Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Dan Li ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Clinicopathological Parameters ◽  
Kaplan Meier ◽  
The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

scholarly journals Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

scholarly journals Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

2020 ◽  
Author(s):  
Li Liu ◽  
She Tian ◽  
Zhu Li ◽  
Yongjun Gong ◽  
Hao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Principal Component ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

scholarly journals Prognostic utility of neutrophil-to-lymphocyte ratio in patients with metastatic colorectal cancer treated using different modalities

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
G. Nogueira-Costa ◽  
I. Fernandes ◽  
R. Gameiro ◽  
J. Gramaça ◽  
A.T. Xavier ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Metastatic Colorectal Cancer ◽  
Real World ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
First Line ◽  
Kaplan Meier

Introduction Inflammation is a critical component in carcinogenesis. The neutrophil-to-lymphocyte ratio (nlr) has been retrospectively studied as a biomarker of prognosis in metastatic colorectal cancer (mcrc). Compared with a low nlr, a high nlr is associated with worse prognosis. In the present study, we compared real-world survival for patients with mcrc based on their nlr group, and we assessed the utility of the nlr in determining first-line chemo­therapy and metastasectomy benefit. Methods In this retrospective and descriptive analysis of patients with mcrc undergoing first-line chemotherapy in a single centre, the last systemic absolute neutrophil and lymphocyte count before treatment was used for the nlr. A receiver operating characteristic curve was used to estimate the nlr cut-off value, dividing the patients into low and high nlr groups. Median overall survival (mos) was compared using Kaplan–Meier curves and the log-rank test. A multivariate analysis was performed using a Cox regression model. Results The 102 analyzed patients had a median follow-up of 15 months. Regardless of systemic therapy, approx­imately 20% of patients underwent metastasectomy. The nlr cut-off was established at 2.35, placing 45 patients in the low-risk group (nlr < 2.35) and 57 in the high-risk group (nlr ≥ 2.35). The Kaplan–Meier analysis showed a mos of 39.1 months in the low-risk group and 14.4 months in the high-risk group (p < 0.001). Multivariate Cox regression on the nlr estimated a hazard ratio of 3.08 (p = 0.01). Survival analysis in each risk subgroup, considering the history of metastasectomy, was also performed. In the low-risk group, mos was longer for patients undergoing metastasectomy than for those not undergoing the procedure (95.2 months vs. 22.6 months, p = 0.05). In the high-risk group, mos was not statistically different for patients undergoing or not undergoing metastasectomy (24.3 months vs. 12.7 months, p = 0.08). Conclusions Our real-world data analysis of nlr in patients with mcrc confirmed that this biomarker is useful in predicting survival. It also suggests that nlr is an effective tool to choose first-line treatment and to predict the benefit of metastasectomy.

scholarly journals Development of a Novel Autophagy-Related Prognostic Signature and Nomogram for Hepatocellular Carcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Qiongxuan Fang ◽  
Hongsong Chen
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Gene Model

BackgroundHepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Autophagy plays a crucial role in the development and progression of HCC.MethodsUnivariate and Lasso Cox regression analyses were performed to determine a gene model that was optimal for overall survival (OS) prediction. Patients in the GSE14520 and GSE54236 datasets of the Cancer Genome Atlas (TCGA) were divided into the high-risk and low-risk groups according to established ATG models. Univariate and multivariate Cox regression analyses were used to identify risk factors for OS for the purpose of constructing nomograms. Calibration and receiver operating characteristic (ROC) curves were used to evaluate model performance. Real-time PCR was used to validate the effects of the presence or absence of an autophagy inhibitor on gene expression in HepG2 and Huh7 cell lines.ResultsOS in the high-risk group was significantly shorter than that in the low-risk group. Gene set enrichment analysis (GSEA) indicated that the association between the low-risk group and autophagy- as well as immune-related pathways was significant. ULK2, PPP3CC, and NAFTC1 may play vital roles in preventing HCC progression. Furthermore, tumor environment analysis via ESTIMATION indicated that the low-risk group was associated with high immune and stromal scores. Based on EPIC prediction, CD8+ T and B cell fractions in the TCGA and GSE54236 datasets were significantly higher in the low-risk group than those in the high-risk group. Finally, based on the results of univariate and multivariate analyses three variables were selected for nomogram development. The calibration plots showed good agreement between nomogram prediction and actual observations. Inhibition of autophagy resulted in the overexpression of genes constituting the gene model in HepG2 and Huh7 cells.ConclusionsThe current study determined the role played by autophagy-related genes (ATGs) in the progression of HCC and constructed a novel nomogram that predicts OS in HCC patients, through a combined analysis of TCGA and gene expression omnibus (GEO) databases.

