scholarly journals Macrophages M1-Related Prognostic Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hao Zhang ◽  
Lin Sun ◽  
Xiao Hu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Prognostic Model ◽  
Immune Checkpoints ◽  
Public Database ◽  
Prognostic Signature ◽  
R Software ◽  
The Public ◽  
Good Ability

A large number of studies have found that macrophages M1 play an important role in the occurrence and development of tumors. The aim of our study is to explore the causes of differential infiltration of macrophages M1 in hepatocellular carcinoma from the perspective of transcriptome and establish a prognostic model of hepatocellular carcinoma. We downloaded gene expression and clinical data from the public database, estimated the content of macrophages M1 in different samples with R software, and found the different genes between high- and low-infiltration groups. Using differentially expressed genes, we constructed a model composed of 7 genes. The risk score of the model has a good ability to predict the prognosis, has a positive correlation with immune checkpoints, and is closely related to other immune cells and immune function. Our model shows good prognostic function and has wide application value.

scholarly journals Development of a Novel CTCs/CTMs Related Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiangyu Li ◽  
Yuanxin Shi ◽  
Kai Zhao ◽  
Yun Lu ◽  
Peng Qiu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Carcinoma Cells ◽  
Immune Checkpoints ◽  
Prognostic Signature ◽  
Characteristic Analysis ◽  
Hepatocellular Carcinoma Cells ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is a common malignancy and the third most deadly cancer worldwide. Previous studies have demonstrated that circulating tumor cells are involved in the occurrence and development of various cancers, including HCC. For this study, we aimed to comprehensively analyze data related to HCC to develop a prognostic model based on CTCs/CTMs related genes (CRGs). Methods: Data were obtained from TCGA, ICGC, and GEO. Firstly, we screened the differentially expressed CRGs and constructed a signature in the TCGA cohort by Lasso‐penalized Cox regression analysis and the multivariate cox regression analysis. Then, the prognostic model was validated in the ICGC dataset and GES14520 dataset with survival analysis and receiver operating characteristic analysis. Moreover, we investigated the clinical significance of prognostic signature, including the correlations with clinical characteristics, immune cell infiltration, and immune checkpoints. Next, we also established the nomogram and to better predict the prognosis of patients. We identified five potential small molecule drugs by Connectivity Map (CMap) and validated them using the Comparative Toxicogenomics Database (CTD). Besides, we further explored the biological role of CDCA8 in hepatocellular carcinoma cells.Results: The prognostic signature exhibited good predictive power and clinical application. Besides, the signature was associated with immune checkpoints (PD-1, PD-L1, and CTLA4), implying that high-risk patients might benefit more from immunotherapy. Additionally, In vitro experiments showed that CDCA8 could promote the proliferation, invasion, and metastasis of hepatocellular carcinoma cells, and silencing CDCA8 could lead to cell cycle arrest and increased apoptosis.Conclusion: We developed a multi-gene classifier that can effectively help the HCC patients benefit from target therapy or immune therapy. And CDCA8 may be the next therapeutic target for hepatocellular carcinoma.

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

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.

scholarly journals Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Shenglan Cai ◽  
Xingwang Hu ◽  
Ruochan Chen ◽  
Yiya Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Genes ◽  
Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

Exploring Prognostic Signatures of Hepatocellular Carcinoma and the potential implications in Tumor Immune Microenvironment

2021 ◽  
Vol 24 ◽  
Author(s):  
Hongxu Chen ◽  
Zhijing Jiang ◽  
Bingshi Yang ◽  
Guiling Yan ◽  
Xiaochen Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Liver Cancer ◽  
Gene Expression Data ◽  
Immune Cells ◽  
Roc Curve ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Expression Data

