scholarly journals Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhuomao Mo ◽  
Daiyuan Liu ◽  
Dade Rong ◽  
Shijun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Immunosuppressive Microenvironment

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

scholarly journals Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

2022 ◽  
Vol 2022 ◽  
pp. 1-24
Author(s):  
Bin-Bin Da ◽  
Shuai Luo ◽  
Ming Huang ◽  
Fei Song ◽  
Rong Ding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Inflammatory Response ◽  
Immune Cell ◽  
Cox Regression ◽  
Inflammatory Responses ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Differential Analysis

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors. However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (HCC) remains unknown. The Cancer Genome Atlas (TCGA) database was analyzed using weighted gene coexpression network analysis (WGCNA) and differential analysis to discover essential inflammatory response-related genes (IFRGs). Cox regression studies, both univariate and multivariate, were employed to develop a prognostic IFRGs signature. Additionally, Gene Set Enrichment Analysis (GSEA) was used to deduce the biological function of the IFRGs signature. Finally, we estimated immune cell infiltration using a single sample GSEA (ssGSEA) and x-cell. Our results revealed that, among the major HCC IFRGs, two (DNASE1L3 and KLKB1) were employed to create a predictive IFRG signature. The IFRG signature could correctly predict overall survival (O.S) as per Kaplan-Meier time-dependent roc curves analysis. It was also linked to pathological tumor stage and T stage and might be used as a prognostic predictor in HCC. GSEA analysis concluded that the IFRG signature might influence the immune response in HCC. Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups. As a result of our findings, DNASILE may play a role in the tumor microenvironment. However, more research is necessary to confirm the role of DNASE1L3 and KLKB1.

scholarly journals Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qiuxian Zheng ◽  
Qin Yang ◽  
Jiaming Zhou ◽  
Xinyu Gu ◽  
Haibo Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Linear Discriminant ◽  
Immune Signature ◽  
Immune Signatures

Abstract Background Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described. Methods HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices. Results Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells. Conclusions The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction.

scholarly journals SERPINE1 associated with remodeling of the tumor microenvironment in colon cancer progression: a novel therapeutic target

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shaokun Wang ◽  
Li Pang ◽  
Zuolong Liu ◽  
Xiangwei Meng
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
The Relationship

Abstract Background The change of immune cell infiltration essentially influences the process of colorectal cancer development. The infiltration of immune cells can be regulated by a variety of genes. Thus, modeling the immune microenvironment of colorectal cancer by analyzing the genes involved can be more conducive to the in-depth understanding of carcinogenesis and the progression thereof. Methods In this study, the number of stromal and immune cells in malignant tumor tissues were first estimated by using expression data (ESTIMATE) and cell-type identification with relative subsets of known RNA transcripts (CIBERSORT) to calculate the proportion of infiltrating immune cell and stromal components of colon cancer samples from the Cancer Genome Atlas database. Then the relationship between the TMN Classification and prognosis of malignant tumors was evaluated. Results By investigating differentially expressed genes using COX regression and protein-protein interaction network (PPI), the candidate hub gene serine protease inhibitor family E member 1 (SERPINE1) was found to be associated with immune cell infiltration. Gene Set Enrichment Analysis (GSEA) further projected the potential pathways with elevated SERPINE1 expression to carcinogenesis and immunity. CIBERSORT was subsequently utilized to investigate the relationship between the expression differences of SERPINE1 and immune cell infiltration and to identify eight immune cells associated with SERPINE1 expression. Conclusion We found that SERPINE1 plays a role in the remodeling of the colon cancer microenvironment and the infiltration of immune cells.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract Background Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aimed to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.Methods Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis and the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. Then the Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature and the CIBERSORT was used for estimating the fractions of immune cell types.Results A total of 397 hypoxia-related DEGs were detected and three genes (PDSS1, CDCA8 and SLC7A11) were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response and the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.Conclusions Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals Identification of the Immune Cell Infiltration Landscape in Hepatocellular Carcinoma to Predict Prognosis and Guide Immunotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Shiyan Yang ◽  
Yajun Cheng ◽  
Xiaolong Wang ◽  
Ping Wei ◽  
Hui Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Principal Component ◽  
Rna Degradation ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Efficacy Evaluation

Background: Globally, hepatocellular carcinoma (HCC) is the sixth most frequent malignancy with a high incidence and a poor prognosis. Immune cell infiltration (ICI) underlies both the carcinogenesis and immunogenicity of tumors. However, a comprehensive classification system based on the immune features for HCC remains unknown.Methods: The HCC dataset from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts was used in this study. The ICI patterns of 571 patients were characterized using two algorithms: the patterns were determined based on the ICI using the ConsensusClusterPlus package, and principal component analysis (PCA) established the ICI scores. Differences in the immune landscape, biological function, and somatic mutations across ICI scores were evaluated and compared, followed by a predictive efficacy evaluation of ICI scores for immunotherapy by the two algorithms and validation using an external immunotherapy cohort.Results: Based on the ICI profile of the HCC patients, three ICI patterns were identified, including three subtypes having different immunological features. Individual ICI scores were determined; the high ICI score subtype was characterized by enhanced activation of immune-related signaling pathways and a significantly high tumor mutation burden (TMB); concomitantly, diminished immunocompetence and enrichment of pathways associated with cell cycle and RNA degradation were found in the low ICI score subtype. Taken together, our results contribute to a better understanding of an active tumor and plausible reasons for its poor prognosis.Conclusion: The present study reveals that ICI scores may serve as valid prognostic biomarkers for immunotherapy in HCC.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract BackgroundHypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.MethodsDifferentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis. Then the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. CIBERSORT was used for estimating the fractions of immune cell types.ResultsA total of 397 hypoxia-related DEGs in HCC were detected and three genes (PDSS1, CDCA8 and SLC7A11) among them were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response. Meanwhile, the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.ConclusionsAltogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract Background Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aimed to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism. Methods Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis and the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. Then the Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature and the CIBERSORT was used for estimating the fractions of immune cell types. Results A total of 397 hypoxia-related DEGs were detected and three genes (PDSS1, CDCA8 and SLC7A11) were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response and the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1. Conclusions Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals Glucose-6-Phosphate Dehydrogenase is a Prognostic Biomarker and Correlated with Immune Infiltrates in Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Yang Bu ◽  
Kejun Liu ◽  
Yiming Niu ◽  
Ji Hao ◽  
Lei Cui ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Liver Cancer ◽  
Immune Cell ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Potential Biomarker ◽  
Glucose 6 Phosphate Dehydrogenase

