Construction of a Promising Tumor-Infiltrating CD8+ T Cells Gene Signature to Improve Prediction of the Prognosis and Immune Response of Uveal Melanoma

Construction of oxidative phosphorylation-related prognostic risk score model in uveal melanoma

BACKGROUND

Uveal melanoma (UVM) is a malignant intraocular tumor in adults. Targeting genes related to oxidative phosphorylation (OXPHOS) may play a role in anti-tumor therapy. However, the clinical significance of oxidative phosphorylation in UVM is unclear.

METHOD

The 134 OXPHOS-related genes were obtained from the KEGG pathway, the TCGA UVM dataset contained 80 samples, served as the training set, while GSE22138 and GSE39717 was used as the validation set. LASSO regression was carried out to identify OXPHOS-related prognostic genes. The coefficients obtained from Cox multivariate regression analysis were used to calculate a risk score, which facilitated the construction of a prognostic model. Kaplan-Meier survival analysis, logrank test and ROC curve using the time "timeROC" package were conducted. The immune cell frequency in low- and high-risk group was analyzed through Cibersort tool. The specific genomic alterations were analyzed by "maftools" R package. The differential expressed genes between low- or high-risk group were analyzed and performed Gene Ontology (GO) and GSEA. Finally, we verified the function of CYC1 in UVM by gene silencing in vitro.

RESULTS

A total of 9 OXPHOS-related prognostic genes were identified, including NDUFB1, NDUFB8, ATP12A, NDUFA3, CYC1, COX6B1, ATP6V1G2, ATP4B and NDUFB4. The UVM prognostic risk model was constructed based on the 9 OXPHOS-related prognostic genes. The prognosis of patients in the high-risk group was poorer than low-risk group. Besides, the ROC curve demonstrated that the area under the curve of the model for predicting the 1 to 5-year survival rate of UVM patients were all more than 0.88. External validation in GSE22138 and GSE39717 dataset revealed that these 9 genes could also be utilized to evaluate and predict the overall survival of patients with UVM. The risk score levels related to immune cell frequency and specific genomic alterations. The DEGs between the low- and high- risk group were enriched in tumor OXPHOS and immune related pathway. In vitro experiments, CYC1 silencing significantly inhibited UVM cell proliferation and invasion, induced cell apoptosis.

CONCLUSION

In sum, a prognostic risk score model based on oxidative phosphorylation-related genes in UVM was developed to enhance understanding of the disease. This prognostic risk score model may help to find potential therapeutic targets for UVM patients. CYC1 acts as an oncogene role in UVM.

Identification of multiomics map and key biomarkers in uveal melanoma with chromosome 3 loss

Purpose

Chromosome 3 loss is an independent risk factor for uveal melanoma (UM), but its exact molecular mechanisms remain unclear. This study was designed to investigate the relationship between chromosome 3 loss and molecular alterations at multiple levels to construct a prognostic model.

Methods

Forty-four UM cases with chromosome 3 loss (chr3 del group) and 36 UM cases without copy number variation on chromosome 3 (chr3 wt group) were collected from the Cancer Genome Atlas (TCGA). The TCGA dataset was subjected to a univariate Cox regression analysis to identify different expressed genes, and a subsequent random forest algorithm analysis revealed significant changes in different expressed genes, which were used to develop key biomarkers for UM. Following that, the immune cell infiltration analysis and drug sensitivity analyses were carried out. The UM cell line was then utilized to investigate the potential functions of the key biomarker via cell apoptosis, proliferation, cycle assays, WB, and RT-qPCR.

Results

By analyzing the 80 cases data in TCGA, the authors unveiled molecular changes relevant to loss of chromosome 3 in UM as well as their poor survival. In addition, machine learning analysis identified three hub genes (GRIN2A, ACAN, and MMP9) as potential therapeutic targets. The differentially enriched pathways between the two groups were mainly about immune-system activity, and hub genes expression was also highly correlated with immune infiltration levels.