scholarly journals Ferroptosis regulator genes are a favorable biomarker for hepatocellular carcinoma

2021 ◽  
Author(s):  
Yongfei He ◽  
Shuqi Zhao ◽  
Zhongliu Wei ◽  
Xin Zhou ◽  
Tianyi Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Information ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Regulator Genes ◽  
The Relationship

Abstract BackgroundIn this study, we comprehensively analyzed the relationship between ferroptosis regulator genes (FRGs) and prognosis of hepatocellular carcinoma (HCC), determined the prognostics value of FRGs, established a prediction model, and explored the relationship with immunotherapy for HCC.MethodsThe mRNA transcriptional levels and clinical information of HCC were obtained from The Cancer Genome Atlas (TCGA) database. The 24 FRGs were combined with the differential expression genes (DEGs) of HCC for further analysis. The prognostics values of differential FRGs via the construction of model and validation by the Cox regression analysis.ResultThere were three genes (CARS1, FANCD2, and SLC7A11) were identified as independent risk factors for HCC, and a predictive model was constructed based on CARS1, FANCD2, and SLC7A11. The model showed that the low-risk group HCC patients with a more prolonged overall survival (OS) than the high-risk group (P=0.001). The high-risk group with higher expression of FRGs than the low-risk group. Finally, the relations between FGEs and immune infiltration showed that CARS1, FANCD2, and SLC7A11 had a positive relationship with macrophage infiltration. From these, three genes might be the potential therapeutic targets.ConclusionOur study indicated that CARS1, FANCD2, and SLC7A11 might have potential value for therapeutic strategies as practical and reliable prognostic tools for HCC.

scholarly journals Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Dan Li ◽  
LingLing Zhuang ◽  
Jian Zhang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Risk Group ◽  
High Risk Group ◽  
Mesenchymal Transition ◽  
Kaplan Meier

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial–mesenchymal transition (EMT) is crucial for cancer progression and associates with a worse prognosis. Thus, we aimed to construct an EMT-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We built an EMT-related lncRNA risk signature in the training set by using Cox regression and LASSO regression based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. Cox regression was performed to explore whether the signature could be used as an independent factor. A nomogram was built involving the risk score and clinicopathological features. Furthermore, we explored the biological functions and immune states in two groups.Results: 12 EMT-related lncRNAs were obtained for constructing the prognosis model in HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse survival than low-risk group. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the validation group. The nomogram was built and could accurately predict survival of HCC patients. GSEA results showed that in high-risk group cancer-related pathways were enriched, and exhibited more cell division activity suggested by Gene Ontology (GO) analysis.Conclusions: We established a novel EMT-related prognostic risk signature including 12 lncRNAs and constructed a nomogram to predict the prognosis in HCC patients, which may improve prognostic predictive accuracy for HCC patients and guide the individualized treatment methods for the patients with HCC.

scholarly journals Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xinyu Gu ◽  
Jun Guan ◽  
Jia Xu ◽  
Qiuxian Zheng ◽  
Chao Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment ◽  
Kaplan Meier ◽  
Immune Related Genes

Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.

scholarly journals A Novel Hypoxic-Angiogenesis-Immune-Related Gene Model for Prognostic and Therapeutic Effect Prediction in Hepatocellular Carcinoma Patients

