A Novel Biomarker TLCD1 Correlates With Prognosis and Immune Infiltrates in Hepatocellular Carcinoma

A Novel Biomarker TLCD1 Correlates with Prognosis and Immune Infiltrates in Hepatocellular Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research A Novel Biomarker TLCD1 Correlates with Prognosis and Immune Infiltrates in Hepatocellular Carcinoma Hanyu Shen, Ailong Huang, Dandan Zhu, Jiali Zhang, Shiqi Ren, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-20580/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Abstract Background: The HCC has seen a spike in the morbidity and the mortality rate in recent times. This calls for an urgent understanding of the underlying molecular mechanisms of the HCC and to speed up the quest for the search of the target molecules to ensure quick diagnosis and prognosis subsequently. TLCD1 is a gene only reported in membrane fluidity. The variations in the TLCD1 expression levels related to the infiltration of the immune cells in HCC and the prognosis shall be examined initially. Methods: The data received from TCGA shall provide the details of the gene expression, clinicopathology analysis and TME estimate, along with the enrichment analysis. Moreover, we performed additional analysis of the bioinformatics available. The immune responses of TLCD1 expression in HCC were analyzed using CIBERSORT and TIMER, while the statistical analysis was handled through R. HPA was used to validate the outcomes. Results: Higher TLCD1 expression is strongly correlated with a poor prognostic and worse overall survival. Specifically, the increase in TLCD1 expression positively correlated with Tregs cells and T cells CD4 memory resting. The pathways strongly associated with TLCD1 was fatty acid metabolism and PPAR signaling pathway. Conclusions: From the outcome of the study, it could be surmised that TLCD1 could be considered as a potential target for future treatment of HCC, as it was observed to be associated with the tumor-infiltrating immune cells in tumor microenvironment, establishing itself as a novel potential prognostic biomarker in HCC. Translational Medicine TLDC1 HCC TCGA fatty acid metabolism TME Tregs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Liver cancer is one of the most common cancer with a rate of high mortality around the world. In the cancer of the liver, the HCC is the most common type, with an increasing trend of incidence presently 1 , 2 . China accounts for about 50% of the morbidity number of liver cancer in the world 2 . For the HCC patients till now, early diagnosis such as the B-mode, CT scan and the serum AFP have been the common tools 3 , 4 . Despite the developments in the detection and management of HCC, patients with HCC remain suffered a bad life for current therapies 5 . Hence, it becomes necessary to understand the underlying molecular mechanisms of HCC and identify the target molecules to assist quick diagnosis and prognosis. TLCD1 is a protein coding gene. The human and mouse TLCD1 gene expression is evident in a variety of tissues, with muscle, heart, fat and liver being the strongest for TLCD1 presence. Using a series of careful immune-detection experiments and the Myc-epitope tagging mechanisms, the TLCD1 was initially found in the plasma membrane of the mammalian cells 6 . Its action at the level of the plasma membrane in limiting the amounts of LCPUFA-containing phospholipids was proved in the previous study by its localization and the effect on PUFA content in membrane phospholipids 7 . In human physiology the LCPUFA are substances with a range of important structural and regulatory functions 8 . LCPUFA are involved in numerous biological processes and are major components of complex lipid molecules 9 , 10 . Liver is an important place of lipid metabolism and lipid is a crucial component of membrane. lipid destruction can reflect their important roles during tumor initiation and disease progression at different points such as disruption of normal tissue architecture, cancer cell migration, interaction of cancer cells with components of the tumor stroma and lipid metabolic reprogramming in cancer cells 11 – 14 . Lipid alterations which might be involved in the onset of cancer and its development, such as lung cancer 15 , breast cancer 16 , 17 , lung cancer 15 and colon cancer 18 . When TLCD1 was removed more unsaturated fats were incorporated, thereby leaving the membranes in a healthy restored state, albeit the excess saturated fat being the medium in which the cells were being grown 7 . HCC is a common type of cancer. However, the role of TLCD1 in HCC remains unknown. In the research, we explored the expression of TLCD1 in human HCC samples founded on microarray data that we downloaded from the TCGA database. Increasing evidence indicates that tumorigenesis is often triggered in a tumor microenvironment (TME), which is significantly influence the gene expression of tumor cells,TME composed extracellular matrix, blood or lymphatic vessels, fibroblasts, immune cells and inflammatory cells 19 . Stromal and immune cells are two main types of nontumor components in the TME, and the investigation of their interaction has been valuable for developing innovative HCC-directed immunotherapies. In this study, we calculated the stromal and immune scores of HCC cohorts from the TCGA database by applying the ESTIMATE algorithm and investigate the relationship between TME cells and TLCD1 expression in patients with HCC 20 . Meanwhile, we used R language (Version 3.5.3) and statistical analysis to examine the correlation of TLCD1 expression with clinical parameters as well as the prognosis in patients with HCC. GEPIA and ICGA data were used to confirm the relationship between TLCD1 and overall survival. Moreover, we performed an evaluation of the landscape of TME and TLCD1 in HCC. To delve deeper into the biological processes involved in the pathogenesis of HCC associated with the TLCD1 regulatory network, we performed the GSEA along with the enhanced GO and KEGG analyses. Materials And Methods Gene Expression Analysis The TCGA official website for the liver provided the pertinent gene expression data (424 files, Workflow Type: HTSeq-FPKM) 21 . Certain customizable functions were made available from the GEPIA online database ( http://gepia.cancer-pku.cn/ ). Adopting a standard processing pipeline the RNA sequencing expression data of 8,587 normal samples and 9,736 tumors from the GTEx and the TCGA projects were analyzed through GEPIA 22 . The TCGA database provided the normal and tumor samples in the GEPIA database. To determine the differential expression of TLCD1, Boxplot, using the disease state as a variable, was graphed. Survival analysis and prognosis analysis The TCGA official website for the liver provided the data of systematic analysis of immune infiltrates and the clinical information (377cases, Data Type: Clinical Supplement). The cases in the TNM stage, distant metastasis, lymph node metastasis, local invasion, overall survival time, and insufficient or missing data on age, were excluded. ICGC The clinical data was further analyzed and retained. The TCGA provided the guidelines for the publication of this study. The GEPIA database computed the correlations of the disease-free survival rate with the TLCD1 expression in HCC. To further analysis the relationship between the survival days of HCC patients and the expression of high degree TLCD1, a total of 203 patients identified and acquired from ICGC dataset( https://icgc.org/ ) were chosen for validation. TME estimate We used the single-sample gene-set enrichment analysis algorithm to quantify the relative abundance of each cell infiltration in the HCC TME and the stromal and immune scores were calculated by applying the ESTIMATE package to the downloaded RNA expression data. We combine the TLCD1 with the TME cells for further analysis 23 . Immune Infiltrates Analysis The relationships between the possible tumor-infiltrating immune cells and the expression of TLCD1 was evaluated with the correlation module of TIMER, an efficient resource helping in the systematic analysis of the immune infiltrates across several types of cancer ( https://cistrome.shinyapps.io/timer/ ) 22 . To determine the abundance of tumor-infiltrating immune cells from gene expression profiles, a previously published statistical deconvolution method was applied by TIMER 24 . The abundance of immune infiltrates could be estimated from the TIMER database that included 10,897 samples across 32 cancer types from TCGA. The correlation of TLCD1 expression with the abundance of immune infiltrates, including the dendritic cells, neutrophils, macrophages, CD8 + T cells, CD4 + T cells, through the gene modules, and the TLCD1 expression in liver cancer was duly analyzed. The left-most pane displays the gene expression levels against the tumor purity 25 . Moreover, a deconvolution algorithm based on gene expression, CIBERSORT ( http://cibersort.stanford.edu/ ), can evaluate the changes in the expression of all the sets of the other genes in the sample against one specific set of genes. In the current analysis, via CIBERSORT, the immune response of 22 immune infiltrates cells in HCC, for determining its correlation with the molecular subpopulation and survival, was gauged. The gene expression datasets were uploaded to CIBERSORT web portal using the standard annotation files with the algorithm running at 1,000 permutations, its default signature matrix. Establishing a measure of confidence in the results a p-value for deconvolution through the Monte Carlo sampling was estimated by CIBERSORT. We used 374 tumor samples from the TCGA divided into 2 groups in order to assess the influence of TLCD1 expression in the immune microenvironment. To select the lymphocyte possibly affected by the expression of TLCD1 the p-value < 0.05 was set as the criterion. Gene Set Enrichment Analysis GSEA was performed using normalized RNA-Seq data by TCGA 26 . The annotated gene sets of c5.all.v7.0.symbols.gmt and c2.cp.kegg.v7.0.symbols.gmt in the Molecular Signatures Database (MSigDB) were selected in GSEA version 3.0. The number of permutations was set at 1,000 to determine the normalized enrichment score. GO terms, KEGG pathways were performed to explore the potential biological functions of TLCD1 by using GSEA. Enrichment results satisfying a nominal P-value < 0.05 and a false discovery rate FDR q-value < 0.25 were considered statistically significant. Human Protein Atlas The human protein atlas database (HPA) ( www.proteinatlas.org ) provides access to 32 human tissues and their protein expressions by using antibody profiling to accurately assess protein localization. 27 Additionally, the HPA provides measurements of RNA levels. HPA database was used to validate protein expression of TLCD1 between normal and liver cancer tissues. Statistical Analysis The R language (Version 3.5.3) conducted the download of the statistical analyses from TCGA. To calculate the 95% CI and the HR the multivariate Cox and the Univariate proportional hazards models were utilized. The comparison of several clinical characteristics with survival was done using the Univariate survival analysis. To evaluate the influence of TLCD1 expression and other clinical pathological factors (lymph node, distant metastasis, tumor status, grade, gender, and age) on survival, the Multivariate Cox analysis was conducted. The cut-off criterion was set with the P -value of TLCD1 expression < 0.05. Using the logistic regression, the correlations between the TCLD1 expression and the clinical characteristics were analyzed. Results TLCD1 expression is significantly upregulated in HCC The TLCD1 mRNA levels in the normal and the tumor tissues of liver cancer type were analyzed by using TCGA database, in order to determine the differences between the TLCD1 expression in the normal and tumor tissues. 50 normal files along with 374 tumor files were transformed to convert count data to values more consistent with the microarray results. The boxplot displayed the expression of TLCD1 between the HCC and the normal data (Fig. 1 A). A significantly higher TLCD1 expression was revealed in the tumor tissues ( p- value = 1.017e-24) by this analysis. Whereas, a significantly increased TLCD1 mRNA expression in HCC compared normal group with liver cancer group ( p- value 1) (Fig. 1 B) was found using the GEPIA database. Relationship between TLCD1 expression and clinical characteristics χ2 tests revealed the relationship between the TLCD1 expression and the clinical characteristics (Table.1). To investigate the association with multivariable characteristics and tumor progression in TCGA patients, we using cox regression(Table.2). Univariate analysis of correlation revealed that some factors, including pathological stage (HR = 1.865, p- value < 0.001), tumor (HR = 1.804, p- value < 0.001) along with the expression of TLCD1(HR = 1.036, p- value = 0.003) are significantly associated with tumor development. In multivariate analysis as a forest boxplot was observed in Fig. 1 C, the TLCD1( p- value = 0.041) expression is an independent prognostic factor for tumor progression. Table 1. Clinical Characteristics of the Patients at Baseline. characteristic n low high Pearson x 2 p total 234 117 117 age ≤60 130 58 72 3.3923 0.0655 >60 104 59 45 gender male 160 82 78 0.3162 0.57389 female 74 35 39 grade I 29 18 11 10.2567 0.01651 II 103 59 44 III 92 38 54 IV 10 2 8 stage I 113 66 47 7.8372 0.0494 II 49 19 30 III 67 31 36 IV 5 1 4 tumor I 115 67 48 14.246 0.002588 II 51 20 31 III 58 27 31 IV 10 3 7 Table 2. Association between TLCD1 expression and clinicopathologic characteristics using logistic regression. Clinical characteristic Odds ratio (OR.95L-OR.95H) P-Value Age 0.69(0.45-1.04) 0.08 Grade (II vs I) 1.60(0.86-3.05) 0.14 Grade (III vs I) 2.78(1.45-5.49) 0.00 Grade (IV vs I) 9.47(2.22-65.89) 0.01 Grade (I,II vs III,IV) 2.12(1.38-3.29) 0.00 Stage (II vs I) 2.00(1.18-3.43) 0.01 Stage (III vs I) 1.60(0.95-2.72) 0.08 Stage (IV vs I) 5.44(0.79-107.72) 0.13 Tumor (III vs I) 2.09(1.26-3.52) 0.00 Relationship between TLCD1 expression and poor overall survival To discover the associations with TLCD1 expression and overall survival in HCC patients, we firstly validated by TCGA datasets, as shown in Fig. 2 A. Patients with higher TLCD1 expression had particularly shorter OS (P = 0.019), Furthermore, we used GEPIA database find high levels of TLCD1 mRNA also strongly correlated with a worse survival ( p- value = 0.0035) (Fig. 2 B). Patients obtained from ICGC database was also used to validate the group of high TLCD1 expression will lead to poor overall survival. The result was considered statistically significant ( p- value < 0.001) (Fig. 2 C). Univariate analysis using logistic regression revealed that TLCD1 expression was associated with poor prognostic clinicopathologic characteristics. Increased TLCD1 expression in HCC as significantly associated with grade (III vs I, p- value = 0.00; IV vs I, p- value = 0.01), stage (II vs I, p- value = 0.01), tumor status (III vs I, p- value = 0.00). These results suggested that liver cancer patients with high TLCD1 expression are more susceptible to a more advanced grade, stage and tumor status than those with low TLCD1 expression (Table.3). Table 3. Correlation between overall survival and multivariable characteristics in TCGA patients via (a) Cox regression (b) Multivariate survival model. characteristic HR HR.95L HR.95H pvalue age 1.005 0.987 1.023 0.591 gender 1.282 0.801 2.053 0.301 grade 1.017 0.746 1.387 0.914 stage 1.865 1.456 2.388 0.000 T 1.804 1.434 2.270 0.000 M 3.850 1.207 12.281 0.023 N 2.022 0.494 8.276 0.328 TLCD1 1.036 1.012 1.060 0.003 characteristic HR HR.95L HR.95H pvalue age 1.008 0.989 1.028 0.392 gender 1.003 0.601 1.674 0.992 grade 1.063 0.766 1.475 0.713 stage 0.895 0.333 2.407 0.826 T 1.961 0.808 4.760 0.137 M 0.975 0.256 3.710 0.971 N 2.519 0.399 15.904 0.326 TLCD1 1.026 1.001 1.051 0.041 Correlation between TLCD1 and the landscape of TME in HCC We first used the single sample GSEA algorithm to depict the enrichment landscape of the abundance of each cell infiltration in the HCC TME, as well as enrichment scores generated from each sample were fully clustered by hierarchical clustering method (Fig. 3 A). Moreover, we proceed to excavate the relationships between TLCD1, stromal cells and immune cells, as shown in Fig. 3 B& 3 C. These results indicated that the expression of TLCD1 was associated with tumor microenvironment components (stromal and immune cells). Correlation between TLCD1 expression and tumor-infiltrating immune cells It was aptly established that tumor-infiltrating lymphocytes were an independent predictor of survival and the sentinel lymph node status in cancers 28 . Hence, whether TLCD1 expression was correlated with the immune infiltration levels in liver cancer was investigated. The correlations of TLCD1 expression with the immune infiltration levels in liver cancer was assessed from TIMER. It was observed that TLCD1 expression had positive correlations with dendritic cells ( p- value = 3.67e-2), macrophages ( p- value = 2.09e-4) and B cell ( p- value = 4.04e-5) as indicated in Fig. 4 A. A specific role in the immune infiltration in liver cancer was played by the TLCD1 as evidenced from the findings. Besides, we examined if the TLCD1 expression was associated with immune infiltration in the liver cancer cases. According to TLCD1 expression, the 374 tumor samples were divided into 2 parts. Overall, the screening criteria was met by the 187 samples of low and high expression groups. To infer the density of 22 types of immune cells and to explore the gene expression profiles of the downloaded samples, the established computational resource CIBERSORT was used. The assessment of the differing concentrations in the low and high TLCD1 expression groups of the 22 immune cell subtypes was done by