Objective: The objective of this study is to construct a prognostic model using genetic markers of liver cancer and explore the signature genes associated with the tumor immune microenviroment. Methods: Cox proportional hazards regression analysis was carried out to screen the significant HR using dataset of TCGA Liver Cancer (LIHC) gene expression data, then LASSO (Least absolute shrinkage and selection operator) was performed to select the minimal variables with significant HR of genes. Thus, the prognostic model was constructed by the minimal variables with their HR and time-dependent receiver-operating characteristic (ROC) curve and area under the ROC curve (AUC) value used to assess the prognostic performance. Then dividing the patients into high and low risk groups by the median of the model, survival analysis was performed by two groups with testing and independent dataset. Furthermore, enrichment analysis of signature mRNAs and lncRNAs and their co-expression genes were performed, then, spearman rank correlation used to calculate the correlation between immune cells and genes in the prognostic model, and testing abundance difference of the immune cells in high and low risks groups. Results: A total of 5989 genes with significant HR were identified, then 6 key genes (three mRNAs: DHX37, SMIM7 and MFSD1, three lncRNAs: PIWIL4, KCNE5 and LOC100128398) screened by LASSO were used to construct the model with their HR value respectively. The AUC values of 1 and 5 year overall survival were 0.78 and 0.76 in discovery data and 0.67 and 0.68 in testing data. Survival analysis performed significantly in discriminating high and low groups with testing and independent data. Furthermore, many immune cells such as nTreg found a significant correlation with the genes in the prognostic model, and many immune cells show significantly different abundance in high and low risk groups. Conclusion: In the study, we used Univariate Cox analyses and LASSO algorithm with TCGA gene expression data to construct the prognostic model in liver cancer patients. And the prognostic model comprising three mRNAs including DHX37, SMIM7, MFSD1, and three lncRNAs including PIWIL4, KCNE5 and LOC100128398. Furthermore, these genes expression levels were associated with the abundance of some immune cells, such as nTreg. Also, many immune cells have significantly different abundance in high and low risk groups. All these results indicated combination with all these six genes could be the potential biomarker for the prognosis of liver cancer.

A new prognostic strategy based on four DNA repair-associated lncRNAs for hepatocellular carcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Hanyi Zeng ◽  
Chengdong Liu ◽  
Xiaohan Zhou ◽  
Li Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Repair ◽  
Clinical Utility ◽  
Prognostic Model ◽  
Cox Regression ◽  
Reduction Process ◽  
Roc Curves ◽  
Clinical Impact ◽  
Functional Enrichment ◽  
Prognostic Signature

Background: Hepatocellular carcinoma (HCC) is a malignant tumour with poor prognosis. The effect of DNA repair on prognosis cannot be ignored; and long non-coding RNA (lncRNA) can regulate the DNA repair process. Objective: : To obtain DNA repair-associated lncRNA (DR-lncRNA) prognostic signature for improved ability to prediction of HCC prognosis. Methods: Our study used the Cancer Genome Atlas database. Gene set variation analysis was performed to differentiate high and low levels of DNA repair to identify DR-lncRNAs. By performing univariate Cox regression, LASSO regression, and multivariate Cox regression analyses, we finally obtained a DR-lncRNA prognostic signature and constructed a nomogram prognostic model. Time-dependent receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact curves were used to assess predictive ability and clinical utility. Differentially expressed genes (DEGs) functional enrichment analysis was performed to further explore the underlying mechanisms that influence HCC prognosis. Results: We obtained a DR-lncRNA prognostic signature—AP002478.1, AC116351.1, LINC02580, and LINC00861. The ROC curves and calibration plots showed good discrimination and calibration properties. Combining the DR-lncRNA prognostic signature and tumour stages, we established a nomogram prognostic model. DCA and clinical impact curves showed the clinical utility of the nomogram prognostic model. DEGs of high-risk and low-risk groups predicted by the DR-lncRNA prognostic were significantly associated with cell cycle and various metabolic pathways and biological processes such as the oxidation-reduction process and cell division. Conclusion: We identified a DR-lncRNA prognostic signature and constructed a nomogram prognostic model, which could be a beneficial prognostic strategy for HCC.

scholarly journals Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

2021 ◽  
Author(s):  
Jixiang Cao ◽  
Xi Chen ◽  
Guang Lu ◽  
Haowei Wang ◽  
Xinyu Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Risk Scores ◽  
Tumor Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

scholarly journals Identification of a novel signature based on unfolded protein response-related gene for predicting prognosis in bladder cancer

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Ke Zhu ◽  
Liu Xiaoqiang ◽  
Wen Deng ◽  
Gongxian Wang ◽  
Bin Fu
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Unfolded Protein Response ◽  
Immune Cells ◽  
Risk Groups ◽  
Immune Checkpoints ◽  
Related Gene ◽  
Prognostic Signature ◽  
Unfolded Protein ◽  
Protein Response

