scholarly journals Development of a multi-gene-based immune prognostic signature in ovarian Cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tiefeng Cao ◽  
Huimin Shen
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Network Analysis ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature. Methods This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes. Conclusions We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2021 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Network Analysis ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background： Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2020 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Prognostic Biomarkers ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background：Chemotherapeutic resistance is responsible for treatment failure. Immunotherapy is important in ovarian cancer (OC). Systematic exploration of immunogenic landscape and reliable immune gene-based prognostic biomarkers or signature is necessary to be identified. This study aims to identify the immune gene-based prognostic biomarkers and regulatory factors, further to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles from RNA-seq data set for The Cancer Genome Atlas (TCGA) ovarian cancer. Differentially expressed and survival-associated immune genes and transcription factors (TFs) were identified using immune genes from ImmPort dataset and TFs from Cistoma database. We developed the prognostic signature based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, Network analysis was performed to uncover the potential molecular mechanisms of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, prognosis, even immunotherapy response of OC patients.

scholarly journals Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Yuhan Zhang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

scholarly journals Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Pancheng Wu ◽  
Yi Zheng ◽  
Yanyu Wang ◽  
Yadong Wang ◽  
Naixin Liang
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Prognostic Signature ◽  
Training Set ◽  
Validation Data ◽  
Data Set ◽  
Screening Programs ◽  
Immune Signature ◽  
Immune Related Genes

Abstract Background The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD. Methods The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts. Results A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design. Conclusions Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

scholarly journals Identification of a Potential Prognostic Signature based on Immune-related Genes in Primary Glioblastoma

2021 ◽  
Author(s):  
Zhaoming Zhou ◽  
Mingyao Lai ◽  
Jiayin Yu ◽  
Jiangfen Zhou ◽  
Qingjun Hu ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Cox Regression ◽  
Sequencing Analysis ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Prognosis Prediction ◽  
Immune Related Genes ◽  
Genome Atlas

Abstract Background: Glioblastoma (GBM) is a common primary brain tumor with a high incidence in adults with malignant and fast-growing biological characteristics. In this study, we explore the immune-related prognosis markers of GBM at the mRNA level.Methods: The sequencing data and clinical information of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). The differentially expressed genes (DEGs) were calculated between normal tissues from the Genotype-Tissue Expression (GTEx) database and tumor tissues from TCGA and CGGA. We obtained immune genes from the ImmProt database. The intersection of DEGs and immune genes were defined as the immune-related differential genes (IR-DEGs), based on which, survival associated IR-DEGs were determined by multivariate Cox regression analysis. The survival risk score (SRS) was determined for each sample with the top 6 prognostic associated IR-DEGs. One-year, two-year, and three-year potential survival were predicted by the prognosis prediction model established by multivariate and univariate Cox regression. In addition, we performed CIBERSORT in GBM patients with samples from TCGA cohort; association analysis was performed with prognostic IR-DEGs and immune cells. Furthermore, the influence of prognostic IR-DEGs on the brain tumor microenvironment (TME) was validated in single-cell sequencing analysis.Results: We found 301 IR-DRGs in GBM primary tumor compared with normal tissue, and 19 of them could predict the overall survival (OS) more accurately in GBM patients. Six IR-DEGs (PLAUR, TNFSF14, CTSB, SOCS3, PTX3, and FCGR2B) were selected to construct the SRS, with which, GBM patients were divided into two different groups which combined with high and low risk. The SRS was found to be an independent prognostic factor for GBM and could predict GBM patients’ possible survival with an acceptable efficiency. Moreover, the expression of 6 IR-DEGs and their co-expressed IR-DEGs could influence TME and were associated with GBM prognosis.Conclusions: This study identified a potential immune prognostic signature of glioblastoma, which could enhance the prognosis prediction ability for GBM patients. The immune-related-genes in the TME could potentially benefit the immunotherapy development for GBM patients.

scholarly journals A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yue Zhao ◽  
Shao-Min Yang ◽  
Yu-Lan Jin ◽  
Guang-Wu Xiong ◽  
Pin Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Scoring System ◽  
Cox Regression ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Prognostic Signature ◽  
Clinical Value ◽  
Cox Regression Analysis

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.

scholarly journals A Novel Ferroptosis-related 6-Gene Signature for Overall Survival Prediction in Patients with Thyroid carcinoma

2021 ◽  
Author(s):  
Zhixing Wang ◽  
Fudan Qiu ◽  
Peilin Shen
Keyword(s):  
Overall Survival ◽  
Thyroid Carcinoma ◽  
Cox Regression ◽  
Independent Predictor ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Survival Prediction ◽  
Prognostic Signature ◽  
Predictive Capacity ◽  
Cox Regression Analysis