applying the CIBERSORT algorithm. The results were exhibited in Fig. 4 B. T cells CD4 memory resting, T cells follicular helper, T cells regulatory (Tregs), Monocytes, Macrophages M0, Macrophages M2, and Mast cells resting were affected by TLCD1 expression. We observed considerable differences in T cells CD4 memory resting and Tregs, macrophages and mast cells between high group and low group. Afterwards, compared with low expression group, Tregs apparently increased ( p- value < 0.001) in high expression group. Moreover, as shown in Fig. 4 C the correlations between the 22 types of immune cells were compared as a correlation heat map. The outcome revealed that the different tumor-infiltrating immune cells subpopulations ratios were moderate to weakly correlated. Excavate correlating Tregs markers The relation between TLCD1 and Tregs gene markers in the liver tissue was determined using the correlation module of GEPIA Pearson correlation analysis. TNFRSF18, IL1R2, JAK1, CTLA4, IL1R1, TNFRSF4 and CD274 are serve as potential biomarkers of Tregs. We analyzed the relationship between these metabolic genes and TLCD1 expression (high VS low), as shown in Fig. 5 A, The correlation between TLCD1 expression and biomarkers expression in the TCGA database in Fig. 5 B. GO and KEGG pathway analysis To explore the potential biological functions and to study the regulatory mechanism of the TLCD1, the KEGG pathways and GO terms were performed using GSEA. In the enrichment of the KEGG pathways and the GO terms the GSEA revealed significant differences (FDR < 0.25, p- value < 0.050). We selected the most significantly enriched signaling pathways based on their normalized enrichment score (NES). As shown in Table.4, the GO annotation in high TLCD1 expression resulted five negative correlated parts: protein activation cascade, vitamin B6 binding, retinoic acid metabolic process, cellular amino acid catabolic process and fatty acid catabolic process. The results revealed that the biological processes and molecular functions strongly associated with TLCD1 was fatty acid catabolic process, as shown in Fig. 6 A. The KEGG pathway analysis showed the TLCD1 was significantly enriched in five negative pathways: tryptophan metabolism, fatty acid metabolism, drug metabolism cytochrome p450, retinol metabolism and PPAR signaling pathway, as shown in Fig. 6 B. It was indicated that the metabolism pathways were strongly associated with TLCD1. In HCC patients all these functions and mechanisms are critically important. Table 4. Signaling pathways most significantly correlated with TLCD1 expression based on their normalized enrichment score (NES) and p-value. NAME NES NOM p-val FDR q-val GO tryptophan metabolism -2.20 0.00 0.00 fatty acid metabolism -2.06 0.00 0.00 drug metabolism cytochrome p450 -2.03 0.00 0.00 retinol metabolism -2.03 0.00 0.00 PPAR signaling pathway -1.87 0.00 0.01 KEGG protein activation cascade -2.19 0.00 0.00 vitamin B6 binding -2.16 0.00 0.00 retinoic acid metabolic process -2.11 0.00 0.01 cellular amino acid catabolic process -2.10 0.00 0.01 fatty acid catabolic process -2.01 0.00 0.01 HPA validation we further analyzed the protein level of TLCD1 in clinical liver tissues from HPA database. The results of immunohistochemical indicated that the protein expression level of TLCD1 significantly abnormal between normal tissues and HCC tissues. Immunohistochemistry analysis available from the HPA showed that in tumor tissues, TLCD1 has higher levels of expression compared to non-tumor tissues (Fig. 6 C). Discussion TLCD1 is a gene only reported in membrane fluidity. Firstly, the variations in TLCD1 expression level related to prognosis and infiltration of immune cells in HCC were determined. Responding to the hormonal signals, the major organ, the liver controls the glucose and the lipid metabolism 29 . The action of TLCD1 at the level of the plasma membrane by limiting the amounts of LCPUFA-containing phospholipids was suggested by the previous study 7 . In the maintenance of the structure and function of the cell membrane and cancer metabolism the polyunsaturated fatty acids played a significant role 30 , 31 . When lipid metabolism and fatty acid catabolic are disorder, a series of pathological changes will occur in the liver. Our studies suggested that TLCD1 could be used as a promising cancer biomarker in HCC as it was found to be having a potential influence on tumor immunology. During our current research, the expression of TLCD1 as a prognostic biomarker in HCC was first explored. All data of HCC patients downloaded from TCGA were performed to estimate the prognostic value. From the perspective of clinical pathology, tumor-infiltrating immune cells and biological functions, it was observed that the up-regulated TLCD1 was an independent prognostic factor for the overall poor survival rate. The liver cancer patients with high TLCD1 expression were found to be more susceptible to a more advanced tumor, grade, and stage status against the low expression of TLCD1. The potential influences of high TLCD1 expression levels on the mechanisms of tumor immunology and tumorigenesis in HCC progression were proposed by our results. For human HCC prognosis, the TLCD1 could serve as a predictor. The correlation between the diverse immune infiltration levels and TLCD1 expression in liver cancer was another important aspect of our study. The study also demonstrated that the infiltration levels of immune cells in HCC could be detected with TIMER. The outcomes revealed that TLCD1 had strongest relationships with B cells, macrophage and dendritic cells. Besides, CIBERSORT confirmed the presence of a moderate to strong positive relationships between the infiltration levels of immune cells and the TLCD1 expression, especially Tregs and dendritic cells. The results in our study could indicate correlation between possible mechanism where TLCD1 regulates Tregs functions in HCC. Regulatory T cells contributes to failure of T cell-mediated immunity 32 . Through cytokine secretion and via cell-to-cell contact, Tregs suppress activation and differentiation of many cell type and sustain tolerance to self-antigens and regulate the immune system 33 . There are opinions that Tregs have a central role in emergence of HCC persistence. We investigated TLCD1 has a strong effect on gene (TNFRSF18 34 , IL1R1, IL1R2 35 , JAK1 36 , CTLA4 37 , TNFRSF4 38 and CD274 39 ) expression related to lipid metabolism. GO term and KEGG pathway analysis in this study revealed that the up-regulated TLCD1 to be primarily linked with fatty acid catabolic process and PPAR signaling pathway. Lipid metabolism, including fatty acid catabolic, is a primary function of the liver 40 . Our study here implicated that overexpression of TLCD1 in HCC patients could induce lipid accumulation and disorder of lipid metabolism. Further studies are needed to confirm if and how TLCD1 supports HCC metastasis in vivo. Evidently, our results could establish the development in the field of TLCD1 biological function in promoting the motility of the HCC cancer cells. Conclusion Our study was first to identify TLCD1 as a new biomarker of the hepatocellular carcinoma thereby helping to determine how the TME cells and the fatty acid catabolic process could promote the development of liver cancer. In HCC studies, it could be a brand-new biomarker. The biomarker therapies could become a promising future option in the treatment of liver diseases with a better understanding of the functional diversity and heterogeneity of TLCD1. An effective design of the therapeutic strategies and diagnosis for treating human HCC could be contributed by TLCD1. Abbreviations HCC: hepatocellular carcinoma, TCGA: The Cancer Genome Atlas, AFP: alpha-fetoprotein, TLCD1: TLC Domain Containing 1, LCPUFA: Long-chain Polyunsaturated Fatty Acids, GEPIA: Expression Profiling Interactive Analysis, GSEA: Gene Set Enrichment Analysis, GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes Declarations Acknowledgements None Authors’ contributions HY-S, ZH-W, YN-D, DD-Z, AL-H came up with the design and conception. The data analysis and visualization were conducted by HY-S, SQ-R, L-D, JL-Z and W-W. The original writing of the draft and its editing were by HY-S, SQ-R, PP-S, ZH-W and YN-D. All authors wrote and reviewed the manuscript, and finally approved the submitted manuscript Funding This study was funded by the National Natural Science Foundation of China, China (No. 81871677) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX20_2839). Availability of data and materials The data was downloaded from TCGA database. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Conflicts of interests: None References Gong D-Y, Chen E-Q, Huang F-J, Leng X-H, Cheng X, Tang H. Role and Functional Domain of Hepatitis B Virus X Protein in Regulating HBV Transcription and Replication in Vitro and in Vivo. Viruses. 