Construction and Validation of an Immune-related Gene Pairs Prognostic Model for Breast Cancer

Abstract Background: Immunotherapy plays an increasingly important role in the treatment of advanced female breast cancer, which has the highest mortality rate among malignant tumors. The purpose of this study was to identify immune-related genes associated with breast cancer prognosis as possible targets of immunotherapy, and their related biological processes and signaling pathways.Methods: Clinical data and gene expression profiles of patients with breast cancer were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and divided into training (n = 1053) and verification (n = 508) groups. CIBERSORT was used to predict differences in immune cell infiltration in patient subsets stratified according to risk. Gene Ontology (GO) enrichment analysis was used to identify pathways associated with immune-related genes in patient subsets stratified according to risk.Results: The prognostic model composed of 27 immune-related gene pairs significantly distinguished between high- and low-risk patients. Univariate and multivariate analyses indicated that the model was an independent prognostic factor for breast cancer. Among the identified genes, APOBEC3G, PLXNB1, and C3AR1 had not been previously studied in breast cancer and warrant further exploration. CCR chemokine receptor binding, regulation of leukocyte-mediated cytotoxicity, T cell migration, T cell receptor complex, and other pathways were significantly enriched in low-risk patients. M2 and M0 macrophages were more highly expressed in high-risk than in low-risk patients. CD8+ T cells and naïve B cells were more abundant in low-risk than in high-risk patients.Conclusion: The immune-related gene pairs prognostic model developed in the current study can help assess breast cancer prognosis and provides a potential target and research direction for breast cancer immunotherapy in the future.

Download Full-text

Development and validation of an individualized immune-related gene pairs prognostic signature in papillary renal cell carcinoma

10.21203/rs.3.rs-29603/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xianghong Zhou ◽

Shi Qiu ◽

Di Jin ◽

Kun Jin ◽

Xiaonan Zheng ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Risk Group ◽

High Risk Group ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene ◽

Immune Related Genes

Abstract Background: Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC.Methods: We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery and validation cohorts, respectively. The immune-related genes in the samples were used to construct gene pairs, and the immune-related genes pairs (IRGPs) with robust impact for overall survival (OS) were screened out to construct the signature by univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis. Then we verified the prognostic role of the signature, and assessed the relationship between this signature with tumor immune infiltration and functional pathways.Results: A total of 315 patients were included in our study, and divided to discovery (n=287) and validation (n=28) cohorts. Finally, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p<0.001). Combined with multiple clinical pathological factors, the results of multivariate analyses confirmed that this signature was an independent predictor of OS (HR, 3.548; 95%CI, 2.096−6.006; p<0.001). The results of immune infiltration analysis demonstrated that the abundance of multiple tumor-infiltration lymphocytes such as CD8+ T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group.Conclusions: We successfully established an individualized prognostic immune-related gene pairs signature, which can accurately and independently predict the OS of patients with PRCC.

Download Full-text

Identifies Immune-Related Gene Pair Signature Associated with Breast Cancer Prognosis

10.21203/rs.3.rs-793025/v1 ◽

2021 ◽

Author(s):

Tianwei Sun ◽

Qixing Tan ◽

Changyuan Wei

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Gene Pair ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Future Research ◽

Breast Cancer Patients ◽

Related Gene ◽

Immune Related Gene ◽

Immune Related Genes

Abstract Background: Breast cancer (BC) is the cancer with the largest number of deaths in women. There is growing evidence that immunity plays an important role in the prognosis of breast cancer. Methods: In this study, we developed and validated an immune-related gene pair signature (IRGPs) to predict the survival of breast cancer patients. Screening immune-related genes from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database for the construction of IRGPs, and patients with breast cancer in these two cohorts were assigned to low- and high- risk subgroups. Additionally, we used Kaplan-Meier survival analysis, univariate and multivariate Cox analysis to investigate IRGPs and their individualized prognostic characteristics, and analysis of immune cell infiltration in breast cancer. Results: A 47-IRGP signature was constructed from 2498 immune genes, which could significantly predict the overall survival (OS) of breast cancer patients in the TCGA and GEO cohorts. Immune infiltration analysis showed that a variety of immune cells are significantly related to the prognostic effects of IRGP characteristics in breast cancer patients, especially CD8+ T cells and macrophages. Conclusions: The IRGP signature constructed in this study can help determine the prognosis of breast cancer and provide new ideas and basis for future research on the role of immune-related genes in breast cancer patients.

