LncRNA-mediated High Expression of TRIP13 Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma

LncRNA-mediated High Expression of TRIP13 Correlates with Poor Prognosis and Tumor Immune Infiltration of 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 Article LncRNA-mediated High Expression of TRIP13 Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma Ya-wen Cao, Jiaxin Ying, Yong-qing Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5276784/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver cancers and is charactered by poor prognosis. The identification of potential prognostic biomarkers is essential for HCC patients. Methods: In this study, Gene Expression Profiling Interative Analysis (GEPIA) online tool was used to the different expression of TRIP13 and has-miR-29c-3p axis between tumor samples and normal samples in multiple cancers including hepatocellular carcinoma (HCC). The oncological value of TRIP13 and has-miR-29c-3p axis in cancer was revealed by Kaplan-Meier analysis. The upstream miRNAs of TRIP13 were predicted by the starBase database and Cytoscape software. Results: In this work, we identified that compared to normal tissues, the expression of TRIP13 was high in tumor samples by The Cancer Genome Atlas (TCGA) and Genotypic-Tissue Expression (GTEx) data. We further revealed that TRIP13 may be a potential oncogene in HCC. Subsequently, hsa-miR-29c-3p was responsible for TRIP13 overexpression, which was identified by expression analysis, correlation analysis and survival analysis of the target gene. Further, hsa-miR-29c-3p was associated with the poor prognosis of cancer patients. Moreover, the expression level of TRIP13 was positively correlated with tumor immune cell infiltration with high infiltrating level of B cells, CD4 + T cells, macrophages, exhausted CD8 + T cells and immune checkpoints including PD1, PD-L1 and CTLA-4. Conclusions: In general, our findings reveal that TRIP13 and hsa-miR-29c-3p promotes the malignant development of hepatocellular carcinoma, which is a potential new therapeutic target and a prognostic biomarker for HCC. Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Genetics Hepatocellular carcinoma bioinformatics prognosis long non-coding RNA TCGA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Hepatocellular carcinoma (HCC), the most common type of primary liver cancer and the third leading cause of cancer-related mortality globally, is characterized by high recurrence, high hazard and high risk[ 1 – 4 ]. It is reported that more than 700,000 people die annually[ 5 ]. However, the pathogenesis of HCC remains unclear. Only 30% of HCC patients can be diagnosed at the early stage of the disease and receive surgery or ablation, but the 5-year recurrence rate is still as high as 70%[ 6 ]. Therefore, it is necessary to explore new oncogenes and their mechanism of action to promote the prognosis of HCC patients. Thyroid hormone receptor-interacting protein 13(TRIP13), located on chromosome 5q15, belongs to the ATPase family associated with various cellular activities (AAA+) ATPase family [ 7 – 9 ]. TRIP13 is involved in numerous cellular physiological processes, such as chromosomal checkpoints, DNA repair and chromosomal synapse[ 10 – 12 ]. For example, Rajat Banerjee et al. reported that TRIP13 highly expressing in non-malignant cells promoted the transformation to malignancy. High expression of TRIP13 in squamous cell carcinoma of the head and neck (SCCHN) promotedtumor growth, enhanced tumor resistance to anti-cancer therapy, and enhanced DNA damage repair[ 13 ]. Tao et al[ 14 ] revealed that overexpression of TRIP13 promoted the proliferation of myeloma cell by inhibiting the spindle checkpoints via the Akt pathway. In addition, TRIP13 has been identified as an oncogene widely involved in the progression of other several malignancies, including colorectal cancer[ 15 ], bladder cancer[ 16 ], and prostate cancer[ 17 ]. In our study, expression analysis and survival analysis of TRIP13 were performed on several types of human cancers. By studying TRIP13 regulation mechanisms associated with micro RNAs (miRNAs) and long non-coding RNA (lncRNAs), TRIP13 expression is correlated with poor progronosis, immune cell infiltration, immune cell biomarkers and immune checkpoints in HCC. We concluded that TRIP13 is a potential target for HCC. 2. Methods Data sources Transcriptome data in our study were downloaded from the Cancer Genome Atlas (TCGA, https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga ), and the Genotype-tissue Expression (GTEx, https://commonfund.nih.gov/GTEx/ ) [ 18 – 20 ]. Meanwhile, its’ corresponding clinical information were from the same portal websites. Screening and analysis of prognostic genes related to hepatocellular carcinoma Perl software was used to homogenize the 33 kinds of cancer gene expression data. Using R (version: x64 4.1.0) limma package, we identified differentially expressed genes (| LogFC | > 1, FDR < 0.05) between normal tissues and tumor tissues in HCC. R survival package was used for survival analysis of the above differential genes, where P values of Kaplan-Meier(KM) analysis and COX analysis were both less than 0.05, which was considered statistically significant. TRIP13 was finally selected as the target gene of this study, and tumor types with more than 5 normal samples were selected for analysis and the generalized cancer expression boxplot was drawn. GEPIA database analysis GEPIA ( http://gepia.cancer-pku.cn/ ) is utilize to determine the expression of TRIP13 and lncRNA in various types of human cancers. In addition, TRIP13 expression was analyzed for Survival in 13 different cancer types, including overall survival (OS) and disease free Survival (DFS). The prognostic value of candidate lncRNAs in HCC patients was evaluated by OS and DFS. HPA database analysis Human Protein Atlas (HPA) database( https://www.Proteinatlas.org/ ) is a large database of proteomics, transcriptomics, and systems biology based on antibody imaging and mass spectrometry. HPA was performed to analyze the immunohistochemical staining of TRIP13 in tumor samples and corresponding normal tissues. Prediction of candidate miRNAs Upstream miRNAs that may bind to TRIP13 were identified using different prediction programs, including PITA, TargetScan, miRmap, microT, miRanda, PicTar and RNA22. The predicted miRNAs were considered as candidate miRNAs for TRIP13, and the results were visualized using Cytoscape software (version 3.8.2). StarBase database analysis StarBase ( http://starbase.sysu.edu.cn/ ) was used for miRNAs and TRIP13, lncRNA and has-miR-29c-3p or lncRNA and TRIP13 expression in hepatocellular carcinoma (HCC) between two correlation analysis. Starbase software was used to analyze the expression level of has-miR-29c-3p in tumor tissues and normal liver tissues. In addition, Starbase was also performed to identify the candidate lncRNAs that might bind to has-Mir-29C-3p, and the results was visualized by cytoscape software (version 3.8.2). Construction of ceRNA network The final lncRNA and miRAN network relationship data tables obtained after screening and analysis by the above methods were imported, and the software was used to visualize the data, adjust the scale and color of the network graph, and finally build the ceRNA expression regulation network. Analysis of TIMER Database The TIMER ( https://cistrome.shinyapps.io/timer/ ) comprehensively analyses the infiltration of tumor immune cells in multiple cancers including HCC. In this study, TIMER was utilized to analyze the correlation between TRIP13 and the infiltration level of immune cells, and between TRIP13 and immune checkpoints in hepatocellular carcinoma. Statistical analysis The statistical analysis in our work was calculated automatically from the above online database. P < 0.05 or log-rank P < 0.05 were considered statistically significant. 3. Results Pan-cancer analysis of TRIP13 expression To investigate the roles of TRIP13 in carcinogenesis, we first analyzed its expression in 18 types of cancers. Compared with normal tissues, TRIP13 was markedly upregulated in tumor tissues of 17 cancer types, including BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, HCC, LUAD, LUSC, PRAD, READ, STAD, THCA, and UCEC ( Fig. 1 ). No significant differences in TRIP13 expression between tumor sample and normal tissue were observed in KICH. We further validated TRIP13 expression in these 18 cancer types by the GEPIA database. TRIP13 expression in BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, HCC, LUAD, LUSC, READ, STAD, and UCEC was significantly increased compared with the corresponding normal tissues ( Fig. 1 ). In conclusion, TRIP13 expression is up-regulated in the 13 cancers mentioned above, suggesting that TRIP13 may play a key regulatory role in the carcinogenesis of these 13 cancers. The prognostic values of TRIP13 in human cancer To investigate the role of TRIP13 in prognosis of cancer patients, we performed the OS and DFS analysis. For OS, TRIP13 with high expression in HCC and LUAD had a poor prognosis, while prognostic analyses in other types of cancers were not statistically significant ( Fig. 2 ). For DFS, the increased expression of TRIP13 was associated with poor prognosis of HCC patients, but for COAD patients, the high expression of TRIP13 leads to a better prognosis ( Fig. 2 ), and there is no statistical significance in the prognostic analysis of other cancers. By combining OS and DFS prognostic analysis, we can speculate on TRIP13 as a new biomarker for poor prognosis in patients with HCC. Prediction and analysis of upstream lncRNAs of TRIP13 To explore which lncRNAs regulate TRIP13, we first predicted upstream miRNAs that may bind to TRIP13 by the starBase database, andfound 35 miRNAs that bind to TRIP13. Cytoscape software was used to visualize the predicted results and establish miRNAs-TRIP13 regulatory network ( Fig. 3 ). Based on the mechanism in which miRNAs regulate the expression of target genes, we hypothesized that there should be a negative correlation between miRNA and TRIP13. Therefore, we conducted an expression correlation analysis for the two RNAs. Among the 35 miRNAs that could bind to TRIP13, hsa-miR-29c-3p and hsa-miR-29a-3p were significantly negatively correlated with TRIP13 expression, while there was no statistical significance between hsa-miR-29a-3p and 33 other predicted miRNAs (COR < -0.2; Pvalue < 0.001; LogFC < 0) ( Table 1 ). Subsequently, we performed an expression and prognostic analysis on the two miRNAs screened