scholarly journals A Novel Lactate Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironment in Hepatocellular Carcinoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Yue Li ◽  
Huanye Mo ◽  
Shengli Wu ◽  
Xin Liu ◽  
Kangsheng Tu
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Tumor Microenvironment ◽  
External Validation ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Lactate Metabolism ◽  
Related Gene ◽  
High Group ◽  
Cox Regression Analysis

Hepatocellular carcinoma (HCC) is the main subtype of primary liver cancer with high malignancy and poor prognosis. Metabolic reprogramming is a hallmark of cancer and has great importance on the tumor microenvironment (TME). As an abundant metabolite, lactate plays a crucial role in cancer progression and the immunosuppressive TME. Nonetheless, the potential roles of lactate in HCC remain unclear. In this study, we downloaded transcriptomic data of HCC patients with corresponding clinical information from the TCGA and ICGC portals. The TCGA-HCC dataset used as the training cohort, while the ICGC-LIRI-JP dataset was served as an external validation cohort. Cox regression analysis and the LASSO regression model were combined to construct the lactate metabolism-related gene signature (LMRGS). Then, we assessed the clinical significance of LMRGS in HCC. Besides, enriched molecular functions, tumor mutation burden (TMB), infiltrating immune cells, and immune checkpoint were comprehensively analyzed in different LMRGS subgroups. In total, 66 differentially expressed lactate metabolism-related genes (LMRGs) were screened. The functions of LMRGs were mainly enriched in mitochondrial activity and metabolic processes. The LMRGS comprised of six key LMRGs (FKTN, PDSS1, PET117, PUS1, RARS1, and RNASEH1) had significant clinical value for independently predicting the prognosis of HCC patients. The overall survival and median survival of patients in the LMRGS-high group were significantly shorter than in the LMRGS-low group. In addition, there were differences in TMB between the two LMRGS subgroups. The probability of genetic mutations was higher in the LMRGS-high group. Most importantly, the LMRGS reflected the TME characteristics. In the LMRGS-high group, the immune microenvironment presented a suppressed state, accompanied by more inhibitory immune cell infiltration, including follicular helper T cells and regulatory T cells. Additionally, the expression of inhibitory checkpoint molecules was much higher in the LMRGS-high group. Our study suggested that the LMRGS was a robust biomarker to predict the clinical outcomes and evaluate the TME of patients with HCC.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals Establishment and Validation of an MTORC1 Signaling-Related Gene Signature to Predict Overall Survival in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zheng Yao ◽  
Song Wen ◽  
Jun Luo ◽  
Weiyuan Hao ◽  
Weiren Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Decision Tree ◽  
Survival Data ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Cancer Hallmarks ◽  
Mtorc1 Signaling

Background. Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients. Methods. The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient. Results. Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals. Conclusions. The present study may contribute to the improvement of current prognostic systems for patients with HCC.

scholarly journals Gene signature to predict prognostic survival of hepatocellular carcinoma

Open Medicine ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. 135-150
Author(s):  
Li Li ◽  
Yundi Cao ◽  
YingRui Fan ◽  
Rong Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Noncoding Rna ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Hypergeometric Test ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis and is the second most fatal cancer, and certain HCC patients also show high heterogeneity. This study developed a prognostic model for predicting clinical outcomes of HCC. RNA and microRNA (miRNA) sequencing data of HCC were obtained from the cancer genome atlas. RNA dysregulation between HCC tumors and adjacent normal liver tissues was examined by DESeq algorithms. Survival analysis was conducted to determine the basic prognostic indicators. We identified competing endogenous RNA (ceRNA) containing 15,364 pairs of mRNA–long noncoding RNA (lncRNA). An imbalanced ceRNA network comprising 8 miRNAs, 434 mRNAs, and 81 lncRNAs was developed using hypergeometric test. Functional analysis showed that these RNAs were closely associated with biosynthesis. Notably, 53 mRNAs showed a significant prognostic correlation. The least absolute shrinkage and selection operator’s feature selection detected four characteristic genes (SAPCD2, DKC1, CHRNA5, and UROD), based on which a four-gene independent prognostic signature for HCC was constructed using Cox regression analysis. The four-gene signature could stratify samples in the training, test, and external validation sets (p <0.01). Five-year survival area under ROC curve (AUC) in the training and validation sets was greater than 0.74. The current prognostic gene model exhibited a high stability and accuracy in predicting the overall survival (OS) of HCC patients.

