A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

Objective. Autophagy and long noncoding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma. Methods. We downloaded RNA sequencing data and clinical information of melanoma from the Cancer Genome Atlas. The coexpression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate Cox regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups. Results. According to the results of the univariate Cox analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate Cox analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group ( p < 0.001 ). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism. Conclusion. The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNA risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

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A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

10.21203/rs.3.rs-57306/v1 ◽

2020 ◽

Author(s):

Yi Ding ◽

Tian Li ◽

Min Li ◽

Tuersong Tayier ◽

MeiLin Zhang ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Risk Model ◽

Risk Group ◽

Clinical Information ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Positive Regulation ◽

Risk Patients ◽

Cox Analysis

Abstract Background: Autophagy and long non-coding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.Methods: We downloaded RNA-sequencing data and clinical information of melanoma from The Cancer Genome Atlas. The co-expression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate COX regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.Results: According to the results of the univariate COX analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate COX analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p<0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.Conclusion: The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNAs risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

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Identification of A Novel Six Autophagy-Related Genes Signature for The Prognostic and A miRNA-Related Autophagy Predictor for Anti-PD-1 Therapy Responses in Prostate Cancer

10.21203/rs.3.rs-42552/v1 ◽

2020 ◽

Author(s):

Lei Wu ◽

Guojun Yue ◽

Wen Quan ◽

Qiong Luo ◽

Dongxu Peng ◽

...

Keyword(s):

Prostate Cancer ◽

High Risk ◽

Regression Model ◽

Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Signature ◽

Potential Biomarker ◽

Risk Patients

Abstract Background: Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.Methods: An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2-3). Based on STRING analysis results, we found that the NKX2-3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2-3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2-3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.Results: We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2-3). Further analysis demonstrated that NKX2-3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.Conclusions: The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

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An autophagy-related long non-coding RNA signature for patients with colorectal cancer

Physiology International ◽

10.1556/2060.2021.00125 ◽

2021 ◽

Author(s):

Dongyan Zhao ◽

Xizhen Sun ◽

Sidan Long ◽

Shukun Yao

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

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Identification of Autophagy-Related Long Non-Coding RNA Prognostic Signature for Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-87560/v1 ◽

2020 ◽

Author(s):

Yankang Cui ◽

Shaobo Zhang ◽

Chenkui Miao ◽

Chao Liang ◽

Xiaochao Chen ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Cell Carcinoma ◽

Prognostic Value ◽

Clear Cell ◽

Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Independent Predictive Factor ◽

Cell Renal Cell Carcinoma

Abstract Background: Studies over the past decade have shown that long non-coding RNAs (lncRNAs) play an essential role in the tumorigenesis and progression of kidney renal clear cell carcinoma (KIRC). Meanwhile, autophagy has been demonstrated to regulate KIRC pathogenesis and targeting therapy resistance. However, the prognostic value of autophagy-related lncRNAs in KIRC patients has not been reported before.Methods: In this study, we obtained transcriptome data of 611 KIRC cases from the TCGA database and 258 autophagy-related mRNAs from the HADb database to identify autophagy-related lncRNAs by co-expression network. A prognostic model was then established basing on these autophagy-related lncRNAs, dividing patients into high-risk and low-risk groups. Survival analysis, clinical variables dependent receiver operating characteristic (ROC) analyses, univariate/multivariate Cox analyses, and clinical correlation analysis were performed based on risk signature with R language. Gene set enrichment analysis (GSEA) was then performed to investigate the potential mechanism of the risk signature promoting KIRC progression with GSEA software. Results: A total of 17 lncRNAs were screened out and all these lncRNAs were found significantly related to KIRC patients' overall survival in subsequent survival analyses. Besides, the overall survival time in the high-risk group was much poorer than in the low-risk group. The ROC analysis revealed that the prognostic value of risk signature was better than age, gender, grade, and N stage. Univariate/multivariate analyses suggested that the risk signature was an independent predictive factor for KIRC patients. Immune-related pathways were dramatically enriched in high-risk patients according to GSEA. Conclusions: In summary, our identified 17 autophagy-related lncRNAs had prognostic value for KIRC patients which may function in Immunomodulation.

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Identification of a novel six autophagy-related genes signature for the prognostic and a miRNA-related autophagy predictor for anti-PD-1 therapy responses in prostate cancer

BMC Cancer ◽

10.1186/s12885-020-07725-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lei Wu ◽

Wen Quan ◽

Guojun Yue ◽

Qiong Luo ◽

Dongxu Peng ◽

...