Conclusion

Chromosome 3 loss has considerable clinical significance for UM, and GRIN2A may be useful in diagnosing, treating, and prognosticating the condition.

Bioinformatics identification of a T-cell-related signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a fatal disease with poor clinical outcomes. T cells play a vital role in the crosstalk between the tumour microenvironment and HCC. Single-cell RNA sequencing data were downloaded from the GSE149614 dataset. The T-cell-related prognostic signature (TRPS) was developed with the integrative procedure including 10 machine learning algorithms. The TRPS was established using 7 T-cell-related markers in the Cancer Genome Atlas cohort with 1-, 2- and 3-year area under curve values of 0.820, 0.725 and 0.678, respectively. TRPS acted as an independent risk factor for HCC patients. HCC patients with a high TRPS-based risk score had a higher Tumour Immune Dysfunction and Exclusion score, lower PD1 and CTLA4 immunophenoscore and lower level of immunoactivated cells, including CD8+ T cells and NK cells. The response rate was significantly higher in patients with low-risk scores in immunotherapy cohorts, including IMigor210 and GSE91061. The TRPS-based nomogram had a relatively good predictive value in evaluating the mortality risk at 1, 3 and 5 years in HCC. Overall, this study develops a TRPS by integrated bioinformatics analysis. This TRPS acted as an independent risk factor for the OS rate of HCC patients. It can screen for HCC patients who might benefit from immunotherapy, chemotherapy and targeted therapy.

Cross talk between tumor stemness and microenvironment for prognosis and immunotherapy of uveal melanoma

PURPOSE

Tumor stem cells have emerged as a crucial focus of investigation and a therapeutic target in the context of cancer metastasis and drug resistance. They represent a promising novel approach to address the treatment of uveal melanoma (UVM).

METHODS

According to the one-class logistic regression (OCLR) approach, we first estimated two stemness indices (mDNAsi and mRNAsi) in a cohort of UVM (n = 80). The prognostic value of stemness indices among four subtypes of UVM (subtype A-D) was investigated. Moreover, univariate Cox regression and Lasso-penalized algorithms were conducted to identify a stemness-associated signature and verify in several independent cohorts. Besides, UVM patients classified into subgroups based on the stemness-associated signature. The differences in clinical outcomes, tumor microenvironment, and probability of immunotherapeutic response were investigated further.

RESULTS

We observed that mDNAsi was significantly linked with overall survival (OS) time of UVM, but no association was discovered between mRNAsi and OS. Stratification analysis indicated that the prognostic value of mDNAsi was only limited in subtype D of UVM. Besides, we established and verified a prognostic stemness-associated gene signature which can classify UVM patients into subgroups with distinct clinical outcomes, tumor mutation, immune microenvironment, and molecular pathways. The high risk of UVM is more sensitive to immunotherapy. Finally, a well-performed nomogram was constructed to predict the mortality of UVM patients.

CONCLUSIONS

This study offers a comprehensive examination of UVM stemness characteristics. We discovered mDNAsi-associated signatures improved the prediction capacity of individualized UVM prognosis and indicated prospective targets for stemness-regulated immunotherapy. Analysis of the interaction between stemness and tumor microenvironment may shed light on combinational treatment that targets both stem cell and the tumor microenvironment.

Comprehensive analysis the diagnosis, malignant progression and immune infiltrate of ANXA6 in prostate cancer

BACKGROUND

Annexins (ANXAs) play a crucial role in the development and progression of tumors. However, their specific involvement in prostate cancer (PCa) remains unclear.

OBJECTIVE

To investigate the function and clinical significance of key ANXAs in PCa.