Abstract Background: Ferroptosis is a new form of regulated cell death (RCD) that plays a crucial role in the genesis and prognosis of tumor. Nevertheless, the relationship between ferroptosis and the prognosis of thyroid carcinoma (THCA) remains unclear and needs to be explored. Methods: By analyzing data from the THCA cohort in the TCGA database, ferroptosis-related differentially expressed genes (DEGs) with prognostic value were identified, which were used to establish a prognostic signature based on Lasso-penalized Cox regression analysis. Then, the model was testified with Kaplan-Meier survival, Cox regression and receiver operating characteristic (ROC) analyses based on overall survival (OS). Finally, DEGs between the low-risk and high-risk groups were identified and used to conduct GO enrichment analysis, KEGG pathways analysis and immune infiltration analysis.Results: A 6-gene signature was constructed which including DPP4, GPX4, GSS, HMGCR, TFRC and PGD. The area under the curve (AUC) were 0.890 (1 year), 0.863 (2 years) and 0.883 (3 years) which validated the prominent predictive capacity of the model. Multivariate Cox regression certified the model as a prognostic-related independent predictor for OS.Conclusion: In this study, we established an innovative prognostic signature of 6 ferroptosis-related genes which can be as a prognostic-related independent predictor for OS in THCA, while the potential mechanisms was still unclear and needed further exploration.

scholarly journals Construction of a novel immune gene-based model for prognosis prediction of colorectal cancer

2021 ◽  
Author(s):  
Bin Xie ◽  
jie lin
Keyword(s):  
Gene Expression ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Set Enrichment Analysis ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Risk Of Death ◽  
Immune Related Genes

Abstract Background Colon adenocarcinoma (COAD) is the third leading cause of cancer-related death. Although surgical treatment and chemotherapy of COAD have made significant progress, its immunotherapy also has great potential, nowadays. Methods Gene expression profiles and clinical data of COAD patients were obtained from The Cancer Genome Atlas_Colon Adenocarcinoma (TCGA_COAD) and Gene Expression Omnibus (GEO) databases, which were further detected for immune-related genes. Immune-related genes were downloaded from Immunology Database and Analysis Portal (ImmPort). LASSO Cox regression analysis was utilized to analyze the immune-related prognostic signature. The prognostic value of the signature was validated by ROC curve. To further detected the potential pathway about immune-related genes in COAD patients, Gene Set Enrichment Analysis (GESA) was used to identify the most significant pathways. Results Finally, a total of 436 immune-related mRNA were identified. Eleven prognosis-related genes were selected to establish an immune-related prognostic signature, which divided patients into high and low risk groups. Several biological processes, such as immune response was enriched. Moreover, our prognosis model has better performance in predicting the 1-, 3-, 5- and 8-years overall survival (OS) for patients from the TCGA and GEO cohort. Also, the complicated signature obtained by combining immune-related signatures with clinical factors provides a more accurate OS predicting compared with individual molecular signatures. Conclusion We have established a prognostic signature consisting of 11 immune-related genes, which may be potential biomarkers for identifying COAD with a high risk of death. Then, the possibility including immunotherapy in personalized COAD treatment was evaluated.

scholarly journals Development and validation of an immune-related prognostic signature for ovarian cancer based on weighted gene co-expression network analysis

2020 ◽  
Author(s):  
Yuanyuan An ◽  
Qing Yang
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Network Analysis ◽  
Immune Responses ◽  
Cancer Patients ◽  
Cox Regression ◽  
M2 Macrophage ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Geo Database

Abstract Background Ovarian cancer is one of the most lethal diseases of women. The prognosis of ovarian cancer patients was closely correlated with immune cell expression and immune responses. Therefore, it is important to identify a robust prognostic signature, which not only correlates with prognoses but also with immune responses in ovarian cancer, thus, providing immune-related patient therapies. Methods Weighted gene co-expression network analysis (WGCNA) was used to identify candidate genes correlated with ovarian cancer prognoses. Univariate and multivariate cox regression analyses were used to construct the prognostic signature. The Kaplan-Meier method was used to predict survival, and the immune-related bioinformatics analysis was performed using R software. The relationship between the signature and clinical parameters was analyzed with GraphPad Prism 7 and SPSS software. Results Gene expression from The Cancer Genome Atlas dataset was used to perform the WGCNA analysis, and identified candidate prognostic-related genes in patients with ovarian cancer. According to the Cox regression analysis, the prognostic signature was constructed, which divided patients into two groups. The high-risk group showed the least favorable prognosis. Three independent cohorts from the Gene Expression Omnibus (GEO) database were used for the validation studies. According to the immune analyses, the GEO database signatures were significantly correlated with the immune statuses of ovarian cancer patients. By analyzing the combination of the prognostic signature and total mutational burden (TMB), ovarian cancer patients were divided into four groups. In these groups, memory B cell, resting memory CD4 T cell, M2 macrophage, resting mast cell and neutrophil were found significant distinctions among these groups. Conclusions This novel signature predicted the prognosis of ovarian cancer patients precisely and independently and showed significant correlations with immune responses. Therefore, this prognostic signature might could indicate targeted immunotherapies for ovarian cancer patients.

scholarly journals Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Fangli Yan ◽  
Yuhua Zheng
Keyword(s):  
Endometrial Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Uterine Corpus ◽  
Immune Genes

Abstract Background Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC. Methods We analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We constructed a transcription factor regulatory network based on Cistrome databases, and also performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using the Cox regression analysis. We then constructed and verified a prognostic signature. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content. Results The immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic signature revealed a ten-gene prognostic signature, comprising of PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC. This signature showed a strong prognostic ability in both the training and testing sets and thus can be used as an independent tool to predict the prognosis of UCEC. In addition, levels of B cells and neutrophils were significantly correlated with the patient’s risk score, while the expression of ten genes was associated with immune cell infiltrates. Conclusions In summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC.

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