\u003cp\u003eLogistic regression analyses of the entire TCGA-LIHC cohort were used to analyze the relationship between risk scores and HCC clinical characteristics. These clinical parameters included age, fender, AFP, Hepatitis B or C status, and alcohol consumption. P\u0026lt;0.05 was the significance threshold.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssessment of the independent prognostic relevance of risk scores\u003c/p\u003e\n\u003cp\u003eThe independent prognostic relevance of hub RBP risk scores, age, sex, tumor grade, tumor stage, and TNM stage was analyzed through univariate and multivariate Cox regression analysis. TCGA entries with incomplete data were omitted from these analyses. P \u0026lt; 0.05 was the significance threshold.\u003c/p\u003e\n\u003cp\u003ePrognostic RBP validation\u003c/p\u003e\n\u003cp\u003eTo analyze the prognostic relevance of identified hub RBPs in HCC patients, we utilized Kaplan-Meier curves. The \u003cem\u003esurvival\u0026nbsp;\u003c/em\u003eR package was used to compute P-values corresponding to these curves via the log-rank test, with P \u0026lt; 0.05 as the significance threshold.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHub RBP-PCG co-expression network construction\u003c/p\u003e\n\u003cp\u003eA co-expression network of hub RBPs and protein-coding genes (PCGs) was additionally constructed in order to further explore the potential mechanisms whereby hub RBPs influence tumor development. Pearson correlation coefficients between RBP and PCG expression levels were calculated, and when these coefficients were \u0026gt; 0.5 or \u0026lt; -0.5 with a p-value \u0026lt; 0.01, this was indicative of a significant correlation. An RBP-PCG co-expression network was constructed using these values, and GO and KEGG enrichment analyses of PCGs were performed.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDifferentially expressed RBP identification\u003c/p\u003e\n\u003cp\u003eIn total, we evaluated the expression of 1542 different RBPs in 374 HCC tumors and 50 normal tissue samples [6]. Of these, we identified 81 differentially expressed RBPs, including 54 and 27 that were upregulated and downregulated, respectively (|log2FC| \u0026gt; 1.0 and P \u0026lt; 0.05) (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analyses\u003c/p\u003e\n\u003cp\u003eGO and KEGG analyses were next used to assess the potential functional roles of up- and down-regulated RBPs in HCC patients. GO analyses revealed upregulated RBPs to be\u0026nbsp;enriched for roles in mRNA metabolic processes, RNA catabolic processes, DNA methylation or demethylation, DNA modification, and mRNA catabolic processes (Figure 2A).\u0026nbsp;In contrast, downregulated RBPs were enriched for roles in RNA catabolic processes, intracellular mRNA localization, translational regulation, 3\u0026apos;\u0026minus;UTR\u0026minus;mediated mRNA destabilization, and RNA phosphodiester bond hydrolysis (Figure 2B). With respect to molecular functions, upregulated RBPs were enriched in mRNA 3\u0026apos;\u0026minus;UTR binding, catalytic activity, acting on RNA, translation regulator activity, poly(U) RNA binding, and poly\u0026minus;pyrimidine tract binding (Figure 2A), whereas downregulated RBPs were enriched in mRNA 3\u0026apos;\u0026minus;UTR AU\u0026minus;rich region binding, AU\u0026minus;rich element binding, mRNA 3\u0026apos;\u0026minus;UTR binding, ribonuclease activity and double\u0026minus;stranded RNA binding (Figure 2B).\u0026nbsp;Upregulated RBPs were additionally enriched in the cytoplasmic ribonucleoprotein granule, ribonucleoprotein granule, cytoplasmic stress granule, telomerase holoenzyme complex, and cytosolic large ribosomal subunit compartments (Figure 2A),\u0026nbsp;while downregulated RBPs were primarily enriched in mRNA cap-binding complex, RNA cap-binding complex, endolysosome membrane, and apical dendrite compartments (Figure 2B). Upregulated RBPs were additionally enriched in the mRNA surveillance pathway, microRNAs in cancer, RNA transport, RNA degradation, DNA replication, and cysteine and methionine metabolism KEGG pathways (Table 1), whereas downregulated RBPs were enriched in the influenza A, mRNA surveillance, and Hepatitis C pathways (Table 1).\u003c/p\u003e\n\u003cp\u003ePPI network construction and analysis\u003c/p\u003e\n\u003cp\u003eWe next utilized Cytoscape (3.7.1) to construct a PPI network based on the STRING database. The resultant network incorporated 66 nodes and 127 edges (Figure 3A). Key co-expressed modules within this network were then identified using the MCODE plugin (Figure 3B). Functional enrichment analyses revealed that hub RBPs within this network were enriched in mRNA catabolic processes, RNA catabolic processes, mRNA surveillance pathways, and ribosome pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIdentification of hub RBPs associated with HCC patient prognosis\u003c/p\u003e\n\u003cp\u003eWe next randomly separated 343 total HCC patients in the TCGA-LIHC dataset that had survived for a minimum of 30 days into a training cohort (n = 172) and a test cohort (n = 171). These two patient cohorts were then used to conduct survival analyses, leading us to identify 22 hub RBPs that were associated with patient OS (Figure 4A). A further multivariate Cox regression analysis determined that seven of these hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, NR0B1) were independently associated with HCC patient OS (Figure 4B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConstruction and validation of a hub RBP-based prognostic model\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe next utilized these seven independent prognostic hub RBPs to construct a prognostic risk score model