Abstract Background The unfolded protein response (UPR) served as a vital role in the progression of tumors, but the molecule mechanisms of UPR in bladder cancer (BLCA) have been not fully investigated. Methods We identified differentially expressed unfolded protein response-related genes (UPRRGs) between BLCA samples and normal bladder samples in the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis and the least absolute shrinkage and selection operator penalized Cox regression analysis were used to construct a prognostic signature in the TCGA set. We implemented the validation of the prognostic signature in GSE13507 from the Gene Expression Omnibus database. The ESTIMATE, CIBERSORT, and ssGSEA algorithms were used to explore the correlation between the prognostic signature and immune cells infiltration as well as key immune checkpoints (PD-1, PD-L1, CTLA-4, and HAVCR2). GDSC database analyses were conducted to investigate the chemotherapy sensitivity among different groups. GSEA analysis was used to explore the potential mechanisms of UPR-based signature. Results A prognostic signature comprising of seven genes (CALR, CRYAB, DNAJB4, KDELR3, CREB3L3, HSPB6, and FBXO6) was constructed to predict the outcome of BLCA. Based on the UPRRGs signature, the patients with BLCA could be classified into low-risk groups and high-risk groups. Patients with BLCA in the low-risk groups showed the more favorable outcomes than those in the high-risk groups, which was verified in GSE13507 set. This signature could serve as an autocephalous prognostic factor in BLCA. A nomogram based on risk score and clinical characteristics was established to predict the over survival of BLCA patients. Furthermore, the signature was closely related to immune checkpoints (PD-L1, CTLA-4, and HAVCR2) and immune cells infiltration including CD8+ T cells, follicular helper T cells, activated dendritic cells, and M2 macrophages. GSEA analysis indicated that immune and carcinogenic pathways were enriched in high-risk group. Conclusions We identified a novel unfolded protein response-related gene signature which could predict the over survival, immune microenvironment, and chemotherapy response of patients with bladder cancer.

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

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.

scholarly journals Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

2021 ◽  
Vol 12 ◽  
Author(s):  
Saadia Ait Ssi ◽  
Dounia Chraa ◽  
Khadija El Azhary ◽  
Souha Sahraoui ◽  
Daniel Olive ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Cox Regression ◽  
Gene Expression Signature ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Tumor Escape ◽  
Cox Regression Analysis ◽  
Patient Prognosis ◽  
Cluster 2

BackgroundGlioma is the most common type of primary brain tumor in adults. Patients with the most malignant form have an overall survival time of <16 months. Although considerable progress has been made in defining the adapted therapeutic strategies, measures to counteract tumor escape have not kept pace, due to the developed resistance of malignant glioma. In fact, identifying the nature and role of distinct tumor-infiltrating immune cells in glioma patients would decipher potential mechanisms behind therapy failure.MethodsWe integrated into our study glioma transcriptomic datasets from the Cancer Genome Atlas (TCGA) cohort (154 GBM and 516 LGG patients). LM22 immune signature was built using CIBERSORT. Hierarchical clustering and UMAP dimensional reduction algorithms were applied to identify clusters among glioma patients either in an unsupervised or supervised way. Furthermore, differential gene expression (DGE) has been performed to unravel the top expressed genes among the identified clusters. Besides, we used the least absolute shrinkage and selection operator (LASSO) and Cox regression algorithm to set up the most valuable prognostic factor.ResultsOur study revealed, following gene enrichment analysis, the presence of two distinct groups of patients. The first group, defined as cluster 1, was characterized by the presence of immune cells known to exert efficient antitumoral immune response and was associated with better patient survival, whereas the second group, cluster 2, which exhibited a poor survival, was enriched with cells and molecules, known to set an immunosuppressive pro-tumoral microenvironment. Interestingly, we revealed that gene expression signatures were also consistent with each immune cluster function. A strong presence of activated NK cells was revealed in cluster 1. In contrast, potent immunosuppressive components such as regulatory T cells, neutrophils, and M0/M1/M2 macrophages were detected in cluster 2, where, in addition, inhibitory immune checkpoints, such as PD-1, CTLA-4, and TIM-3, were also significantly upregulated. Finally, Cox regression analysis further corroborated that tumor-infiltrating cells from cluster 2 exerted a significant impact on patient prognosis.ConclusionOur work brings to light the tight implication of immune components on glioma patient prognosis. This would contribute to potentially developing better immune-based therapeutic approaches.

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