METHODS

Multiple databases were used to analyze the expression levels, genetic variations, potential prognostic value and clinical significance of ANXAs in PCa. Then, the co-expressed genes of ANXA6 were identified, and the correlation between ANXA6 and immune cell infiltration was validated using the Tumor Immune Estimation Resource (TIMER) database. Additionally, in vitro assays such as Cell Counting Kit-8 (CCK-8), Colony Formation, Transwell and T-cell Chemotaxis assays were conducted to validate the functions of ANXA6. Moreover, multiple types of in vivo assays were performed to further validate the identified ANXA6 functions.

RESULTS

The results demonstrated that ANXA2, ANXA6 and ANXA8 were significantly downregulated in PCa. ANXA6 upregulation was significantly associated with improved PCa patients' overall survival. Enrichment analysis revealed that ANXA6 and its co-expressed genes were involved in tumor progression, and ANXA6 overexpression could effectively inhibit the proliferation, migration and invasion of PC-3 cells. In vivo studies also demonstrated that ANXA6 overexpression suppressed tumor growth. Importantly, ANXA6 was found to promote the chemotaxis of CD4+ T cells and CD8+ T cells towards PC-3 cells, and the overexpression of ANXA6 in PC-3 cells promoted the polarization of macrophages into M1 macrophages in the supernatant of PCa cells.

CONCLUSIONS

ANXA6 demonstrated promising potential for consideration as a prognostic biomarker in PCa as it was found to play key roles in regulating immune cell infiltration and the malignant progression to PCa.

The immune checkpoint VISTA is associated with prognosis in patients with malignant uveal melanoma

Introduction

Uveal melanoma (UM) is a rare yet deadly tumor. It is known for its high metastatic potential, which makes it one of the most aggressive and lethal cancers. Recently, immune checkpoints such as Programmed cell Death protein-1 (PD1) and Cytotoxic T-Lymphocyte-Associated significantly increasing patient survival in multiple human cancers, especially cutaneous melanoma. However, patients with UMs were excluded from these studies because of their molecular characteristics, which tend to be widely different from those of cutaneous melanoma. This study aimed to analyze the expression of V domain Ig Suppressor T-cell Activation (VISTA), a novel immune checkpoint, to evaluate its prognosis significance and its correlation with PD1 and CTLA-4.

Methods

Evaluation of VISTA, CTLA-4, and PD1 expression was performed through TCGA database analysis and immunohistochemistry using two independent cohorts with primary malignant UM.

Results and discussion

Our results showed that VISTA expression was associated with tumor aggressiveness, T cell exhaustion, and the shortest median overall survival among patients. Surprisingly, PD1 protein expression was negative in all patients, whereas CTLA-4 expression was high in patients with advanced stages. Our findings suggest that VISTA may be a prognostic marker and an attractive treatment strategy for immunotherapy in patients with UM. Exploring its expression profile may predict response to immunotherapy and may lead to the improvement of precision therapy in malignant uveal melanoma patients.

Identification of co-expressed gene networks promoting CD8+ T cell infiltration and having prognostic value in uveal melanoma

Current immunotherapies are unsatisfactory against uveal melanoma (UM); however, elevated CD8+ T cell infiltration level indicates poor prognosis in UM. Here, we aimed to identify co-expressed gene networks promoting CD8+ T cell infiltration in UM and created a prognostic hazard model based on the identified hub genes. Raw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Stromal-immune comprehensive score (ESTIMATE) was used to evaluate the immune-infiltration landscape of the tumor microenvironment. Single-Sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Correlation Network Analysis (WGCNA) were used to quantify CD8+ T cell infiltration level and identify hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to analyze the biological processes. Least absolute shrinkage and selection operator (LASSO) Cox regression were used to establish a prognostic model, which was further validated. Finally, pan-cancer analysis evaluated these genes to be associated with CD8+ T cell infiltration in other tumors. In conclusion, the proposed four-gene (PTPN12, IDH2, P2RX4, and KDELR2) prognostic hazard model had satisfactory prognostic ability. These hub genes may promote CD8+ T cell infiltration in UM through antigen presentation, and CD8+ T cell possibly function as Treg, resulting in poor prognosis. These findings might facilitate the development of novel immunotherapies.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Background

Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses.

