SLC35G2 as a Prognostic Biomarker in Hepatocellular carcinoma and Its Correlation with Immunity

SLC35G2 as a Prognostic Biomarker in Hepatocellular carcinoma and Its Correlation with Immunity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SLC35G2 as a Prognostic Biomarker in Hepatocellular carcinoma and Its Correlation with Immunity Yanqiu Meng, Lebing Yuan, Xianbin Huang, Youhua Li, Sansan Fu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2902000/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hepatocellular carcinoma (HCC) is the major cause of the worldwide cancer burden, especially in China. Solute Carrier Family 35 Member G2 (SLC35G2), a methylation-related gene, plays an essential role during tumorigenesis. However, its roles in key biological functions, the tumor microenvironment, mutations, and single-cell sequencing analysis remain unclear in HCC. This study aimed to identify the correlation between SLC35G2 and prognosis, biological roles, and immune features in HCC. The abnormal expression of SLC35G2 was associated with multiple tumor types, and there was a significant upregulation in HCC samples compared to normal tissues, which was an independent prognostic factor for predicting poor overall survival (OS) and disease-specific survival (DSS) in HCC. A nomogram based on SLC35G2, age, gender, histologic grade, and T-, N-, and M-stages was constructed, and the prognostic model performed well as shown by calibration curves for the 1-, 3-, and 5-year OS. Gene set enrichment analysis showed that SLC35G2 was closely related to tumorigenesis and immune response pathways, including Hippo-merlin, PI3K-AKT, IL-8, and IL-10 signaling pathways. In addition, SLC35G2 expression was inversely correlated with eosinophils and Th17 cells, and increased SLC35G2 expression was significantly associated with immune checkpoint molecules (GI24, CTLA4, PD-L1, B7-H3, TIM-3, and TGF-β). Furthermore, single-cell sequencing analysis showed that SLC35G2 expression was primarily localized in NK/T cells. In conclusion, SLC35G2 was identified as a new prognostic marker and had important potential implications for immunotherapy in HCC. SLC35G2 hepatocellular carcinoma prognosis Immunity bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-associated mortality[ 1 ]. Hepatitis B virus infection, cirrhosis, non-alcoholic steatohepatitis, aristolochic acid, tobacco, and diabetes mellitus are becoming rising etiologies of HCC[ 2 – 4 ]. HCC has a bad outlook, with a 5-year overall survival (OS) under 20%, despite the use of a multifaceted approach[ 5 – 7 ]. In the current state, the translation of molecular functions and immunologic characteristics into biomarkers to guide therapeutic practice is still under investigation. It is vital to develop new molecular indicators for HCC. Solute Carrier Family 35 Member G2 (SLC35G2, also known as TMEM 22), is a polytopic transmembrane protein identified in the Golgi apparatus, endosomes, and lysosomes that is possibly involved in nucleoside-sugar transport[ 8 – 10 ]. Previous research demonstrated the significance of SLC35G2 expression for cell proliferation in renal cell carcinoma[ 11 ]. SLC35G2 was utilized for the prognostic prediction of survival in patients with hepatitis-positive HCC[ 12 ]. SLC35G2 expression was upregulated in peripheral blood mononuclear cells (PBMs) one year after nephrectomy[ 13 ]. Those studies suggest that SLC35G2 may be closely associated with tumorigenesis. Nevertheless, no studies have examined its effects on relevant biological functions, the tumor microenvironment (TME), immune checkpoints, mutations, and single-cell sequencing analysis in HCC. In our study, Tumor Immune Estimation Resource (TIMER 2.0), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Kaplan-Meier Plotter databases were used for SLC35G2 expression, clinicopathology, survival analysis, and evaluating prognosis. Functional enrichment analysis and protein-protein interaction (PPI) networks were performed in HCC using Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and STRING databases. The association of SLC35G2 with TME and immune checkpoint molecules was explored utilizing the SangerBox tool and the Gene Expression Profiling Interactive Analysis (GEPIA2) database, respectively. SLC35G2 mutations in HCC were carried out by the CBio Cancer Genomics Portal (cBioPortal). Additionally, hepatocellular carcinoma single-cell sequencing analysis was employed to better comprehend its ecosystem. The connection between SLC35G2 and HCC was first carefully examined. The biological functions of SLC35G2 in the tumor immune microenvironment were deeply explored. A comprehensive understanding of the potential mechanisms and functions of SLC35G2 in HCC may help predict prognosis and guide treatment. Results 1 The upregulation of SLC35G2 expression and its correlation with clinicopathology in HCC First, using data from the TIMER 2.0 database, the pan-cancer analysis showed that SLC35G2 was highly expressed in CHOL, ESCA, HNSC, KIRC, LIHC, and STAD (Fig. 1 A). The corresponding abbreviations for tumors were listed in Supplementary Table S1 . According to the TCGA-LIHC data, both the unpaired tissues (50 normal and 374 tumor tissues) and the matched tissues (50 paired samples) demonstrated noticeably higher levels of SLC35G2 expression in the HCC tissues (P < 0.001) (Fig. 1 B–C). Furthermore, by getting sequencing data sets (GSE45267 and GSE121248) from the GEO database, the above-mentioned result was further confirmed (Fig. 1 D–E). SLC35G2 had better predictive efficacy, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of 0.716 in differentiating HCC tissues from normal tissues (Supplementary Figure S1 ). In addition, to explore the relationship between various clinicopathological factors in SLC35G2 high and low groups in HCC, high SLC35G2 expression was significantly associated with histologic grade (G1–G2 vs G3–G4; P = 0.002) and OS events (P = 0.005) (Table 1 and Fig. 2 A–B). Table 1 The baseline characteristics of low- and high-SLC35G2 expression groups Characteristic Levels Low expression of SLC35G2 High expression of SLC35G2 P value n 187 187 Gender, n (%) Female 61 (16.3%) 60 (16%) 1.000 Male 126 (33.7%) 127 (34%) Age, n (%) 60 102 (27.3%) 94 (25.2%) T stage, n (%) T1 93 (25.1%) 90 (24.3%) 0.876 T2 44 (11.9%) 51 (13.7%) T3 41 (11.1%) 39 (10.5%) T4 7 (1.9%) 6 (1.6%) N stage, n (%) N0 128 (49.6%) 126 (48.8%) 1.000 N1 2 (0.8%) 2 (0.8%) M stage, n (%) M0 138 (50.7%) 130 (47.8%) 1.000 M1 2 (0.7%) 2 (0.7%) Histologic grade, n (%) G1 36 (9.8%) 19 (5.1%) 0.018 G2 92 (24.9%) 86 (23.3%) G3 50 (13.6%) 74 (20.1%) G4 6 (1.6%) 6 (1.6%) Pathologic stage, n (%) Stage I 89 (25.4%) 84 (24%) 0.804 Stage II 40 (11.4%) 47 (13.4%) Stage III 44 (12.6%) 41 (11.7%) Stage IV 2 (0.6%) 3 (0.9%) AFP (ng/ml), n (%) 400 32 (11.4%) 33 (11.8%) Child-Pugh grade, n (%) A 114 (47.3%) 105 (43.6%) 0.428 B 9 (3.7%) 12 (5%) C 0 (0%) 1 (0.4%) Fibrosis ishak score, n (%) 0 39 (18.1%) 36 (16.7%) 0.472 1/2 20 (9.3%) 11 (5.1%) 3/4 13 (6%) 15 (7%) 5/6 40 (18.6%) 41 (19.1%) Vascular invasion, n (%) No 100 (31.4%) 108 (34%) 0.327 Yes 60 (18.9%) 50 (15.7%) OS event, n (%) Alive 134 (35.8%) 110 (29.4%) 0.013 Dead 53 (14.2%) 77 (20.6%) DSS event, n (%) Alive 150 (41%) 137 (37.4%) 0.185 Dead 34 (9.3%) 45 (12.3%) Abbreviations: AFP, α-fetoprotein; OS, overall survival; DSS, disease-specific survival. Bold values denote two-sided p < 0.05. 2 Assessment of the prognostic value of SLC35G2 in HCC The relationship between SLC35G2 expression and OS was explored. Univariate analysis showed that T stage (T3 vs T1 (HR = 2.674, 95% CI = 1.761–4.060, p < 0.001), T4 vs T1(HR = 5.386, 95% CI = 2.690–10.784, p < 0.001)), M stage (HR = 4.007, 95% CI = 1.281–12.973, p = 0.017), and SLC35G2 expression (HR = 1.729, 95% CI = 1.218–2.455, p = 0.002) had a worse survival prognosis. Multivariate analysis revealed that T stage (T3 vs T1 (HR = 2.562, 95% CI = 1.685–3.896, p < 0.001), T4 vs T1(HR = 5.500, 95% CI = 2.745–11.020, p < 0.001)) and SLC35G2 expression (HR = 1.693, 95% CI = 1.189–2.411, p = 0.004) shortened OS (Table 2 ), and it could be seen that high expression of SLC35G2 was an independent risk prognostic factor. Furthermore, SLC35G2 expression distribution, survivor status, and prognostic risk score were plotted as risk factors (Fig. 2 C), which suggests that higher SLC35G2 expression was associated with increased survival risk. Furthermore, the 1-, 3-, and 5-year ROC analyses were performed, and the AUC was 0.619, 0.616, and 0.635, respectively, indicating moderate accuracy of forecasting (Fig. 2 D). High expression of SLC35G2 was associated with poor OS and DSS (Fig. 2 E–F). A nomogram was used to predict 1-, 3-, and 5-year survival rates in HCC patients (Fig. 3 A). To assess the predictive power of the nomogram, 1-, 3-, and 5-year calibration curves showed the prognostic model performed well (Fig. 3 B–D). Table 2 Relationship between overall survival and multivariable characteristics Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Age 373 60 196 1.205 (0.850–1.708) 0.295 Gender 374 Female 121 Reference Male 253 0.793 (0.557–1.130) 0.200 T stage 371 T1 183 Reference T2 95 1.431 (0.902–2.268) 0.128 1.442 (0.909–2.286) 0.120 T3 80 2.674 (1.761–4.060) < 0.001 2.562 (1.685–3.896) < 0.001 T4 13 5.386 (2.690-10.784) < 0.001 5.500 (2.745–11.020) < 0.001 N stage 258 N0 254 Reference N1 4 2.029 (0.497–8.281) 0.324 M stage 272 M0 268 Reference M1 4 4.077 (1.281–12.973) 0.017 Histologic grade 369 G1 55 Reference G2 178 1.162 (0.686–1.969) 0.576 G3 124 1.185 (0.683–2.057) 0.545 G4 12 1.681 (0.621–4.549) 0.307 SLC35G2 374 Low 187 Reference High 187 1.729 (1.218–2.455) 0.002 1.693 (1.189–2.411) 0.004 Candidate variables were considered with a p < 0.01 in the multivariate Cox regression analysis. Bold values represent two-sided p < 0.05. 