Download Full-text

Identification of Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.752643 ◽

2021 ◽

Vol 12 ◽

Author(s):

Na Li ◽

Jiahong Wang ◽

Xianquan Zhan

Keyword(s):

Prognostic Model ◽

Immune Cell ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Prognostic Models ◽

Related Gene ◽

Immune Related Gene ◽

Immune Related Genes

Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Download Full-text

Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.715728 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiaofei Feng ◽

Shanshan Mu ◽

Yao Ma ◽

Wenji Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Expression Profiles ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene ◽

Risk Patients

With the increasing prevalence of Hepatocellular carcinoma (HCC) and the poor prognosis of immunotherapy, reliable immune-related gene pairs (IRGPs) prognostic signature is required for personalized management and treatment of patients. Gene expression profiles and clinical information of HCC patients were obtained from the TCGA and ICGC databases. The IRGPs are constructed using immune-related genes (IRGs) with large variations. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct IRGPs signature. The IRGPs signature was verified through the ICGC cohort. 1,309 IRGPs were constructed from 90 IRGs with high variability. We obtained 50 IRGPs that were significantly connected to the prognosis and constructed a signature that included 17 IRGPs. In the TCGA and ICGC cohorts, patients were divided into high and low-risk patients by the IRGPs signature. The overall survival time of low-risk patients is longer than that of high-risk patients. After adjustment for clinical and pathological factors, multivariate analysis showed that the IRGPs signature is an independent prognostic factor. The Receiver operating characteristic (ROC) curve confirmed the accuracy of the signature. Besides, gene set enrichment analysis (GSEA) revealed that the signature is related to immune biological processes, and the immune microenvironment status is distinct in different risk patients. The proposed IRGPs signature can effectively assess the overall survival of HCC, and provide the relationship between the signature and the reactivity of immune checkpoint therapy and the sensitivity of targeted drugs, thereby providing new ideas for the diagnosis and treatment of the disease.

Download Full-text

Development and verification of a personalized immune prognostic feature in breast cancer

Experimental Biology and Medicine ◽

10.1177/1535370220936964 ◽

2020 ◽

Vol 245 (14) ◽

pp. 1242-1253 ◽

Cited By ~ 1

Author(s):

HongLei Wang ◽

Li Wu ◽

HongTao Wang

Keyword(s):

Breast Cancer ◽

Prognostic Indicator ◽

Immune Gene ◽

Survival Ratio ◽

Related Gene ◽

Training Set ◽

Gene Markers ◽

Gene Pairs ◽

Immune Related Gene ◽

Immune Related Genes

Immune-related genes have great potential as prognostic markers in many types of cancer. Therefore, we have attempted to develop immune-related gene markers to enhance the prognosis of breast cancer; 1159 samples of breast cancer gene expression data and clinical follow-up messages were downloaded from TCGA and GEO, which were classified into training set, test set, and validation set. In the training set, the gene pairs are established according to the relative expression levels between 320 immune genes, in which the prognosis-related gene pairs are screened, and Lasso is used for feature selection to screen the robust biomarkers. A prognostic model of immune gene correlation was set up and verified. Sixty-six IRGPs were obtained, and 17-IRGPs signature was established. 17-IRGPs signature is an independent prognostic indicator for BC patients, which can stratify the risk in the training set and testing series, and AUC of five years survival was greater than 0.7; 17-IRGPs signature had better classification performance in patients with advanced BC. In addition, we compared the prognostic characteristics of 17-IRGPs with four reported breast cancers and clinical stages; 17-IRGPs achieved the highest average C index (0.7, P < 0.05), and functional analysis found that the dysregulated immune environment may be the cause of the observed difference in survival between patient groups defined by our characteristics. 17-IRGPs signature was constructed as a newly developed prognostic indicator to calculate the survival of BC patients. Impact statement Breast cancer is among the highest prevalent malignant tumors worldwide with a low survival ratio. Immune-related genes have great potential as prognostic indicator in many types of tumors. Therefore, we have attempted to develop immune-related gene markers to enhance the prognosis of breast cancer. 17-IRGPs signature was constructed as a newly developed prognostic indicator to predict the survival of BC patients.