out that were significantly related to TRIP13 to determine the miRNAs for further analysis. As shown in Fig. 3 , hsa-miR-29c-3p was greatly down-regulated, and was positively correlated with the prognosis of patients in HCC, while the expression and prognosis of hsa-miR-29a-3p were not statistically significant. All findings above suggested that hsa-miR-29c-3p may be the most promising miRNA for regulating TRIP13 expression in HCC. Table 1 The correlation between lncRNAs predicted by starBase database and miRNA expression in hepatocellular carcinoma was analyzed. lncRNA miRNA cor pvalue logFC diffPval HCG18 hsa-miR-29c-3p -0.322335839 2.74E-10 0.465368062 2.01E-18*** THUMPD3-AS1 hsa-miR-29c-3p -0.307607996 1.83E-09 0.698874601 6.85E-25*** LINC01224 hsa-miR-29c-3p -0.297122169 5.61E-09 0.243608381 1.89E-14*** SNHG20 hsa-miR-29c-3p -0.293682559 1.00E-08 0.683379527 3.89E-24*** VASH1-AS1 hsa-miR-29c-3p -0.285695688 2.57E-08 0.192794036 1.29E-05*** SNHG17 hsa-miR-29c-3p -0.283317372 3.37E-08 0.836762598 3.19E-16*** TUG1 hsa-miR-29c-3p -0.278515252 5.82E-08 0.901816194 2.66E-19*** H19 hsa-miR-29c-3p -0.276497272 7.30E-08 -1.44865181 2.16E-07*** KCNQ1OT1 hsa-miR-29c-3p -0.263531311 2.99E-07 0.03030447 1.89E-13*** GAS5 hsa-miR-29c-3p -0.246460687 1.72E-06 1.960769197 3.75E-25*** AFDN-DT hsa-miR-29c-3p -0.212264003 4.01E-05 0.305059358 1.38E-09*** CRNDE hsa-miR-29c-3p -0.173499264 0.000816946 0.978296027 7.73E-19*** LINC01521 hsa-miR-29c-3p -0.167998228 0.001180047 0.193177353 2.92E-09*** RAD51-AS1 hsa-miR-29c-3p -0.164409587 0.001525759 0.556194889 7.47E-20*** NOP14-AS1 hsa-miR-29c-3p -0.163421445 0.001629971 0.431565576 4.54E-21*** LINC00852 hsa-miR-29c-3p -0.160457147 0.001960868 0.183263523 1.54E-12*** LINC00638 hsa-miR-29c-3p -0.149268577 0.004038057 0.281435539 3.97E-12*** DUXAP8 hsa-miR-29c-3p -0.141306725 0.00647771 0.160597246 3.42E-16*** HOXA-AS3 hsa-miR-29c-3p -0.12040699 0.020521589 0.022884196 9.18E-10*** STAG3L5P-PVRIG2P-PILRB hsa-miR-29c-3p -0.112479174 0.030577736 0.32523122 6.71E-15*** LINC00511 hsa-miR-29c-3p -0.10592571 0.04171425 0.402893301 6.96E-12*** DNAJC27-AS1 hsa-miR-29c-3p -0.10184178 0.050329294 0.139905357 1.38E-08*** ARRDC1-AS1 hsa-miR-29c-3p -0.092161967 0.076644643 0.776007016 8.45E-24*** PCBP1-AS1 hsa-miR-29c-3p -0.091449822 0.078956896 0.161218575 1.86E-08*** MIR4458HG hsa-miR-29c-3p -0.089351293 0.0861011 0.474344814 1.95E-09*** LINC00879 hsa-miR-29c-3p -0.08812079 0.090532407 0.112587878 0.001020221** CCDC144NL-AS1 hsa-miR-29c-3p -0.080786464 0.120844889 0.12632652 0.001414051** SNHG15 hsa-miR-29c-3p -0.062833499 0.227806466 0.438151972 8.83E-06*** XIST hsa-miR-29c-3p -0.061809657 0.235604965 0.012765208 0.675672464 OIP5-AS1 hsa-miR-29c-3p -0.054773187 0.293199411 0.241149513 0.000913576*** LIFR-AS1 hsa-miR-29c-3p -0.047001782 0.367303117 0.018543991 0.184265318 NPTN-IT1 hsa-miR-29c-3p -0.045130115 0.386710175 0.057876265 0.007878119** LINC00943 hsa-miR-29c-3p -0.039033143 0.45412105 0.025985214 1.45E-05*** MIR646HG hsa-miR-29c-3p -0.01923981 0.712226826 0.074685285 0.188284507 EBLN3P hsa-miR-29c-3p -0.017789521 0.732941213 0.388929474 3.52E-06*** LINC01907 hsa-miR-29c-3p -0.007143892 0.8910685 0.009407702 0.000260429*** MIRLET7BHG hsa-miR-29c-3p -0.006676652 0.8981055 0.08240944 7.65E-12*** NEAT1 hsa-miR-29c-3p -0.003723834 0.943063569 0.929263755 9.87E-09*** MIR762HG hsa-miR-29c-3p -0.003514882 0.946253535 0.194821539 8.93E-13*** PVT1 hsa-miR-29c-3p 0.003409458 0.947863315 0.628921817 2.90E-20*** LINC00689 hsa-miR-29c-3p 0.0049803 0.923938005 0.066842258 0.1446275 HOXA10-AS hsa-miR-29c-3p 0.035524611 0.495724424 0.00719357 0.251420012 MIAT hsa-miR-29c-3p 0.037888775 0.467470989 0.099225703 2.59E-05*** LINC01270 hsa-miR-29c-3p 0.04367098 0.402088733 0.489136102 2.59E-13*** FAM30A hsa-miR-29c-3p 0.05509667 0.290501237 0.034583582 0.01060002* MIR193BHG hsa-miR-29c-3p 0.116719349 0.02480247 0.251260733 0.003567614** MIR497HG hsa-miR-29c-3p 0.145933153 0.004948131 0.028395181 0.260089322 HCP5 hsa-miR-29c-3p 0.215360387 3.07E-05 0.962049837 1.13E-09*** MIR29B2CHG hsa-miR-29c-3p 0.408439009 0 0.06319898 5.38E-06*** Identifying the upstream lncRNA of hsa-miR-29c-3p by starBase database We utilized starBase database to identify the upstream lncRNAs of hsa-miR-29c-3p. The results showed that 49 lncRNAs were the upstream lncRNAs of hsa-miR-29c-3p. Then, we visulizated the lncRNA- hsa-miR-29c-3p regulatory network using Cytoscape software ( Fig. 4 ). According to the screening conditions (COR < -0.2; P value 0), the expression of these lncRNAs in HCC was analyzed. Among all 49 lncRNAs, only 10 lncRNAs, including AFDN-DT, GAS5, KCNQ1OT1, TUG1, SNHG17, VASH1-AS1, SNHG20, LINC01224, ThumPD3-AS1 and HCG18, were remarkedly upregulated in HCC ( Fig. 5 ). Subsequently, the role of these 10 lncRNAs in the prognosis of HCC patients was analyzed.Only patients with high expression of SNHG17 and SNHG20 had poor OS and DFS ( Fig. 6 ). According to the hypothesis of the competitive endogenous RNA (ceRNA), lncRNAs can up-regulate mRNA expression through competitive binding with corresponding miRNAs. Therefore, there should be a negative correlation between lncRNA and miRNA, or a positive correlation between lncRNA and mRNA. Therefore, we also analyzed the expression correlation between the above 10 screened lncRNAs and TRIP13 ( Table 2 ). Considering the results above, this study suggested that SNHG17 and SNHG20 may be the two most potential upstream lncRNAs of hsa-miR-29c-3p /TRIP13 axis in HCC. Table 2 Correlation analysis between lncRNA and TRIP13 in hepatocellular carcinoma identified by Starbase database lncRNA Gene cor pvalue logFC diffPval LINC01224 TRIP13 0.545720493 4.23E-30 0.243608381 1.89E-14*** KCNQ1OT1 TRIP13 0.395054196 1.75E-15 0.03030447 1.89E-13*** AFDN-DT TRIP13 0.188800424 0.000266227 0.305059358 1.38E-09*** SNHG17 TRIP13 0.395286839 1.63E-15 0.836762598 3.19E-16*** GAS5 TRIP13 0.315236899 6.93E-10 1.960769197 3.75E-25*** TUG1 TRIP13 0.47864946 0 0.901816194 2.66E-19*** SNHG20 TRIP13 0.473833363 0 0.683379527 3.89E-24*** THUMPD3-AS1 TRIP13 0.524733701 0 0.698874601 6.85E-25*** HCG18 TRIP13 0.591473217 0 0.465368062 2.01E-18*** VASH1-AS1 TRIP13 0.202349414 9.16E-05 0.192794036 1.29E-05*** TRIP13 positively correlates with immune cell infiltration in HCC A recent study showed that DCZ04145, an inhibitor targeting TRIP13, can treat colon cancer by upregulating cytotoxic mediators to activate anti-tumor immune responses[ 21 ].Therefore, in order to further investigate the role of TRIP13 in tumor immunity, we performed the TIMER database to identify the correlation between TRIP13 and the infiltration of immune cells in HCC. TRIP13 expression was positively correlated with the infiltrating level of the immune cells, including B cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells, exclusive of CD8 + T cell ( Fig. 7 ). These findings partially suggested that TRIP13 be involving in immune cell infiltration. The relationship of TRIP13 and immune checkpoint in hepatocellular carcinoma Based on previous studies, we know that PD1/PD-L1 and CTLA-4 are the important immunosuppresive molecules responsible for tumor immune invasion. Considering the potential carcinogenic role of TRIP13 in HCC, we evaluated the relationship between TRIP13 and PD1, PD-L1 or CTLA-4. TIMER database analysis with purity adjustment showed that the expression of TRIP13 was greatly positively correlated with PD1, PD-L1 and CTLA-4 ( Fig. 8 ). Similar to the results of the TIMER database analysis, we also found that TRIP13 was significantly positively correlated with PD-1, PD-L1 or CTLA-4 in hepatocellular carcinoma in the GEPIA database (Fig. 8 ). These results suggested that TRIP13 may be involved in tumor immune evasion in HCC. 4. Discussion HCC is notorious for its poor prognosis and still lacks effective therapies. Understanding of the molecular mechanism of HCC may promote the development of effective therapeutic targets and identify the prognostic biomarkers. There is growing evidence that TRIP13 plays a significant role in the initiation and malignant development of multiple cancers, including HCC. However, the role of TRIP13 in HCC remain unclear and further research is necessary. In this study, we first used TCGA to conduct a pan-cancer analysis of TRIP13 expression, and then further validated the expression of TRIP13 using GEPIA database. Survival analysis of TRIP13 in these cancer types showed thatTRIP13 is associated with poor prognosis in HCC. Zhu reported that TRIP13 is a promising candidate oncogene in HCC, and mechanistically cell migration, invasion, and metastasis is promoted by the interaction of TRIP13 and ACTN4 in the AKT/mTOR signaling pathway[ 22 ]. Zhou et al. found that miR-29b-3p was an important suppressor of HCC and negative regulator of HCP5 which promote tumor grow and metastatis [ 23 ]. Masamichi Hayashi et al. found that miR-23b-3p was an oncogenic microRNA in HCC cell lines, and its overexpression in HCC tissues is an important biomarker for prognosis[ 24 ]. To investigate miRNAs regulating TRIP13, we performed seven prediction programs, including PITA, RNA22, miRmap, microT, miRanda, PicTar and Targescan, to identify the miRNAs that may bind to TRIP13. Finally, 13 miRNAs were identified. Most of these miRNAs played the suppressive role in HCC. For example, Xu et al reported that miR-885-5p can regulate glucose metabolism of HCC cells in the hypoxic area, and partly reduced the malignant growth of HCC cells in vivo and in vitro by inhibiting several enzymes in the glycolysis pathway[ 25 ]. Other studies have also revealed hsa-miR-29c-3p as an suppressive role in the proliferation and migration of HCC. Wu et al. showed that miR-29c-3p plays a tumor suppressor miRNA in hepatocellular carcinoma. miR-29c-3p inhibits the malignant progression of hepatocellular carcinoma by reducing DNMT3B expression and promoting LATS1 demethylation, thereby restoring its expression and subsequently activating the Hippo signaling pathway[ 26 ].Based on the ceRNA hypothesis, potential lncRNAs of the hsa-miR-29c-3p /TRIP13 axis was carcinogenic lncRNAs in HCC. Next, the upstream lncRNAs of hsa-miR-29c-3p /TRIP13 axis were predicted and the potential lncRNAs were found. Through expression analysis, survival analysis and correlation analysis, SNHG17 and SNHG20 were identified as the most potentially upregulated lncRNAs in HCC. These two lncRNAs act as oncogenes in a variety of malignant tumors, including HCC. SNHG17 is up-regulated in HCC tissues, and overexpression of SNHG17 can promote the growth, invasion and metastasis of HCC. Meanwhile, SNHG17 regulates its function and upregulates RFX1 by sponging miR-3180-3p[ 27 ]. SNHG20 mediated- suppression depends in part on the regulation of e-cadherin expression through interaction with EZH2[ 28 ]. Our study suggests that the SNHG17/SNHG20/ hsa-miR-29c-3p /TRIP13 axis may be a potential pathway for the progress of hepatocellular carcinoma. The increasing evidence confirmed that tumor immune cell infiltration may play a important role in tumor immune evasion and immune tolerance, and may become a new therapeutic target for tumor, which affects the efficacy of chemotherapy, radiotherapy and immunotherapy for tumor patients[ 29 – 31 ].Our study shows that TRIP13 expression is involved in the infiltration of a variety of immune cells, including B cells, CD4 + T cells, macrophages, neutrophils and dendritic cells. TRIP13 was also significantly positively associated with immune chechpoints. These results suggest that the carcinogenic effect of TRIP13 in HCC may be related to tumor immune invasion. 