2013. Fan X, Wang P, Sun Y, et al. Induction of apoptosis by an oleanolic acid derivative in SMMC-7721 human hepatocellular carcinoma cells is associated with mitochondrial dysfunction. 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Transcriptional and epigenetic basis of Treg cell development and function: its genetic anomalies or variations in autoimmune diseases. Cell research. 2020;30(6):465-474. Jacquemin C, Augusto JF, Scherlinger M, et al. OX40L/OX40 axis impairs follicular and natural Treg function in human SLE. JCI insight. 2018;3(24). Di Pilato M, Kim EY, Cadilha BL, et al. Targeting the CBM complex causes T(reg) cells to prime tumours for immune checkpoint therapy. Nature. 2019;570(7759):112-116. Yan G, Li X, Peng Y, et al. The Fatty Acid beta-Oxidation Pathway is Activated by Leucine Deprivation in HepG2 Cells: A Comparative Proteomics Study. Sci Rep. 2017;7(1):1914. Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-20580","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":59007860,"identity":"269aa882-11c2-487b-8147-20cc12490346","order_by":0,"name":"Hanyu Shen","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hanyu","middleName":"","lastName":"Shen","suffix":""},{"id":59007861,"identity":"5e6b9030-4dbc-408a-a31a-bebc80d8db2b","order_by":1,"name":"Ailong Huang","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ailong","middleName":"","lastName":"Huang","suffix":""},{"id":59007862,"identity":"5442d3b0-bd10-4868-afe8-2d5120479865","order_by":2,"name":"Dandan Zhu","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Zhu","suffix":""},{"id":59007863,"identity":"98ad8d18-b83e-4347-92cf-ee20a4b6a599","order_by":3,"name":"Jiali Zhang","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiali","middleName":"","lastName":"Zhang","suffix":""},{"id":59007864,"identity":"480e615c-fb6c-4dac-b348-3d7633341754","order_by":4,"name":"Shiqi Ren","email":"","orcid":"","institution":"Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226000, People's Republic of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Ren","suffix":""},{"id":59007865,"identity":"22aa3e78-5fae-43d5-8664-8fd2b177ede9","order_by":5,"name":"Lian Duan","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lian","middleName":"","lastName":"Duan","suffix":""},{"id":59007866,"identity":"76b854dc-7f7f-450f-b5bf-bb0bd6375afd","order_by":6,"name":"Pingping Sun","email":"","orcid":"","institution":"Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226000, People's Republic of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pingping","middleName":"","lastName":"Sun","suffix":""},{"id":59007867,"identity":"c8f62c69-96f8-4bd1-b3f2-079eb5473775","order_by":7,"name":"Ziheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2RsUrEMBjHvxKIy1e7RjzaV/ikg8i9TILgrU6Hg0iOQrPcAxR8isMXSAl0knN1cOh0890ityimIB5Ir3QUzA+SIfx/fPknAIHAH4Rntdmrz3vMGLPQHs6PK6eCabHlTXphuAT5kx5QUnGizyrOcnhBGqfw84XOEblaFPguVOngMlsSbOcOkkfdr0xqfY1iogyLn0g+O7gqkaJq7UC82X4FlHZI3ZR41co7B9QgsdiPIyGPKf5KknViayV9Kx9DilBFVFlf32F0mBINKViXsNP+kQue+y4zpObmtl6uZyhe+5XMmI2v478ycRuxK6cpObdq9/NpmlT9ym8K7HbrF47Kex7GBgOBQOAf8QUsRFqlUrAkhgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8873-732X","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ziheng","middleName":"","lastName":"Wang","suffix":""},{"id":59007868,"identity":"6c3e155f-6a3b-4d83-8a75-343d4b08a305","order_by":8,"name":"Yinong Duan","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yinong","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2020-04-01 10:20:09","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-20580/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-20580/v3","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14876401,"identity":"04cd8272-3959-4bb2-afba-03f6690b0743","added_by":"auto","created_at":"2020-07-16 18:12:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56988,"visible":true,"origin":"","legend":"(A)The expression of TLCD1 between normal and tumor tissues in TCGA (B) TLCD1 mRNA expression levels in normal and HCC tissues, as obtained from GEPIA (C)Multivariate Cox analysis of TLCD1 expression and other clinicopathological variables","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42288/v1/Fig1.JPG"},{"id":14876402,"identity":"0204b139-0d26-4394-90ca-67c08a4a3ab4","added_by":"auto","created_at":"2020-07-16 18:12:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99998,"visible":true,"origin":"","legend":"Kaplan-Meier analyses of TLCD1 expression for patient survival. (A)TCGA database (B)GEPIA database (C)ICGC database","description":"","filename":"Fig2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42288/v1/Fig2.JPG"},{"id":14876403,"identity":"2bd05e88-a1ec-4c2d-883d-0532748e454d","added_by":"auto","created_at":"2020-07-16 18:12:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126865,"visible":true,"origin":"","legend":"(A) Hierarchical clustering heatmap of each cell infiltration in the HCC (B) The relationships between TLCD1 expression and stromal score (C) The relationships between TLCD1 expression and immune cells","description":"","filename":"Fig3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42288/v1/Fig3.JPG"},{"id":14876404,"identity":"f7783a3f-12af-48c7-b9f9-872b2524cc47","added_by":"auto","created_at":"2020-07-16 18:12:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126596,"visible":true,"origin":"","legend":"(A) Correlations between TLCD1 expression and immune infiltration levels (B) The varied proportions of 22 subtypes of immune cells in high and low TLCD1 expression groups in tumor samples (C) Heatmap of 22 immune infiltration cells in tumor samples.","description":"","filename":"Fig4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42288/v1/Fig4.JPG"},{"id":14876405,"identity":"e041721f-44a4-4725-896e-a28785f228c6","added_by":"auto","created_at":"2020-07-16 18:12:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78246,"visible":true,"origin":"","legend":"(A) Box plot of metabolic genes and TLCD1 expression (B) Correlation between TLCD1 expression and Treg-related biomarkers expression","description":"","filename":"Fig5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42288/v1/Fig5.JPG"},{"id":14876406,"identity":"9564eaeb-e638-4c18-a34f-29ce4c9c2a93","added_by":"auto","created_at":"2020-07-16 18:12:14","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":71934,"visible":true,"origin":"","legend":"(A) GO term analysis revealed five correct groups (B) KEGG pathway showed five correlated groups. (C) Representative immunohistochemistry staining results reveal the protein level expression of TLCD1 in HCC and normal tissues in HPA database.","description":"","filename":"Fig6.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42288/v1/Fig6.JPG"},{"id":14876399,"identity":"2a826695-6987-41b1-be48-72c8f197d242","added_by":"auto","created_at":"2021-09-17 02:29:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":911610,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-42288/v1/c13e0a1d-b6fe-4f50-bb0a-b550b59f5456.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eA Novel Biomarker TLCD1 Correlates with Prognosis and Immune Infiltrates in Hepatocellular Carcinoma\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eLiver cancer is one of the most common cancer with a rate of high mortality around the world. In the cancer of the liver, the HCC is the most common type, with an increasing trend of incidence presently\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. China accounts for about 50% of the morbidity number of liver cancer in the world\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For the HCC patients till now, early diagnosis such as the B-mode, CT scan and the serum AFP have been the common tools \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Despite the developments in the detection and management of HCC, patients with HCC remain suffered a bad life for current therapies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Hence, it becomes necessary to understand the underlying molecular mechanisms of HCC and identify the target molecules to assist quick diagnosis and prognosis.