Download Full-text

Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer

Bioscience Reports ◽

10.1042/bsr20204301 ◽

2021 ◽

Author(s):

Bo Liu ◽

Tingting Fu ◽

Ping He ◽

Chenyou Du ◽

Ke Xu

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

High Risk ◽

Prognostic Model ◽

Risk Group ◽

Group Versus ◽

Low Risk ◽

Risk Patients ◽

Validation Set ◽

Immune Related Genes

Purpose: To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC). Methods: Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and ImmPort were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452. Results: In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group. Conclusions: The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.

Download Full-text

A Novel Signature of 23 Immunity-related Gene Pairs is Prognostic of Cutaneous Melanoma

10.21203/rs.3.rs-38060/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yanan Xue ◽

Yinan Xue ◽

Zhengcai Wang ◽

Yongzhen Mo ◽

Pinyan Wang ◽

...

Keyword(s):

Cutaneous Melanoma ◽

Prognostic Model ◽

Risk Group ◽

Risk Groups ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene ◽

Technical Bias ◽

Tcga Dataset

Abstract Background: We aimed to identify immune-related signature for predicting cutaneous melanoma (CM) prognosis. Methods: We used TCGA samples (n=471) to develop the best 23 Immune related gene pairs (23-IRGP) prognostic signature and divided patients into high- and low-immune risk group in TCGA dataset and validation datasets: GSE65904 (n=214), GSE59455 (n=141), and GSE22153 (n=79). Results: 23-IRGP presented precise ability in cutaneous melanoma (CM) which high-risk groups showed poor prognosis and indicated significant predict power in immune micro-environment and biological analysis as well. Conclusions: we established a novel promising prognostic model in CM and built the bridge between immune micro-environment and CM patient results. This approach can be applied to discover the signatures in other diseases without technical bias from different platforms.

Download Full-text

Prognostic Implications of Immune-Related Gene Pairs Signatures in Bladder Cancer

Journal of Oncology ◽

10.1155/2021/5345181 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Hui Xiong ◽

Hui Gao ◽

Jinding Hu ◽

Yun Dai ◽

Hanbo Wang ◽

...

Keyword(s):

Bladder Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cells ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Related Gene ◽

Gene Pairs ◽

Immune Related Gene

Compelling evidence indicates that immune function is correlated with the prognosis of bladder cancer (BC). Here, we aimed to develop a clinically translatable immune-related gene pairs (IRGPs) prognostic signature to estimate the overall survival (OS) of bladder cancer. From the 251 prognostic-related IRGPs, 37 prognostic-related IRGPs were identified using LASSO regression. We identified IRGPs with the potential to be prognostic markers. The established risk scores divided BC patients into high and low risk score groups, and the survival analysis showed that risk score was related to OS in the TCGA-training set ( p < 0.001 ; HR = 7.5 [5.3, 10]). ROC curve analysis showed that the AUC for the 1-year, 3-year, and 5-year follow-up was 0.820, 0.883, and 0.879, respectively. The model was verified in the TCGA-testing set and external dataset GSE13507. Multivariate analysis showed that risk score was an independent prognostic predictor in patients with BC. In addition, significant differences were found in gene mutations, copy number variations, and gene expression levels in patients with BC between the high and low risk score groups. Gene set enrichment analysis showed that, in the high-risk score group, multiple immune-related pathways were inhibited, and multiple mesenchymal phenotype-related pathways were activated. Immune infiltration analysis revealed that immune cells associated with poor prognosis of BC were upregulated in the high-risk score group, whereas immune cells associated with a better prognosis of BC were downregulated in the high-risk score group. Other immunoregulatory genes were also differentially expressed between high and low risk score groups. A 37 IRGPs-based risk score signature is presented in this study. This signature can efficiently classify BC patients into high and low risk score groups. This signature can be exploited to select high-risk BC patients for more targeted treatment.

Download Full-text