3 Identification of differentially expressed genes Based on the expression level of SLC35G2, 1028 (89.7%) genes were upregulated and 118 (10.3%) genes were downregulated, and a volcano map was visualized with an adjusted p 1 (Fig. 4 A and Supplementary Table S2 ). The correlation between the expression levels of SLC35G2 and the TOP10 differentially expressed genes (MT1B, CLPS, CYP11B2, SEPTIN14, OLIG3, RHOXF2B, GPR1-AS, CPA2, CT55) was visualized using a co-expression heat map (Fig. 4 B). 4 Functional enrichment analysis of SLC35G2 in HCC The biological functions of SLC35G2 were enriched and analyzed by GO and GSEA. The GO term showed an obvious correlation between SLC35G2 and signal release, cell-cell adhesion, hydrogen peroxide metabolic process, transmembrane transporter complex, ion channel activity, endopeptidase regulator activity, passive transmembrane transporter activity, and G protein-coupled receptor binding (Fig. 4 C–E, Supplementary Table S3 ). The interaction between SLC35G2 and related genes was explored by the STRING database (Fig. 4 F). GSEA analysis revealed that the expression of SLC35G2 in HCC patients was closely correlated with the immune response, tumor growth, cytokine secretion, and cell-cell signal transmission (Fig. 4 G - J, Supplementary Table S4 ). Additionally, the immunosuppressive and pro-tumor signaling pathways were highly enriched in the SLC35G2 high expression group, indicating that SLC35G2 high expression may facilitate the occurrence and progression of HCC. 5 Correlation between SLC35G2 expression and the tumor immune microenvironment The immune microenvironment is critical to the development of tumors [14] . SLC35G2 expression levels were positively correlated with the levels of T helper, Th2, Tem, and macrophages (both p < 0.001), Tcm (p = 0.003), Tgd (p = 0.007), aDC (p = 0.027), and Th1 cells (p = 0.044), and negatively correlated with the levels of eosinophils (p = 0.009) and Th17 cells (p < 0.001) (Fig. 5 A). The immune cell enrichments in the high- and low-SLC35G2 groups were explored. Th2 and T helper cells (both P < 0.001), Tem (P = 0.007), aDC (P = 0.041), macrophages (P = 0.011), Tcm (P = 0.036), and Tgd (P = 0.027) were substantially enriched in the SLC35G2 high expression group, whereas Th17 cells (P < 0.001) and eosinophils (P = 0.004) were significantly reduced (Fig. 5 B). SLC35G2 was significantly associated with Stromal Score (P < 0.001) and Estimate Score (p = 0.03) in TME but not with Immune Score (p = 0.20) (Fig. 5 C–E). For the occurrence and growth of tumors, “immune checkpoint blockade” disrupts harmful immune regulation checkpoints and activates pre-existing anti-tumor immune reactions[ 15 ]. We investigated how SLC35G2 expression correlated with the common immune checkpoint markers[ 16 ] and discovered that SLC35G2 was positively correlated with the expression of GI24, CTLA4, PD-L1, B7-H3, TIM-3, and TGFβ (P < 0.001) (Fig. 5 F–K). Additionally, there was a positive correlation with SLC35G2, PD-1, IDO-1, VEGFA, and VEGFB (Supplementary Figure S2 ). 6 SLC35G2 mutations and single-cell sequencing analysis We investigated the SLC35G2 mutation characteristics in pan-cancer from TCGA using the cBioPortal tool and found that the frequency of the SLC35G2 mutations was 0.27% in HCC (Fig. 6 A). Further, to analyze the potential functions of SLC35G2 at the single-cell level, we used HCC single-cell sequencing [ 17 ]. As shown in Fig. 6 B, cell clusters were annotated with a t-SNE projection. Additionally, a scatter plot of SLC35G2 expression with t-SNE projection was displayed at single-cell levels (Fig. 6 C). SLC35G2 was discovered to be mostly expressed in NK/T cells. Discussion There is an urgent need for new prognostic and diagnostic markers to enable personalized therapy because of the heterogeneity of HCC. SLC35G2 was focused on the methylation role in certain diseases[ 12 , 18 , 19 ]. DNA methylation is widely recognized as an epigenetic mechanism of gene regulation[ 20 ]. SLC35G2, as one of the methylation-driver prognostic genes, had a prognostic risk score signature in hepatitis-positive HCC[ 12 ]. Aberrant methylation of the promoter CpG islands of SLC35G2 was found in melanocytes[ 18 ]. SLC35G2 could classify glioblastoma subtypes on the basis of relevant biological functions and different degrees of gene methylation[ 19 ]. However, the molecule's specific biochemical functions and interactions with TME have not been examined in HCC. In our research, we found SLC35G2 expression was reduced in 7 cancer tissues compared to normal tissues while being incremental in 6 other cancer tissues, including HCC. SLC35G2 expression levels vary between tumor kinds and may indicate various mechanisms and functions. We used a variety of accessible databases described earlier to demonstrate that SLC35G2 was highly expressed in HCC samples. Different clinical-pathological factors were correlated with SLC35G2 expression, and higher SLC35G2 expression was significantly linked to a higher histological grade and death event than lower SLC35G2 expression. Furthermore, our data showed that an elevated SLC35G2 level was significantly correlated with poor OS and DSS in HCC. The nomogram has been extensively utilized to support clinical judgment[ 21 ]. In order to predict OS probabilities in HCC, we created a nomogram based on SLC35G2, age, gender, histological grade, and T-, N-, and M-stages. The nomogram's predictive power was good, as shown by calibration curves for the 1-, 3-, and 5-year OS. The biological roles and signaling network of SLC35G2 call for further investigation because these studies fall short of explaining the fundamental mechanisms of SLC35G2 in HCC. According to the GO and GSEA analyses, SLC35G2 expression was primarily involved in signal release, cell-cell adhesion, hydrogen peroxide metabolic process, transmembrane transporter complex, ion channel activity, endopeptidase regulator activity, passive transmembrane transporter activity, G protein-coupled receptor binding, tumor-promoting signaling pathways, and immune response pathways. The pathways related to tumorigenesis and immune response mainly included Hippo-merlin, PI3K-AKT, IL-8, and IL-10 signaling pathways. The tumor suppressor Merlin (also known as NF2) is the upstream regulator genetically linked to the Hippo kinase cascade[ 22 ]. The Hippo-YAP pathway is involved in tumor suppression and the innate immune system[ 23 , 24 ]. The PI3K/AKT signaling pathway has a profound impact on cell survival, growth, and proliferation[ 25 ]. Inhibition of the PI3K-AKT-mTOR signaling pathway can delay the progression of tumors and improve immunosurveillance[ 26 ]. IL-8 mediates Sorafenib resistance through the PI3K/AKT/mTOR pathway[ 27 ]. IL-8 has an adaptive immunosuppressive effect, and there is a strong correlation between tumor-derived IL-8 and tolerogenic myeloid-cell infiltration in the tumor microenvironment[ 28 ]. Overexpression of IL-10 is linked with a bad prognosis in malignancies [29] . Tumor-infiltrating Treg cells, via the expression of IL-10, may contribute to the depletion of intratumoral CD8 + T cells [ 30 ]. The anti-tumor response of CD8 + T cells is inhibited by GABA-IL-10 production[ 31 ]. Tumorigenesis may often exhibit abnormalities in the above signaling pathways. TME can lead to immunosuppression and resistance to chemotherapy, thus impacting cancer growth and metastasis[ 32 ]. The kind and number of tumor-infiltrating immune cells are closely related to TME and tumor prognosis[ 33 ]. However, the potential relationship between SLC35G2 and immunity was not investigated. In our study, SLC35G2 was negatively correlated with eosinophils and Th17 cells. Eosinophils