5. Conclusion TRIP13highly expresses in multiple types of human tumors, and is positively associated with poor prognosis of hepatocellular carcinoma. We identified the upstream regulatory mechanism of TRIP13 in hepatocellular carcinoma, SNHG17/SNHG20/ hsa-miR-29c-3p /TRIP13 axis ( Fig. 9 ). In addition, TRIP13 may play a pro-tumor role by increasing tumor immunosuppressive cell infiltration and immune checkpoint expression. However, this study has some limitations that above results were only obtained by bioinformatics analysis. Therefore, the role of TRIP13 in HCC should be further investigated by more basic experiments and large clinical trials. Declarations Acknowledgements We wish to thank the timely help given by those whose work we could not cite due to space limitations. Author Contributions: Conceptualization, Cao YW and Ye YQ.; methodology, Ye YQ.; software, Cao YW.; validation, Ying JX and Ye YQ; formal analysis, Ye YQ; investigation, Cao YW.; resources,Cao YW; data curation, Ying JX.; writing—original draft preparation, Cao YW and Ye YQ; writing—review and editing, Ye YQ and Cao YW; visualization, Cao YW and Ye YQ; supervision, Ye YQ; project administration, Ye YQ; funding acquisition, Ying JX; All authors have read and agreed to the published version of the manuscript. Funding No funding. Availability and data materials The datasets for this study can be found in the following website: https://commonfund.nih.gov/GTEx/, http://gepia.cancer-pku.cn/, https://www.Proteinatlas.org/, http://starbase.sysu.edu.cn/, https://cistrome.shinyapps.io/timer/. All data generated or analyzed during this study are available upon reasonable request from the corresponding author. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68 , 394–424 (2018). Luo, Z. et al. CircCAMSAP1 promotes hepatocellular carcinoma progression through miR-1294/GRAMD1A pathway. J. Cell. Mol. Med. 25 , 3793–3802 (2021). Forner, A., Llovet, J. M. & Bruix, J. Hepatocellular carcinoma. Lancet . 379 , 1245–1255 (2012). Wong, M. C. et al. International incidence and mortality trends of liver cancer: a global profile. Sci. Rep. 7 , 45846 (2017). Affo, S., Yu, L. X. & Schwabe, R. F. The Role of Cancer-Associated Fibroblasts and Fibrosis in Liver Cancer. Annu. Rev. Pathol. 12 , 153–186 (2017). Oikonomopoulos, G., Aravind, P. & Sarker, D. Lenvatinib: a potential breakthrough in advanced hepatocellular carcinoma? Future Oncol. 12 , 465–476 (2016). Wang, K. et al. Thyroid hormone receptor interacting protein 13 (TRIP13) AAA-ATPase is a novel mitotic checkpoint-silencing protein. J. Biol. Chem. 289 , 23928–23937 (2014). Wang, Y. et al. A Small-Molecule Inhibitor Targeting TRIP13 Suppresses Multiple Myeloma Progression. Cancer Res. 80 , 536–548 (2020). Ye, Q. et al. TRIP13 is a protein-remodeling AAA plus ATPase that catalyzes MAD2 conformation switching. Elife , 4 (2015). Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N. & Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat. Genet. 38 , 1043–1048 (2006). Ma, H. T. & Poon, R. Y. C. TRIP13 Regulates Both the Activation and Inactivation of the Spindle-Assembly Checkpoint. Cell. Rep. 14 , 1086–1099 (2016). Eytan, E. et al. Disassembly of mitotic checkpoint complexes by the joint action of the AAA-ATPase TRIP13 and p31(comet). Proc. Natl. Acad. Sci. U S A . 111 , 12019–12024 (2014). Banerjee, R. et al. N.J. D'Silva, TRIP13 promotes error-prone nonhomologous end joining and induces chemoresistance in head and neck cancer. Nat. Commun. 5 , 4527 (2014). Tao, Y. et al. TRIP13 impairs mitotic checkpoint surveillance and is associated with poor prognosis in multiple myeloma. Oncotarget . 8 , 26718–26731 (2017). Sheng, N. et al. TRIP13 promotes tumor growth and is associated with poor prognosis in colorectal cancer. Cell. Death Dis. 9 , 402 (2018). Gao, Y. et al. Increased expression of TRIP13 drives the tumorigenesis of bladder cancer in association with the EGFR signaling pathway. Int. J. Biol. Sci. 15 , 1488–1499 (2019). Dong, L. et al. TRIP13 is a predictor for poor prognosis and regulates cell proliferation, migration and invasion in prostate cancer. Int. J. Biol. Macromol. 121 , 200–206 (2019). Consortium, G. T. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science . 348 , 648–660 (2015). Tomczak, K., Czerwinska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. (Pozn) . 19 , A68–77 (2015). Hudson, T. J. et al. International network of cancer genome projects. Nature . 464 , 993–998 (2010). Agarwal, S. et al. DCZ0415, a small-molecule inhibitor targeting TRIP13, inhibits EMT and metastasis via inactivation of the FGFR4/STAT3 axis and the Wnt/beta-catenin pathway in colorectal cancer. Mol. Oncol. 16 , 1728–1745 (2022). Zhu, M. X. et al. Elevated TRIP13 drives the AKT/mTOR pathway to induce the progression of hepatocellular carcinoma via interacting with ACTN4. J. Exp. Clin. Cancer Res. 38 , 409 (2019). Zhou, Y. et al. Long non-coding RNA HCP5 functions as a sponge of miR-29b-3p and promotes cell growth and metastasis in hepatocellular carcinoma through upregulating DNMT3A. Aging (Albany NY) . 13 , 16267–16286 (2021). Hayashi, M. et al. miR-23b-3p Plays an Oncogenic Role in Hepatocellular Carcinoma. Ann. Surg. Oncol. 28 , 3416–3426 (2021). Xu, F. et al. miR-885-5p Negatively Regulates Warburg Effect by Silencing Hexokinase 2 in Liver Cancer. Mol. Ther. Nucleic Acids . 18 , 308–319 (2019). Wu, H. et al. miR-29c-3p regulates DNMT3B and LATS1 methylation to inhibit tumor progression in hepatocellular carcinoma. Cell. Death Dis. 10 , 48 (2019). Ma, T. et al. Long Non-coding RNA SNHG17 Upregulates RFX1 by Sponging miR-3180-3p and Promotes Cellular Function in Hepatocellular Carcinoma. Front. Genet. 11 , 607636 (2020). Liu, J. et al. Long non-coding RNA SNHG20 predicts a poor prognosis for HCC and promotes cell invasion by regulating the epithelial-to-mesenchymal transition. Biomed. Pharmacother . 89 , 857–863 (2017). Zheng, Y. et al. PD-L1(+)CD8(+) T cells enrichment in lung cancer exerted regulatory function and tumor-promoting tolerance. iScience . 25 , 103785 (2022). Ji, D. et al. Combination of radiotherapy and suppression of Tregs enhances abscopal antitumor effect and inhibits metastasis in rectal cancer. J. Immunother Cancer , 8 (2020). Peng, Z. et al. GSDME enhances Cisplatin sensitivity to regress non-small cell lung carcinoma by mediating pyroptosis to trigger antitumor immunocyte infiltration. Signal. Transduct. Target. Ther. 5 , 159 (2020). Additional Declarations No competing interests reported. Supplementary Files Supplementary1.png.jpg Supplementary2.png.jpg Supplementary3.png.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5276784","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377272234,"identity":"95142592-884c-4b27-b075-92eb1cc452d9","order_by":0,"name":"Ya-wen Cao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ya-wen","middleName":"","lastName":"Cao","suffix":""},{"id":377272235,"identity":"59abb6cd-d0df-4a3c-b31e-8ffccb046c99","order_by":1,"name":"Jiaxin Ying","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Ying","suffix":""},{"id":377272236,"identity":"1f3bae26-7df5-49f0-850f-d0c40eb6134b","order_by":2,"name":"Yong-qing Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie3PMUvDQBjG8TccXB3emPVCA/oRTg4i0mL8KBEhXToUXBwPApmCc4uLX8HF+ULALMGsBx1s6eqQbkVaMUtHrx0F7z8ez4+XA7DZ/nAIQJRqH4bo9eTRhN4up3US+Lk6/pAQblYOub4x73iVX6wmX4Pg0nsOGdIGQYPTrscGUtdCzB5HeDVdJIzhHJ0nSfzZq4Hocdh38xK5Vm+MszmSQFHimsjH554UGYv5O1IWHyAawz5uOtKkhKtYIR4ifp3cC1eOuivUWUp1hwyL1PiX06p8WeF2EPGmacvd93UUVWnRrg3kXJ1wcDICwOL9myN/33edyd4CYNsRTxmHNpvN9o/7ATMKV2UUtpHkAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yong-qing","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2024-10-16 14:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5276784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5276784/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70919748,"identity":"87166f30-738e-4399-8a19-48a190994604","added_by":"auto","created_at":"2024-12-09 08:35:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5855716,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis for TRIP13 in multiple cancers. TRIP13 expression in 18 human cancers based on TCGA tumor data and normal data(A). TRIP13 expression in GEPIA (B); * represents P \u0026lt; 0.05; ** represents P \u0026lt; 0.01; *** means P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure01.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/874116eb396b7683fd22274c.jpg"},{"id":70919749,"identity":"ac0c4ffe-de34-41d9-8387-a54d66e0ed6f","added_by":"auto","created_at":"2024-12-09 08:35:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5385864,"visible":true,"origin":"","legend":"\u003cp\u003eOS analysis and DFS analysisof TRP13 in multiple cancers by the GEPIA database. (A-B) Overall survival plot of TRIP13 in HCC and LUAD. (C-D) RFS analysis of TRIP13 in HCC and COAD\u003c/p\u003e","description":"","filename":"Figure02.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/a33b884596ddf2ffb4ea1010.jpg"},{"id":70919754,"identity":"95a018fa-8e83-409b-909b-08ed392667ed","added_by":"auto","created_at":"2024-12-09 08:35:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8771215,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of hsa-miR-29c-3p as a potential upstream miRNA of TRIP13 in hepatocellular carcinoma. Mirna-trip13 regulatory network established by Cytoscape software(A); Expression of hsa-miR-29c-3p in hepatocellular carcinoma tissues and normal tissue samples determined by starBase database (B); The prognostic value of hsa-miR-29c-3p in hepatocellular carcinoma was evaluated by Kaplan-Meier plotter (C).