as follows: risk score =0.7291*ExpressionSMG5+0.4424*ExpressionBOP1+0.0610*ExpressionLIN28B+0.0936*ExpressionRNF17+(0.2779)*ExpressionANG+0.6005*ExpressionLARP1B+0.0731*ExpressionNR0B1. Risk scores for each patient in the training set were then calculated, and the \u003cem\u003eSurvminer\u0026nbsp;\u003c/em\u003eR package was used to calculate the median risk score in this patient cohort. This median value was used to stratify patients into low- and high-risk groups, and survival outcomes between these groups were then compared via Kaplan-Meier survival and time-dependent ROC analyses. This analysis confirmed that the OS of HCC patients in the high-risk group was significantly reduced relative to that of patients in the low-risk group (Figure 5A), with an area under the ROC curve value of 0.801 for this seven RBP risk score model (Figure 5B), consistent with its moderate diagnostic performance. In Figure 5C, mRNA expression levels, survival status, and risk score values for patients in the low- and high-risk groups are shown. We then utilized this same risk score formula to analyze patients in the test cohort (n=171) (Figure 6A-C). Consistent with the above results, HCC patients in the low-risk group exhibited an OS that was significantly longer than that of patients in the high-risk group, with an area under the ROC curve of 0.676. This thus indicates that our prognostic model was able to successfully predict HCC patient survival outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConstruction of a hub RBP-based prognostic nomogram\u003c/p\u003e\n\u003cp\u003eA nomogram incorporating the results of the above multivariate Cox regression analysis pertaining to the seven hub RBPs was next constructed and used to predict 1-, 3-, and 5-year HCC patient OS (Figure 7) in our training dataset. This analysis revealed that patient 1-, 3-, and 5-year OS declined as risk scores increased, consistent with our above results, confirming the prognostic value of this risk nomogram.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relationship between risk scores and clinical parameters\u003c/p\u003e\n\u003cp\u003eLogistic regression analyses were used to assess the relationship between risk scores and HCC clinical characteristics, revealing that high risk scores were associated with low histological grade (G3-4 vs G1-2, OR=2.060) and high AFP levels (\u0026gt;20 ng/mL vs \u0026lt;=20 ng/mL, OR=1.986) (P\u0026lt;0.05). In contrast, these scores were unrelated to hepatitis status, vascular invasion, or alcohol intake (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRBP risk scores independently predict HCC patient prognosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe next conducted univariate Cox analyses or factors associated with prognosis in 226 patients that survived for a minimum of 30 days and for whom complete clinical data were available. These analyses revealed that cancer tissue stage, T stage, and risk scores were all associated with HCC patient OS (P \u0026lt; 0.001) (Figure 8A). Subsequent multivariate Cox analysis confirmed that the RBP risk score was an independent predictor of HCC patient OS, with a hazard ratio (HR) of 1.160 and a 95% confidence interval of 1.095-1.229 (P=4.305E-07)(Figure 8B).\u003c/p\u003e\n\u003cp\u003eValidation of hub RBP prognostic value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLastly, the relationship between identified hub RBPs and HCC patient OS was evaluated using the Kaplan-Meier plotter database. This analysis confirmed that 4/7 hub RBPs (ANG, LIN28B, SMG5, and NR0B1) were significantly associated with HCC patient OS, with respective P-values of 0.017, 0.013, 0.002, and 0.003 (Figure 9A-D).\u003c/p\u003e\n\u003cp\u003eSMG5-PCG co-expression network analysis\u003c/p\u003e\n\u003cp\u003eA correlation analysis of SMG5 and PCGs revealed that there were 3756 total PCGs correlated with SMG5, of which 7 were negatively correlated and 3749 were positively correlated. The top five PCGs positively correlated with SMG5 expression were ISG20L2, DENND4B, UBQLN4, PI4KB, and SLC39A1 (Table 3), while the top five PCGs negatively correlated with SMG5 expression were TTC36, CLEC4M, FCN2, MFSD2, and MT1X (Table 3). GO and KEGG analyses of these SMG5-related PCGs revealed them to be primarily enriched in the mTOR, AMPK, VEGF, and hepatitis B signaling pathways, indicating that they are closely related to tumor development.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile available treatments for HCC have improved significantly in recent years [20], it remains a condition associated with high rates of morbidity and mortality [21]. As such, it is essential that novel diagnostic and prognostic biomarkers of HCC be identified in order to improve patient outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRBP dysregulation has been shown to be a hallmark of many tumor types [8]. In gliomas [10], breast cancer [11], and B-ALL [12], RBPs have been found to be directly related to tumor development and patient prognosis. In the present study, we identified 81 RBPs that were differentially expressed in HCC tissues relative to healthy control tissues in the TCGA-LIHC dataset. We analyzed the biological roles of these RBPs through functional enrichment analyses and by constructing a PPI network, after which we employed Cox regression analyses, survival analyses, and time-dependent ROC analyses of key hub RBPs within this network to construct a prognostic risk model. This model was capable of predicting HCC patient OS based upon the intratumoral expression of seven key RBPs. As such, our results highlight these RBPs as novel prognostic biomarkers of HCC, and additionally identify these genes as potential