have regulating actions toward other immune cell groups in the tumor microenvironment and direct cytotoxic functions against tumor cells[ 34 , 35 ]. It was discovered that eosinophil degranulation was the basis for the eradication of metastasis in a melanoma rodent model[ 36 ]. The antitumor functions of IL-4 in mice were proven by tumor regression driven primarily by eosinophils[ 37 ]. Th17 cells inhibit tumor growth and promote tumor cell apoptosis by secreting TNFα, IFN γ, IL-17F, IL-21, and IL-22[ 38 ]. And so, increased SLC35G2 expression may result in immunosuppression. Furthermore, SLC35G2 was found to be significantly related to Stromal Score and Estimate Score. increased SLC35G2 expression was significantly associated with immune checkpoint molecules (GI24, CTLA4, PD-L1, B7-H3, TIM-3, and TGF-β). Results of exploratory research indicated that SLC35G2 mutations were uncommon in HCC. As revealed by single-cell sequencing analysis, SLC35G2 expression was primarily localized in NK/T cells. This further illustrated that SLC35G2 was closely linked to the immune response. Although our investigation revealed SLC35G2 mutations, single-cell expression patterns, immune microenvironment modifications, and survival outcomes in HCC, some restrictions still need to be taken into consideration. Firstly, the data came from online databases, and we were unable to acquire some crucial clinical data, such as patient-specific information regarding chemotherapy and radiotherapy. Second, some vital pathological parameters were lacking, like pathologic stage and histologic grade. Third, the potential biological roles of SLC35G2 in HCC need to be further explored and validated in vivo and in vitro. Conclusions In this study, increased SLC35G2 expression was associated with a poor survival prognosis and had independent prognostic value in HCC. Furthermore, SLC35G2 played important roles in the immune microenvironment and immune-related response pathways. In short, SLC35G2 was identified as a new predictive marker of HCC and had important potential implications for immunotherapy. But more research is required to figure out how SLC35G2 controls the progression and development of HCC. Materials and Methods Data collection and analysis TIMER 2.0 ( http://timer.cistrome.org/ ) was used to compare SLC35G2 expression patterns in adjacent normal tissues and pan-cancer [39] . From TCGA database ( https://cancergenome.nih.gov/ ), the expression level of SLC35G2 mRNA and clinical information in TPM format were obtained for HCC[ 40 ]. Duplicate samples were not included in our study. Additionally, to further verify the reliability of the above conclusion, GSE121248[ 41 ]and GSE45267[ 42 , 43 ]were retrieved from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/gds/ ). The "ggplot2" R tool (version 3.3.3) was used to visualize the data. Scatterplots were used to show the association between clinicopathological factors and SLC35G2 expression. Survival analysis The "survival" R package (version 3.3.1) was used to perform univariate and multivariate Cox regression models to explore the influence of clinical factors on patient outcomes with HCC. Candidate variables were considered with a P < 0.01 in the multivariate Cox regression analysis. Using Cox regression analysis, risk factor plots for overall survival prediction were constructed. We used the “pROC” R tool (version 1.17.0) to forecast the diagnostic power of SLC35G2 in HCC and normal tissues. Using the log-rank test, the Kaplan-Meier plotter[ 44 ] database was utilized to perform overall survival (OS) and disease-specific survival (DSS) in HCC. In order to assess the SLC35G2 predictive accuracy for OS, the time-dependent receiver operating characteristic (ROC) curves were applied by the "timeROC" R program (version 0.4). The nomogram's construction and validation Applying the Cox regression model for overall survival prediction, a prognostic nomogram for HCC was constructed using the "survival" and "rms" (version 6.2.0) R packages based on several clinicopathologic parameters, and an assessment of the nomogram's performance used calibration curves[ 45 ]. Differentially expressed gene analysis The median score of SLC35G2 expression was used as a reference point for high and low expression of SLC35G2 mRNA from the TCGA-LIHC dataset. Differentially expressed genes were determined[ 46 ] using DESeq2 (version 1.26.0) with an adjusted p 1. Using Spearman correlation analysis, co-expression heat maps of the top 10 DEGs and SLC35G2 were presented. Protein-protein interactions and functional enrichment analysis The STRING database ( https://cn.string-db.org/ )[ 47 ]was used to examine possible connections between SLC35G2 and other genes in HCC. As part of the GO analysis, three annotation categories were available: BP, CC, and MF. The “clusterProfiler” R package (version 3.14.3) was used to perform GO analysis[ 48 ], and the z-score value corresponding to each enrichment entry was calculated using the “GOplot” R package (version 1.0.2) [ 49 ]. GSEA enrichment analysis was carried out using the “clusterProfiler” R program to investigate the potential molecular roles of SLC35G2 on the Kyoto Encyclopedia of Genes and Genomes (KEGG), Wiki Pathways (WP) databases, Pathway Interaction Database (PID), and REACTOME[ 50 , 51 ], and statistically significantly enriched function or pathway terms were defined as meeting an adjusted p < 0.05 and a false discovery rate (FDR) < 0.25. Immune cell infiltration analysis Using the "GSVA" R package (version 1.46.0) [ 52 ], we investigated the correlation between the expression of SLC35G2 and the degree of 24 immune cell infiltrations [ 53 ]. Based on the median expression level of SLC35G2, the Wilcoxon rank sum test was used to determine the degree of 24 immune cell infiltrations in the high and low groups. SangerBox ( http://www.sangerbox.com/home.html ) was used to assess the correlation of SLC35G2 expression with Immune Score, Stromal Score, and Estimate Score. The association between immune checkpoint molecules and SLC35G2 expression was further investigated using Spearman's analysis, which was obtained from GEPIA 2.0 ( http://gepia2.cancer-pku.cn/#correlation ). SLC35G2 gene mutations and single-cell sequencing analysis We used cBioPortal[ 54 ] ( https://www.cbioportal.org/ ) to explore the mutation types and frequencies of SLC35G2. We gained a deeper knowledge of the HCC ecosystem through the HCC single-cell sequencing atlas ( http://omic.tech/scrna-hcc/ ) [ 17 ]. These data were derived from 71915 single-cell transcriptomes generated from 10 HCC patients, containing four major sites of interest: primary tumor, portal vein tumor thrombus, metastatic lymph nodes, and non-tumor liver. Based on single-cell sequencing data, the link between SLC35G2 expression and TME was investigated. Declarations Acknowledgements We acknowledge our use of R packages, TCGA, GEO, TIMER 2.0, STRING, SangerBox, GEPIA 2.0, cBioPortal, and the HCC single-cell sequencing atlas databases. We appreciate the platforms and the authors who uploaded their data. Author contributions YQM conceived the idea and analyzed the data. LBY provided the technical support. XBH collected the data. YHL and SSF wrote the manuscript. XDP reviewed the manuscript. All authors have read and approved the final submitted manuscript. Funding There is no fund to support it. Availability of data and materials The Cancer Genome Atlas: (https://cancergenome.nih.gov/) Gene Expression Omnibus: (https://www.ncbi.nlm.nih.gov/gds/) TIMER2.0: (http://timer.cistrome.org/) STRING: (https://cn.string-db.org/) SangerBox: (http://www.sangerbox.com/home.html) GEPIA2.0: (http://gepia2.cancer-pku.cn/#correlation) cBioPortal: (https://www.cbioportal.org/) the HCC single-cell sequencing atlas: (http://omic.tech/scrna-hcc/) Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS: Hepatocellular carcinoma. Nature reviews Disease primers 2021, 7(1):6. 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Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology 2014, 15(12):550. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P et al: Correction to 'The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets'. Nucleic acids research 2021, 49(18):10800. Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology 2012, 16(5):284-287. Walter W, Sánchez-Cabo F, Ricote M: GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics (Oxford, England) 2015, 31(17):2912-2914. Bild A, Febbo PG: Application of a priori established gene sets to discover biologically important differential expression in microarray data. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(43):15278-15279. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(43):15545-15550. Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics 2013, 14:7. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A et al: Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013, 39(4):782-795. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science signaling 2013, 6(269):pl1. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1ROCcurveforclassifyingHCCversusnormaltissues.docx Supplementary 1. Figure S1: ROC curve for classifying HCC versus normal tissues. SupplementaryFigureS2correlationbetweenSLC35G2andotherimmunecheckpointmoleculesPD1IDO1VEGFAandVEGFB.docx Supplementary 2. Figure S2: correlation between SLC35G2 and other immune checkpoint molecules (PD-1, IDO-1, VEGFA, and VEGFB). SupplementaryTableS1thefullnamesofTCGAtumorabbreviation.xlsx Supplementary 3. Table S1: the full names of TCGA tumor abbreviation. SupplementaryTableS2differentiallyexpressedgenessummary.xlsx Supplementary 4. Table S2: differentially expressed genes summary. SupplementaryTableS3GObiologicalfunctionalenrichmentanalysissummary.xlsx Supplementary 5. Table S3: GO biological functional enrichment analysis summary. SupplementaryTableS4GSEApathwayenrichmentanalysissummary.xlsx Supplementary 6. Table S4: GSEA pathway enrichment analysis summary. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2902000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":204798161,"identity":"f4c7c4c7-e713-4bd8-ad0d-02bfe773523f","order_by":0,"name":"Yanqiu Meng","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanqiu","middleName":"","lastName":"Meng","suffix":""},{"id":204798162,"identity":"3eb7541a-3a1d-48c8-9401-74e0664b9216","order_by":1,"name":"Lebing Yuan","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lebing","middleName":"","lastName":"Yuan","suffix":""},{"id":204798163,"identity":"11cc412a-a76b-44ec-ba43-7ce0b7ef6b32","order_by":2,"name":"Xianbin Huang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xianbin","middleName":"","lastName":"Huang","suffix":""},{"id":204798164,"identity":"64123ad2-4ab1-4ae1-b875-4baed6ab51aa","order_by":3,"name":"Youhua Li","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Youhua","middleName":"","lastName":"Li","suffix":""},{"id":204798165,"identity":"5b4f6785-4d89-4ae1-baaf-0a0c57e7db67","order_by":4,"name":"Sansan Fu","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sansan","middleName":"","lastName":"Fu","suffix":""},{"id":204798166,"identity":"6a65c22e-8d31-479f-bf36-32f3cb7c4a28","order_by":5,"name":"Xiaodong Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYHACA2nGhgNybOztB0jTYszHcyaBNC2J8yQcDIhTby6RvPF24Y476W0SDAkMPyq2EdZi2XOs2HrmmWe5bdKNBxh7ztwmwlXHe8ykedsO57bJHEhgZmwjRsthHrCWdDaJBAMitUBtSSBeC9gvvGeeGbYBA/kgUX4Bhxjvjjvy8u3tBx/8qCDGYcicA4TVo2sZBaNgFIyCUYAVAAD9Xj55t7XNjgAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Nanchang University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2023-05-06 14:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2902000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2902000/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37688936,"identity":"c440ecac-e6c0-4756-b156-ab159d27d941","added_by":"auto","created_at":"2023-05-30 17:43:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1030930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential analysis of SLC35G2 expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) SLC35G2 expression in pan-cancer. SLC35G2 expression in unpaired HCC samples (B) and matched HCC samples (C). Expression of SLC35G2 in the GSE45267 dataset(D) and the GSE121248 dataset(E). (∗∗∗p \u0026lt; 0.001; ∗∗p \u0026lt; 0.01; ∗p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/64798202e520079233ae9eb8.png"},{"id":37687289,"identity":"56c0a1bf-2f07-465e-8005-d4ddd22f6873","added_by":"auto","created_at":"2023-05-30 17:27:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":521243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation of SLC35G2 expression with clinicopathological parameters and survival prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression of SLC35G2 was significantly different between (A) Histologic grade and (B) OS event. (C) SLC35G2 expression distribution and survival status. (D) The 1-, 3-, and 5-year time-dependent ROC analyses were performed for OS prediction based on SLC35G2 expression. Correlation of SLC35G2 expression level with OS(E) and DSS(F). OS, overall survival; DSS, disease specific survival(∗∗∗p \u0026lt; 0.001; ∗∗p \u0026lt; 0.01; ∗p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/fd846489b30a96a7a6b8bbef.png"},{"id":37687297,"identity":"7b25ab40-0a2d-4ec3-89c9-4233c6b4efbc","added_by":"auto","created_at":"2023-05-30 17:27:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":206648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of Nomogram.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Nomogram construction based on SLC35G2 expression and clinicopathological parameters. Calibration curves of 1-(B),3-(C),5-(D) year.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/1415eb59ea155bf625f75b30.png"},{"id":37688588,"identity":"f581c446-6fb6-4c31-b864-08c7c386f864","added_by":"auto","created_at":"2023-05-30 17:35:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":737628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes analysis and functional enrichment of SLC35G2 in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A volcano map of Differentially expressed genes. (B)A heatmap of the relationships between the top 10 differentially expressed genes and SLC35G2 expression. Significant Gene Ontology terms associated with SLC35G2, including BP(C), CC(D), and MF(E). (F) PPI networks. Significant GSEA results associated with SLC35G2, including KEGG(G), WP(H), PID(I), and REACTOME(J) pathways. BP, biological process; CC, cellular component; MF, molecular function; PPI, protein-protein interaction; KEGG, the Kyoto Encyclopedia of Genes and Genomes; WP, Wiki pathways; PID, Pathway Interaction Database;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/7dc68f7e6afe3e18569463da.png"},{"id":37687293,"identity":"f6c57199-b143-4567-adb3-46647ce05278","added_by":"auto","created_at":"2023-05-30 17:27:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":638234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSLC35G2 expression associated with immunity in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlations between SLC35G2 expression and immune cell infiltrations levels. (B) The varied proportions of immune cells in high- and low- SLC35G2 groups. (C-E) Relationships between SLC35G2 expression and the tumor microenvironment. (F-K) Relationships between SLC35G2 and the immune checkpoint molecules (GI24, CTLA4, PD-L1, B7-H3, TIM-3, and TGF-β).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/320ae4b1f1be545e2312ea1f.png"},{"id":37687296,"identity":"6c4e6174-5ddc-4568-a4b1-7e43717b2612","added_by":"auto","created_at":"2023-05-30 17:27:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":507168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSLC35G2 mutation characterizations and single-cell sequencing analysis in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The mutation types and frequencies of SLC35G2 from the cBioPortal tool. (B) cell clusters were annotated with a t-SNE projection. (C) a scatter plot of SLC35G2 expression with t-SNE projection.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/a5c7f4d0c83035fe8ac4b86a.png"},{"id":38457936,"identity":"fc6ae240-48f7-4561-8c0d-8667f3061cb3","added_by":"auto","created_at":"2023-06-13 10:29:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2920188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/3b876b83-6af9-40b7-841f-9b70dfa18379.pdf"},{"id":37688586,"identity":"667f4028-6204-4fe7-82d4-624cce5e19aa","added_by":"auto","created_at":"2023-05-30 17:35:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":69263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 1. Figure S1: \u003c/strong\u003eROC curve for classifying HCC versus normal tissues.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS1ROCcurveforclassifyingHCCversusnormaltissues.docx","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/50d368b0b36ff1da677e4bc4.docx"},{"id":37688589,"identity":"7137a288-77cf-430a-b1b7-ac1c87556ae0","added_by":"auto","created_at":"2023-05-30 17:35:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":594065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 2. Figure S2:\u003c/strong\u003e correlation between SLC35G2 and other immune checkpoint molecules (PD-1, IDO-1, VEGFA, and VEGFB).