Keyword(s):

Prostate Cancer ◽

High Risk ◽

Regression Model ◽

Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Signature ◽

Potential Biomarker ◽

Risk Patients

Abstract Background Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear. Methods An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2–3). Based on STRING analysis results, we found that the NKX2–3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2–3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2–3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa. Results We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2–3). Further analysis demonstrated that NKX2–3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa. Conclusions The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

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Gastrointestinal Acuity Score (GAS): A Comprehensive Risk Score Incorporating Histologic Grade and Clinical Factors to Predict Need for Second Line Therapy, Non-Relapse Mortality, and Overall Survival in Lower Gastrointestinal Graft Vs. Host Disease

Blood ◽

10.1182/blood.v126.23.3128.3128 ◽

2015 ◽

Vol 126 (23) ◽

pp. 3128-3128

Author(s):

Jin S. Im ◽

Rima M Saliba ◽

Susan C Abraham ◽

Asif Rashid ◽

William Ross ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Risk Group ◽

Low Risk ◽

Intermediate Risk ◽

Second Line ◽

Second Line Therapy ◽

Cart Analysis ◽

Line Therapy ◽

Risk Patients

Abstract Lower Gastrointestinal Graft versus Host Disease (GI GVHD) is a major cause for GVHD related non-relapsed mortality (NRM). High dose corticosteroid is the initial therapy for suspected lower GI GVHD, and additional therapy is often required for steroid refractory cases. The identification of high-risk patients is critical as timely intensification of treatment through early institution of second line therapy improves outcomes. To develop a reliable lower GI GVHD risk scoring system, Gastrointestinal Acuity Score (GAS), we evaluated 210 consecutive patients who underwent endoscopic biopsy for suspected lower GI GVHD within 6 months from the transplant from 2009 to 2012 at M.D. Anderson Cancer Center. We first identified 5 significant prognostic factors that accounted for the increased NRM in lower GI GVHD using univariate analysis: histologic grade, clinical stage, age, multi-organ GVHD, and donor type (Table 1). Gender, donor cell type, conditioning regimen (myeloablative vs non-myeloablative), time of diagnosis, disease status did not significantly influence NRM. Next, we performed multivariate Classification and Regression Tree (CART) analysis that utilizes sequential binary decision nodes to categorize 197 patients into subgroups according to their risk profile and outcome (Figure 1). The decision tree identified 7 subgroups starting with the presence of multi-organ GVHD as the first major decision node. This was followed by clinical stage 3/4, Age > 40 years, histology grade 3/4, and histology grade 1. We then consolidated subgroups with comparable risk profiles into low, intermediate, and high NRM risk groups (Figure 1). The 3 NRM risk groups correlated with day 28 response, the need for second line therapy and overall survival (Figure 2). The complete response rates at day 28 from the initiation of steroid therapy were significantly lower in the high (15%, p<0.001) and intermediate (43%, p=0.003) risk group compared to the low-risk group (70%). Accordingly, the high-risk patients were 4.3 (p < 0.001) and 1.6 (p=0.02) folds more likely to need second line therapy compared to low and intermediate risk patients, respectively. The intermediate risk patients were 2.7 (p=0.02) folds more likely to need second line therapy compared to the low risk group. In addition, the high-risk group was associated with the highest NRM (HR 5.4, p<0.001), followed by the intermediate risk (HR 3.8, p<0.001) compared to the low risk group. This translated into worse overall survival at 1 year for the high-risk patients. Lastly, there was a trend towards a lower chronic GVHD rate in the low risk group (25%, HR=0.6, p 0.06) compared to the high and intermediate risk groups (37%). In conclusion, we developed a novel risk scoring system, Gastrointestinal Acuity Score (GAS), incorporating histologic grades and clinical factors through multivariate CART analysis, and demonstrated that GAS predicts both early outcomes (day 28 CR rate and need for second line therapy) and late outcomes (NRM and overall survival) in patients with lower GI GVHD. Once prospectively validated in a larger cohort of lower GI GVHD patients, it will be of great use for clinicians with limited access to GVHD biomarker analysis in making treatment decisions as our new risk scoring system utilizes readily available variables. Figure 1. Univariate Analysis of risk factors for NRM at 1 year in Lower GI GVHD Figure 1. Univariate Analysis of risk factors for NRM at 1 year in Lower GI GVHD Figure 2. CART Analysis: Decision Tree for Risk Assessment Figure 2. CART Analysis: Decision Tree for Risk Assessment Figure 3. Need for second line therapy, NRM and OS Figure 3. Need for second line therapy, NRM and OS Disclosures Alousi: Therakos, Inc: Research Funding.

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Identifying novel cell glycolysis related gene signature predictive of overall survival in bladder urothelial carcinoma

10.21203/rs.3.rs-30639/v1 ◽

2020 ◽

Author(s):

Xin Zhao ◽

Jia Li ◽

Jiafeng Li ◽

Wenjun Xiong ◽

Rui Jiang

Keyword(s):

Data Mining ◽

Overall Survival ◽

High Risk ◽

Urothelial Carcinoma ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Bladder Urothelial Carcinoma

