scholarly journals Construction of a Prognostic m6A-related lncRNA Signature to Predict Immunotherapy and Chemotherapy Response in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Yiping Zou ◽  
Zhihong Chen ◽  
Hongwei Han ◽  
Qi Lou ◽  
Yuanpeng Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Tumor Development ◽  
Risk Groups ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Response

Abstract Hepatocellular carcinoma (HCC) is one of the main causes of cancer-related deaths worldwide. N6-methyladenosine (m6A) and long noncoding RNA (lncRNA) are two common modifications that affect tumor development and play vital roles in the prognosis of HCC. Therefore, we comprehensively analyzed transcriptome data from the Cancer Genome Atlas (TCGA) and identified lncRNAs related to m6A regulators. Univariate, LASSO, and multivariate Cox regression analyses were used to build a prognostic model. Patients were then divided into low- and high-risk groups according to the optimal cut-off point. The prognosis value of the novel model was evaluated using Kaplan-Meier analysis and the receiver operating characteristic curve. Besides, mutation and immune profiles together with immune checkpoint expressions were further explored to identify the difference in somatic alteration and tumor immune landscape between the two groups. Additionally, a better response to conventional chemotherapy and immunotherapy was found in patients with the high-risk group, but they were more resistant to sorafenib. The m6A-related lncRNAs model might be used to predict the prognosis and drug responses in patients with HCC.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals Expression pattern and prognostic value of N6-methyladenosine RNA methylation key regulators in hepatocellular carcinoma

Mutagenesis ◽  
2021 ◽  
Vol 36 (5) ◽  
pp. 369-379
Author(s):  
Min Deng ◽  
Lin Fang ◽  
Shao-Hua Li ◽  
Rong-Ce Zhao ◽  
Jie Mei ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Critical Role ◽  
Risk Groups ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Hepatocellular carcinoma (HCC) is still one of the most common malignancies worldwide. The accuracy of biomarkers for predicting the prognosis of HCC and the therapeutic effect is not satisfactory. N6-methyladenosine (m6A) methylation regulators play a crucial role in various tumours. Our research aims further to determine the predictive value of m6A methylation regulators and establish a prognostic model for HCC. In this study, the data of HCC from The Cancer Genome Atlas (TCGA) database was obtained, and the expression level of 15 genes and survival was examined. Then we identified two clusters of HCC with different clinical factors, constructed prognostic markers and analysed gene set enrichment, proteins’ interaction and gene co-expression. Three subgroups by consensus clustering according to the expression of the 13 genes were identified. The risk score generated by five genes divided HCC patients into high-risk and low-risk groups. In addition, we developed a prognostic marker that can identify high-risk HCC. Finally, a novel prognostic nomogram was developed to accurately predict HCC patients’ prognosis. The expression levels of 13 m6A RNA methylation regulators were significantly upregulated in HCC samples. The prognosis of cluster 1 and cluster 3 was worse. Patients in the high-risk group show a poor prognosis. Moreover, the risk score was an independent prognostic factor for HCC patients. In conclusion, we reveal the critical role of m6A RNA methylation modification in HCC and develop a predictive model based on the m6A RNA methylation regulators, which can accurately predict HCC patients’ prognosis and provide meaningful guidance for clinical treatment.

scholarly journals Utility of radiomics for predicting patient survival in hepatocellular carcinoma with portal vein tumor thrombosis treated with stereotactic body radiotherapy

2020 ◽  
Author(s):  
Kui Wu ◽  
Yongjie Shui ◽  
Wenzheng Sun ◽  
Sheng Lin ◽  
Haowen Pang
Keyword(s):  
High Risk ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Patient Survival ◽  
Risk Group ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk

Abstract Objective This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in HCC with PVTT treated with SBRT. Materials and Methods The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the radiomic feature. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results A total of seven radiomic features and five clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [AUC = 0.859 (CI: 0.770–0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641–0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to facilitate individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.

scholarly journals An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism

2021 ◽  
Vol 8 ◽  
Author(s):  
Hao Zhang ◽  
Renzheng Liu ◽  
Lin Sun ◽  
Xiao Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Rate ◽  
Liver Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
The Difference ◽  
Experimental Group

Liver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on The Cancer Genome Atlas (TCGA) database, we first identified 1066 lncRNAs with differential expression. The patient data obtained from TCGA were divided into the experimental group and the verification group. According to the difference in lncRNAs, we used single-factor and multi-factor Cox regression to select the genes needed to build the model in the experimental group, which were verified in the verification group. The results showed that the model could accurately predict the survival rate of patients in the high and low risk groups. The reliability of the model was also confirmed by the area under the receiver operating characteristic curve. Our model is significantly correlated with different clinicopathological features. Finally, we built a ceRNA network based on lncRNAs, which was used to display miRNAs and mRNAs related to lncRNAs. In summary, we constructed an lncRNA model to predict the survival rate of patients with hepatocellular carcinoma.