\u003c/p\u003e","description":"","filename":"SupplementaryFigureS2correlationbetweenSLC35G2andotherimmunecheckpointmoleculesPD1IDO1VEGFAandVEGFB.docx","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/26e0e8b7bfa746e6c68fbf78.docx"},{"id":37687288,"identity":"ecefea0d-aa3b-4f04-b9c8-c80dd84d5653","added_by":"auto","created_at":"2023-05-30 17:27:03","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 3. Table S1:\u003c/strong\u003e the full names of TCGA tumor abbreviation.\u003c/p\u003e","description":"","filename":"SupplementaryTableS1thefullnamesofTCGAtumorabbreviation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/27f466feb65d2977941be305.xlsx"},{"id":37687299,"identity":"14bd69e6-eca3-44bf-bd7c-d46e93f11911","added_by":"auto","created_at":"2023-05-30 17:27:04","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6059125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 4. Table S2:\u003c/strong\u003e differentially expressed genes summary.\u003c/p\u003e","description":"","filename":"SupplementaryTableS2differentiallyexpressedgenessummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/c923b0ae4a2595faf4dfb37b.xlsx"},{"id":37687292,"identity":"b2399dd5-7658-4d75-b68b-44b8a26cfed9","added_by":"auto","created_at":"2023-05-30 17:27:04","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":23108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 5. Table S3:\u003c/strong\u003e GO biological functional enrichment analysis summary.\u003c/p\u003e","description":"","filename":"SupplementaryTableS3GObiologicalfunctionalenrichmentanalysissummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/cf50dc0ff51df3c54af6f5c3.xlsx"},{"id":37688590,"identity":"bd034520-e4ee-47ca-8cde-a384119f9c7c","added_by":"auto","created_at":"2023-05-30 17:35:04","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":213707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary 6. Table S4:\u003c/strong\u003e GSEA pathway enrichment analysis summary.\u003c/p\u003e","description":"","filename":"SupplementaryTableS4GSEApathwayenrichmentanalysissummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2902000/v1/695944667025cc5033455e78.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"SLC35G2 as a Prognostic Biomarker in Hepatocellular carcinoma and Its Correlation with Immunity","fulltext":[{"header":"Background","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the fourth leading cause of cancer-associated mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hepatitis B virus infection, cirrhosis, non-alcoholic steatohepatitis, aristolochic acid, tobacco, and diabetes mellitus are becoming rising etiologies of HCC[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. HCC has a bad outlook, with a 5-year overall survival (OS) under 20%, despite the use of a multifaceted approach[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the current state, the translation of molecular functions and immunologic characteristics into biomarkers to guide therapeutic practice is still under investigation. It is vital to develop new molecular indicators for HCC.\u003c/p\u003e \u003cp\u003eSolute Carrier Family 35 Member G2 (SLC35G2, also known as TMEM 22), is a polytopic transmembrane protein identified in the Golgi apparatus, endosomes, and lysosomes that is possibly involved in nucleoside-sugar transport[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous research demonstrated the significance of SLC35G2 expression for cell proliferation in renal cell carcinoma[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. SLC35G2 was utilized for the prognostic prediction of survival in patients with hepatitis-positive HCC[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. SLC35G2 expression was upregulated in peripheral blood mononuclear cells (PBMs) one year after nephrectomy[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Those studies suggest that SLC35G2 may be closely associated with tumorigenesis. Nevertheless, no studies have examined its effects on relevant biological functions, the tumor microenvironment (TME), immune checkpoints, mutations, and single-cell sequencing analysis in HCC.\u003c/p\u003e \u003cp\u003eIn our study, Tumor Immune Estimation Resource (TIMER 2.0), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Kaplan-Meier Plotter databases were used for SLC35G2 expression, clinicopathology, survival analysis, and evaluating prognosis. Functional enrichment analysis and protein-protein interaction (PPI) networks were performed in HCC using Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and STRING databases. The association of SLC35G2 with TME and immune checkpoint molecules was explored utilizing the SangerBox tool and the Gene Expression Profiling Interactive Analysis (GEPIA2) database, respectively. SLC35G2 mutations in HCC were carried out by the CBio Cancer Genomics Portal (cBioPortal). Additionally, hepatocellular carcinoma single-cell sequencing analysis was employed to better comprehend its ecosystem.\u003c/p\u003e \u003cp\u003eThe connection between SLC35G2 and HCC was first carefully examined. The biological functions of SLC35G2 in the tumor immune microenvironment were deeply explored. A comprehensive understanding of the potential mechanisms and functions of SLC35G2 in HCC may help predict prognosis and guide treatment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1 The upregulation of SLC35G2 expression and its correlation with clinicopathology in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, using data from the TIMER 2.0 database, the pan-cancer analysis showed that SLC35G2 was highly expressed in CHOL, ESCA, HNSC, KIRC, LIHC, and STAD (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The corresponding abbreviations for tumors were listed in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. According to the TCGA-LIHC data, both the unpaired tissues (50 normal and 374 tumor tissues) and the matched tissues (50 paired samples) demonstrated noticeably higher levels of SLC35G2 expression in the HCC tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026ndash;C). Furthermore, by getting sequencing data sets (GSE45267 and GSE121248) from the GEO database, the above-mentioned result was further confirmed (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD\u0026ndash;E). SLC35G2 had better predictive efficacy, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of 0.716 in differentiating HCC tissues from normal tissues (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, to explore the relationship between various clinicopathological factors in SLC35G2 high and low groups in HCC, high SLC35G2 expression was significantly associated with histologic grade (G1\u0026ndash;G2 vs G3\u0026ndash;G4; P\u0026thinsp;=\u0026thinsp;0.002) and OS events (P\u0026thinsp;=\u0026thinsp;0.005) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;B).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe baseline characteristics of low- and high-SLC35G2 expression groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow expression of SLC35G2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh expression of SLC35G2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistologic grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFP (ng/ml), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild-Pugh grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFibrosis ishak score, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular invasion, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS event, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSS event, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: AFP, \u0026alpha;-fetoprotein; OS, overall survival; DSS, disease-specific survival.