diagnostic or therapeutic targets. These differentially expressed RBPs were found to be functionally enriched in pathways relating to the regulation of mRNA metabolism, RNA catabolism, DNA methylation or demethylation, DNA modification, translation regulation, mRNA3\u0026apos;-UTR binding, ribonuclease activity, , ribonucleoprotein granule, telomerase holoenzyme complex, and dsRNA binding. It has been reported that human ribosomal protein S3 (RPS3) regulates the expression of silent information regulator 1 (SIRT1) after transcription to promote liver cancer [22]. IGF2 mRNA-binding proteins (IGF2BPs) can specifically bind to the lncRNA HULC (Highly Up-regulated in Liver Cancer) HULC, thereby controlling its expression [23]. Polypyrimidine tract-binding protein 1 (PTBP1) is highly expressed in hepatocellular carcinoma and promotes the translation of cyclin D3 (CCND3) via interacting with the 5\u0026apos;-untranslated region (5\u0026apos;-UTR) of its mRNA, thereby playing a role in the development of hepatocellular carcinoma [24].RBPs are capable of specifically binding to conserved 3\u0026rsquo;-UTR sequences in target mRNAs, thereby modulating their stability and subsequent translation [25,26]. Appropriate regulation of DNA modification is essential to ensure that chromosomes replicate correctly, and that genes are expressed or silenced in a context-appropriate manner [27]. Promoter or gene body hypermethylation can lead to the inactivation of key tumor suppressor genes, and methylation-based epigenetic silencing of specific genes is a hallmark of many forms of cancer [28].\u0026nbsp;There are also many studies that show that telomerase plays an important role in the development of liver cirrhosis and liver cancer [29].\u0026nbsp;Our KEGG pathway analyses further suggested that these dysregulated RBPs may be linked to HCC onset and progression owing to their ability to influence mRNA monitoring pathway, microRNA, RNA transport, RNA degradation, and DNA replication pathway. For example, microRNAs have been shown to play an important role in post-transcriptional regulation of gene expression. Indeed, microRNA dysregulation is thought to be associated with tumor suppressor gene inactivation and oncogene activation in liver cancer [30]. The mRNA monitoring pathway is essential for maintaining homeostasis such that when this regulation is disrupted it can facilitate tumor pathogenesis [31]. As such, these mechansims may explain how differentially expressed RBPs are associated with the development of liver cancer.\u003c/p\u003e\n\u003cp\u003eThrough Cox regression analyses, we detected seven key RBPs that were associated with HCC patient prognosis, including SMG5, BOP1, LIN28B, RNF17, ANG, and LARP1B. These seven hub RBPs exhibit telomerase RNA binding, ribonucleoprotein complex binding, DNA binding, ribonuclease, DNA-binding transcription factor, and RNA polymerase II-specific functions [32-34]. They are additionally involved in the regulation of telomere maintenance, the cell cycle, RNA 3\u0026apos;-end processing, cell migration, and in the negative regulation of transcription [33] [35-38]. These genes are closely linked to tumor development. In prior studies, BOP1 has been shown to promote liver cancer development via driving epithelial to mesenchymal transition [39]. Lin28b is a miR-125a target gene that, when downregulated, can inhibit liver cancer cell proliferation [40], NR0B1 (also called DAX-1) can inhibit the proliferation of liver cancer cells by suppressing the transcriptional activity of \u0026beta;-catenin [41]. In HCC patients, plasma samples contain high levels of angiopoietin-1 (Ang-1), and patients with low angiopoietin-2 (Ang-2) levels exhibit better OS [42].We then employed a multivariate stepwise Cox regression analysis to establish a risk model incorporating these seven hub RBPs that can be used to predict HCC patient prognosis. Time-dependent ROC curve analyses revealed that these seven genes offered good diagnostic ability, and that our risk model could be readily used to identify HCC patients with a poor prognosis. However, few studies to date have explored the molecular mechanisms whereby these hub RBPs influence HCC pathogenesis, and as such, further research is essential. We additionally constructed a nomogram capable of predicting the 1-, 3-, and 5-year HCC patient OS. In addition, we utilized Kaplan-Meier curves to assess the prognostic value of these seven hub RBPs, with four of them being found to be associated with patient outcomes. We also constructed a co-expression network of SMG5 and correlated PCGs in order to discover its potential downstream target genes and to explore the possible regulation of RBPs involved in the development of liver cancer. We found that PCGs associated with SMG5 were related to tumorigenesis. Closely-related genes such as UBQLN4 are upregulated in aggressive tumors and promote non-homologous end binding (NHEJ) during DSB repair, resulting in DNA mismatches [43]. HCC patients exhibiting CLEC4M overexpression have a better OS, and CLEC4M overexpression inhibits the proliferation of liver cancer cells and promotes their apoptotic death [44]. KEGG pathway enrichment analyses revealed that these PCGs were enriched in the mTOR, AMPK, and VEGF signaling pathways, all of which are closely linked to cancer development and progression [45-47]. These pathways may thus be one mechanism whereby these RBPs participate in the occurrence and development of liver cancer and other malignancies. As such, these differentially expressed hub RBPs offer clear value in the assessment of HCC patients and may represent viable therapeutic targets. Despite the lack of effective adjuvant therapy for liver cancer, the application of targeted drugs provides a promising opportunity for imporiving the prognosis of those affected by this disease [48].