\u003c/p\u003e \u003cp\u003eTLCD1 is a protein coding gene. The human and mouse TLCD1 gene expression is evident in a variety of tissues, with muscle, heart, fat and liver being the strongest for TLCD1 presence. Using a series of careful immune-detection experiments and the Myc-epitope tagging mechanisms, the TLCD1 was initially found in the plasma membrane of the mammalian cells\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Its action at the level of the plasma membrane in limiting the amounts of LCPUFA-containing phospholipids was proved in the previous study by its localization and the effect on PUFA content in membrane phospholipids\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In human physiology the LCPUFA are substances with a range of important structural and regulatory functions \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. LCPUFA are involved in numerous biological processes and are major components of complex lipid molecules\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Liver is an important place of lipid metabolism and lipid is a crucial component of membrane. lipid destruction can reflect their important roles during tumor initiation and disease progression at different points such as disruption of normal tissue architecture, cancer cell migration, interaction of cancer cells with components of the tumor stroma and lipid metabolic reprogramming in cancer cells\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Lipid alterations which might be involved in the onset of cancer and its development, such as lung cancer\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, breast cancer\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, lung cancer\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and colon cancer\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. When TLCD1 was removed more unsaturated fats were incorporated, thereby leaving the membranes in a healthy restored state, albeit the excess saturated fat being the medium in which the cells were being grown\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHCC is a common type of cancer. However, the role of TLCD1 in HCC remains unknown. In the research, we explored the expression of TLCD1 in human HCC samples founded on microarray data that we downloaded from the TCGA database. Increasing evidence indicates that tumorigenesis is often triggered in a tumor microenvironment (TME), which is significantly influence the gene expression of tumor cells,TME composed extracellular matrix, blood or lymphatic vessels, fibroblasts, immune cells and inflammatory cells\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Stromal and immune cells are two main types of nontumor components in the TME, and the investigation of their interaction has been valuable for developing innovative HCC-directed immunotherapies. In this study, we calculated the stromal and immune scores of HCC cohorts from the TCGA database by applying the ESTIMATE algorithm and investigate the relationship between TME cells and TLCD1 expression in patients with HCC\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Meanwhile, we used R language (Version 3.5.3) and statistical analysis to examine the correlation of TLCD1 expression with clinical parameters as well as the prognosis in patients with HCC. GEPIA and ICGA data were used to confirm the relationship between TLCD1 and overall survival. Moreover, we performed an evaluation of the landscape of TME and TLCD1 in HCC. To delve deeper into the biological processes involved in the pathogenesis of HCC associated with the TLCD1 regulatory network, we performed the GSEA along with the enhanced GO and KEGG analyses.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGene Expression Analysis\u003c/h2\u003e \u003cp\u003eThe TCGA official website for the liver provided the pertinent gene expression data (424 files, Workflow Type: HTSeq-FPKM)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Certain customizable functions were made available from the GEPIA online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003c/span\u003e). Adopting a standard processing pipeline the RNA sequencing expression data of 8,587 normal samples and 9,736 tumors from the GTEx and the TCGA projects were analyzed through GEPIA \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The TCGA database provided the normal and tumor samples in the GEPIA database. To determine the differential expression of TLCD1, Boxplot, using the disease state as a variable, was graphed.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eSurvival analysis and prognosis analysis\u003c/h2\u003e \u003cp\u003eThe TCGA official website for the liver provided the data of systematic analysis of immune infiltrates and the clinical information (377cases, Data Type: Clinical Supplement). The cases in the TNM stage, distant metastasis, lymph node metastasis, local invasion, overall survival time, and insufficient or missing data on age, were excluded. ICGC The clinical data was further analyzed and retained. The TCGA provided the guidelines for the publication of this study. The GEPIA database computed the correlations of the disease-free survival rate with the TLCD1 expression in HCC. To further analysis the relationship between the survival days of HCC patients and the expression of high degree TLCD1, a total of 203 patients identified and acquired from ICGC dataset(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icgc.org/\u003c/span\u003e\u003c/span\u003e) were chosen for validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eTME estimate\u003c/h2\u003e \u003cp\u003eWe used the single-sample gene-set enrichment analysis algorithm to quantify the relative abundance of each cell infiltration in the HCC TME and the stromal and immune scores were calculated by applying the ESTIMATE package to the downloaded RNA expression data. We combine the TLCD1 with the TME cells for further analysis\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eImmune Infiltrates Analysis\u003c/h2\u003e \u003cp\u003eThe relationships between the possible tumor-infiltrating immune cells and the expression of TLCD1 was evaluated with the correlation module of TIMER, an efficient resource helping in the systematic analysis of the immune infiltrates across several types of cancer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e22\u003c/sup\u003e. To determine the abundance of tumor-infiltrating immune cells from gene expression profiles, a previously published statistical deconvolution method was applied by TIMER\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The abundance of immune infiltrates could be estimated from the TIMER database that included 10,897 samples across 32 cancer types from TCGA. The correlation of TLCD1 expression with the abundance of immune infiltrates, including the dendritic cells, neutrophils, macrophages, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, through the gene modules, and the TLCD1 expression in liver cancer was duly analyzed. The left-most pane displays the gene expression levels against the tumor purity\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Moreover, a deconvolution algorithm based on gene expression, CIBERSORT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cibersort.stanford.edu/\u003c/span\u003e\u003c/span\u003e), can evaluate the changes in the expression of all the sets of the other genes in the sample against one specific set of genes. In the current analysis, via CIBERSORT, the immune response of 22 immune infiltrates cells in HCC, for determining its correlation with the molecular subpopulation and survival, was gauged. The gene expression datasets were uploaded to CIBERSORT web portal using the standard annotation files with the algorithm running at 1,000 permutations, its default signature matrix. Establishing a measure of confidence in the results a p-value for deconvolution through the Monte Carlo sampling was estimated by CIBERSORT. We used 374 tumor samples from the TCGA divided into 2 groups in order to assess the influence of TLCD1 expression in the immune microenvironment. To select the lymphocyte possibly affected by the expression of TLCD1 the p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set as the criterion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eGene Set Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGSEA was performed using normalized RNA-Seq data by TCGA\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The annotated gene sets of c5.all.v7.0.symbols.gmt and c2.cp.kegg.v7.0.symbols.gmt in the Molecular Signatures Database (MSigDB) were selected in GSEA version 3.0. The number of permutations was set at 1,000 to determine the normalized enrichment score. GO terms, KEGG pathways were performed to explore the potential biological functions of TLCD1 by using GSEA. Enrichment results satisfying a nominal P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a false discovery rate FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eHuman Protein Atlas\u003c/h2\u003e \u003cp\u003eThe human protein atlas database (HPA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://gepia.cancer-pku.cn/\" target=\"_blank\"\u003ewww.proteinatlas.org\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e) provides access to 32 human tissues and their protein expressions by using antibody profiling to accurately assess protein localization.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Additionally, the HPA provides measurements of RNA levels. HPA database was used to validate protein expression of TLCD1 between normal and liver cancer tissues.