2022 ◽  
Vol 2022 ◽  
pp. 1-27
Author(s):  
Wen Lv ◽  
Qi Yao
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Liver Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Dysfunction ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant tumors that have been discovered so far, which makes the prognostic prediction difficult. The hypoxia, angiogenesis, and immunity-related genes (HAIRGs) are closely related to the development of liver cancer. However, the prognostic and treatment effect of hypoxia, angiogenesis, and immunity-related genes in HCC continues to be further clarified. Methods. The gene expression quantification data and clinical information in patients with liver cancer were downloaded from the TCGA database, and HAIRG signature was built by using the least absolute shrinkage and selection operator (LASSO) technique. Patient from the ICGC database validated the model. Then, tumor immune dysfunction and exclusion (TIDE) algorithm was applied to estimate the clinical response to immunotherapy and the sensitivity of drugs was evaluated by the half-maximal inhibitory concentration (IC50). Result. The HAIRGs were identified between the HCC patients and normal patients in the TCGA database. In univariate Cox regression analysis, seventeen differentially expressed genes (DEGs) were associated with overall survival (OS). An eight HAIRG signature model was constructed and was used to divide the patients into two groups according to the median value of the risk score base on the TCGA dataset. Patients in the high-risk group had a significant reduction in OS compared to those in the low-risk group ( P < 0.001 in the TCGA, P < 0.001 in the ICGC). For TCGA and ICGC databases of univariate Cox regression analyses, the risk score was used as an independent predictor of OS ( HR > 1 , P < 0.001 ). Functional analysis showed that the relevant immune pathways and immune responses were enriched, cellular component analysis showed that the immunoglobulin complex and other related substances were enriched, and immune status existed a difference in the high- and low-risk groups. Then, the tumor immune dysfunction and exclusion (TIDE) algorithm presented differences in immune response in the high- and low-risk groups ( P < 0.05 ), and based on drug sensitivity prediction, patients in the high-risk group were more sensitive to cisplatin compared to those in the low-risk group in both the TCGA and ICGC cohorts ( P < 0.05 ). Conclusions. HAIRG signature can be utilized for prognostic prediction in HCC, while it can be considered a prediction model for clinical evaluation of immunotherapy response and chemotherapy sensitivity in HCC.

Assessing the impact of comorbidities on autologous hematopoietic cell transplant (AHCT) outcomes using the hematopoietic cell transplant-comorbidity index (HCT-CI) in lymphoma

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 7102-7102
Author(s):  
M. Kassar ◽  
S. A. Gregory ◽  
K. Shell ◽  
P. Venugopal ◽  
J. Shammo ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Hematopoietic Cell ◽  
Low Risk ◽  
Risk Scores ◽  
Cell Transplant ◽  
Hematopoietic Cell Transplant ◽  
The Impact

7102 Background: Sorror et al. has identified HCT-CI as a valid scoring of pretransplant comorbidities that predicted nonrelapse mortality and survival after allogeniec HCT. We recently reported on the validity of HCT-CI in predicting morbidity outcomes after AHCT for lymphoma (BBMT in press). High HCT-CI score predicted for prolonged hospitalization and high incidence of hospital re-admission after AHCT. The objective of this study is to evaluate the impact of HCT-CI on mortality risk after AHCT. Methods: We included pts above age of 40 with advanced HL or NHL, who underwent AHCT in our institution between 01/98 & 05/06. Median follow up was 29.4 mo. Pts were assigned scores based on the HCT-CI. Defenition of comorbidities were recently reported (Kassar et al, BBMT in press). Results: 80 pts were included (NHL: 74, HL: 6). 61 pts were male. Median age was 56 years (42–76). Comorbidities (points, prevalence%): mild hepatic (1,14), cardiac (1,15), cerebrovascular (1,4), arrhythmia (1,9), moderate pulmonary (2,11), severe pulmonary (3,8), rheumatologic (2,5), DM (1,23), inflammatory bowel disease (1,3), psychiatric (1,11), infection (1,6), obesity (1,11), and renal (2,1). Median HCT-CI was 1 (0: 37 %, 1: 26%, 2–7: 37%). 22 pts died: 15 from relapse and 7 from non relapse mortality (NRM) causes. Cumulative day-100 NRM and 1-year NRM rates are: 1.3% and 4%, respectively. Pts were categorized into 2 groups: low-risk (scores of 0–1) and high-risk (scores 2–7). Using Cox Regression model and adjusting for age and histology, low-risk group had a significantly better OS (1 wk-58.6 mo, median 34.1 mo) compared to high-risk group (5 days-23.6 mo, median 6 mo) (HR: 3.73, p = .01, 95% CI 1.32, 10.54). 1-year OS rate was 75% vs. 25%, respectively (p=.04). Conclusion: HCT-CI is a valid scoring of pre-transplant comorbidities that predicted mortality after AHCT for pts with lymphoma. The physiologic burden of comorbidities is likely to impact the tolerance to AHCT or to other therapies administered upon relapse after transplant. HCT-CI can serve as important tool for both the transplant administrator when planning for resources allocation and clinical trials, and for pts during pre-transplant evaluation and counseling. No significant financial relationships to disclose.

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

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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