\u003c/p\u003e","description":"","filename":"Figure03.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/aebf73918e060edf3f6cd8b9.jpg"},{"id":70921960,"identity":"e2e49c2b-1add-4d7f-ab24-f1eea201348b","added_by":"auto","created_at":"2024-12-09 08:43:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5864989,"visible":true,"origin":"","legend":"\u003cp\u003eLncRNAs -miRNA regulatory network established by Cytoscape software.\u003c/p\u003e","description":"","filename":"Figure04.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/e059fbdd0c95cecce36bc766.jpg"},{"id":70919757,"identity":"2f290290-307b-45d8-a301-78b47ba30c01","added_by":"auto","created_at":"2024-12-09 08:35:39","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4963367,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis of upstream lncRNAs of hsa-miR-29c-3p in hepatocellular carcinoma.\u003cstrong\u003e \u003c/strong\u003eExpression analysis compared with control group for AFDN-DT(A), GAS5(B), HCG18(C), KCNQ1OT1(D), LINC01224(E), SNHG17(F), SNHG20(G), THUMPD3-AS1(H), TUG1(I), and VASH1-AS1(J).\u003c/p\u003e","description":"","filename":"Figure05.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/00b5f61d9d7fe9d3fee46dcb.jpg"},{"id":70921957,"identity":"d8e657ca-0b61-4aac-8ba4-fa79e7df9239","added_by":"auto","created_at":"2024-12-09 08:43:39","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5522022,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis and DRF of upstream lncRNAs of hsa-miR-29c-3p in hepatocellular carcinoma. The Paired survival analysis (OS and RFS) for SNHG17(A-B), SNHG20(C-D).\u003c/p\u003e","description":"","filename":"Figure06.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/f478d24e06dcb261429ef3a8.jpg"},{"id":70921959,"identity":"764177ab-a236-427b-a765-9cf4a5caa30b","added_by":"auto","created_at":"2024-12-09 08:43:39","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":9024254,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation of TRIP13 expression level with B cells (A), macrophages (B), neutrophils (C), dendritic cells (D), CD8+T cells (E) and CD4+T cells (F) in hepatocellular carcinoma tissues.\u003c/p\u003e","description":"","filename":"Figure07.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/d4705d051377734b03a4fd82.jpg"},{"id":70922659,"identity":"6d758dbb-35d4-4ef9-9c4c-9c9434f03215","added_by":"auto","created_at":"2024-12-09 08:51:39","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6607539,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between TRIP13 expression and PD-1, PD-L1 and CTLA-4 expression in hepatocellular carcinoma Spearman correlation between TRIP13 and PD-1 expression in hepatocellular carcinoma (tumor purity correction using TIMER database)(A); GEPIA database analysis of TRIP13 and PD-1 expression in hepatocellular carcinoma (B); Spearman correlation between TRIP13 and PD-L1 expression in hepatocellular carcinoma (tumor purity correction using TIMER database)(c); GEPIA database analysis of TRIP13 and PD-L1 expression in hepatocellular carcinoma (D); Spearman correlation between TRIP13 and ctLA-4 expression in hepatocellular carcinoma (tumor purity correction using TIMER database)(e); GEPIA database analysis of TRIP13 and CTLA-4 expression in hepatocellular carcinoma (F).\u003c/p\u003e","description":"","filename":"Figure08.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/bbe77e4b78cd1c87128621d0.jpg"},{"id":70919752,"identity":"78459a1f-b9b3-41f9-8f05-e1dcaad088d0","added_by":"auto","created_at":"2024-12-09 08:35:39","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1543495,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of SNHG17/ SNHG20-hsa-miR-29c-3p-TRIP13 axis in hepatocellular carcinoma.\u003c/p\u003e","description":"","filename":"Figure09.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/4b527baedcb456274c521dd6.jpg"},{"id":81177011,"identity":"6b4d96b3-c990-42c7-8608-de2e00f12ebf","added_by":"auto","created_at":"2025-04-23 06:32:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":54575102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/92186cd6-0156-45ee-ace5-42d35d476944.pdf"},{"id":70921964,"identity":"62a29418-c8b7-479d-80ce-bfc88e6304ec","added_by":"auto","created_at":"2024-12-09 08:43:41","extension":"jpg","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1220949,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.png.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/22e400e4a877ca61343df4c1.jpg"},{"id":70922660,"identity":"44d929d3-ac79-4a53-b4eb-776b248e12a9","added_by":"auto","created_at":"2024-12-09 08:51:39","extension":"jpg","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":1206005,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2.png.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/4124639584f66b9967c0dcae.jpg"},{"id":70924285,"identity":"db3b453a-0c6e-4a13-a458-9538bb146849","added_by":"auto","created_at":"2024-12-09 08:59:39","extension":"jpg","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":1375794,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary3.png.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5276784/v1/84ae7ac3c608edab325d2147.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"LncRNA-mediated High Expression of TRIP13 Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC), the most common type of primary liver cancer and the third leading cause of cancer-related mortality globally, is characterized by high recurrence, high hazard and high risk[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is reported that more than 700,000 people die annually[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the pathogenesis of HCC remains unclear. Only 30% of HCC patients can be diagnosed at the early stage of the disease and receive surgery or ablation, but the 5-year recurrence rate is still as high as 70%[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, it is necessary to explore new oncogenes and their mechanism of action to promote the prognosis of HCC patients.\u003c/p\u003e \u003cp\u003eThyroid hormone receptor-interacting protein 13(TRIP13), located on chromosome 5q15, belongs to the ATPase family associated with various cellular activities (AAA+) ATPase family [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. TRIP13 is involved in numerous cellular physiological processes, such as chromosomal checkpoints, DNA repair and chromosomal synapse[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For example, Rajat Banerjee et al. reported that TRIP13 highly expressing in non-malignant cells promoted the transformation to malignancy. High expression of TRIP13 in squamous cell carcinoma of the head and neck (SCCHN) promotedtumor growth, enhanced tumor resistance to anti-cancer therapy, and enhanced DNA damage repair[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Tao et al[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] revealed that overexpression of TRIP13 promoted the proliferation of myeloma cell by inhibiting the spindle checkpoints via the Akt pathway. In addition, TRIP13 has been identified as an oncogene widely involved in the progression of other several malignancies, including colorectal cancer[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], bladder cancer[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and prostate cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, expression analysis and survival analysis of TRIP13 were performed on several types of human cancers. By studying TRIP13 regulation mechanisms associated with micro RNAs (miRNAs) and long non-coding RNA (lncRNAs), TRIP13 expression is correlated with poor progronosis, immune cell infiltration, immune cell biomarkers and immune checkpoints in HCC. We concluded that TRIP13 is a potential target for HCC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e \u003cb\u003eData sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTranscriptome data in our study were downloaded from the Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Genotype-tissue Expression (GTEx, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://commonfund.nih.gov/GTEx/\u003c/span\u003e\u003cspan address=\"https://commonfund.nih.gov/GTEx/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Meanwhile, its\u0026rsquo; corresponding clinical information were from the same portal websites.\u003c/p\u003e \u003cp\u003e \u003cb\u003eScreening and analysis of prognostic genes related to hepatocellular carcinoma\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePerl software was used to homogenize the 33 kinds of cancer gene expression data. Using R (version: x64 4.1.0) limma package, we identified differentially expressed genes (| LogFC | \u0026gt; 1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between normal tissues and tumor tissues in HCC. R survival package was used for survival analysis of the above differential genes, where P values of Kaplan-Meier(KM) analysis and COX analysis were both less than 0.05, which was considered statistically significant. TRIP13 was finally selected as the target gene of this study, and tumor types with more than 5 normal samples were selected for analysis and the generalized cancer expression boxplot was drawn.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGEPIA database analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is utilize to determine the expression of TRIP13 and lncRNA in various types of human cancers. In addition, TRIP13 expression was analyzed for Survival in 13 different cancer types, including overall survival (OS) and disease free Survival (DFS). The prognostic value of candidate lncRNAs in HCC patients was evaluated by OS and DFS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHPA database analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHuman Protein Atlas (HPA) database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.Proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.Proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a large database of proteomics, transcriptomics, and systems biology based on antibody imaging and mass spectrometry. HPA was performed to analyze the immunohistochemical staining of TRIP13 in tumor samples and corresponding normal tissues.