A Novel Nomogram Based on Lipid Metabolism-Related Risk Gene Expression Can Better Predict Overall Survival for Hepatocellular Carcinoma

Abstract Metabolic reprogramming has been proven to be a hallmark of cancer. The pathogenic factors involved in Hepatocellular carcinoma (HCC) lead to an abnormal lipid metabolism that facilitates the malignant transformation of liver cells . However, the association between lipid metabolism and the prognosis of HCC has not been systematically delineated. In this study, the training set comprised 221 patients from The Cancer Genome Atlas (TCGA) based on the gene expression details, whereas 230 patients within the International Cancer Genome Consortium (ICGC) comprised the validation set. Ten lipid metabolism-related risk genes were screened; they were found to be significantly related to the prognosis of HCC. The risk score was calculated based on ten screened lipid metabolism-related risk genes and was confirmed to be an independent prognostic factor for HCC even when excluding clinical features. Therefore, a novel nomogram integrating the risk score and other proven clinical attributes was constructed. The results of the area under the receiver operating characteristics curve (AUC), C index, and calibration plot supported the better predictive capacity of the nomogram over others. Treatment with metformin significantly positively affected the expression of four out of ten genes; this was beneficial to longer overall survival. The results provide a new insight into accurate prognostic prediction, as well as understanding the carcinogenesis and process of HCC .

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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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Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-020-07734-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang ◽

Hongjing Dong ◽

Xiaoqiang Liu ◽

...

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. This study aims to explore the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma (HCC) to provide new methods of treatment for HCC. Methods The study selected 343 HCC patients with complete survival information (survival time > = 1 month) in the Cancer Genome Atlas (TCGA) as study subjects. Kaplan-Meier survival analysis assisted in determining the relationship between macrophage infiltration and overall survival (OS), and Pearson correlation tests were used to identify metabolic reprogramming genes (MRGs) associated with tumor macrophage abundance. Lasso regression algorithms were used on prognosis-related MRGs identified by Kaplan-Meier survival analysis and univariate Cox regression analysis to construct a risk score; another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) was used to verify prognostic signature externally. Results A risk score composed of 8 metabolic genes could accurately predict the OS of a training cohort (TCGA) and a testing cohort (ICGC). The risk score could be widely used for people with different clinical characteristics, and it is a predictor that is independent of other clinical factors that affect prognosis. As expected, compared with the low-risk group, the high-risk group exhibited an obviously higher macrophage abundance, together with a positive correlation between the risk score and the expression levels of three commonly used immune checkpoints (PD1, PDL1, and CTLA4). Conclusion Our study constructed and validated a novel eight-gene signature for predicting HCC patient OS, which may contribute to clinical treatment decisions.

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Development and validation of a m6A-related gene signature for predicting the prognosis of hepatocellular carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2020-0178 ◽

2020 ◽

Vol 14 (13) ◽

pp. 1217-1228

Author(s):

Weihao Kong ◽

Xutong Li ◽

Honghai Xu ◽

Yufeng Gao

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Multivariate Regression Analysis ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Risk Genes ◽

Related Risk ◽

Cancer Genome Atlas

Background: This study aimed to investigate the prognostic role of m6A methylation regulators in hepatocellular carcinoma (HCC). Materials & methods: Gene expression matrices were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, and Gene Expression Omnibus databases. Univariate and multivariate regression analysis were utilized to determine the m6A risk genes. Results: Two m6A-related risk genes (YTHDF1, YTHDF2) were identified in the TCGA HCC cohort. The m6A-correlated risk score is an independent risk factor for the overall survival of the TCGA HCC cohort. Finally, we verified the reliability of our results using three external datasets. Conclusion: The m6A-correlated gene signature has prognostic value in HCC patients and thus provides guidance for the treatment of HCC.

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A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

BioMed Research International ◽

10.1155/2020/7310768 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Jun Liu ◽

Jianjun Lu ◽

Zhanzhong Ma ◽

Wenli Li

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Clinical Practice ◽

Correlation Coefficient ◽

Cox Regression ◽

Prognostic Significance ◽

Gene Signature ◽

Cancer Genome ◽

Survival Prediction

Background. Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. Methods. We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. Results. Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. Conclusion. This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

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A Calcium-Related Immune Signature in Prognosis Prediction of Patients With Glioma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.723103 ◽

2021 ◽

Vol 9 ◽

Author(s):

