scholarly journals Development of a prognostic signature for esophageal cancer based on nine immune related genes

2020 ◽  
Author(s):  
zhi Zhang ◽  
cheng chen ◽  
ying fang ◽  
sheng Li ◽  
xiaohua Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. Methods: This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). Results: We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM ,STC2 ,NGPTL3 and NR2F2) by multivariate Cox regression analysis. The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusions: Prognosis related IRGs were identified and a prognostic signature for esophageal cancer based on nine IRGs was developed.

scholarly journals Development of a prognostic signature for esophageal cancer based on nine immune related genes

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhi Zhang ◽  
Cheng Chen ◽  
Ying Fang ◽  
Sheng Li ◽  
Xiaohua Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. Methods This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). Results We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM, STC2, NGPTL3 and NR2F2) by multivariate Cox regression analysis. The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusions Prognosis related IRGs were identified and a prognostic signature for esophageal cancer based on nine IRGs was developed.

scholarly journals Development of a prognostic model for esophageal cancer based on nine immune related genes

2020 ◽  
Author(s):  
Zhi Zhang ◽  
Cheng Chen ◽  
Ying Fang ◽  
Sheng Li ◽  
Xiaohua Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Regulatory Networks ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. Methods: This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). Results: We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM ,STC2 ,NGPTL3 and NR2F2) by multivariate Cox regression analysis. The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusions: These molecules may serve as potential therapeutic targets and biomarkers for the new-immunotherapy of EC.

scholarly journals Development of a prognostic model for esophageal cancer based on nine immune related genes

2020 ◽  
Author(s):  
zhi Zhang ◽  
cheng chen ◽  
ying fang ◽  
sheng Li ◽  
xiaohua Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Regulatory Networks ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. Methods: We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM ,STC2 ,NGPTL3 and NR2F2) by multivariate Cox regression analysis. Results: The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusion: These molecules may serve as potential therapeutic targets and biomarkers for the new-immunotherapy of EC.

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

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

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

scholarly journals Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Jimin He ◽  
Chun Zeng ◽  
Yong Long
Keyword(s):  
Risk Stratification ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Genome Atlas

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module ( cor = 0.67 ; P < 0.001 ) and its genes ( n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome ( P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas’ survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

scholarly journals The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dankun Luo ◽  
Wenchao Yao ◽  
Qiang Wang ◽  
Qiu Yang ◽  
Xuxu Liu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prognostic Model ◽  
Invasive Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Pathway Enrichment

AbstractLong non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

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

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.

scholarly journals Identification of Prognostic Signature and Immune Infiltration in the Skin Cutaneous Melanoma Microenvironment

2021 ◽  
Author(s):  
Xiaoyong Li ◽  
Xueyi Jian ◽  
Xiaofeng Wei ◽  
Yan Lin ◽  
Zena Huang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Lymphocyte Activation ◽  
Risk Scores ◽  
Training Dataset ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Tumor Immune Microenvironment

Abstract Skin cutaneous melanoma (SKCM), characterized by high immunogenicity, has an increasing incidence in recent years. The development of immunotherapy recently offered a promising treatment for patients with SKCM. Unfortunately, not all patients derive benefit from such treatment, so we still face considerable challenges. Hence, it is imperative to develop novel prognostic signature and identify immunotherapeutic targets. In the present study, patients in high immune scores group presented a higher survival rate, while no statistical difference was observed in groups with different stromal scores. 493 DEGs were identified for functional analysis, which were enriched in function related to immune regulation such as lymphocyte activation and cytokine-cytokine receptor interaction. Subsequently, 84 DEGs intersected from Univariate Cox regression analysis and top 100 hub genes in PPI network were identified for the construction of prognostic model. Finally, a prognostic signature including 3 genes (HLA-DQB2, CD80 and GBP4) was established in TCGA training dataset, which was effectively validated in test dataset. Moreover, the model was considered as an independent prognostic factor via univariate and multivariate analysis. Besides, CIBERSORT and correlation analysis demonstrated that the expression level of risk scores was significantly correlated to infiltration levels of immune cells in SKCM. Above all, our study developed a novel prognostic signature, serving as potential prognostic biomarker for SKCM patients. A closely correlation between the prognostic model and tumor immune microenvironment was confirmed, offering a novel insight for the pathogenesis and potential immunotherapy for SKCM.

