Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma

Background: Liver cancer stem cells, characterized by self-renewal and initiating cancer cells, were decisive drivers of progression and therapeutic resistance in hepatocellular carcinoma (HCC). However, a comprehensive understanding of HCC stemness has not been identified.Methods: RNA sequencing information, corresponding clinical annotation, and mutation data of HCC were downloaded from The Cancer Genome Atlas-LIHC project. Two stemness indices, mRNA expression-based stemness index (mRNAsi) and epigenetically regulated-mRNAsi, were used to comprehensively analyze HCC stemness. Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data and single-sample gene-set enrichment analysis algorithm were performed to characterize the context of tumor immune microenvironment (TIME). Next, differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify significant mRNAsi-related modules with hub genes. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment pathways were analyzed to functionally annotate these key genes. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a prognostic signature. Kaplan–Meier survival curves and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS) were utilized to draw the landscape of TIME. Finally, the mutation data were analyzed by employing “maftools” package.Results: mRNAsi was significantly elevated in HCC samples. mRNAsi escalated as tumor grade increased, with poor prognosis presenting the higher stemness index. The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. Additionally, prognostic risk-clinical nomogram was drawn to estimate risk quantitatively. Moreover, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was elucidated.Conclusion: This work comprehensively elucidated that stemness characteristics served as a crucial player in clinical outcome, complexity of TIME, and immunotherapeutic prediction from both mRNAsi and mRNA level. Quantitative identification of stemness characteristics in individual tumor will contribute into predicting clinical outcome, mapping landscape of TIME further optimizing precision immunotherapy.

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Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.11273 ◽

2021 ◽

Vol 9 ◽

pp. e11273

Author(s):

Lei Yang ◽

Weilong Yin ◽

Xuechen Liu ◽

Fangcun Li ◽

Li Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Overall Survival ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Enrichment Score ◽

Hub Genes ◽

Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

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Human Nuclear Receptors (NRs) Genes have Prognostic Significance in Hepatocellular Carcinoma Patients

10.21203/rs.3.rs-52493/v1 ◽

2020 ◽

Author(s):

Guangtao Sun ◽

Kejian Sun ◽

Chao Shen

Keyword(s):

Hepatocellular Carcinoma ◽

Nuclear Receptors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Prognostic Significance ◽

Prognostic Signature ◽

Term Survival ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world. Human nuclear receptors (NRs) have been identified to closely related to various cancer. However, the prognostic significance of NRs on HCC patients has not been studied in detail.Method: We downloaded the mRNA profiles and clinical information of 371 HCC patients from TCGA database and analyzed the expression of 48 NRs. The consensus clustering analysis with the mRNA levels of 48 NRs was performed by the "ConsensusClusterPlus". The Univariate cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs which were selected. Then Multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors including risk score and TNM Stage was used to predict the long-term survival of HCC patients.Results: NRs could effectively separate HCC samples with different prognosis. The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and TNM Stage and could better predict the long-term survival for 3- and 5-year of HCC patients.Conclusion: Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.

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

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.611626 ◽

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.

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The novel immune-related genes predict the prognosis of patients with hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-021-89747-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lunxu Li ◽

Shilin Xia ◽

Xueying Shi ◽

Xu Chen ◽

Dong Shang

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Risk Score ◽

Cox Regression ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prediction Ability ◽

Cox Regression Analysis ◽

Immune Related Genes

AbstractHepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.

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Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

10.21203/rs.3.rs-16348/v1 ◽

2020 ◽

Author(s):

Ze-bing Song ◽

Guo-pei Zhang ◽

shaoqiang li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

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Development of an Immune-Related LncRNA Prognostic Signature for Glioma

Frontiers in Genetics ◽

10.3389/fgene.2021.678436 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yudong Cao ◽

Hecheng Zhu ◽

Jun Tan ◽

Wen Yin ◽

Quanwei Zhou ◽

...

Keyword(s):

Regression Analysis ◽

Clinical Outcome ◽

Immune Cell ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.