scholarly journals Identification of Metabolic Reprogramming Related Gene Signature to Predict the Prognosis of Bladder Cancer Patients

2020 ◽  
Author(s):  
Tinghao Li ◽  
Hang Tong ◽  
Hubin Yin ◽  
Honghao Cao ◽  
Junlong Zhu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Treatment Options ◽  
External Validation ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Delayed Treatment ◽  
Emerging Treatments

Abstract Background: Different kinds of metabolic reprogramming have been widely researched in multifarious cancer types and show up as a guaranteed prognostic predictor, while bladder cancer (BLCA) is most frequent urothelium carcinoma but with poor prognosis despite there are emerging treatments, for lack of reliable predicting biomarkers to early predict the prognosis and delayed treatment options for patients in the terminal stage. Our study aims to explore new prognostic factors related to metabolism in BLCA and make these genes up as novel risk stratification.Methods: We selected a large number of samples downloaded from TCGA (The Cancer Genome Atlas) to find out the possible glycolysis-related genes that correlated with differentiation from cancer sample to normal tissue, aimed to find out a more credible model. To make our signature more believable, we chose the clinical features information from GEO (Gene Expression Omnibus) database as external validation cohort.Results: Finally, we established a three glycolysis-related gene signature based on the expression of AK3, GALK1 and NUP205 to make a prediction on the prognosis of BLCA patients, which were also validated by external cohort and whole mixed cohort. As a result, we built a three glycolysis-related gene signature and found its prognosis value is more valuable in high malignancy patients, which may help physicians to make a more aggressive choice.

scholarly journals Development and External Validation of a Novel Immune Checkpoint–Related Gene Signature for Prediction of Overall Survival in Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Enfa Zhao ◽  
Shimin Chen ◽  
Ying Dang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Immune Checkpoint ◽  
Immune Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Gene Signature ◽  
Cancer Genome ◽  
Related Gene

Objective: The purpose of this study was to develop and validate a novel immune checkpoint–related gene signature for prediction of overall survival (OS) in hepatocellular carcinoma (HCC).Methods: mRNA expression profiles and clinical follow-up information were obtained in the International Cancer Genome Consortium database. An external dataset from The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma database was used to validate the results. The univariate and multivariate Cox regression analyses were performed based on the differentially expressed genes. We generated a four-mRNA signature to predict patient survival. Furthermore, the reliability and validity were validated in TCGA cohort. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value.Results: A four-gene (epidermal growth factor, mutated in colorectal cancer, mitogen-activated protein kinase kinase 2, and NRAS proto-oncogene, GTPase) signature was built to classify patients into two risk groups using a risk score with different OS in two cohorts (all P &lt; 0.0001). Multivariate regression analysis demonstrated the signature was an independent predictor of HCC. Furthermore, the signature presented an excellent diagnostic power in differentiating HCC and adjacent tissues. Immune cell infiltration analysis revealed that the signature was associated with a number of immune cell subtypes.Conclusion: We identified a four–immune checkpoint–related gene signature as a robust biomarker with great potential for clinical application in risk stratification and OS prediction in HCC patients and could be a potential indicator of immunotherapy in HCC. The diagnostic signature had been validated to accurately distinguish HCC from adjacent tissues.

Identification of a Novel Epithelial-to-Mesenchymal-related Gene Signature in Predicting Survival of Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Simeng Xiao ◽  
Junjie Hu ◽  
Na Hu ◽  
Lei Sheng ◽  
Hui Rao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Signature ◽  
Risk Scores ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Mesenchymal Transformation

Background: Epithelial-mesenchymal transformation (EMT) promotes cancer metastasis including hepatocellular carcinoma. Therefore, EMT-related gene signature was explored. Objective: The present study was designed to develop an EMT-related gene signature for predicting the prognosis of patients with hepatocellular carcinoma. Methods: We conducted an integrated gene expression analysis based on tumor data of the patients with hepatocellular carcinoma from The Cancer Genome Atlas (TCGA), HCCDB18 and GSE14520 dataset. An EMT-related gene signature was constructed by least absolute shrinkage and selection operator (LASSO) and COX regression analysis of univariate and multivariate survival. Results: A 3-EMT gene signature was developed and validated based on gene expression profiles of hepatocellular carcinoma from three microarray platforms. Patients with a high risk score had a significantly worse overall survival (OS) than those with low risk scores. The EMT-related gene signature showed a high performance in accurately predicting prognosis and in examining the clinical characteristics and immune score analysis. Univariate and multivariate Cox regression analyses confirmed that the EMT-related gene signature was an independent prognostic factor for predicting survival in hepatocellular carcinoma patients. Compared with the existing models, our EMT-related gene signature reached higher area under curve (AUC). Conclusion: Our findings provide novel insight into understanding EMT and help identify hepatocellular carcinoma patients with poor prognosis.