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eBold values denote two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2 Assessment of the prognostic value of SLC35G2 in HCC\u003c/h3\u003e\n\u003cp\u003eThe relationship between SLC35G2 expression and OS was explored. Univariate analysis showed that T stage (T3 vs T1 (HR\u0026thinsp;=\u0026thinsp;2.674, 95% CI\u0026thinsp;=\u0026thinsp;1.761\u0026ndash;4.060, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T4 vs T1(HR\u0026thinsp;=\u0026thinsp;5.386, 95% CI\u0026thinsp;=\u0026thinsp;2.690\u0026ndash;10.784, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)), M stage (HR\u0026thinsp;=\u0026thinsp;4.007, 95% CI\u0026thinsp;=\u0026thinsp;1.281\u0026ndash;12.973, p\u0026thinsp;=\u0026thinsp;0.017), and SLC35G2 expression (HR\u0026thinsp;=\u0026thinsp;1.729, 95% CI\u0026thinsp;=\u0026thinsp;1.218\u0026ndash;2.455, p\u0026thinsp;=\u0026thinsp;0.002) had a worse survival prognosis. Multivariate analysis revealed that T stage (T3 vs T1 (HR\u0026thinsp;=\u0026thinsp;2.562, 95% CI\u0026thinsp;=\u0026thinsp;1.685\u0026ndash;3.896, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T4 vs T1(HR\u0026thinsp;=\u0026thinsp;5.500, 95% CI\u0026thinsp;=\u0026thinsp;2.745\u0026ndash;11.020, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)) and SLC35G2 expression (HR\u0026thinsp;=\u0026thinsp;1.693, 95% CI\u0026thinsp;=\u0026thinsp;1.189\u0026ndash;2.411, p\u0026thinsp;=\u0026thinsp;0.004) shortened OS (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), and it could be seen that high expression of SLC35G2 was an independent risk prognostic factor. Furthermore, SLC35G2 expression distribution, survivor status, and prognostic risk score were plotted as risk factors (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC), which suggests that higher SLC35G2 expression was associated with increased survival risk. Furthermore, the 1-, 3-, and 5-year ROC analyses were performed, and the AUC was 0.619, 0.616, and 0.635, respectively, indicating moderate accuracy of forecasting (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). High expression of SLC35G2 was associated with poor OS and DSS (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE\u0026ndash;F). A nomogram was used to predict 1-, 3-, and 5-year survival rates in HCC patients (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). To assess the predictive power of the nomogram, 1-, 3-, and 5-year calibration curves showed the prognostic model performed well (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026ndash;D).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship between overall survival and multivariable characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 25.9929%;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 25.9929%;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003e\u0026lt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.205 (0.850\u0026ndash;1.708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e0.793 (0.557\u0026ndash;1.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.431 (0.902\u0026ndash;2.268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.442 (0.909\u0026ndash;2.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e2.674 (1.761\u0026ndash;4.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e2.562 (1.685\u0026ndash;3.896)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e5.386 (2.690-10.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e5.500 (2.745\u0026ndash;11.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e2.029 (0.497\u0026ndash;8.281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e4.077 (1.281\u0026ndash;12.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eHistologic grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.162 (0.686\u0026ndash;1.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.185 (0.683\u0026ndash;2.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.681 (0.621\u0026ndash;4.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eSLC35G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.4386%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 13.3542%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 7.3925%;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.729 (1.218\u0026ndash;2.455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.5543%;\"\u003e\n \u003cp\u003e1.693 (1.189\u0026ndash;2.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.4386%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 73.6864%;\"\u003eCandidate variables were considered with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in the multivariate Cox regression analysis.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 73.6864%;\"\u003eBold values represent two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3 Identification of differentially expressed genes\u003c/h3\u003e\n\u003cp\u003eBased on the expression level of SLC35G2, 1028 (89.7%) genes were upregulated and 118 (10.3%) genes were downregulated, and a volcano map was visualized with an adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |Log2-FC| \u0026gt; 1 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA and Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). The correlation between the expression levels of SLC35G2 and the TOP10 differentially expressed genes (MT1B, CLPS, CYP11B2, SEPTIN14, OLIG3, RHOXF2B, GPR1-AS, CPA2, CT55) was visualized using a co-expression heat map (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003e4 Functional enrichment analysis of SLC35G2 in HCC\u003c/h3\u003e\n\u003cp\u003eThe biological functions of SLC35G2 were enriched and analyzed by GO and GSEA. The GO term showed an obvious correlation between SLC35G2 and signal release, cell-cell adhesion, hydrogen peroxide metabolic process, transmembrane transporter complex, ion channel activity, endopeptidase regulator activity, passive transmembrane transporter activity, and G protein-coupled receptor binding (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;E, Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). The interaction between SLC35G2 and related genes was explored by the STRING database (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). GSEA analysis revealed that the expression of SLC35G2 in HCC patients was closely correlated with the immune response, tumor growth, cytokine secretion, and cell-cell signal transmission (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG - J, Supplementary Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e). Additionally, the immunosuppressive and pro-tumor signaling pathways were highly enriched in the SLC35G2 high expression group, indicating that SLC35G2 high expression may facilitate the occurrence and progression of HCC.\u003c/p\u003e\n\u003ch3\u003e5 Correlation between SLC35G2 expression and the tumor immune microenvironment\u003c/h3\u003e\n\u003cp\u003eThe immune microenvironment is critical to the development of tumors\u003csup\u003e[14]\u003c/sup\u003e. SLC35G2 expression levels were positively correlated with the levels of T helper, Th2, Tem, and macrophages (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Tcm (p\u0026thinsp;=\u0026thinsp;0.003), Tgd (p\u0026thinsp;=\u0026thinsp;0.007), aDC (p\u0026thinsp;=\u0026thinsp;0.027), and Th1 cells (p\u0026thinsp;=\u0026thinsp;0.044), and negatively correlated with the levels of eosinophils (p\u0026thinsp;=\u0026thinsp;0.009) and Th17 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The immune cell enrichments in the high- and low-SLC35G2 groups were explored. Th2 and T helper cells (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Tem (P\u0026thinsp;=\u0026thinsp;0.007), aDC (P\u0026thinsp;=\u0026thinsp;0.041), macrophages (P\u0026thinsp;=\u0026thinsp;0.011), Tcm (P\u0026thinsp;=\u0026thinsp;0.036), and Tgd (P\u0026thinsp;=\u0026thinsp;0.027) were substantially enriched in the SLC35G2 high expression group, whereas Th17 cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and eosinophils (P\u0026thinsp;=\u0026thinsp;0.004) were significantly reduced (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). SLC35G2 was significantly associated with Stromal Score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Estimate Score (p\u0026thinsp;=\u0026thinsp;0.03) in TME but not with Immune Score (p\u0026thinsp;=\u0026thinsp;0.20) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;E). For the occurrence and growth of tumors, \u0026ldquo;immune checkpoint blockade\u0026rdquo; disrupts harmful immune regulation checkpoints and activates pre-existing anti-tumor immune reactions[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. We investigated how SLC35G2 expression correlated with the common immune checkpoint markers[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] and discovered that SLC35G2 was positively correlated with the expression of GI24, CTLA4, PD-L1, B7-H3, TIM-3, and TGF\u0026beta; (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF\u0026ndash;K). Additionally, there was a positive correlation with SLC35G2, PD-1, IDO-1, VEGFA, and VEGFB (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e6 SLC35G2 mutations and single-cell sequencing analysis\u003c/h3\u003e\n\u003cp\u003eWe investigated the SLC35G2 mutation characteristics in pan-cancer from TCGA using the cBioPortal tool and found that the frequency of the SLC35G2 mutations was 0.27% in HCC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Further, to analyze the potential functions of SLC35G2 at the single-cell level, we used HCC single-cell sequencing [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB, cell clusters were annotated with a t-SNE projection. Additionally, a scatter plot of SLC35G2 expression with t-SNE projection was displayed at single-cell levels (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). SLC35G2 was discovered to be mostly expressed in NK/T cells.