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrediction of candidate miRNAs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUpstream miRNAs that may bind to TRIP13 were identified using different prediction programs, including PITA, TargetScan, miRmap, microT, miRanda, PicTar and RNA22. The predicted miRNAs were considered as candidate miRNAs for TRIP13, and the results were visualized using Cytoscape software (version 3.8.2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStarBase database analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/\u003c/span\u003e\u003cspan address=\"http://starbase.sysu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for miRNAs and TRIP13, lncRNA and has-miR-29c-3p or lncRNA and TRIP13 expression in hepatocellular carcinoma (HCC) between two correlation analysis. Starbase software was used to analyze the expression level of has-miR-29c-3p in tumor tissues and normal liver tissues. In addition, Starbase was also performed to identify the candidate lncRNAs that might bind to has-Mir-29C-3p, and the results was visualized by cytoscape software (version 3.8.2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConstruction of ceRNA network\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe final lncRNA and miRAN network relationship data tables obtained after screening and analysis by the above methods were imported, and the software was used to visualize the data, adjust the scale and color of the network graph, and finally build the ceRNA expression regulation network.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of TIMER Database\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe TIMER (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) comprehensively analyses the infiltration of tumor immune cells in multiple cancers including HCC. In this study, TIMER was utilized to analyze the correlation between TRIP13 and the infiltration level of immune cells, and between TRIP13 and immune checkpoints in hepatocellular carcinoma.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe statistical analysis in our work was calculated automatically from the above online database. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003ePan-cancer analysis of TRIP13 expression\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the roles of TRIP13 in carcinogenesis, we first analyzed its expression in 18 types of cancers. Compared with normal tissues, TRIP13 was markedly upregulated in tumor tissues of 17 cancer types, including BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, HCC, LUAD, LUSC, PRAD, READ, STAD, THCA, and UCEC ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences in TRIP13 expression between tumor sample and normal tissue were observed in KICH. We further validated TRIP13 expression in these 18 cancer types by the GEPIA database. TRIP13 expression in BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, HCC, LUAD, LUSC, READ, STAD, and UCEC was significantly increased compared with the corresponding normal tissues ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ). In conclusion, TRIP13 expression is up-regulated in the 13 cancers mentioned above, suggesting that TRIP13 may play a key regulatory role in the carcinogenesis of these 13 cancers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe prognostic values of TRIP13 in human cancer\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the role of TRIP13 in prognosis of cancer patients, we performed the OS and DFS analysis. For OS, TRIP13 with high expression in HCC and LUAD had a poor prognosis, while prognostic analyses in other types of cancers were not statistically significant ( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For DFS, the increased expression of TRIP13 was associated with poor prognosis of HCC patients, but for COAD patients, the high expression of TRIP13 leads to a better prognosis ( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ), and there is no statistical significance in the prognostic analysis of other cancers. By combining OS and DFS prognostic analysis, we can speculate on TRIP13 as a new biomarker for poor prognosis in patients with HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrediction and analysis of upstream lncRNAs of TRIP13\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo explore which lncRNAs regulate TRIP13, we first predicted upstream miRNAs that may bind to TRIP13 by the starBase database, andfound 35 miRNAs that bind to TRIP13. Cytoscape software was used to visualize the predicted results and establish miRNAs-TRIP13 regulatory network ( Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the mechanism in which miRNAs regulate the expression of target genes, we hypothesized that there should be a negative correlation between miRNA and TRIP13. Therefore, we conducted an expression correlation analysis for the two RNAs. Among the 35 miRNAs that could bind to TRIP13, hsa-miR-29c-3p and hsa-miR-29a-3p were significantly negatively correlated with TRIP13 expression, while there was no statistical significance between hsa-miR-29a-3p and 33 other predicted miRNAs (COR \u0026lt; -0.2; Pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001; LogFC\u0026thinsp;\u0026lt;\u0026thinsp;0) ( Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ). Subsequently, we performed an expression and prognostic analysis on the two miRNAs screened out that were significantly related to TRIP13 to determine the miRNAs for further analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, hsa-miR-29c-3p was greatly down-regulated, and was positively correlated with the prognosis of patients in HCC, while the expression and prognosis of hsa-miR-29a-3p were not statistically significant. All findings above suggested that hsa-miR-29c-3p may be the most promising miRNA for regulating TRIP13 expression in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between lncRNAs predicted by starBase database and miRNA expression in hepatocellular carcinoma was analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ediffPval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.322335839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.74E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.465368062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.01E-18***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHUMPD3-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.307607996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.698874601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.85E-25***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.297122169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.61E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.243608381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89E-14***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.293682559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683379527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.89E-24***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVASH1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.285695688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.192794036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29E-05***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.283317372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.836762598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.19E-16***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.278515252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.82E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.901816194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.66E-19***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.276497272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.30E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.44865181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.16E-07***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCNQ1OT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.263531311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.99E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03030447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89E-13***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.246460687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.960769197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.75E-25***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFDN-DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.212264003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.01E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305059358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38E-09***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRNDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.173499264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000816946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.978296027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.73E-19***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.167998228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001180047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193177353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.92E-09***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAD51-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.164409587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001525759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.556194889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.47E-20***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOP14-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.163421445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001629971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.431565576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.54E-21***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.160457147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001960868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183263523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54E-12***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.149268577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004038057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281435539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.97E-12***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDUXAP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.141306725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00647771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160597246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.42E-16***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOXA-AS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