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe R language (Version 3.5.3) conducted the download of the statistical analyses from TCGA. To calculate the 95% CI and the HR the multivariate Cox and the Univariate proportional hazards models were utilized. The comparison of several clinical characteristics with survival was done using the Univariate survival analysis. To evaluate the influence of TLCD1 expression and other clinical pathological factors (lymph node, distant metastasis, tumor status, grade, gender, and age) on survival, the Multivariate Cox analysis was conducted. The cut-off criterion was set with the \u003cem\u003eP\u003c/em\u003e-value of TLCD1 expression\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Using the logistic regression, the correlations between the TCLD1 expression and the clinical characteristics were analyzed.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003ch2\u003eTLCD1 expression is significantly upregulated in HCC\u003c/h2\u003e\n\u003cp\u003eThe TLCD1 mRNA levels in the normal and the tumor tissues of liver cancer type were analyzed by using TCGA database, in order to determine the differences between the TLCD1 expression in the normal and tumor tissues. 50 normal files along with 374 tumor files were transformed to convert count data to values more consistent with the microarray results. The boxplot displayed the expression of TLCD1 between the HCC and the normal data (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). A significantly higher TLCD1 expression was revealed in the tumor tissues (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;1.017e-24) by this analysis. Whereas, a significantly increased TLCD1 mRNA expression in HCC compared normal group with liver cancer group (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.010, |Log2FC|\u0026gt;1) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB) was found using the GEPIA database.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eRelationship between TLCD1 expression and clinical characteristics\u003c/h2\u003e\n\u003cp\u003e\u0026chi;2 tests revealed the relationship between the TLCD1 expression and the clinical characteristics (Table.1). To investigate the association with multivariable characteristics and tumor progression in TCGA patients, we using cox regression(Table.2). Univariate analysis of correlation revealed that some factors, including pathological stage (HR\u0026thinsp;=\u0026thinsp;1.865, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor (HR\u0026thinsp;=\u0026thinsp;1.804, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001) along with the expression of TLCD1(HR\u0026thinsp;=\u0026thinsp;1.036, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.003) are significantly associated with tumor development. In multivariate analysis as a forest boxplot was observed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC, the TLCD1(\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.041) expression is an independent prognostic factor for tumor progression.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\u003ccaption\u003eTable 1.\u0026nbsp;Clinical Characteristics of the Patients at Baseline.\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003echaracteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003elow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003ehigh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\n\u003cp\u003ePearson x\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003etotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026le;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\n\u003cp\u003e3.3923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e0.0655\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026gt;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003egender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\n\u003cp\u003e0.3162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e0.57389\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003egrade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\n\u003cp\u003e10.2567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e0.01651\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003estage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\n\u003cp\u003e7.8372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e0.0494\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003etumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\n\u003cp\u003e14.246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e0.002588\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"122\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\u003ccaption\u003eTable 2.\u0026nbsp;Association between TLCD1 expression and clinicopathologic characteristics using logistic regression.\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eClinical characteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003eOdds ratio (OR.95L-OR.95H)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003eP-Value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e0.69(0.45-1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eGrade (II vs I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e1.60(0.86-3.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eGrade (III vs I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e2.78(1.45-5.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eGrade (IV vs I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e9.47(2.22-65.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eGrade (I,II vs III,IV)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e2.12(1.38-3.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eStage (II vs I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e2.00(1.18-3.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eStage (III vs I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e1.60(0.95-2.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eStage (IV vs I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e5.44(0.79-107.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eTumor (III vs I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"197\"\u003e\n\u003cp\u003e2.09(1.26-3.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eRelationship between TLCD1 expression and poor overall survival\u003c/h2\u003e\n\u003cp\u003eTo discover the associations with TLCD1 expression and overall survival in HCC patients, we firstly validated by TCGA datasets, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA. Patients with higher TLCD1 expression had particularly shorter OS (P\u0026thinsp;=\u0026thinsp;0.019), Furthermore, we used GEPIA database find high levels of TLCD1 mRNA also strongly correlated with a worse survival (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.0035) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Patients obtained from ICGC database was also used to validate the group of high TLCD1 expression will lead to poor overall survival. The result was considered statistically significant (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Univariate analysis using logistic regression revealed that TLCD1 expression was associated with poor prognostic clinicopathologic characteristics. Increased TLCD1 expression in HCC as significantly associated with grade (III vs I, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.00; IV vs I, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.01), stage (II vs I, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.01), tumor status (III vs I, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.00). These results suggested that liver cancer patients with high TLCD1 expression are more susceptible to a more advanced grade, stage and tumor status than those with low TLCD1 expression (Table.3).\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\u003ccaption\u003eTable 3. Correlation between overall survival and multivariable characteristics in TCGA patients via (a) Cox regression (b) Multivariate survival model.\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003echaracteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eHR.95L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eHR.95H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.591\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003egender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.801\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e2.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.301\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003egrade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003estage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.865\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e2.388\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.804\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e2.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e3.850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e12.281\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e2.