Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients

Background. Hepatocellular carcinoma (HCC) is the leading liver cancer with special immune microenvironment, which played vital roles in tumor relapse and poor drug responses. In this study, we aimed to explore the prognostic immune signatures in HCC and tried to construct an immune-risk model for patient evaluation. Methods. RNA sequencing profiles of HCC patients were collected from the cancer genome Atlas (TCGA), international cancer genome consortium (ICGC), and gene expression omnibus (GEO) databases (GSE14520). Differentially expressed immune genes, derived from ImmPort database and MSigDB signaling pathway lists, between tumor and normal tissues were analyzed with Limma package in R environment. Univariate Cox regression was performed to find survival-related immune genes in TCGA dataset, and in further random forest algorithm analysis, significantly changed immune genes were used to generate a multivariate Cox model to calculate the corresponding immune-risk score. The model was examined in the other two datasets with recipient operation curve (ROC) and survival analysis. Risk effects of immune-risk score and clinical characteristics of patients were individually evaluated, and significant factors were then used to generate a nomogram. Results. There were 52 downregulated and 259 upregulated immune genes between tumor and relatively normal tissues, and the final immune-risk model (based on SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV and MAP4K2) can better differentiate patients into high and low immune-risk subpopulations, in which high score patients showed worse outcomes after resection ( p < 0.05 ). The differentially enriched pathways between the two groups were mainly about cell proliferation and cytokine production, and calculated immune-risk score was also highly correlated with immune infiltration levels. The nomogram, constructed with immune-risk score and tumor stages, showed high accuracy and clinical benefits in prediction of 1-, 3- and 5-year overall survival, which is useful in clinical practice. Conclusion. The immune-risk model, based on expression of SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV, and MAP4K2, can better differentiate patients into high and low immune-risk groups. Combined nomogram, using immune-risk score and tumor stages, could make accurate prediction of 1-, 3- and 5-year survival in HCC patients.

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Comprehensive Analysis to Identify the Epithelial–Mesenchymal Transition-Related Immune Signatures as a Prognostic and Therapeutic Biomarkers in Hepatocellular Carcinoma

Frontiers in Surgery ◽

10.3389/fsurg.2021.742443 ◽

2021 ◽

Vol 8 ◽

Author(s):

Guozhi Wu ◽

Yuan Yang ◽

Yu Zhu ◽

Yemao Li ◽

Zipeng Zhai ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Cancer Genome ◽

Mesenchymal Transition ◽

Immune Genes

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with the high rates of the morbidity and mortality due to the lack of the effective prognostic model for prediction.Aim: To construct a risk model composed of the epithelial–mesenchymal transition (EMT)-related immune genes for the assessment of the prognosis, immune infiltration status, and chemosensitivity.Methods: We obtained the transcriptome and clinical data of the HCC samples from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) databases. The Pearson correlation analysis was applied to identify the differentially expressed EMT-related immune genes (DE-EMTri-genes). Subsequently, the univariate Cox regression was introduced to screen out the prognostic gene sets and a risk model was constructed based on the least absolute shrinkage and selection operator-penalized Cox regression. Additionally, the receiver operating characteristic (ROC) curves were plotted to compare the prognostic value of the newly established model compared with the previous model. Furthermore, the correlation between the risk model and survival probability, immune characteristic, and efficacy of the chemotherapeutics were analyzed by the bioinformatics methods.Results: Six DE-EMTri-genes were ultimately selected to construct the prognostic model. The area under the curve (AUC) values for 1-, 2-, and 3- year were 0.773, 0.721, and 0.673, respectively. Stratified survival analysis suggested that the prognosis of the low-score group was superior to the high-score group. Moreover, the univariate and multivariate analysis indicated that risk score [hazard ratio (HR) 5.071, 95% CI 3.050, 8.432; HR 4.396, 95% CI 2.624, 7.366; p < 0.001] and stage (HR 2.500, 95% CI 1.721, 3.632; HR 2.111, 95% CI 1.443, 3.089; p < 0.001) served as an independent predictive factors in HCC. In addition, the macrophages, natural killer (NK) cells, and regulatory T (Treg) cells were significantly enriched in the high-risk group. Finally, the patients with the high-risk score might be more sensitive to cisplatin, doxorubicin, etoposide, gemcitabine, and mitomycin C.Conclusion: We established a reliable EMTri-genes-based prognostic signature, which may hold promise for the clinical prediction.

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Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.616998 ◽

2021 ◽

Vol 11 ◽

Author(s):

Huadi Shi ◽

Fulan Zhong ◽

Xiaoqiong Yi ◽

Zhenyi Shi ◽

Feiyan Ou ◽

...

Keyword(s):

Cervical Cancer ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Survival Prediction ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

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Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/5564040 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Fanbo Qin ◽

Junyong Zhang ◽

Jianping Gong ◽

Wenfeng Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Value ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

Clinical Parameters ◽

Cox Regression Analysis ◽

Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

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Genomics and Prognosis Analysis of Circadian clock genes in Hepatocellular Carcinoma

10.21203/rs.3.rs-35099/v1 ◽

2020 ◽

Author(s):

Qikuan He ◽

Pengyi Guo ◽

Yunshou Lin ◽

Zhongjing Zhang ◽

Yanning Lv ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Circadian Clock ◽

Risk Score ◽

Clock Genes ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Normal Tissues ◽