.12040699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020521589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022884196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.18E-10***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAG3L5P-PVRIG2P-PILRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.112479174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030577736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32523122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.71E-15***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.10592571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04171425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402893301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.96E-12***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNAJC27-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.10184178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050329294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.139905357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38E-08***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARRDC1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.092161967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076644643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776007016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.45E-24***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCBP1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.091449822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078956896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161218575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.86E-08***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIR4458HG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.089351293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0861011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.474344814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95E-09***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08812079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.090532407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112587878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001020221**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCDC144NL-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.080786464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120844889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12632652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001414051**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.062833499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227806466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.438151972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.83E-06***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.061809657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235604965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012765208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.675672464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOIP5-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.054773187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.293199411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241149513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000913576***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIFR-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.047001782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.367303117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018543991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.184265318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPTN-IT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.045130115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.386710175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057876265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007878119**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.039033143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45412105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025985214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45E-05***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIR646HG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01923981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.712226826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074685285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.188284507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBLN3P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.017789521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.732941213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.388929474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.52E-06***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.007143892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8910685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009407702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000260429***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIRLET7BHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.006676652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8981055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08240944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.65E-12***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.003723834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.943063569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.929263755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.87E-09***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIR762HG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.003514882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.946253535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.194821539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.93E-13***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003409458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.947863315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.628921817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.90E-20***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0049803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923938005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066842258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1446275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOXA10-AS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035524611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.495724424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00719357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.251420012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037888775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.467470989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099225703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.59E-05***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04367098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.402088733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.489136102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.59E-13***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM30A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05509667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290501237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034583582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01060002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIR193BHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116719349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02480247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.251260733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003567614**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIR497HG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145933153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004948131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028395181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.260089322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.215360387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.962049837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13E-09***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIR29B2CHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.408439009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06319898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.38E-06***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentifying the upstream lncRNA of hsa-miR-29c-3p by starBase database\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe utilized starBase database to identify the upstream lncRNAs of hsa-miR-29c-3p. The results showed that 49 lncRNAs were the upstream lncRNAs of hsa-miR-29c-3p. Then, we visulizated the lncRNA- hsa-miR-29c-3p regulatory network using Cytoscape software ( Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the screening conditions (COR \u0026lt; -0.2; P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001; LogFC\u0026thinsp;\u0026gt;\u0026thinsp;0), the expression of these lncRNAs in HCC was analyzed. Among all 49 lncRNAs, only 10 lncRNAs, including AFDN-DT, GAS5, KCNQ1OT1, TUG1, SNHG17, VASH1-AS1, SNHG20, LINC01224, ThumPD3-AS1 and HCG18, were remarkedly upregulated in HCC ( Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Subsequently, the role of these 10 lncRNAs in the prognosis of HCC patients was analyzed.Only patients with high expression of SNHG17 and SNHG20 had poor OS and DFS ( Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). According to the hypothesis of the competitive endogenous RNA (ceRNA), lncRNAs can up-regulate mRNA expression through competitive binding with corresponding miRNAs. Therefore, there should be a negative correlation between lncRNA and miRNA, or a positive correlation between lncRNA and mRNA. Therefore, we also analyzed the expression correlation between the above 10 screened lncRNAs and TRIP13 ( Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ). Considering the results above, this study suggested that SNHG17 and SNHG20 may be the two most potential upstream lncRNAs of hsa-miR-29c-3p /TRIP13 axis in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis between lncRNA and TRIP13 in hepatocellular carcinoma identified by Starbase