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is an urgent need for new prognostic and diagnostic markers to enable personalized therapy because of the heterogeneity of HCC. SLC35G2 was focused on the methylation role in certain diseases[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. DNA methylation is widely recognized as an epigenetic mechanism of gene regulation[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. SLC35G2, as one of the methylation-driver prognostic genes, had a prognostic risk score signature in hepatitis-positive HCC[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Aberrant methylation of the promoter CpG islands of SLC35G2 was found in melanocytes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. SLC35G2 could classify glioblastoma subtypes on the basis of relevant biological functions and different degrees of gene methylation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the molecule's specific biochemical functions and interactions with TME have not been examined in HCC.\u003c/p\u003e \u003cp\u003eIn our research, we found SLC35G2 expression was reduced in 7 cancer tissues compared to normal tissues while being incremental in 6 other cancer tissues, including HCC. SLC35G2 expression levels vary between tumor kinds and may indicate various mechanisms and functions. We used a variety of accessible databases described earlier to demonstrate that SLC35G2 was highly expressed in HCC samples. Different clinical-pathological factors were correlated with SLC35G2 expression, and higher SLC35G2 expression was significantly linked to a higher histological grade and death event than lower SLC35G2 expression.\u003c/p\u003e \u003cp\u003eFurthermore, our data showed that an elevated SLC35G2 level was significantly correlated with poor OS and DSS in HCC. The nomogram has been extensively utilized to support clinical judgment[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In order to predict OS probabilities in HCC, we created a nomogram based on SLC35G2, age, gender, histological grade, and T-, N-, and M-stages. The nomogram's predictive power was good, as shown by calibration curves for the 1-, 3-, and 5-year OS.\u003c/p\u003e \u003cp\u003eThe biological roles and signaling network of SLC35G2 call for further investigation because these studies fall short of explaining the fundamental mechanisms of SLC35G2 in HCC. According to the GO and GSEA analyses, SLC35G2 expression was primarily involved in signal release, cell-cell adhesion, hydrogen peroxide metabolic process, transmembrane transporter complex, ion channel activity, endopeptidase regulator activity, passive transmembrane transporter activity, G protein-coupled receptor binding, tumor-promoting signaling pathways, and immune response pathways. The pathways related to tumorigenesis and immune response mainly included Hippo-merlin, PI3K-AKT, IL-8, and IL-10 signaling pathways. The tumor suppressor Merlin (also known as NF2) is the upstream regulator genetically linked to the Hippo kinase cascade[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Hippo-YAP pathway is involved in tumor suppression and the innate immune system[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The PI3K/AKT signaling pathway has a profound impact on cell survival, growth, and proliferation[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Inhibition of the PI3K-AKT-mTOR signaling pathway can delay the progression of tumors and improve immunosurveillance[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. IL-8 mediates Sorafenib resistance through the PI3K/AKT/mTOR pathway[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. IL-8 has an adaptive immunosuppressive effect, and there is a strong correlation between tumor-derived IL-8 and tolerogenic myeloid-cell infiltration in the tumor microenvironment[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Overexpression of IL-10 is linked with a bad prognosis in malignancies\u003csup\u003e[29]\u003c/sup\u003e. Tumor-infiltrating Treg cells, via the expression of IL-10, may contribute to the depletion of intratumoral CD8\u0026thinsp;+\u0026thinsp;T cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The anti-tumor response of CD8\u0026thinsp;+\u0026thinsp;T cells is inhibited by GABA-IL-10 production[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Tumorigenesis may often exhibit abnormalities in the above signaling pathways.\u003c/p\u003e \u003cp\u003eTME can lead to immunosuppression and resistance to chemotherapy, thus impacting cancer growth and metastasis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The kind and number of tumor-infiltrating immune cells are closely related to TME and tumor prognosis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the potential relationship between SLC35G2 and immunity was not investigated. In our study, SLC35G2 was negatively correlated with eosinophils and Th17 cells. Eosinophils have regulating actions toward other immune cell groups in the tumor microenvironment and direct cytotoxic functions against tumor cells[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It was discovered that eosinophil degranulation was the basis for the eradication of metastasis in a melanoma rodent model[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The antitumor functions of IL-4 in mice were proven by tumor regression driven primarily by eosinophils[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Th17 cells inhibit tumor growth and promote tumor cell apoptosis by secreting TNFα, IFN γ, IL-17F, IL-21, and IL-22[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. And so, increased SLC35G2 expression may result in immunosuppression. Furthermore, SLC35G2 was found to be significantly related to Stromal Score and Estimate Score. increased SLC35G2 expression was significantly associated with immune checkpoint molecules (GI24, CTLA4, PD-L1, B7-H3, TIM-3, and TGF-β). Results of exploratory research indicated that SLC35G2 mutations were uncommon in HCC. As revealed by single-cell sequencing analysis, SLC35G2 expression was primarily localized in NK/T cells. This further illustrated that SLC35G2 was closely linked to the immune response.\u003c/p\u003e \u003cp\u003eAlthough our investigation revealed SLC35G2 mutations, single-cell expression patterns, immune microenvironment modifications, and survival outcomes in HCC, some restrictions still need to be taken into consideration. Firstly, the data came from online databases, and we were unable to acquire some crucial clinical data, such as patient-specific information regarding chemotherapy and radiotherapy. Second, some vital pathological parameters were lacking, like pathologic stage and histologic grade. Third, the potential biological roles of SLC35G2 in HCC need to be further explored and validated in vivo and in vitro.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, increased SLC35G2 expression was associated with a poor survival prognosis and had independent prognostic value in HCC. Furthermore, SLC35G2 played important roles in the immune microenvironment and immune-related response pathways. In short, SLC35G2 was identified as a new predictive marker of HCC and had important potential implications for immunotherapy. But more research is required to figure out how SLC35G2 controls the progression and development of HCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eData collection and analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTIMER 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to compare SLC35G2 expression patterns in adjacent normal tissues and pan-cancer\u003csup\u003e[39]\u003c/sup\u003e. From TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the expression level of SLC35G2 mRNA and clinical information in TPM format were obtained for HCC[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Duplicate samples were not included in our study. Additionally, to further verify the reliability of the above conclusion, GSE121248[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]and GSE45267[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]were retrieved from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The \"ggplot2\" R tool (version 3.3.3) was used to visualize the data. Scatterplots were used to show the association between clinicopathological factors and SLC35G2 expression.