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, in the present study we developed a predictive model of HCC patient survival based upon the expression of seven key RBPs within tumor tissues. While this model exhibited significant prognostic value, this study is limited by the fact that it is solely based upon data within the TCGA database and lacks any external validation. In addition, we have not explored the functional roles of these RBPs in the context of HCC, and as such, future \u003cem\u003ein vitro \u003c/em\u003eand \u003cem\u003ein vivo \u003c/em\u003eanalyses will be necessary to confirm and expand upon our findings. In addition, candidate RBPs may provide insight into the regulation of HCC while offering value as prognostic biomarkers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHCC: hepatocellular carcinoma; RBPs: RNA-binding proteins; TCGA-LIHC: the Cancer Genome Atlas - liver HCC; OS: overall survival; PCGs: protein-coding genes; RFA: recurrence-free survival; TACE: transcatheter arterial chemoembolization; RFS: recurrence-free survival; TCGA: The Cancer Genome Atlas; PPI: protein-protein interaction; GO: gene ontology; KEGG: Kyoto gene and genome encyclopedia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in the The Cancer Genome Atlas database（\u003ca href=\"https://portal.gdc.cancer.gov/\"\u003ehttps://portal.gdc.cancer.gov/\u003c/a\u003e）.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was funded by Guangxi colleges and universities young and middle-aged teachers basic ability improvement project, Grant/Award Number: 2018KY0126; The central government guides local science and technology development projects (local professional technology innovation platforms), Grant/Award Number: GUIKE ZY1949017\u003c/p\u003e\n\u003cp\u003eThe funding agency did not have any role in design of the study, data collection, analysis, interpretation of the results and writing of the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eMW conceived the study and performed the bioinformatics analyses. MW and ZC downloaded and organized the clinical and gene expression data. MW, ZC and ZH performed the statistical analyses. MW and SH wrote the manuscript. KL,CC,GW and YZ critically revised the article for essential intellectual content and administrative support. All authors read and approved the final.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank you for sharing the data in the Cancer Genome Atlas (TCGA) database.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; Information\u003c/p\u003e\n\u003cp\u003eORCID:\u003c/p\u003e\n\u003cp\u003eMin Wang,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0002-5474-449x\"\u003ehttps://orcid.org/0000-0002-5474-449x\u003c/a\u003e\u003cu\u003ehttps://orcid.org/0000-0002-5474-449x\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eShan Huang,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0001-8581-3649\"\u003ehttps://orcid.org/0000-0001-8581-3649\u003c/a\u003e\u003cu\u003ehttps://orcid.org/\u003c/u\u003e\u003cu\u003e0000-0001-8581-3649\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eZefeng Chen,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0002-8446-4878\"\u003ehttps://orcid.org/0000-0002-8446-4878\u003c/a\u003e\u003cu\u003ehttps://orcid.org/\u003c/u\u003e\u003cu\u003e0000-0002-8446-4878\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eZhiwei Han,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0002-2669-2354\"\u003ehttps://orcid.org/0000-0002-2669-2354\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eKezhi Li,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0002-4205-4088\"\u003ehttps://orcid.org/0000-0002-4205-4088\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eChuang Chen,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0001-5367-4349\"\u003ehttps://orcid.org/0000-0001-5367-4349\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGuobin Wu,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0003-0505-9637\"\u003ehttps://orcid.org/0000-0003-0505-9637\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYinnong Zhao,\u0026nbsp;\u003ca href=\"https://orcid.org/0000-0002-3080-1912\"\u003ehttps://orcid.org/0000-0002-3080-1912\u003c/a\u003e\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 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The Journal of cell biology 2005, 170(3):367-378.\u003c/li\u003e\n\u003cli\u003eHeo I, Joo C, Kim YK, Ha M, Yoon MJ, Cho J, Yeom KH, Han J, Kim VN: TUT4 in concert with Lin28 suppresses microRNA biogenesis through pre-microRNA uridylation. Cell 2009, 138(4):696-708.\u003c/li\u003e\n\u003cli\u003eKim HM, Kang DK, Kim HY, Kang SS, Chang SI: Angiogenin-induced protein kinase B/Akt activation is necessary for angiogenesis but is independent of nuclear translocation of angiogenin in HUVE cells. Biochem Biophys Res Commun 2007, 352(2):509-513.\u003c/li\u003e\n\u003cli\u003eNedumaran B, Hong S, Xie YB, Kim YH, Seo WY, Lee MW, Lee CH, Koo SH, Choi HS: DAX-1 acts as a novel corepressor of orphan nuclear receptor HNF4alpha and negatively regulates gluconeogenic enzyme gene expression. 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Cell Physiol Biochem 2014, 34(3):734-742.\u003c/li\u003e\n\u003cli\u003ePestana RC, Hassan MM, Abdel-Wahab R, Abugabal YI, Girard LM, Li D, Chang P, Raghav K, Morris J, Wolff RA et al: Clinical and prognostic significance of circulating levels of angiopoietin-1 and angiopoietin-2 in hepatocellular carcinoma. Oncotarget 2018, 9(102):37721-37732.