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.494\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e8.276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eTLCD1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cbr /\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003echaracteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eHR.95L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eHR.95H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.392\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003egender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.601\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.992\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003egrade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.475\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.713\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003estage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e2.407\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.826\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.808\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e4.760\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e3.710\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.971\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e2.519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e15.904\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.326\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eTLCD1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e1.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003eCorrelation between TLCD1 and the landscape of TME in HCC\u003c/h2\u003e\n\u003cp\u003eWe first used the single sample GSEA algorithm to depict the enrichment landscape of the abundance of each cell infiltration in the HCC TME, as well as enrichment scores generated from each sample were fully clustered by hierarchical clustering method (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Moreover, we proceed to excavate the relationships between TLCD1, stromal cells and immune cells, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026amp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC. These results indicated that the expression of TLCD1 was associated with tumor microenvironment components (stromal and immune cells).\u003c/p\u003e\n\u003ch2\u003eCorrelation between TLCD1 expression and tumor-infiltrating immune cells\u003c/h2\u003e\n\u003cp\u003eIt was aptly established that tumor-infiltrating lymphocytes were an independent predictor of survival and the sentinel lymph node status in cancers\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Hence, whether TLCD1 expression was correlated with the immune infiltration levels in liver cancer was investigated. The correlations of TLCD1 expression with the immune infiltration levels in liver cancer was assessed from TIMER. It was observed that TLCD1 expression had positive correlations with dendritic cells (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;3.67e-2), macrophages (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;2.09e-4) and B cell (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;4.04e-5) as indicated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA. A specific role in the immune infiltration in liver cancer was played by the TLCD1 as evidenced from the findings. Besides, we examined if the TLCD1 expression was associated with immune infiltration in the liver cancer cases. According to TLCD1 expression, the 374 tumor samples were divided into 2 parts. Overall, the screening criteria was met by the 187 samples of low and high expression groups. To infer the density of 22 types of immune cells and to explore the gene expression profiles of the downloaded samples, the established computational resource CIBERSORT was used. The assessment of the differing concentrations in the low and high TLCD1 expression groups of the 22 immune cell subtypes was done by applying the CIBERSORT algorithm. The results were exhibited in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB. T cells CD4 memory resting, T cells follicular helper, T cells regulatory (Tregs), Monocytes, Macrophages M0, Macrophages M2, and Mast cells resting were affected by TLCD1 expression.\u003c/p\u003e\n\u003cp\u003eWe observed considerable differences in T cells CD4 memory resting and Tregs, macrophages and mast cells between high group and low group. Afterwards, compared with low expression group, Tregs apparently increased (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) in high expression group. Moreover, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC the correlations between the 22 types of immune cells were compared as a correlation heat map. The outcome revealed that the different tumor-infiltrating immune cells subpopulations ratios were moderate to weakly correlated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003eExcavate correlating Tregs markers\u003c/h2\u003e\n\u003cp\u003eThe relation between TLCD1 and Tregs gene markers in the liver tissue was determined using the correlation module of GEPIA Pearson correlation analysis. TNFRSF18, IL1R2, JAK1, CTLA4, IL1R1, TNFRSF4 and CD274 are serve as potential biomarkers of Tregs. We analyzed the relationship between these metabolic genes and TLCD1 expression (high VS low), as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, The correlation between TLCD1 expression and biomarkers expression in the TCGA database in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eGO and KEGG pathway analysis\u003c/h2\u003e\n\u003cp\u003eTo explore the potential biological functions and to study the regulatory \u003cem\u003emechanism\u003c/em\u003e of the TLCD1, the KEGG pathways and GO terms were performed using GSEA. In the enrichment of the KEGG pathways and the GO terms the GSEA revealed significant differences (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.050). We selected the most significantly enriched signaling pathways based on their normalized enrichment score (NES). As shown in Table.4, the GO annotation in high TLCD1 expression resulted five negative correlated parts: protein activation cascade, vitamin B6 binding, retinoic acid metabolic process, cellular amino acid catabolic process and fatty acid catabolic process. The results revealed that the biological processes and molecular functions strongly associated with TLCD1 was fatty acid catabolic process, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA. The KEGG pathway analysis showed the TLCD1 was significantly enriched in five negative pathways: tryptophan metabolism, fatty acid metabolism, drug metabolism cytochrome p450, retinol metabolism and PPAR signaling pathway, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB. It was indicated that the metabolism pathways were strongly associated with TLCD1. In HCC patients all these functions and mechanisms are critically important.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\u003ccaption\u003eTable 4. Signaling pathways most significantly correlated with TLCD1 expression based on their normalized enrichment score (NES) and p-value.\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eNAME\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eNES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eNOM p-val\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003eFDR q-val\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" width=\"60\"\u003e\n\u003cp\u003eGO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003etryptophan metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003efatty acid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003edrug metabolism cytochrome p450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eretinol metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003ePPAR signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-1.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" width=\"60\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eprotein activation cascade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003evitamin B6 binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eretinoic acid metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003ecellular amino acid catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003efatty acid catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e-2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003eHPA validation\u003c/h2\u003e\n\u003cp\u003ewe further analyzed the protein level of TLCD1 in clinical liver tissues from HPA database. The results of immunohistochemical indicated that the protein expression level of TLCD1 significantly abnormal between normal tissues and HCC tissues. Immunohistochemistry analysis available from the HPA showed that in tumor tissues, TLCD1 has higher levels of expression compared to non-tumor tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eTLCD1 is a gene only reported in membrane fluidity. Firstly, the variations in TLCD1 expression level related to prognosis and infiltration of immune cells in HCC were determined. Responding to the hormonal signals, the major organ, the liver controls the glucose and the lipid metabolism \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The action of TLCD1 at the level of the plasma membrane by limiting the amounts of LCPUFA-containing phospholipids was suggested by the previous study \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In the maintenance of the structure and function of the cell membrane and cancer metabolism the polyunsaturated fatty acids played a significant role \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. When lipid metabolism and fatty acid catabolic are disorder, a series of pathological changes will occur in the liver. Our studies suggested that TLCD1 could be used as a promising cancer biomarker in HCC as it was found to be having a potential influence on tumor immunology.