Development and verification of an immune‐related gene pairs prognostic signature in ovarian cancer

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.

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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.

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Development and Validation of an Individualized Immune-Related Gene Pairs Prognostic Signature in Papillary Renal Cell Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.569884 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xianghong Zhou ◽

Shi Qiu ◽

Di Jin ◽

Kun Jin ◽

Xiaonan Zheng ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Papillary Renal Cell Carcinoma ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene ◽

Development And Validation

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Individualized prognostic signature for pancreatic carcinoma validated by integrating immune-related gene pairs (IRGPs)

Bioengineered ◽

10.1080/21655979.2020.1860493 ◽

2021 ◽

Vol 12 (1) ◽

pp. 88-95

Author(s):

Yecheng Li ◽

Pingan Yao ◽

Kui Zhao ◽

Zhenyu Ye ◽

Haobo Zhang ◽

...

Keyword(s):

Pancreatic Carcinoma ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene

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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.

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Prediction of Survival Outcome in Lower-Grade Glioma Using a Prognostic Signature with 33 Immune-Related Gene Pairs

International Journal of General Medicine ◽

10.2147/ijgm.s338135 ◽

2021 ◽

Vol Volume 14 ◽

pp. 8149-8160

Author(s):

Shaohua Chen ◽

Yongchu Sun ◽

Xiaodong Zhu ◽

Zengnan Mo

Keyword(s):

Survival Outcome ◽

Lower Grade ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene ◽

Lower Grade Glioma ◽

Prediction Of Survival

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Development of an immune-related gene pairs index for the prognosis analysis of metastatic melanoma

Scientific Reports ◽

10.1038/s41598-020-80858-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rong-zhi Huang ◽

Min Mao ◽

Jie Zheng ◽

Hai-qi Liang ◽

Feng-ling Liu ◽

...

Keyword(s):

Decision Making ◽

Skin Cancer ◽

Metastatic Melanoma ◽

Clinical Decision Making ◽

Clinical Decision ◽

Gene Set Enrichment Analysis ◽

Comprehensive Treatment ◽

Related Gene ◽

Gene Pairs ◽

Immune Related Gene

AbstractMelanoma is a skin cancer with great metastatic potential, which is responsible for the major deaths in skin cancer. Although the prognosis of melanoma patients has been improved with the comprehensive treatment, for patients with metastasis, the complexity and heterogeneity of diffuse diseases make prognosis prediction and systematic treatment difficult and ineffective. Therefore, we established a novel personalized immune-related gene pairs index (IRGPI) to predict the prognosis of patients with metastatic melanoma, which was conducive to provide new insights into clinical decision-making and prognostic monitoring for metastatic melanoma. Through complex analysis and filtering, we identified 24 immune-related gene pairs to build the model and obtained the optimal cut-off value from receiver operating characteristic curves, which divided the patients into high and low immune-risk groups. Meantime, the Kaplan–Meier analysis, Cox regression analysis and subgroup analysis showed that IRGPI had excellent prognostic value. Furthermore, IRGPI was shown that was closely associated with immune system in the subsequent tumor microenvironment analysis and gene set enrichment analysis. In addition, we broken through the data processing limitations of traditional researches in different platforms through the application of gene pairs, which would provide great credibility for our model. We believe that our research would provide a new perspective for clinical decision-making and prognostic monitoring in metastatic melanoma.

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Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-83120-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chunlei Wu ◽

Quanteng Hu ◽

Dehua Ma

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Related Gene ◽

Cox Regression Analysis ◽

Gene Pairs ◽

Immune Related Gene ◽

Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

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Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e15565 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e15565-e15565

Author(s):

Qiqi Zhu ◽

Du Cai ◽

Wei Wang ◽

Min-Er Zhong ◽

Dejun Fan ◽

...

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Prognostic Value ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Related Gene ◽

Training Cohort ◽

Gene Pairs ◽

Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

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