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Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.618976 ◽

2021 ◽

Vol 11 ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

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Identification of Prognostic Immune-Related Genes in Pancreatic Adenocarcinoma and Establishment of a Prognostic Nomogram: A Bioinformatic Study

BioMed Research International ◽

10.1155/2020/1346045 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Guolin Wu ◽

Zhenfeng Deng ◽

Zongrui Jin ◽

Jilong Wang ◽

Banghao Xu ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Evaluation ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Related Genes

Background. The prognosis of pancreatic adenocarcinoma (PAAD) is extremely poor and has not been improved. Thus, an effective method to assess the prognosis of patients must be established to improve their survival rate. Method. This study investigated immune-related genes that could be used as potential therapeutic targets for PAAD. Level 3 gene expression data from the PAAD cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. For validation, other PAAD datasets (DSE62452) were downloaded from the Gene Expression Omnibus (GEO) database. The PAAD datasets from TCGA and GEO were used to screen immune-related genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping immune-related genes of the two datasets were identified. Coexpression networks of the immune-related genes were constructed. Results. A signature of three immune-related genes (CKLF, ERAP2, and EREG) was identified in patients with PAAD. The signature could be used to divide the patients with PAAD into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of PAAD. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model based on the clinical characteristics and risk score of the TCGA PAAD dataset. The accuracy of the prognostic signature was further evaluated through functional evaluation and principal component analysis. Conclusions. The results indicated that the signature of three immune-related genes had excellent predictive value for PAAD. These findings might help improve personalized treatment and medical decisions.

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Human nuclear receptors (NRs) genes have prognostic significance in hepatocellular carcinoma patients

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02246-x ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Guangtao Sun ◽

Kejian Sun ◽

Chao Shen

Keyword(s):

Hepatocellular Carcinoma ◽

Nuclear Receptors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Prognostic Significance ◽

Prognostic Signature ◽

Term Survival ◽

Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world. Method We downloaded the mRNA profiles and clinical information of 371 HCC patients from The Cancer Genome Atlas (TCGA) database. The consensus clustering analysis with the mRNA levels of 48 nuclear receptors (NRs) was performed by the “ConsensusClusterPlus.” The univariate Cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs. Then multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors was used to predict the long-term survival of HCC patients. Results The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG, and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and tumor node metastasis (TNM) stage and could better predict the long-term survival for 3- and 5-year of HCC patients. Conclusion Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.

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A Prognostic Autophagy-Related Gene Pair Signature and Small-Molecule Drugs for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.689801 ◽

2021 ◽

Vol 12 ◽

Author(s):

ZeBing Song ◽

GuoPei Zhang ◽

Yang Yu ◽

ShaoQiang Li

Keyword(s):

Hepatocellular Carcinoma ◽

Small Molecule ◽

Risk Score ◽

Cox Regression ◽

Cancer Genome ◽

Related Gene ◽

Prognostic Signature ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Small Molecule Drugs

Dysregulation of autophagy-related genes (ARGs) is related to the prognosis of cancers. However, the aberrant expression of ARGs signature in the prognosis of hepatocellular carcinoma (HCC) remain unclear. Using The Cancer Genome Atlas and the International Cancer Genome Consortium database, 188 common autophagy-related gene pairs (ARGPs) were identified. Through univariate, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis, a prognostic signature of the training set was constructed on the basis of 6 ARGPs. Further analysis revealed that the ARGP based signature performed more accurately in overall survival (OS) prediction compared to other published gene signatures. In addition, a high risk of HCC was closely related to CTLA4 upregulation, LC3 downregulation, low-response to axitinib, rapamycin, temsirolimus, docetaxel, metformin, and high-response to bleomycin. Univariate Cox and multivariate Cox analysis revealed that the risk score was an independent prognostic factor for HCC. These results were internally validated in the test and TCGA sets and externally validated in the ICGC set. A nomogram, consisting of the risk score and the TNM stage, performed well when compared to an ideal nomogram. In conclusion, a 6-ARGP-based prognostic signature was identified and validated as an effective predictor of OS of patients with HCC. Furthermore, we recognized six small-molecule drugs, which may be potentially effective in treating HCC.

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