scholarly journals Prognostic lncRNAs, miRNAs, and mRNAs Form a Competing Endogenous RNA Network in CESC

2021 ◽  
Author(s):  
Lang Li ◽  
Qiusheng Guo ◽  
Gaochen Lan ◽  
Fei Liu ◽  
Wenwu Wang ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Rank Test ◽  
Prognostic Signature ◽  
Competing Endogenous Rna ◽  
Genetic Characteristics ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumorigenesis involves a combination of multiple genetic alteration processes. Constructing a survival-associated competing endogenous RNA (ceRNA) network and a multi-mRNA-based prognostic signature model can help us better understand the complexity and genetic characteristics of CESC.Methods: The RNA-seq data and clinical information of CESC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs, lncRNAs and miRNAs were identified by edgeR package. Constructing prognostic model used the differentially expressed RNAs. The Kaplan-Meier method and log-rank test were performed to assess survival rates. The relationships between overall survival (OS) and clinical parameters were evaluated by Cox regression analysis. A survival-associated ceRNA network was constructed by multiMiR package and miRcode database. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) were used to identify the functional role of the ceRNA network in the prognosis of CESC.Results: Differentially expressed 298 mRNAs, 8 miRNAs, and 29 lncRNAs were significantly associated with the prognosis of CESC. The prognostic signature model based on 4 mRNAs (OPN3, DAAM2, HENMT1, and CAVIN3) was constructed. The prognostic ability was 0.726 for this model. Patients in the high-risk group were significantly associated with worse OS. The KEGG pathways were significantly enriched in the TGF-β and Cell adhesion molecules signaling pathways.Conclusion: This study identified several potential prognostic biomarkers to construct a multi-mRNA-based prognostic model for CESC.

scholarly journals A Potential Immune-Related Long Non-coding RNA Prognostic Signature for Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xue Pan ◽  
Fangfang Bi
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Factors ◽  
Cox Regression ◽  
Risk Group ◽  
Characteristic Curve ◽  
Gene Expression Omnibus ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Ovarian cancer (OC), the most lethal gynecologic malignancy, ranks fifth in cancer deaths among women, largely because of late diagnosis. Recent studies suggest that the expression levels of immune-related long non-coding RNAs (lncRNAs) play a significant role in the prognosis of OC; however, the potential of immune-related lncRNAs as prognostic factors in OC remains unexplored. In this study, we aimed to identify a potential immune-related lncRNA prognostic signature for OC patients. We used RNA sequencing and clinical data from The Cancer Genome Atlas and the Gene Expression Omnibus database to identify immune-related lncRNAs that could serve as useful biomarkers for OC diagnosis and prognosis. Univariate Cox regression analysis was used to identify the immune-related lncRNAs with prognostic value. Functional annotation of the data was performed through the GenCLiP310 website. Seven differentially expressed lncRNAs (AC007406.4, AC008750.1, AL022341.2, AL133351.1, FAM74A7, LINC02229, and HOXB-AS2) were found to be independent prognostic factors for OC patients. The Kaplan-Meier curve indicated that patients in the high-risk group had a poorer survival outcome than those in the low-risk group. The receiver operating characteristic curve revealed that the predictive potential of the immune-related lncRNA signature for OC was robust. The prognostic signature of the seven lncRNAs was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the signature of the seven lncRNAs was an independent prognostic factor for OC patients. Finally, we constructed a nomogram model and a competing endogenous RNA network related to the lncRNA prognostic signature. In conclusion, our study reveals novel immune-related lncRNAs that may serve as independent prognostic factors in OC.

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