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 Differentially expressed liver exosome-related genes as a candidate prognostic biomarker for hepatocellular carcinoma

2020 ◽  
Author(s):  
Bangyou Zuo ◽  
Haitao Zhao ◽  
Jin Bian ◽  
Junyun Long ◽  
Xu Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Cell Communication ◽  
High Risk Group ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed

Abstract Background The function of exosome includes cell-to-cell communication, neovascularization, and metastasis of cancer cell and drug resistance, which plays an important part in the occurrence and progression of hepatocellular carcinoma (HCC). Because the mechanism in this area is less studied, our goal is to identify exosome-related genes in HCC, establish a reliable prognostic model for liver cancer patients, and explore its underlying mechanisms. Methods The exoRbase database and The Cancer Genome Atlas (TCGA) database were used to analyze differentially expressed genes (DEGs). Cox regression and LASSO analysis were applied to determine DEGs closely related to overall survival (OS). Then the exosome-related prognostic model was constructed in TCGA and validated in the database of International Cancer Genome Consortium (ICGC). Nomogram graph was performed to predict the survival. CIBERSORT was used to estimate the score of different type of immune cells. DEGs related to immunotherapy are used to predict the effect of immunotherapy. Results 48 exosome-related DEGs were obtained and five genes (XPO1, IFI30, FBXO16, CALM1, MORC3) among them were selected to construct predictive model. Then we divided the HCC patients into low-risk and high-risk groups by the best cut-off value according to the X-tile software. The high-risk related to exosome were significantly associated with a poor prognosis. Moreover, the features related to exosome could positively regulate immune response. At the same time, the proportion of T cell regulatory factors (Tregs) and macrophages M2 is higher in the high-risk group, and high-risk group exhibited higher expression of immune checkpoint molecular including PD-L1, PD-L2, TIGIT, and IDO1. Conclusions Overall, our research showed that markers related to exosomes were potential biomarkers for the prognosis of HCC, providing an immunological perspective for the development of precision treatment.

scholarly journals Comprehensive Analysis of the Immune-Oncology Targets and Immune Infiltrates of N6-Methyladenosine-Related Long Noncoding RNA Regulators in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoqiang Zhang ◽  
Li Shen ◽  
Ruyu Cai ◽  
Xiafei Yu ◽  
Junzhe Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
Noncoding Rna ◽  
Immune Cell ◽  
Cox Regression ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Oncology

Breast cancer (BRCA) has become the highest incidence of cancer due to its heterogeneity. To predict the prognosis of BRCA patients, sensitive biomarkers deserve intensive investigation. Herein, we explored the role of N6-methyladenosine-related long non-coding RNAs (m6A-related lncRNAs) as prognostic biomarkers in BRCA patients acquired from The Cancer Genome Atlas (TCGA; n = 1,089) dataset and RNA sequencing (RNA-seq) data (n = 196). Pearson’s correlation analysis, and univariate and multivariate Cox regression were performed to select m6A-related lncRNAs associated with prognosis. Twelve lncRNAs were identified to construct an m6A-related lncRNA prognostic signature (m6A-LPS) in TCGA training (n = 545) and validation (n = 544) cohorts. Based on the 12 lncRNAs, risk scores were calculated. Then, patients were classified into low- and high-risk groups according to the median value of risk scores. Distinct immune cell infiltration was observed between the two groups. Patients with low-risk score had higher immune score and upregulated expressions of four immune-oncology targets (CTLA4, PDCD1, CD274, and CD19) than patients with high-risk score. On the contrary, the high-risk group was more correlated with overall gene mutations, Wnt/β-catenin signaling, and JAK-STAT signaling pathways. In addition, the stratification analysis verified the ability of m6A-LPS to predict prognosis. Moreover, a nomogram (based on risk score, age, gender, stage, PAM50, T, M, and N stage) was established to evaluate the overall survival (OS) of BRCA patients. Thus, m6A-LPS could serve as a sensitive biomarker in predicting the prognosis of BRCA patients and could exert positive influence in personalized immunotherapy.

scholarly journals Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes

2021 ◽  
Author(s):  
Yuanbin Zhong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Tumour Progression ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Cell Types ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature

Abstract Background Hepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear. Materials and methods We identified differentially expressed PRGs in tumours versus normal tissues. Through univariate, LASSO, and multivariate Cox regression analyses, a prognostic PRG signature was established. The signature effectiveness was evaluated by time-dependent receiver operating characteristic (ROC) curve and Kaplan–Meier (KM) survival analysis. The signature was validated in the ICGC (LIRI-JP) cohort. In addition, single-sample gene enrichment analysis (ssGSEA) showed the infiltration of major immune cell types and the activity of common immune pathways in different subgroups. Results Twenty-nine pyroptosis-related DEGs from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were detected, and four genes (CTSV, CXCL8, MKI67 and PRF1) among them were selected to construct a prognostic signature. Then, the patients were divided into high- and low-risk groups. The pyroptosis-related signature was significantly associated with overall survival (OS). In addition, the patients in the high-risk group had lower levels of immune infiltration. Conclusion The prognostic signature for HCC based on 4 pyroptosis-related genes has reliable prognostic and predictive value for HCC patients.