Methods

The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.

Conclusion

The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients

BACKGROUND

Deep learning has been extensively used in digital histopathology. The purpose of this study was to test deep learning (DL) algorithms for predicting the vital status of whole-slide image (WSI) of uveal melanoma (UM).

METHODS

We developed a deep learning model (Google-net) to predict the vital status of UM patients from histopathological images in TCGA-UVM cohort and validated it in an internal cohort. The histopathological DL features extracted from the model and then were applied to classify UM patients into two subtypes. The differences between two subtypes in clinical outcomes, tumor mutation, and microenvironment, and probability of drug therapeutic response were investigated further.

RESULTS

We observed that the developed DL model can achieve a high accuracy of >  = 90% for patches and WSIs prediction. Using 14 histopathological DL features, we successfully classified UM patients into Cluster1 and Cluster2 subtypes. Compared to Cluster2, patients in the Cluster1 subtype have a poor survival outcome, increased expression levels of immune-checkpoint genes, higher immune-infiltration of CD8 + T cell and CD4 + T cells, and more sensitivity to anti-PD-1 therapy. Besides, we established and verified prognostic histopathological DL-signature and gene-signature which outperformed the traditional clinical features. Finally, a well-performed nomogram combining the DL-signature and gene-signature was constructed to predict the mortality of UM patients.

CONCLUSIONS

Our findings suggest that DL model can accurately predict vital status in UM patents just using histopathological images. We found out two subgroups based on histopathological DL features, which may in favor of immunotherapy and chemotherapy. Finally, a well-performing nomogram that combines DL-signature and gene-signature was constructed to give a more straightforward and reliable prognosis for UM patients in treatment and management.

A T-cell-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma based on transcriptomics and single-cell sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is the fifth most frequently diagnosed malignancy and the third leading cause of cancer death globally. T cells are significantly correlated with the progression, therapy and prognosis of cancer. Limited systematic studies regarding the role of T-cell-related markers in HCC have been performed.

METHODS

T-cell markers were identified with single-cell RNA sequencing (scRNA-seq) data from the GEO database. A prognostic signature was developed with the LASSO algorithm in the TCGA cohort and verified in the GSE14520 cohort. Another three eligible immunotherapy datasets, GSE91061, PRJEB25780 and IMigor210, were used to verify the role of the risk score in the immunotherapy response.

RESULTS

With 181 T-cell markers identified by scRNA-seq analysis, a 13 T-cell-related gene-based prognostic signature (TRPS) was developed for prognostic prediction, which divided HCC patients into high-risk and low-risk groups according to overall survival, with AUCs of 1 year, 3 years, and 5 years of 0.807, 0.752, and 0.708, respectively. TRPS had the highest C-index compared with the other 10 established prognostic signatures, suggesting a better performance of TRPS in predicting the prognosis of HCC. More importantly, the TRPS risk score was closely correlated with the TIDE score and immunophenoscore. The high-risk score patients had a higher percentage of SD/PD, and CR/PR occurred more frequently in patients with low TRPS-related risk scores in the IMigor210, PRJEB25780 and GSE91061 cohorts. We also constructed a nomogram based on the TRPS, which had high potential for clinical application.

CONCLUSION

Our study proposed a novel TRPS for HCC patients, and the TRPS could effectively indicate the prognosis of HCC. It also served as a predictor for immunotherapy.

Identification and validation of immunogenic cell death-related score in uveal melanoma to improve prediction of prognosis and response to immunotherapy

BACKGROUND

Immunogenic cell death (ICD) could activate innate and adaptive immune response. In this work, we aimed to develop an ICD-related signature in uveal melanoma (UVM) patients and facilitate assessment of their prognosis and immunotherapy.

METHODS

A set of machine learning methods, including non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithms were used to evaluate the infiltration of immune cells. The Genomics of Drug Sensitivity in Cancer (GDSC), cellMiner and tumor immune dysfunction and exclusion (TIDE) databases were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures was also compared.