Circadian Clock Genes ◽

Cancer Genome Atlas ◽

Genome Consortium

Abstract Background: Circadian clock genes have been reported to exhibit a regulatory effect on the carcinogenesis and progression of numerous cancers. Nevertheless, the specific relationship between hepatocellular carcinoma (HCC) and circadian rhythm associated genes still remain to be clarified. Therefore, we evaluate the prognosis function of circadian clock genes in HCC with the online datasets of The Cancer Genome Atlas (TCGA) and the international cancer genome consortium (ICGC). Methods: In our research, the RNA-seq of the selected core circadian genes in HCC patients and their relevant clinical data were acquired from the online TCGA database and the ICGC database. R software and cBioPortal website were performed. Results: As consequence, among the 22 typical circadian clock genes, 16 genes were statistically expressed between HCC and adjacent normal tissues. Accordingly, 11 clock genes with regression coefficients were used to constitute a new risk score formula, which was related to the prognosis in HCC. Moreover, the new nomogram, which consisting risk score and several clinical traits , could be applied for the purpose of accurate prediction of the OS time for the patients. Finally, we identified a novel nomogram related with OS in HCC patients with a comprehensive analysis of circadian clock genes and other clinical characteristics profiles. It was also the first time we systematically demonstrated the relationship between clock genes and the HCC prognosis, which would contribute to the treatment of HCC. Conclusions: The current study demonstrated the potential of circadian clock genes as clinically associated biomarkers for prognosis prediction in HCC, which may make a significant contribution to the further investigations of HCC progression.

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Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20203263 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Xiaofei Wang ◽

Jie Qiao ◽

Rongqi Wang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Score ◽

Expression Profiles ◽

Univariate Analysis ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Good Prediction ◽

Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

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Eight-Gene Metabolic Signature Related with Tumor-Associated Macrophages Predicting Overall Survival for Hepatocellular Carcinoma

10.21203/rs.3.rs-36554/v1 ◽

2020 ◽

Author(s):

Junyu Huo ◽

Yunjin Zang ◽

Hongjing Dong ◽

Xiaoqiang Liu ◽

Fu He ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Survival Analysis ◽

Risk Score ◽

Risk Group ◽

Cancer Genome ◽

Metabolic Reprogramming ◽

Tumor Associated Macrophages ◽

Kaplan Meier ◽

The Relationship

Abstract Background: In recent years, the relationship between tumor associated macrophages (TAMs) and solid tumors has become a research hotspot. The study aims at exploring the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma(HCC), in order to provide a new way of treatment for HCC.Materials and methods: The study selected 343 HCC patients with complete survival information(survival time >= 1month) in the Cancer Genome Atlas (TCGA) as the study objects. Kaplan-Meier survival analysis assisted in figuring out the relationship between macrophage infiltration level and overall survival (OS), and Pearson correlation test to identify metabolic reprogramming genes(MRGs) related to tumor macrophage abundance. Lasso regression algorithm were conducted on prognosis related MRGs screened by Univariate Cox regression analysis and Kaplan-Meier survival analysis to construct the riskscore, another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) were used for external validation regarding the prognostic signature.Results: A risk score composed of 8 metabolic genes can accurately predict the OS of training cohort(TCGA) and testing cohort(ICGC). It is important that the risk score could widely used for people with different clinical characteristics, and is an independent predictor independent of other clinical factors affecting prognosis. As expected, high-risk group exhibited an obviously higher macrophage abundance relative to low-risk group, and the risk score presented a positive relation to the expression level of three commonly used immune checkpoints(PD1,PDL1,CTLA4).Conclusion: Our study constructed and validated a novel eight‑gene signature for predicting HCC patients’ OS, which possibly contributed to making clinical treatment decisions.

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Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.796795 ◽

2022 ◽

Vol 11 ◽

Author(s):

Zehua Liu ◽

Rongfang Pan ◽

Wenxian Li ◽

Yanjiang Li

Keyword(s):

Prostate Cancer ◽

Cell Cycle ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Functional Enrichment ◽

Cancer Center ◽

Clinical Prognosis ◽

Normal Tissues

This study aimed to identify critical cell cycle-related genes (CCRGs) in prostate cancer (PRAD) and to evaluate the clinical prognostic value of the gene panel selected. Gene set variation analysis (GSVA) of dysregulated genes between PRAD and normal tissues demonstrated that the cell cycle-related pathways played vital roles in PRAD. Patients were classified into four clusters, which were associated with recurrence-free survival (RFS). Moreover, 200 prognostic-related genes were selected using the Kaplan–Meier (KM) survival analysis and univariable Cox regression. The prognostic CCRG risk score was constructed using random forest survival and multivariate regression Cox methods, and their efficiency was validated in Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70770. We identified nine survival-related genes: CCNL2, CDCA5, KAT2A, CHTF18, SPC24, EME2, CDK5RAP3, CDC20, and PTTG1. Based on the median risk score, the patients were divided into two groups. Then the functional enrichment analyses, mutational profiles, immune components, estimated half-maximal inhibitory concentration (IC50), and candidate drugs were screened of these two groups. In addition, the characteristics of nine hub CCRGs were explored in Oncomine, cBioPortal, and the Human Protein Atlas (HPA) datasets. Finally, the expression profiles of these hub CCRGs were validated in RWPE-1 and three PRAD cell lines (PC-3, C4-2, and DU-145). In conclusion, our study systematically explored the role of CCRGs in PRAD and constructed a risk model that can predict the clinical prognosis and immunotherapeutic benefits.

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Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.657051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shenglan Cai ◽

Xingwang Hu ◽

Ruochan Chen ◽

Yiya Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Genes ◽

Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

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Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

10.21203/rs.3.rs-349666/v1 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5970085 ◽

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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