Cha Lin ◽

Jian Chen ◽

Zhaoying Su ◽

Pei Liu ◽

Zheyu Liu ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

T Cell ◽

Calcium Signaling ◽

Clinical Features ◽

Risk Score ◽

Immune Checkpoint ◽

Normal Brain ◽

Related Gene ◽

Related Risk

Background: Immune checkpoint inhibitors have been successfully used in a variety of tumors, however, the efficacy of immune checkpoint blockade therapy for patients with glioma is limited. In this study, we tried to clarify gene expression signatures related to the prognosis of gliomas and construct a signature to predict the survival of patients with gliomas.Methods: Calcium-related differential expressed genes (DEGs) between gliomas and normal brain tissues were comprehensively analyzed in two independent databases. Univariate, multivariate Cox regression analysis and proportional hazards model were used to identify the prognostic of calcium-related risk score signature. The CIBERSORT algorithm and association analysis were carried out to evaluate the relationship between calcium-related signature and characteristic clinical features, tumor-infiltrating immune cell signatures as well as immune checkpoint molecules in glioma. A nomogram model was developed for predicting the overall survival for patients with gliomas.Results: We found the intersection of 415 DEGs between gliomas and normal brain tissues, and identified that an eighteen calcium-related gene panel was significantly enriched in these DEGs. A calcium-related signature derived risk score was developed to divide patients into high- and low-risk groups. Low levels of calcium-related gene expression in high-risk score cases were accompanied with worse outcomes of patients. Calcium-related risk scores were significantly associated with characteristic clinical features, immune infiltrating signatures of tumor microenvironment, and exhausted T cell markers including programmed cell death 1 (PD-1), lymphocyte activating 3 (LAG3), and T cell membrane protein 3 (TIM-3), which contribute to an adverse therapeutic effect of immunotherapy. Calcium-related signature risk score was considered as an independent prognostic parameter to predict the of overall survival of patients with gliomas in nomogram model.Conclusion: Our study demonstrated that calcium signaling pathway is highly associated with immunosuppression of gliomas and overall survival of patients. Targeting the calcium signaling pathway might be a new strategy to reverse the immunosuppressive microenvironment of gliomas and improve the efficacy of glioma immunotherapy.

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Prognostic Power of a Chaperonin Containing TCP-1 Subunit Genes Panel for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.668871 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wenli Li ◽

Jun Liu ◽

Hetong Zhao

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Gene Signature ◽

Cancer Genome ◽

Medical Decision ◽

Survival Prediction ◽

Cox Regression Analysis ◽

T Complex

Chaperonin containing TCP-1 (T-complex protein 1) (CCT) is a large molecular weight complex that contains nine subunits (TCP1, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT6B, CCT7, CCT8). This study aimed to reveal key genes which encode CCT subunits for prognosis and establish prognostic gene signatures based on CCT subunit genes. The data was downloaded from The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus. CCT subunit gene expression levels between tumor and normal tissues were compared. Corresponding Kaplan-Meier analysis displayed a distinct separation in the overall survival of CCT subunit genes. Correlation analysis, protein-protein interaction network, Gene Ontology analysis, immune cells infiltration analysis, and transcription factor network were performed. A nomogram was constructed for the prediction of prognosis. Based on multivariate Cox regression analysis and shrinkage and selection method for linear regression model, a three-gene signature comprising CCT4, CCT6A, and CCT6B was constructed in the training set and significantly associated with prognosis as an independent prognostic factor. The prognostic value of the signature was then validated in the validation and testing set. Nomogram including the signature showed some clinical benefit for overall survival prediction. In all, we built a novel three-gene signature and nomogram from CCT subunit genes to predict the prognosis of hepatocellular carcinoma, which may support the medical decision for HCC therapy.

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Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients

BMC Gastroenterology ◽

10.1186/s12876-021-01638-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wenjie Wang ◽

Chen Zhang ◽

Qihong Yu ◽

Xichuan Zheng ◽

Chuanzheng Yin ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Lipid Metabolism ◽

Liver Cancer ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Patient Prognosis

Abstract Background Liver cancer is one of the most common malignancies worldwide. HCC (hepatocellular carcinoma) is the predominant pathological type of liver cancer, accounting for approximately 75–85 % of all liver cancers. Lipid metabolic reprogramming has emerged as an important feature of HCC. However, the influence of lipid metabolism-related gene expression in HCC patient prognosis remains unknown. In this study, we performed a comprehensive analysis of HCC gene expression data from TCGA (The Cancer Genome Atlas) to acquire further insight into the role of lipid metabolism-related genes in HCC patient prognosis. Methods We analyzed the mRNA expression profiles of 424 HCC patients from the TCGA database. GSEA(Gene Set Enrichment Analysis) was performed to identify lipid metabolism-related gene sets associated with HCC. We performed univariate Cox regression and LASSO(least absolute shrinkage and selection operator) regression analyses to identify genes with prognostic value and develop a prognostic model, which was tested in a validation cohort. We performed Kaplan-Meier survival and ROC (receiver operating characteristic) analyses to evaluate the performance of the model. Results We identified three lipid metabolism-related genes (ME1, MED10, MED22) with prognostic value in HCC and used them to calculate a risk score for each HCC patient. High-risk HCC patients exhibited a significantly lower survival rate than low-risk patients. Multivariate Cox regression analysis revealed that the 3-gene signature was an independent prognostic factor in HCC. Furthermore, the signature provided a highly accurate prediction of HCC patient prognosis. Conclusions We identified three lipid-metabolism-related genes that are upregulated in HCC tissues and established a 3-gene signature-based risk model that can accurately predict HCC patient prognosis. Our findings support the strong links between lipid metabolism and HCC and may facilitate the development of new metabolism-targeted treatment approaches for HCC.

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Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.597996 ◽

2021 ◽

Vol 10 ◽

Author(s):

Siyuan Tian ◽

Jingyi Liu ◽

Keshuai Sun ◽

Yansheng Liu ◽

Jiahao Yu ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Risk Score ◽

Prognostic Model ◽

Rna Binding ◽

Risk Group ◽

Clinical Stage ◽

Cancer Genome ◽

Clinicopathological Parameters ◽

Transcriptional Level

BackgroundEvidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients.MethodsDifferently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability.ResultsThe prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis.ConclusionsThe RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.

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A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

BioData Mining ◽

10.1186/s13040-021-00261-y ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Qian Yan ◽

Wenjiang Zheng ◽

Boqing Wang ◽

Baoqian Ye ◽

Huiyan Luo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Risk Score ◽

Immune Cells ◽

Prognostic Model ◽

Risk Groups ◽

Low Risk ◽

Prognostic Performance ◽

Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

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