database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ecor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ediffPval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.545720493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.23E-30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.243608381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.89E-14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCNQ1OT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.395054196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.75E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.03030447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.89E-13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFDN-DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.188800424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.000266227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.305059358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.38E-09***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.395286839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.63E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.836762598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.19E-16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.315236899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.93E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.960769197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.75E-25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47864946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.901816194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.66E-19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.473833363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.683379527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.89E-24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHUMPD3-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.524733701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.698874601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.85E-25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.591473217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.465368062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.01E-18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVASH1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.202349414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9.16E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.192794036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.29E-05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTRIP13 positively correlates with immune cell infiltration in HCC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA recent study showed that DCZ04145, an inhibitor targeting TRIP13, can treat colon cancer by upregulating cytotoxic mediators to activate anti-tumor immune responses[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].Therefore, in order to further investigate the role of TRIP13 in tumor immunity, we performed the TIMER database to identify the correlation between TRIP13 and the infiltration of immune cells in HCC. TRIP13 expression was positively correlated with the infiltrating level of the immune cells, including B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and dendritic cells, exclusive of CD8\u0026thinsp;+\u0026thinsp;T cell ( Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings partially suggested that TRIP13 be involving in immune cell infiltration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relationship of TRIP13 and immune checkpoint in hepatocellular carcinoma\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on previous studies, we know that PD1/PD-L1 and CTLA-4 are the important immunosuppresive molecules responsible for tumor immune invasion. Considering the potential carcinogenic role of TRIP13 in HCC, we evaluated the relationship between TRIP13 and PD1, PD-L1 or CTLA-4. TIMER database analysis with purity adjustment showed that the expression of TRIP13 was greatly positively correlated with PD1, PD-L1 and CTLA-4 ( Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e ). Similar to the results of the TIMER database analysis, we also found that TRIP13 was significantly positively correlated with PD-1, PD-L1 or CTLA-4 in hepatocellular carcinoma in the GEPIA database (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e ). These results suggested that TRIP13 may be involved in tumor immune evasion in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHCC is notorious for its poor prognosis and still lacks effective therapies. Understanding of the molecular mechanism of HCC may promote the development of effective therapeutic targets and identify the prognostic biomarkers. There is growing evidence that TRIP13 plays a significant role in the initiation and malignant development of multiple cancers, including HCC. However, the role of TRIP13 in HCC remain unclear and further research is necessary. In this study, we first used TCGA to conduct a pan-cancer analysis of TRIP13 expression, and then further validated the expression of TRIP13 using GEPIA database. Survival analysis of TRIP13 in these cancer types showed thatTRIP13 is associated with poor prognosis in HCC. Zhu reported that TRIP13 is a promising candidate oncogene in HCC, and mechanistically cell migration, invasion, and metastasis is promoted by the interaction of TRIP13 and ACTN4 in the AKT/mTOR signaling pathway[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Zhou et al. found that miR-29b-3p was an important suppressor of HCC and negative regulator of HCP5 which promote tumor grow and metastatis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Masamichi Hayashi et al. found that miR-23b-3p was an oncogenic microRNA in HCC cell lines, and its overexpression in HCC tissues is an important biomarker for prognosis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo investigate miRNAs regulating TRIP13, we performed seven prediction programs, including PITA, RNA22, miRmap, microT, miRanda, PicTar and Targescan, to identify the miRNAs that may bind to TRIP13. Finally, 13 miRNAs were identified. Most of these miRNAs played the suppressive role in HCC. For example, Xu et al reported that miR-885-5p can regulate glucose metabolism of HCC cells in the hypoxic area, and partly reduced the malignant growth of HCC cells in vivo and in vitro by inhibiting several enzymes in the glycolysis pathway[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Other studies have also revealed hsa-miR-29c-3p as an suppressive role in the proliferation and migration of HCC. Wu et al. showed that miR-29c-3p plays a tumor suppressor miRNA in hepatocellular carcinoma. miR-29c-3p inhibits the malignant progression of hepatocellular carcinoma by reducing DNMT3B expression and promoting LATS1 demethylation, thereby restoring its expression and subsequently activating the Hippo signaling pathway[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].Based on the ceRNA hypothesis, potential lncRNAs of the hsa-miR-29c-3p /TRIP13 axis was carcinogenic lncRNAs in HCC. Next, the upstream lncRNAs of hsa-miR-29c-3p /TRIP13 axis were predicted and the potential lncRNAs were found. Through expression analysis, survival analysis and correlation analysis, SNHG17 and SNHG20 were identified as the most potentially upregulated lncRNAs in HCC. These two lncRNAs act as oncogenes in a variety of malignant tumors, including HCC. SNHG17 is up-regulated in HCC tissues, and overexpression of SNHG17 can promote the growth, invasion and metastasis of HCC. Meanwhile, SNHG17 regulates its function and upregulates RFX1 by sponging miR-3180-3p[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. SNHG20 mediated- suppression depends in part on the regulation of e-cadherin expression through interaction with EZH2[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our study suggests that the SNHG17/SNHG20/ hsa-miR-29c-3p /TRIP13 axis may be a potential pathway for the progress of hepatocellular carcinoma.\u003c/p\u003e \u003cp\u003eThe increasing evidence confirmed that tumor immune cell infiltration may play a important role in tumor immune evasion and immune tolerance, and may become a new therapeutic target for tumor, which affects the efficacy of chemotherapy, radiotherapy and immunotherapy for tumor patients[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].Our study shows that TRIP13 expression is involved in the infiltration of a variety of immune cells, including B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils and dendritic cells. TRIP13 was also significantly positively associated with immune chechpoints. These results suggest that the carcinogenic effect of TRIP13 in HCC may be related to tumor immune invasion.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTRIP13highly expresses in multiple types of human tumors, and is positively associated with poor prognosis of hepatocellular carcinoma. We identified the upstream regulatory mechanism of TRIP13 in hepatocellular carcinoma, SNHG17/SNHG20/ hsa-miR-29c-3p /TRIP13 axis ( Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In addition, TRIP13 may play a pro-tumor role by increasing tumor immunosuppressive cell infiltration and immune checkpoint expression. However, this study has some limitations that above results were only obtained by bioinformatics analysis. Therefore, the role of TRIP13 in HCC should be further investigated by more basic experiments and large clinical trials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank the timely help given by those whose work we could not cite due to space limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Cao YW and Ye YQ.; methodology, Ye YQ.; software, Cao YW.; validation, Ying JX and Ye YQ; formal analysis, Ye YQ; investigation, Cao YW.; resources,Cao YW; data curation, Ying JX.; writing\u0026mdash;original draft preparation, Cao YW and Ye YQ; writing\u0026mdash;review and editing, Ye YQ and Cao YW; visualization, Cao YW and Ye YQ; supervision, Ye YQ; project administration, Ye YQ; funding acquisition, Ying JX; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability and data materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets for this study can be found in the following website: https://commonfund.nih.gov/GTEx/,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttp://gepia.cancer-pku.cn/, https://www.Proteinatlas.org/, http://starbase.sysu.edu.cn/, https://cistrome.shinyapps.io/timer/. \u0026nbsp;All data generated or analyzed during this study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 394\u0026ndash;424 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, Z. et al. CircCAMSAP1 promotes hepatocellular carcinoma progression through miR-1294/GRAMD1A pathway. \u003cem\u003eJ. Cell. Mol. Med.