\u003c/li\u003e\n\u003cli\u003eJachimowicz RD, Beleggia F, Isensee J, Velpula BB, Goergens J, Bustos MA, Doll MA, Shenoy A, Checa-Rodriguez C, Wiederstein JL et al: UBQLN4 Represses Homologous Recombination and Is Overexpressed in Aggressive Tumors. Cell 2019, 176(3):505-519.e522.\u003c/li\u003e\n\u003cli\u003eYu Q, Gao K: CLEC4M overexpression inhibits progression and is associated with a favorable prognosis in hepatocellular carcinoma. Molecular medicine reports 2020, 22(3):2245-2252.\u003c/li\u003e\n\u003cli\u003eMossmann D, Park S, Hall MN: mTOR signalling and cellular metabolism are mutual determinants in cancer. 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Nat Rev Clin Oncol 2018, 15(10):599-616.\u003c/li\u003e\n\u003c/ol\u003e\n"},{"header":"Tables","content":"\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:center;text-indent:0in;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;'\u003eTable1: analysis of KEGG pathway of aberrantly expressed RBPs\u003c/p\u003e\n\u003ctable style=\"border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120.5pt;border-top: 1.5pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border-top: 1.5pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eterm\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border-top: 1.5pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003ecount\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border-top: 1.5pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003ep-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003eUp-regulated RBPs\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003emRNA surveillance pathway\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e1.71E-06\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eMicroRNAs in cancer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.00061399\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eRNA transport\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.00072374\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eRNA degradation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.000789978\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eDNA replication\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.00317763\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eCysteine and methionine metabolism\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.005824133\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003eDown-regulated RBPs\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:177.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:42.25pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:74.85pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eInfluenza A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.000219498\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003emRNA surveillance pathway\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.000578381\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;border-bottom:solid windowtext 1.5pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1.5pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eHepatitis C\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.25pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1.5pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.85pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1.5pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e0.002696851\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:justify;text-indent:24.0pt;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:center;text-indent:24.0pt;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;background:white;\"\u003eTable 2: The relationship between risk scores and HCC clinical characteristics\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width: 4.4e+2pt;border: none;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:41.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family: \"Times New Roman\",serif;color:black;'\u003eparameter\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:60.9pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:41.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003ecount (N)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:137.1pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:41.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003eOdds ratio in riskScore\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:50.1pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:41.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003ep-Value \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003eage(\u0026gt;60vs\u0026lt;=60)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e343\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e1.279 (0.837- 1.958)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.256\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 190.45pt;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eSex (male vs female)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e343\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e1.164 (0.740 -1.836)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.511\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 190.45pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eAFP(\u0026gt;20\u003c/span\u003e \u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eng/mLvs\u0026lt;=20\u003c/span\u003e \u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eng/mL)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e260\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e1.986 (1.215 -3.270)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.006\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eHepatitis B or C (Yes vs No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e315\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.670 (0.428 -1.046)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.079\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eAlcohol consumption (Yes vs No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e315\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e1.161 (0.731 -1.846)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.528\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eCirrhosis status (Yes vs No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e199\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.612 (0.334- 1.110)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.108\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eVascular invasion (Yes vs No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e289\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;1.042(0.642- 1.692)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.868\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eGrade (G3-4 vs G1-2)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e338\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e2.060 (1.316 -3.249)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.002\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eStage (Stage III-IV vs Stage I-II)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e321\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e1.649 (0.997 -2.751)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.053\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:14.45pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eT (T3-4 vs T1-2)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e340\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;1.541 (0.946- 2.527)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.084\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;padding:0in 5.4pt 0in 5.4pt;height:13.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eN (N1 vs N0)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e242\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;2.017 (0.191 -43.740)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;padding: 0in 5.4pt;height: 13.9pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.569\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:190.45pt;border:none;border-bottom:solid windowtext 1.5pt;padding:0in 5.4pt 0in 5.4pt;height:14.45pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eM (M1 vs M0)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.9pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1.5pt solid windowtext;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e248\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137.1pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1.5pt solid windowtext;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;2.016 (0.191- 43.723)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.1pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1.5pt solid windowtext;padding: 0in 5.4pt;height: 14.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.569\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:justify;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;color:black;background:white;'\u003eBold values indicate P\u0026lt;0.05.\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:justify;line-height:150%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;color:black;background:white;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:justify;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:24.0pt;'\u003e\u003cspan style='font-size:16px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003eTable 3: The top 10 PCGs correlated with SMG5 expression\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width:390.9pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003eCorrelated\u0026nbsp;\u003c/span\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003ePCG\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:148.85pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Spearman\u0026apos;s Correlation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:121.55pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003ep-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eISG20L2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:148.85pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:5.0pt;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:121.55pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e9.48E-96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eDENND4B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:148.85pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:121.55pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e8.02E-96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003eUBQLN4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:148.85pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.801\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:121.55pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e4.57E-96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003ePI4KB\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:148.85pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.805\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:121.55pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e1.19E-97\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003eSLC39A1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:148.85pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.808\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:121.55pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:right;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height: 107%;font-family:\"Times New