scholarly journals Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Wenzheng Chen ◽  
Jianfeng Huang ◽  
Jianbo Xiong ◽  
Pengcheng Fu ◽  
Changyu Chen ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Treatment Response ◽  
Risk Group ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene ◽  
Cox Regression Analysis

Background. The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. Methods. We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME-related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME-related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated. Results. High immune and stromal scores are significantly associated with poor overall survival ( p < 0.05 ). We identified 773 differential TME-related prognostic genes associated with survival; these genes were enriched in immune-related pathways. Nine key prognostic genes were identified and were used to construct a TME-related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high-risk group with worse survival outcomes, based on Kaplan-Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high-risk and low-risk groups. High-risk patients had higher expression of PD-1, PDL-1, and CTLA-4 and lower expression of LAG-3 and VSIR. Infiltration of dendritic cells was higher in the high-risk group. Conclusions. We identified a novel prognostic TME-related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients.

scholarly journals A Novel Signature Based On Mtorc1 Pathway In Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Zhuomao Mo ◽  
Shuqiao Zhang ◽  
Shijun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Regression Analysis ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Signal Pathway ◽  
Cancer Genome ◽  
Cox Regression Analysis

Abstract BackgroundmTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature.MethodsHCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot.ResultsThe signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found significantly associated with the infiltration of B cells, CD4+T-cells, CD8+T-cells, dendritic cells, macrophages and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Finally, the nomogram involved age, gender, stage and signature have been established.ConclusionThe mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC. *These authors contributed equally.

scholarly journals Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Changjing Huang ◽  
Chenyue Zhang ◽  
Jie Sheng ◽  
Dan Wang ◽  
Yingke Zhao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cell ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Cell Infiltration ◽  
Related Gene

Background: Hepatocellular carcinoma (HCC) is a typical inflammatory-related malignant tumor with complex immune tolerance microenvironment and poor prognosis. In this study, we aimed to construct a novel immune-related gene signature for the prognosis of HCC patients, exploring tumor microenvironment (TME) cell infiltration characterization and potential mechanisms.Methods: A total of 364 HCC samples with follow-up information in the TCGA-LIHC dataset were analyzed for the training of the prognostic signature. The Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs was conducted to identify the prognostic genes and establish an immune risk signature. The immune cell infiltration in TME was estimated via the CIBERSORT method. Gene Set Variation Analysis (GSVA) was conducted to compare the biological pathways involved in the low-risk and high-risk groups. Furthermore, paraffin sections of HCC tissue microarrays containing 77 patients from Fudan University Shanghai Cancer Center were used for IHC staining. The clinical characteristics of the 77 HCC patients were collected and summarized for survival analysis validation via the Kaplan–Meier (KM) method.Results: Three-gene signature with close immune correlation (Risk score = EPO * 0.02838 + BIRC5 * 0.02477 + SPP1 * 0.0002044) was constructed eventually and proven to be an effective prognostic factor for HCC patients. The patients were divided into a high-risk and a low-risk group according to the optimal cutoff, and the survival analysis revealed that HCC samples with high-risk immuno-score had significantly poorer outcomes than the low-risk group (p &lt; 0.0001). The results of CIBERSORT suggested that the immune cell activation was relatively higher in the low-risk group with better prognosis. Besides, GSVA analysis showed multiple signaling differences between the high- and low-risk group, indicating that the three-gene prognostic model can affect the prognosis of patients by affecting immune-related mechanisms. Tissue microarray (TMA) results further confirmed that the expression of three genes in HCC tissues was closely related to the prognosis of patients, respectively.Conclusion: In this study, we constructed and validated a robust three-gene signature with close immune correlation in HCC, which presented a reliable performance in the prediction of HCC patients’ survival.

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