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 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 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 Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Jincai Wu ◽  
Yuliang Zhang ◽  
Zhehao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Signature ◽  
Clinical Samples ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Cancer Tissues

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes.Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples.Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages.Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

scholarly journals Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

scholarly journals Identification of key N6-Methyladenosine-Related lncRNAs in Colon Adenocarcinoma using Bioinformatics Analysis

2021 ◽  
Author(s):  
Yuancheng Huang ◽  
Yanhua Yan ◽  
Chaoyuan Huang ◽  
Xiaotao Jiang ◽  
Zehong Yang ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Clinical Characteristics ◽  
Prognostic Model ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Prognostic Indicator ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Purpose: The purpose of this study was to investigate the role of m6A-related lncRNAs in colon adenocarcinoma (COAD) and determine their prognostic value.Material and methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas database. Correlation and univariate Cox regression analysis were conducted to identify m6A-related prognostic lncRNAs. A prognostic signature was established via least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The prognostic value of risk scores was evaluated using the Kaplan-Meier method, receiver operating characteristic curves, and univariate and multivariate regression analyses. Whether the prognostic model could serve as a prognostic indicator for overall survival (OS) in subgroups of patients with different clinical characteristics were explored. Next, We established a competing endogenous RNA network. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes pathway, and Gene Ontology analysis were performed for biological functional analysis.Results: 36 lncRNAs that were highly correlated with OS of patients were identified. A prognostic signature comprising 11 m6A-related lncRNAs was constructed, which had significant value in predicting the OS of patients . Univariate and Multivariate Cox regression analyses suggested that the risk score was an independent prognostic factor. This m6A-related lncRNA prognostic model could serve as a prognostic indicator for OS in subgroups of patients with different clinical characteristics. Biological processes and pathways associated with cancer were identified.Conclusion: We revealed the role and prognostic value of m6A-related lncRNAs in COAD. Our finding refreshed the understanding of m6A-related lncRNAs and provided novel insights to identify predictive biomarkers and develop targeted therapy for COAD.

scholarly journals Development of a novel immune-related genes prognostic signature for osteosarcoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zuo-long Wu ◽  
Ya-jun Deng ◽  
Guang-zhi Zhang ◽  
En-hui Ren ◽  
Wen-hua Yuan ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Information ◽  
Prognostic Indicator ◽  
Tissue Expression ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature’s prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

scholarly journals Construction and validation of a novel prognostic signature for uveal melanoma based on five metabolism-related genes

2021 ◽  
Vol 18 (6) ◽  
pp. 8045-8063
Author(s):  
Han Zhao ◽  
◽  
Yun Chen ◽  
Peijun Shen ◽  
Lan Gong ◽  
...  
Keyword(s):  
Uveal Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Molecular Signature ◽  
Prognostic Models ◽  
Prognostic Signature ◽  
Cox Regression Analysis

<abstract> <sec><title>Background</title><p>Uveal melanoma (UM) is the most aggressive intraocular tumor worldwide. Accurate prognostic models are urgently needed. The present research aimed to construct and validate a prognostic signature is associated with overall survival (OS) for UM patients based on metabolism-related genes (MRGs).</p> </sec> <sec><title>Methods</title><p>MRGs were obtained from molecular signature database (MSigDB). The gene expression profiles and patient clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. In the training datasets, MRGs were analyzed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) Cox analyses to build a prognostic model. The GSE84976 was treated as the validation cohort. In addition, time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curve analyses the reliability of the developed model. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. Nomogram that combined the five-gene signature was used to evaluate the predictive OS value of UM patients.</p> </sec> <sec><title>Results</title><p>Five MRGs were identified and used to establish the prognostic model for UM patients. The model was successfully validated using the testing cohort. Moreover, ROC analysis demonstrated a strong predictive ability that our prognostic signature had for UM prognosis. Multivariable Cox regression analysis revealed that the risk model was an independent predictor of prognosis. UM patients with a high-risk score showed a higher level of immune checkpoint molecules.</p> </sec> <sec><title>Conclusion</title><p>We established a novel metabolism-related signature that could predict survival and might be therapeutic targets for the treatment of UM patients.</p> </sec> </abstract>

scholarly journals Identification of IGF2BP3 as an Adverse Prognostic Biomarker of Gliomas