Abstract Background: Glucose-6-phosphate dehydrogenase (G6PD) plays an important role in the metabolic and immunological aspects of tumors. In hepatocellular carcinoma (HCC), the alteration of tumor microenvironment influences recurrence and metastasis. We extracted G6PD-related data from public databases of HCC tissues and used a bioinformatics approach to explore the correlation between G6PD expression and clinicopathological features and prognosis of immune cell infiltration in HCC.Methods: We extract G6PD expression information from TCGA and GEO databases in liver cancer tissues and normal tissues, validated by immunohistochemistry, and the correlation between G6PD expression and clinical features is analyzed, and the clinical significance of G6PD in liver cancer is assessed by Kaplan-Meier, Cox regression and prognostic line graph models. Functional enrichment analysis is performed by protein-protein interaction (PPI) network, GO/KEGG, GSEA and G6PD-associated differentially expressed genes (DEGs). TIMER and ssGSEA packages are used to assess the correlation between expression and the level of immune cell infiltration.Results: Our results show that G6PD expression is significantly upregulated in hepatocellular carcinoma tissues (P < 0.001). G6PD expression is associated with histological grade, pathological stage, T-stage, vascular infiltration and AFP level (P < 0.05); HCC patients in the low G6PD expression group had longer overall survival and better prognosis compared with the high G6PD expression group (P < 0.05). The level of G6PD expression also affects the levels of macrophages, unactivated dendritic cells, B cells, and follicular helper T cells in the tumor microenvironment.Conclusion: High expression of G6PD is a potential biomarker for poor prognosis of hepatocellular carcinoma, and G6PD may be a target for immunotherapy of HCC.

scholarly journals Significant correlation between HSPA4 and prognosis and immune regulation in hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12315
Author(s):  
Bing-Bing Shang ◽  
Jun Chen ◽  
Zhi-Guo Wang ◽  
Hui Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Roc Curve ◽  
Immune Regulation ◽  
Immune Cell ◽  
Genetic Alterations ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Cancer Stage

Background Hepatocellular carcinoma (HCC) is an inflammation-associated tumor involved in immune tolerance and evasion in the immune microenvironment. Heat shock proteins (HSPs) are involved in the occurrence, progression, and immune regulation of tumors. Therefore, HSPs have been considered potential therapeutic targets. Here, we aimed to elucidate the value of HSP family A (Hsp70) member 4 (HSPA4) in the diagnosis and predicting prognosis of HCC, and its relationship with immune cell infiltration, immune cell biomarkers, and immune checkpoints. Gene mutation, DNA methylation, and the pathway involved in HCC were also analyzed. Methods The gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to compare HSPA4 expression, and the results were confirmed by immunohistochemical staining of clinical samples. R package was used to analyze the correlation between HSPA4 and cancer stage, and to establish receiver operating characteristic (ROC) curve of diagnosis, time-dependent survival ROC curve, and a nomogram model. cBioPortal and MethSurv were used to identify genetic alterations and DNA methylation, and their effect on prognosis. The Tumor Immune Estimation Resource (TIMER) was used to analyze immune cell infiltration, immune cell biomarkers, and immune checkpoints. The STRING database was used to analyze protein–protein interaction network information. Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the functions of HSPA4 and its functional partner genes. Results Overexpression of HSPA4 was identified in 25 cancers. Overexpression of HSPA4 considerably correlated with cancer stage and alpha-fetoprotein (AFP) level in HCC. Patients with higher HSPA4 expression showed poorer prognosis. HSPA4 expression can accurately identify tumor from normal tissue (AUC = 0.957). The area under 1-, 3-, and 5-year survival ROCs were above 0.6. The HSPA4 genetic alteration rate was 1.3%. Among the 14 DNA methylation CpG sites, seven were related to the prognosis of HCC. HSPA4 was positively related to immune cell infiltration and immune checkpoints (PD-1 and CTLA-4) in HCC. The KEGG pathway enrichment analysis revealed HSPA4 enrichment in antigen processing and presentation together with HSPA8 and HSP90AA1. We verified the value of HSPA4 in the diagnosis and predicting prognosis of HCC. HSPA4 may not only participate in the occurrence and progression but also the immune regulation of HCC. Therefore, HSPA4 can be a potential diagnostic and prognostic biomarker and a therapeutic target for HCC.

scholarly journals Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Honglan Guo ◽  
Qinqiao Fan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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