\u003c/p\u003e \u003cp\u003eDuring our current research, the expression of TLCD1 as a prognostic biomarker in HCC was first explored. All data of HCC patients downloaded from TCGA were performed to estimate the prognostic value. From the perspective of clinical pathology, tumor-infiltrating immune cells and biological functions, it was observed that the up-regulated TLCD1 was an independent prognostic factor for the overall poor survival rate. The liver cancer patients with high TLCD1 expression were found to be more susceptible to a more advanced tumor, grade, and stage status against the low expression of TLCD1. The potential influences of high TLCD1 expression levels on the mechanisms of tumor immunology and tumorigenesis in HCC progression were proposed by our results. For human HCC prognosis, the TLCD1 could serve as a predictor.\u003c/p\u003e \u003cp\u003eThe correlation between the diverse immune infiltration levels and TLCD1 expression in liver cancer was another important aspect of our study. The study also demonstrated that the infiltration levels of immune cells in HCC could be detected with TIMER. The outcomes revealed that TLCD1 had strongest relationships with B cells, macrophage and dendritic cells. Besides, CIBERSORT confirmed the presence of a moderate to strong positive relationships between the infiltration levels of immune cells and the TLCD1 expression, especially Tregs and dendritic cells. The results in our study could indicate correlation between possible mechanism where TLCD1 regulates Tregs functions in HCC. Regulatory T cells contributes to failure of T cell-mediated immunity\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Through cytokine secretion and via cell-to-cell contact, Tregs suppress activation and differentiation of many cell type and sustain tolerance to self-antigens and regulate the immune system\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. There are opinions that Tregs have a central role in emergence of HCC persistence. We investigated TLCD1 has a strong effect on gene (TNFRSF18\u003csup\u003e34\u003c/sup\u003e, IL1R1, IL1R2\u003csup\u003e35\u003c/sup\u003e, JAK1\u003csup\u003e36\u003c/sup\u003e, CTLA4\u003csup\u003e37\u003c/sup\u003e, TNFRSF4\u003csup\u003e38\u003c/sup\u003e and CD274\u003csup\u003e39\u003c/sup\u003e) expression related to lipid metabolism.\u003c/p\u003e \u003cp\u003eGO term and KEGG pathway analysis in this study revealed that the up-regulated TLCD1 to be primarily linked with fatty acid catabolic process and PPAR signaling pathway. Lipid metabolism, including fatty acid catabolic, is a primary function of the liver\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our study here implicated that overexpression of TLCD1 in HCC patients could induce lipid accumulation and disorder of lipid metabolism. Further studies are needed to confirm if and how TLCD1 supports HCC metastasis in vivo. Evidently, our results could establish the development in the field of TLCD1 biological function in promoting the motility of the HCC cancer cells.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eOur study was first to identify TLCD1 as a new biomarker of the hepatocellular carcinoma thereby helping to determine how the TME cells and the fatty acid catabolic process could promote the development of liver cancer. In HCC studies, it could be a brand-new biomarker. The biomarker therapies could become a promising future option in the treatment of liver diseases with a better understanding of the functional diversity and heterogeneity of TLCD1. An effective design of the therapeutic strategies and diagnosis for treating human HCC could be contributed by TLCD1.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eHCC: hepatocellular carcinoma, TCGA: The Cancer Genome Atlas, AFP: alpha-fetoprotein, TLCD1: TLC Domain Containing 1, LCPUFA: Long-chain Polyunsaturated Fatty Acids, GEPIA: Expression Profiling Interactive Analysis, GSEA: Gene Set Enrichment Analysis, GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eHY-S, ZH-W, YN-D, DD-Z, AL-H came up with the design and conception. The data analysis and visualization were conducted by HY-S, SQ-R, L-D, JL-Z and W-W. The original writing of the draft and its editing were by HY-S, SQ-R, PP-S, ZH-W and YN-D. All authors wrote and reviewed the manuscript, and finally approved the submitted manuscript\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China, China (No. 81871677) and Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province (No. KYCX20_2839).\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data was downloaded from TCGA database.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eConflicts of interests: \u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGong D-Y, Chen E-Q, Huang F-J, Leng X-H, Cheng X, Tang H. 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The Fatty Acid beta-Oxidation Pathway is Activated by Leucine Deprivation in HepG2 Cells: A Comparative Proteomics Study. \u003cem\u003eSci Rep. \u003c/em\u003e2017;7(1):1914.\u003c/li\u003e\n\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":"TLDC1, HCC, TCGA, fatty acid metabolism, TME, Tregs","lastPublishedDoi":"10.21203/rs.3.rs-20580/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-20580/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe HCC has seen a spike in the morbidity and the mortality rate in recent times. This calls for an urgent understanding of the underlying molecular mechanisms of the HCC and to speed up the quest for the search of the target molecules to ensure quick diagnosis and prognosis subsequently. TLCD1 is a gene only reported in membrane fluidity. The variations in the TLCD1 expression levels related to the infiltration of\u003cstrong\u003e \u003c/strong\u003ethe\u003cstrong\u003e \u003c/strong\u003eimmune cells in HCC and the prognosis shall be examined initially.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe data received from TCGA shall provide the details of the gene expression, clinicopathology analysis and TME estimate, along with the enrichment analysis. Moreover, we performed additional analysis of the bioinformatics available. The immune responses of TLCD1 expression in HCC were analyzed using CIBERSORT and TIMER, while the statistical analysis was handled through R. HPA was used to validate the outcomes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eHigher TLCD1 expression is strongly correlated with a poor prognostic and worse overall survival. Specifically, the increase in TLCD1 expression positively correlated with Tregs cells and T cells CD4 memory resting. The pathways strongly associated with TLCD1 was fatty acid metabolism and PPAR signaling pathway.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eFrom the outcome of the study, it could be surmised that TLCD1 could be considered as a potential target for future treatment of HCC, as it was observed to be associated with the tumor-infiltrating immune cells in tumor microenvironment, establishing itself as a novel potential prognostic biomarker in HCC.\u003c/p\u003e","manuscriptTitle":"A Novel Biomarker TLCD1 Correlates with Prognosis and Immune Infiltrates in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-07-16 18:12:12","doi":"10.21203/rs.3.rs-20580/v3","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}},{"code":2,"date":"2020-06-26 17:25:07","doi":"10.21203/rs.3.rs-20580/v2","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}},{"code":1,"date":"2020-04-06 16:38:20","doi":"10.21203/rs.3.rs-20580/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":"ff51e121-abfe-47cd-93d8-f3868b1778e2","owner":[],"postedDate":"July 16th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8092041,"name":"Translational Medicine"}],"tags":[],"updatedAt":"2021-10-25T16:16:41+00:00","versionOfRecord":[],"versionCreatedAt":"2020-07-16 18:12:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v3","identity":"rs-20580","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-20580","identity":"rs-20580","version":["v3"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}