2021 ◽  
Vol 12 ◽  
Author(s):  
Chao Sun ◽  
Xin Zheng ◽  
Yingxin Sun ◽  
Ju Yu ◽  
Minfeng Sheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Roc Curve ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Low Grade ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Underlying Mechanisms ◽  
Genome Atlas

N6-methyladenosine (m6A) RNA modification can alter gene expression and function by regulating RNA splicing, stability, translocation, and translation. It is involved in various types of cancer. However, its role in gliomas is not well known. This study aimed to determine the prognostic value of the m6A RNA methylation regulator in gliomas and investigate the underlying mechanisms of the aberrant expression of m6A-related genes.mRNA expression profiles and clinical information of 448 glioma samples were obtained from The Cancer Genome Atlas and cBioportal. The expression of m6A-related genes in normal controls and low-grade glioma and glioblastoma was obtained from Gene Expression Profiling Interactive Analysis. Further, m6A-related gene expression and its relationship with prognosis were obtained through The Chinese Glioma Genome Atlas (CGGA). Multivariate Cox regression analyses were performed, and a nomogram was built with potential risk factors based on a multivariate Cox analysis to predict survival probability. Online tools such as Gene Set Enrichment Analysis, STRING, Cytoscape, and Molecular Complex Detection were applied for bioinformatics analysis and to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. The multivariate Cox regression analysis found that higher expression levels of YTHDC2 and insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3, also called IMP3) were independent negative and positive prognostic factors for overall survival (OS), respectively. Data from the CGGA database showed that IGF2BP3 expression increased when the tumor grade increased. Receiver operating characteristic (ROC) curve was used to evaluate the predictive specificity and sensitivity. The area under the ROC curve indicated that the OS prediction was 0.92 (1-year) and 0.917 (3-years), indicating that m6A-related genes could predict patient survival. In addition, IGF2BP3 was closely related to the shorter survival period of patients. Copy number variation and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of IGF2BP3 in gliomas. Significantly altered genes were identified, and the protein–protein interaction network was constructed. Based on the data presented, our study identified several m6A-related genes, especially IGF2BP3, that could be potential prognostic biomarkers of gliomas. The study unveiled the potential regulatory mechanism of IGF2BP3 in gliomas.

KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

2021 ◽  
Vol 24 ◽  
Author(s):  
Ping Lin ◽  
Yuean Zhao ◽  
Xiaoqian Li ◽  
Zongan Liang
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Malignant Pleural Mesothelioma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Pleural Mesothelioma ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

Identification of molecular subtyping system and four-gene prognostic signature with immune-related genes for uveal melanoma

2021 ◽  
pp. 153537022110538
Author(s):  
Fei Xia ◽  
Zhilong Yu ◽  
Aijun Deng ◽  
Guohong Gao
Keyword(s):  
Uveal Melanoma ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Gene Modules ◽  
Melanoma Patients ◽  
Immune Related Genes

Immunotherapy is the most promising treatment for uveal melanoma patients with metastasis. Tumor microenvironment plays an essential role in tumor progression and greatly affects the efficacy of immunotherapy. This research constructed an immune-related subtyping system and discovered immune prognostic genes to further understand the immune mechanism in uveal melanoma. Immune-related genes were determined from literature. Gene expression profiles of uveal melanoma were clustered using consensus clustering based on immune-related genes. Subtypes were further divided by applying immune landscape, and weighted correlation network analysis was performed to construct immune gene modules. Univariate Cox regression analysis was conducted to generate a prognostic model. Enriched immune cells were determined after gene set enrichment analysis. Three major immune subtypes (IS1, IS2, and IS3) were identified, and IS2 could be further divided into IS2A and IS2B. The subtypes were closely associated with uveal melanoma prognosis. IS3 group had the most favorable prognosis and was sensitive to PD-1 inhibitor. Immune genes in IS1 group showed an overall higher expression than IS3 group. Six immune gene modules were identified, and the enrichment score of immune genes varied within immune subtypes. Four immune prognostic genes ( IL32, IRF1, SNX20, and VAV1) were found to be closely related to survival. This novel immune subtyping system and immune landscape provide a new understanding of immunotherapy in uveal melanoma. The four prognostic genes can predict prognosis of uveal melanoma patients and contribute to new development of targeted drugs.

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