RESULTS

The ICDscore could predict the prognosis of UVM patients in both the training and four validating cohorts. The ICDscore outperformed 19 previously published signatures. Patients with high ICDscore exhibited a substantial increase in immune cell infiltration and expression of immune checkpoint inhibitor-related genes, leading to a higher response rate to immunotherapy. Furthermore, the downregulation of poly (ADP-ribose) polymerase family member 8 (PARP8), a critical gene involved in the development of the ICDscore, resulted in decreased cell proliferation and slower migration of UVM cells.

CONCLUSION

In conclusion, we developed a robust and powerful ICD-related signature for evaluating the prognosis and benefits of immunotherapy that could serve as a promising tool to guide decision-making and surveillance for UVM patients.

A Novel Glycolysis-Related Signature for Predicting the Prognosis and Immune Infiltration of Uveal Melanoma

INTRODUCTION

As the most common aggressive intraocular cancer in adults, uveal melanoma (UVM) threatens the survival and vision of many people. Glycolysis is a novel hallmark of cancer, but the role of glycolysis-related genes in UVM prognosis remains unknown. The purpose of the study was to establish a glycolysis-related gene signature (GRGS) to predict UVM prognosis.

METHODS

Raw data were obtained from TCGA-UVM and GSE22138 datasets. The GRGS was established by univariate, LASSO, and multivariate Cox regression analyses. Kaplan-Meier survival and time-dependent receiver operating characteristic curves were used to evaluate the predictive ability of the GRGS. The relationships of the GRGS with infiltrating immune cell levels and mutations were analyzed with CIBERSORT and maftools.

RESULTS

A novel GRGS (risk score = 0.690861*ISG20 + 0.070991*MET - 0.227520*SDC2 + 0.690223*FBP1 + 0.048008*CLN6 - 0.128520*SDC3) was developed for predicting UVM prognosis. The GRGS had robust predictive stability in UVM. Enrichment annotation suggested that the high-risk group had stronger adaptive immune responses and that the low-risk group had more innate immune cell infiltration. Moreover, BAP1 mutation was related to high risk, and SF3B1 mutation was related to low risk.

CONCLUSIONS

This study developed and validated a novel GRGS to predict UVM prognosis and immune infiltration. The signature revealed an association between glycolysis-related genes and the tumor microenvironment, providing new insights into the role of glycolysis in UVM.

Digital Quantification of Intratumoral CD8+ T-Cells Predicts Relapse and Unfavorable Outcome in Uveal Melanoma

Although it is a disease that occurs mainly in the Caucasian population, uveal melanoma (UM) is the most common primary intraocular tumor in adults. Here, we used digital pathology and image analysis for the diagnosis of UM and the prediction of the prognosis. Our retrospective study included a total of 404 histopathological slides from 101 patients. A digital image acquisition and quantitative analysis of tissue immune biomarkers (CD4, CD8, CD68, CD163) were performed. A negative impact of the intratumoral CD8 positive cell density higher than 13.3 cells/mm² was detected for both RFS (HR 2.08, 95% Cl 1.09 to 3.99, p = 0.027) and OS (HR 3.30, 95% CI 1.58 to 6.88, p = 0.001). Moreover, we confirmed that older age and stage III were independent negative prognostic factors for both RFS and OS. Our results suggest that a specific distribution profile of CD8 in UM might predict the risk of relapse and death, with potential implications for determining which subgroups of UMs are amenable to specific pharmacological treatment regimens.

PTPRO-related CD8+ T-cell signatures predict prognosis and immunotherapy response in patients with breast cancer

BACKGROUND

Poor immunogenicity and extensive immunosuppressive T-cell infiltration in the tumor immune microenvironment (TIME) have been identified as potential barriers to immunotherapy success in "immune-cold" breast cancers. Thus, it is crucial to identify biomarkers that can predict immunotherapy efficacy. Protein tyrosine phosphatase receptor type O (PTPRO) regulates multiple kinases and pathways and has been implied to play a regulatory role in immune cell infiltration in various cancers.