scholarly journals Identification of a Novel Metastasis-Related miRNAs-Based Signature for Predicting the Prognosis of Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yuan Chen ◽  
Guifu Wang ◽  
Hao Xu ◽  
Hao Wang ◽  
Dousheng Bai
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Human Cancer ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Signature

Hepatocellular carcinoma (HCC) is one of the most common internal malignancies worldwide and is associated with a poor prognosis. Abnormal expression of miRNAs is believed to play a role in the recurrent metastasis of HCC. However, limited studies on the role of miRNAs in HCC metastasis have been carried out. Therefore, this study is aimed at exploring the potential value of metastasis-related miRNAs (MRMs) in HCC. We retrieved MRMs were from the Human Cancer Metastasis Database. Differential miRNAs were identified for tumor samples of HCC patients and normal samples based on the TCGA database. Further, univariate and multivariate Cox regression analyses were used to screen MRMs known to be independent prognostic factors in HCC. These MRMs were then used to build a prognostic signature. All patients were classified into high-risk and low-risk groups based on the median of the signature scores. Moreover, GO and KEGG pathway enrichment analyses were performed to predict the function of these MRMs. Finally, a nomogram was constructed to predict the OS of patients at 1, 2, and 3 years. In our study, a total of seven prognostic MRMs (miR-140-3p, miR-9-5p, miR-942-5p, miR-324-3p, miR-29c-5p, miR-551a, and miR-149-5p) were identified and used for constructing the prognostic signature based on the training cohort. Patients in the low-risk HCC group showed better overall survival (OS) than those in the high-risk group. The results were validated using the validation cohort. In summary, the findings of this study provide evidence that MRMs-based prognostic signature is an independent biomarker in the prognosis of HCC patients.

scholarly journals Prognostic Value of a Pyroptosis-Related Long Noncoding RNA Signature Associated with Osteosarcoma Microenvironment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xinxin Bu ◽  
Jiuxiang Liu ◽  
Rong Ding ◽  
Zhi Li
Keyword(s):  
High Risk ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Bone Cancer ◽  
Predictive Biomarkers ◽  
Clinical Information ◽  
Risk Groups ◽  
High Risk Group ◽  
Immune Checkpoints ◽  
Kaplan Meier

Background. Osteosarcoma is the most prevalent bone cancer that affects young adults and adolescents. It is the most frequent malignancy of the bone. In spite of the fact that complete surgical resection and chemotherapy have increased the overall survival of osteosarcoma patients considerably, the prognosis remains dismal in patients with recurring and/or metastasized osteosarcoma. Thus, finding predictive biomarkers representing osteosarcoma's biological variability may result in more effective treatment for osteosarcoma patients. Methods. In this research, RNA data and clinical information were obtained from TARGET database. The risk score was calculated using a technique that incorporated both univariate and multivariate Cox regression. A variety of statistical methods were employed to assess the risk score's accuracy. These included ROC curves, nomograms, and Kaplan-Meier curves. Following that, bioinformatics studies were carried out in order to investigate the possible biological processes that influence the prognosis of osteosarcoma patients. GSEA was used to investigate the variations in pathway enrichment among the different groups of genes. To examine the disparities in the immune microenvironment, the analytical methods CIBERSORT and ssGSEA were employed. Results. We discovered three differentially expressed lncRNAs (RPARP-AS1, AC009159.3, and AC124312.3) that are linked to osteosarcoma prognosis. Kaplan-Meier analysis showed the presence of a signature of high-risk lncRNAs linked with a poor prognosis for osteosarcoma. Furthermore, the AUC of the lncRNAs signature was 0.773, indicating that they are useful in predicting osteosarcoma prognosis in certain cases. In predicting osteosarcoma prognosis, our risk assessment approach outperformed conventional clinicopathological characteristics. In the high-risk group of people, GSEA showed the presence of tumor-related pathways as well as immune-related pathways. Furthermore, TARGET revealed that immune-related functions such as checkpoint, T-cell coinhibition, and costimulation were significantly different between the high-risk and low-risk groups. LAIR1, LAG3, CD44, and CD22, as well as other immune checkpoints, were shown to be expressed differentially across the two risk groups. Conclusion. This study established that pyroptosis-derived lncRNAs had a significant predictive value for osteosarcoma patients' survival, indicating that they may be a viable target for future therapy.

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