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 3793\u0026ndash;3802 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForner, A., Llovet, J. M. \u0026amp; Bruix, J. Hepatocellular carcinoma. \u003cem\u003eLancet\u003c/em\u003e. \u003cb\u003e379\u003c/b\u003e, 1245\u0026ndash;1255 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong, M. C. et al. International incidence and mortality trends of liver cancer: a global profile. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 45846 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAffo, S., Yu, L. X. \u0026amp; Schwabe, R. F. The Role of Cancer-Associated Fibroblasts and Fibrosis in Liver Cancer. \u003cem\u003eAnnu. Rev. Pathol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 153\u0026ndash;186 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOikonomopoulos, G., Aravind, P. \u0026amp; Sarker, D. Lenvatinib: a potential breakthrough in advanced hepatocellular carcinoma? \u003cem\u003eFuture Oncol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 465\u0026ndash;476 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, K. et al. Thyroid hormone receptor interacting protein 13 (TRIP13) AAA-ATPase is a novel mitotic checkpoint-silencing protein. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e289\u003c/b\u003e, 23928\u0026ndash;23937 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. et al. A Small-Molecule Inhibitor Targeting TRIP13 Suppresses Multiple Myeloma Progression. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e, 536\u0026ndash;548 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe, Q. et al. TRIP13 is a protein-remodeling AAA plus ATPase that catalyzes MAD2 conformation switching. \u003cem\u003eElife\u003c/em\u003e, 4 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N. \u0026amp; Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 1043\u0026ndash;1048 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, H. T. \u0026amp; Poon, R. Y. C. TRIP13 Regulates Both the Activation and Inactivation of the Spindle-Assembly Checkpoint. \u003cem\u003eCell. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1086\u0026ndash;1099 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEytan, E. et al. Disassembly of mitotic checkpoint complexes by the joint action of the AAA-ATPase TRIP13 and p31(comet). \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e111\u003c/b\u003e, 12019\u0026ndash;12024 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee, R. et al. N.J. D'Silva, TRIP13 promotes error-prone nonhomologous end joining and induces chemoresistance in head and neck cancer. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 4527 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao, Y. et al. TRIP13 impairs mitotic checkpoint surveillance and is associated with poor prognosis in multiple myeloma. \u003cem\u003eOncotarget\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 26718\u0026ndash;26731 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, N. et al. TRIP13 promotes tumor growth and is associated with poor prognosis in colorectal cancer. \u003cem\u003eCell. Death Dis.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 402 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, Y. et al. Increased expression of TRIP13 drives the tumorigenesis of bladder cancer in association with the EGFR signaling pathway. \u003cem\u003eInt. J. Biol. Sci.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1488\u0026ndash;1499 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, L. et al. TRIP13 is a predictor for poor prognosis and regulates cell proliferation, migration and invasion in prostate cancer. \u003cem\u003eInt. J. Biol. Macromol.\u003c/em\u003e \u003cb\u003e121\u003c/b\u003e, 200\u0026ndash;206 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConsortium, G. T. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. \u003cem\u003eScience\u003c/em\u003e. \u003cb\u003e348\u003c/b\u003e, 648\u0026ndash;660 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomczak, K., Czerwinska, P. \u0026amp; Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. \u003cem\u003eContemp. Oncol. (Pozn)\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e, A68\u0026ndash;77 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudson, T. J. et al. International network of cancer genome projects. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e464\u003c/b\u003e, 993\u0026ndash;998 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal, S. et al. DCZ0415, a small-molecule inhibitor targeting TRIP13, inhibits EMT and metastasis via inactivation of the FGFR4/STAT3 axis and the Wnt/beta-catenin pathway in colorectal cancer. \u003cem\u003eMol. Oncol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1728\u0026ndash;1745 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, M. X. et al. Elevated TRIP13 drives the AKT/mTOR pathway to induce the progression of hepatocellular carcinoma via interacting with ACTN4. \u003cem\u003eJ. Exp. Clin. Cancer Res.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 409 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Y. et al. Long non-coding RNA HCP5 functions as a sponge of miR-29b-3p and promotes cell growth and metastasis in hepatocellular carcinoma through upregulating DNMT3A. \u003cem\u003eAging (Albany NY)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 16267\u0026ndash;16286 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayashi, M. et al. miR-23b-3p Plays an Oncogenic Role in Hepatocellular Carcinoma. \u003cem\u003eAnn. Surg. Oncol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 3416\u0026ndash;3426 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, F. et al. miR-885-5p Negatively Regulates Warburg Effect by Silencing Hexokinase 2 in Liver Cancer. \u003cem\u003eMol. Ther. Nucleic Acids\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 308\u0026ndash;319 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, H. et al. miR-29c-3p regulates DNMT3B and LATS1 methylation to inhibit tumor progression in hepatocellular carcinoma. \u003cem\u003eCell. Death Dis.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 48 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, T. et al. Long Non-coding RNA SNHG17 Upregulates RFX1 by Sponging miR-3180-3p and Promotes Cellular Function in Hepatocellular Carcinoma. \u003cem\u003eFront. Genet.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 607636 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J. et al. Long non-coding RNA SNHG20 predicts a poor prognosis for HCC and promotes cell invasion by regulating the epithelial-to-mesenchymal transition. \u003cem\u003eBiomed. Pharmacother\u003c/em\u003e. \u003cb\u003e89\u003c/b\u003e, 857\u0026ndash;863 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Y. et al. PD-L1(+)CD8(+) T cells enrichment in lung cancer exerted regulatory function and tumor-promoting tolerance. \u003cem\u003eiScience\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 103785 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, D. et al. Combination of radiotherapy and suppression of Tregs enhances abscopal antitumor effect and inhibits metastasis in rectal cancer. \u003cem\u003eJ. Immunother Cancer\u003c/em\u003e, \u003cb\u003e8\u003c/b\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, Z. et al. GSDME enhances Cisplatin sensitivity to regress non-small cell lung carcinoma by mediating pyroptosis to trigger antitumor immunocyte infiltration. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 159 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, bioinformatics, prognosis, long non-coding RNA, TCGA","lastPublishedDoi":"10.21203/rs.3.rs-5276784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5276784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver cancers and is charactered by poor prognosis. The identification of potential prognostic biomarkers is essential for HCC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u0026nbsp; \u003c/strong\u003eIn this study, Gene Expression Profiling Interative Analysis (GEPIA) online tool was used to the different expression of TRIP13 and has-miR-29c-3p axis between tumor samples and normal samples in multiple cancers including hepatocellular carcinoma (HCC). The oncological value of TRIP13 and has-miR-29c-3p axis in cancer was revealed by Kaplan-Meier analysis. The upstream miRNAs of TRIP13 were predicted by the starBase database and Cytoscape software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this work, we identified that compared to normal tissues, the expression of TRIP13 was high in tumor samples by The Cancer Genome Atlas (TCGA) and Genotypic-Tissue Expression (GTEx) data. We further revealed that TRIP13 may be a potential oncogene in HCC. Subsequently, hsa-miR-29c-3p was responsible for TRIP13 overexpression, which was identified by expression analysis, correlation analysis and survival analysis of the target gene. Further, hsa-miR-29c-3p was associated with the poor prognosis of cancer patients. Moreover, the expression level of TRIP13 was positively correlated with tumor immune cell infiltration with high infiltrating level of B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, macrophages, exhausted CD8\u003csup\u003e+\u003c/sup\u003e T cells and immune checkpoints including PD1, PD-L1 and CTLA-4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn general, our findings reveal that TRIP13 and hsa-miR-29c-3p promotes the malignant development of hepatocellular carcinoma, which is a potential new therapeutic target and a prognostic biomarker for HCC.\u003c/p\u003e","manuscriptTitle":"LncRNA-mediated High Expression of TRIP13 Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 08:35:34","doi":"10.21203/rs.3.rs-5276784/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c61cde23-c64d-457e-9d37-7a6bb55cd26d","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40165147,"name":"Biological sciences/Cancer"},{"id":40165148,"name":"Biological sciences/Cell biology"},{"id":40165149,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2025-04-23T06:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-09 08:35:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5276784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5276784","identity":"rs-5276784","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}