Roman\",serif;color:black;'\u003e5.05E-99\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:120.5pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:left;line-height:107%;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;line-height:107%;font-family:\"Times New Roman\",serif;color:black;'\u003eTTC36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:148.85pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp 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Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"RNA binding protein, hepatocellular carcinoma, prognosis, comprehensive bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-40802/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-40802/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Finally, find protein-coding genes (PCGs) related to hub RBPs were used to construct a hub RBP-PCG co-expression network.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-11-13 19:59:21","doi":"10.21203/rs.3.rs-40802/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2020-11-10T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-11-08T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-11-07T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-11-07T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2020-09-22 15:47:47","doi":"10.21203/rs.3.rs-40802/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2020-10-05T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-10-05T12:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable pending editorial decision\n"},{"type":"reviewersInvited","content":"","date":"2020-09-29T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-09-18T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-09-17T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-09-17T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-07-10 14:57:10","doi":"10.21203/rs.3.rs-40802/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-09-07T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-09-06T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nI read with great interest the study by Wang et al. They have explored upon the role of RBPs in HCC prognostication. They have been able to identify certain RBPs associated with survival. This study generates interest as to how RBPs might be put into clinical practice both in terms of treatment and HCC prognostication. I have few questions from authors.\n1) What was the association between RBPs and poor prognostic markers we often use in the clinical settings. It would be interesting to know if BAD RBPs were associated with increased AFP. What was the relation between RBPs and poor grade, microvascular invasion, tumor size etc.\n2) Is it possible that we can have good long term outcomes in patients with high AFP/poor grade etc based on expression of absence of certain RBPs.\n3) Authors have not included any of the treatments in the multivariate analysis. It would be interesting to see if expression of certain RBPs within the same treatment group or AFP cutoffs yielded different outcomes.\n\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I have no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please publish my name with my report.**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-08-27T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-08-24T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nThis work by Wang et al set out to identify RBP signature that can reliably be used to predict HCC patient OS. They relied on the data from TCGA and performed a series of statistical and clinically oriented analyses to resolve the prognostic relevance of these genes. A panel of seven RBPs was then shown to exhibit good prognostic performance. This is overall a well-written and clearly-designed study, uncovering potentially important biomarker with prognostic value.\n\nSome concerns and suggestions:\n1. The biological relevance of these marker genes should be elaborated in the discussion. This will help provide some mechanistic insights into how these altered RBP might impact cancer progression and outcome.\n2. Since RBPs are linked to gene/transcript expression control, is it possible to decipher the gene network downstream of the RBPs? To this end, the authors could construct co-expression network in association with at least one of the candidate RBPs. This information (identity of the co-expressed genes) would strengthen the functional relevance of RBP in tumor biology. \n\n\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-08-03T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorAssigned","content":"","date":"2020-07-20T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-07-20T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-07-09T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-07-09T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-07-02T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8979509a-ca99-4ac7-baf4-455a14d6bf03","owner":[],"postedDate":"November 13th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":1075191,"name":"Cancer Biology"},{"id":1075192,"name":"Oncology"}],"tags":[],"updatedAt":"2020-11-29T15:03:12+00:00","versionOfRecord":{"articleIdentity":"rs-40802","link":"https://doi.org/10.1186/s12885-020-07625-3","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2020-11-23 15:00:55","publishedOnDateReadable":"November 23rd, 2020"},"versionCreatedAt":"2020-11-13 19:59:21","video":"","vorDoi":"10.1186/s12885-020-07625-3","vorDoiUrl":"https://doi.org/10.1186/s12885-020-07625-3","workflowStages":[]},"version":"v3","identity":"rs-40802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-40802","identity":"rs-40802","version":["v3"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}