Abstract Background: Bladder urothelial carcinoma (BLCA) is the most common pathological type of bladder cancer and featured by a high risk for relapse and metastasis. Although many biomarkers have been developed by data mining and experimental studies to predict the prognosis of BLCA, a single-gene biomarker usually has poor specificity and sensitivity, leading to unsatisfactory prediction. Therefore, novel gene signatures are needed to more accurately predict the prognosis of BLCA.Methods: Data mining was performed for expression profile analysis of 433 mRNA expression data from the TCGA BLCA patients (n=412). Gene Set Enrichment Analysis (GSEA) was used to interpret the glycolysis-related gene sets. Gene signature related to the prognosis of BLCA was identified by univariate and multivariate Cox regression. A risk score was computed based on three genes by linear regression model and its relation with overall survival was investigated by Kaplan-Meier analysis.Results: Three genes (CHPF, AK3, NUP188) were found to be significantly correlated to the overall survival of BLCA patients. Based on the signature composed of these three genes, 412 BLCA patients were divided into high-risk and low-risk groups. The survival time of the high-risk group was significantly shorter than that of the low-risk group, indicating a worse prognosis.Conclusion: A signature composed of three glycolysis-related genes was developed as biomarkers to predict the prognosis of BLCA and to provide a meaningful reference for the clinical treatment of BLCA.

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Identification of Five Immune-Related lncRNAs Predicting Survival and Tumor Microenvironment Characteristics in Breast Cancer

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6676692 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ran Xiao ◽

Meng Yang ◽

Yuanyuan Tan ◽

Rumeng Ding ◽

Duolu Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Tumor Microenvironment ◽

Risk Model ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Clinical Samples ◽

Cox Regression Analysis

A common cancer in females, breast cancer (BRCA) mortality has been recently reduced; however, the prognosis of BRCA patients remains poor. This study attempted to develop prognostic immune-related long noncoding RNAs (lncRNAs) for BRCA and identify the effects of these lncRNAs on the tumor microenvironment (TME). Gene expression data from The Cancer Genome Atlas (TCGA) database were collected in order to select differentially expressed lncRNAs. Immune-related lncRNAs were downloaded from the ImmLnc database, where 316 immune-related lncRNAs were identified, 12 of which were found to be significantly related to the prognosis of BRCA patients. Multivariate cox regression analysis was then applied to construct prognostic immune-related lncRNAs as the risk model, including C6orf99, LINC00987, SIAH2-AS1, LINC01010, and ELOVL2-AS1. High-risk and low-risk groups were distinguished according to the median of immune-related risk scores. Accordingly, the overall survival (OS) in the high-risk group was observed to be shorter than that in the low-risk group. qRT-PCR analysis demonstrated that lncRNA expression levels in BRCA cell lines were in basic agreement with predictions except for LINC00987. By validating numerous clinical samples, lncRNA C6orf99 was shown to be highly expressed in the advanced stage, while LINC01010 and SIAH2-AS1 decreased in the advanced T-stage and M-stage. Moreover, the expression of LINC0098 was found to be significantly decreased among the groups (>50 years old). Gene set enrichment analysis (GSEA) was applied to analyze the cancer hallmarks and immunological characteristics of the high-risk and low-risk groups. Importantly, the TIMER database demonstrated that this immune-related lncRNA risk model for breast cancer is related to the infiltration of immune cells. In conclusion, the results indicated that five immune-related lncRNAs could be used as a prognostic model and may even accelerate immunotherapy for BRCA patients.

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Development and validation of a novel ten-gene prognostic signature for hepatocellular carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e16665 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e16665-e16665

Author(s):

Taicheng Zhou ◽

Zhihua Cai ◽

Ning Ma ◽

Wenzhuan Xie ◽

Chan Gao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognosis Prediction

e16665 Background: Hepatocellular carcinoma (HCC) remains a major challenge for public health worldwide and long-term outcomes remained dismal despite availability of curative treatment. We aimed to construct a multi-gene model for prognosis prediction to inform clinical management of HCC. Methods: RNA-seq data of paired tumor and normal tissue samples of HCC patients from the TCGA and GEO database were used to identify differentially expressed genes (DEGs). DEGs shared by both cohorts along with patients’ survival data of the TCGA cohort were further analyzed using univariate Cox regression and LASSO Cox regression to build a prognostic 10-gene signature, followed by validation of the signature via ICGC cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: Of 571 patients (70.93% men and 29.07% women; median age [IQR], 65 [56-72] years), a signature of 10 genes was constructed using the training cohort. In the testing and validation cohorts, the signature significantly stratified patients into low- vs high-risk groups in terms of overall survival across and within subpopulations with stage I/II and III/IV disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 0.13 [95% CI, 0.07-0.24; P < 0 .001] to 0.38 [95% CI, 0.2-0.71; P < 0.001]) after adjusting for clinicopathological factors. Prognosis was significantly worse in the high-risk group than in the low-risk group across cohorts (P < 0.001 for all). The 10-gene signature achieved a higher accuracy (C-index, 0.84; AUCs for 1-, 3- and 5-year OS, 0.84, 0.81 and 0.85, respectively) than 8 previously reported multigene signatures (C-index range, 0.67 to 0.73; AUCs range, 0.68 to 0.79, 0.68 to 0.80 and 0.67 to 0.78, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor stage and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the pathways related to cell cycle regulation were more prominently enriched in the high-risk group while the low-risk group had higher enrichment of metabolic process. Conclusions: Taken together, we established a robust 10-gene signature and a nomogram to predict overall survival of HCC patients, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

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