\u003c/p\u003e\n\u003ch3\u003eSurvival analysis\u003c/h3\u003e\n\u003cp\u003eThe \"survival\" R package (version 3.3.1) was used to perform univariate and multivariate Cox regression models to explore the influence of clinical factors on patient outcomes with HCC. Candidate variables were considered with a P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in the multivariate Cox regression analysis. Using Cox regression analysis, risk factor plots for overall survival prediction were constructed. We used the \u0026ldquo;pROC\u0026rdquo; R tool (version 1.17.0) to forecast the diagnostic power of SLC35G2 in HCC and normal tissues. Using the log-rank test, the Kaplan-Meier plotter[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] database was utilized to perform overall survival (OS) and disease-specific survival (DSS) in HCC. In order to assess the SLC35G2 predictive accuracy for OS, the time-dependent receiver operating characteristic (ROC) curves were applied by the \"timeROC\" R program (version 0.4).\u003c/p\u003e\n\u003ch3\u003eThe nomogram's construction and validation\u003c/h3\u003e\n\u003cp\u003eApplying the Cox regression model for overall survival prediction, a prognostic nomogram for HCC was constructed using the \"survival\" and \"rms\" (version 6.2.0) R packages based on several clinicopathologic parameters, and an assessment of the nomogram's performance used calibration curves[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDifferentially expressed gene analysis\u003c/h3\u003e\n\u003cp\u003eThe median score of SLC35G2 expression was used as a reference point for high and low expression of SLC35G2 mRNA from the TCGA-LIHC dataset. Differentially expressed genes were determined[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] using DESeq2 (version 1.26.0) with an adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2-fold-change (FC)| \u0026gt; 1. Using Spearman correlation analysis, co-expression heat maps of the top 10 DEGs and SLC35G2 were presented.\u003c/p\u003e\n\u003ch3\u003eProtein-protein interactions and functional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]was used to examine possible connections between SLC35G2 and other genes in HCC. As part of the GO analysis, three annotation categories were available: BP, CC, and MF. The \u0026ldquo;clusterProfiler\u0026rdquo; R package (version 3.14.3) was used to perform GO analysis[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and the z-score value corresponding to each enrichment entry was calculated using the \u0026ldquo;GOplot\u0026rdquo; R package (version 1.0.2) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. GSEA enrichment analysis was carried out using the \u0026ldquo;clusterProfiler\u0026rdquo; R program to investigate the potential molecular roles of SLC35G2 on the Kyoto Encyclopedia of Genes and Genomes (KEGG), Wiki Pathways (WP) databases, Pathway Interaction Database (PID), and REACTOME[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and statistically significantly enriched function or pathway terms were defined as meeting an adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e\n\u003ch3\u003eImmune cell infiltration analysis\u003c/h3\u003e\n\u003cp\u003eUsing the \"GSVA\" R package (version 1.46.0) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], we investigated the correlation between the expression of SLC35G2 and the degree of 24 immune cell infiltrations [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Based on the median expression level of SLC35G2, the Wilcoxon rank sum test was used to determine the degree of 24 immune cell infiltrations in the high and low groups. SangerBox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.sangerbox.com/home.html\u003c/span\u003e\u003cspan address=\"http://www.sangerbox.com/home.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to assess the correlation of SLC35G2 expression with Immune Score, Stromal Score, and Estimate Score. The association between immune checkpoint molecules and SLC35G2 expression was further investigated using Spearman's analysis, which was obtained from GEPIA 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#correlation\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#correlation\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSLC35G2 gene mutations and single-cell sequencing analysis\u003c/h3\u003e\n\u003cp\u003eWe used cBioPortal[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to explore the mutation types and frequencies of SLC35G2. We gained a deeper knowledge of the HCC ecosystem through the HCC single-cell sequencing atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://omic.tech/scrna-hcc/\u003c/span\u003e\u003cspan address=\"http://omic.tech/scrna-hcc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These data were derived from 71915 single-cell transcriptomes generated from 10 HCC patients, containing four major sites of interest: primary tumor, portal vein tumor thrombus, metastatic lymph nodes, and non-tumor liver. Based on single-cell sequencing data, the link between SLC35G2 expression and TME was investigated.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge our use of R packages, TCGA, GEO, TIMER 2.0, STRING, SangerBox, GEPIA 2.0, cBioPortal, and the HCC single-cell sequencing atlas databases. We appreciate the platforms and the authors who uploaded their data.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYQM conceived the idea and analyzed the data. LBY provided the technical support. XBH collected the data. YHL and SSF wrote the manuscript. XDP reviewed the manuscript. All authors have read and approved the final submitted manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no fund to support it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cancer Genome Atlas: (https://cancergenome.nih.gov/)\u003c/p\u003e\n\u003cp\u003eGene Expression Omnibus: (https://www.ncbi.nlm.nih.gov/gds/)\u003c/p\u003e\n\u003cp\u003eTIMER2.0: (http://timer.cistrome.org/)\u003c/p\u003e\n\u003cp\u003eSTRING: (https://cn.string-db.org/) \u003c/p\u003e\n\u003cp\u003eSangerBox: (http://www.sangerbox.com/home.html) \u003c/p\u003e\n\u003cp\u003eGEPIA2.0: (http://gepia2.cancer-pku.cn/#correlation) \u003c/p\u003e\n\u003cp\u003ecBioPortal: (https://www.cbioportal.org/) \u003c/p\u003e\n\u003cp\u003ethe HCC single-cell sequencing atlas: (http://omic.tech/scrna-hcc/) \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLlovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS: Hepatocellular carcinoma. 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Science signaling 2013, 6(269):pl1.\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":"SLC35G2, hepatocellular carcinoma, prognosis, Immunity, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-2902000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2902000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma (HCC) is the major cause of the worldwide cancer burden, especially in China. Solute Carrier Family 35 Member G2 (SLC35G2), a methylation-related gene, plays an essential role during tumorigenesis. However, its roles in key biological functions, the tumor microenvironment, mutations, and single-cell sequencing analysis remain unclear in HCC. This study aimed to identify the correlation between SLC35G2 and prognosis, biological roles, and immune features in HCC. The abnormal expression of SLC35G2 was associated with multiple tumor types, and there was a significant upregulation in HCC samples compared to normal tissues, which was an independent prognostic factor for predicting poor overall survival (OS) and disease-specific survival (DSS) in HCC. A nomogram based on SLC35G2, age, gender, histologic grade, and T-, N-, and M-stages was constructed, and the prognostic model performed well as shown by calibration curves for the 1-, 3-, and 5-year OS. Gene set enrichment analysis showed that SLC35G2 was closely related to tumorigenesis and immune response pathways, including Hippo-merlin, PI3K-AKT, IL-8, and IL-10 signaling pathways. In addition, SLC35G2 expression was inversely correlated with eosinophils and Th17 cells, and increased SLC35G2 expression was significantly associated with immune checkpoint molecules (GI24, CTLA4, PD-L1, B7-H3, TIM-3, and TGF-β). Furthermore, single-cell sequencing analysis showed that SLC35G2 expression was primarily localized in NK/T cells. In conclusion, SLC35G2 was identified as a new prognostic marker and had important potential implications for immunotherapy in HCC.\u003c/p\u003e","manuscriptTitle":"SLC35G2 as a Prognostic Biomarker in Hepatocellular carcinoma and Its Correlation with Immunity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-30 17:26:58","doi":"10.21203/rs.3.rs-2902000/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":"c7de34f1-97c1-4e14-adfe-94f10bdfc22a","owner":[],"postedDate":"May 30th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-21T05:30:10+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-30 17:26:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2902000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2902000","identity":"rs-2902000","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}