METHODS

ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) were performed to uncover the TIME landscape. The correlation analysis of PTPRO and immune infiltration was performed to characterize the immune features of PTPRO. Univariate and multivariate Cox analyses were applied to determine the prognostic value of various variables and construct the PTPRO-related CD8⁺ T-cell signatures (PTSs). The Kaplan-Meier curve and the receiver operating characteristic (ROC) curve were used to estimate the performance of PTS in assessing prognosis and immunotherapy response in multiple validation datasets.

RESULTS

High PTPRO expression was related to high infiltration levels of CD8⁺ T cells, as well as macrophages, activated dendritic cells (aDCs), tumor-infiltrating lymphocytes (TILs), and Th1 cells. Given the critical role of CD8⁺ T cells in the TIME, we focused on the impact of PTPRO expression on CD8⁺ T-cell infiltration. The prognostic PTS was then constructed using the TCGA training dataset. Further analysis showed that the PTS exhibited favorable prognostic performance in multiple validation datasets. Of note, the PTS could accurately predict the response to immune checkpoint inhibitors (ICIs).

CONCLUSION

PTPRO significantly impacts CD8⁺ T-cell infiltration in breast cancer, suggesting a potential role of immunomodulation. PTPRO-based PTS provides a new immune cell paradigm for prognosis, which is valuable for immunotherapy decisions in cancer patients.

The Immunological Contribution of a Novel Metabolism-Related Signature to the Prognosis and Anti-Tumor Immunity in Cervical Cancer

Cervical cancer is the most frequently diagnosed malignancy in the female reproductive system. Conventional stratification of patients based on clinicopathological characters has gradually been outpaced by a molecular profiling strategy. Our study aimed to identify a reliable metabolism-related predictive signature for the prognosis and anti-tumor immunity in cervical cancer. In this study, we extracted five metabolism-related hub genes, including ALOX12B, CA9, FAR2, F5 and TDO2, for the establishment of the risk score model. The Kaplan-Meier curve suggested that patients with a high-risk score apparently had a worse prognosis in the cervical cancer training cohort (TCGA, n = 304, p < 0.0001), validation cohort (GSE44001, n = 300, p = 0.0059) and pan-cancer cohorts (including nine TCGA tumors). Using a gene set enrichment analysis (GSEA), we observed that the model was correlated with various immune-regulation-related pathways. Furthermore, pan-cancer cohorts and immunohistochemical analysis showed that the infiltration of tumor infiltrating lymphocytes (TILs) was lower in the high-score group. Additionally, the model could also predict the prognosis of patients with cervical cancer based on the expression of immune checkpoints (ICPs) in both the discovery and validation cohorts. Our study established and validated a metabolism-related prognostic model, which might improve the accuracy of predicting the clinical outcome of patients with cervical cancer and provide guidance for personalized treatment.

Immunological significance of survival-related alternative splicing in uveal melanoma

Uveal melanoma (UM) is a highly malignant intraocular tumor. The imbalance of alternative splicing (AS) is a landmark of tumor initiation and progression. However, there are few studies of AS in UM. Thus, this study aimed to identify a new AS-based prognostic signature and reveal its relationship with tumor-infiltrating immune cells. Univariable Cox regression analysis identified survival-related AS events. The prognostic signature was constructed using the univariable and multivariable Cox regression analyses. Kaplan-Meier survival analysis, the proportional hazard model, and receiver operating characteristic curves verified its prognostic value. Single-sample gene set enrichment analysis was used to analyze immune cell enrichment. The correlation of the risk score with tumor-infiltrating immune cells and immune checkpoint blockade (ICB) genes was examined. We screened 2886 survival-related AS events, of which five were selected to build a prognostic predictor. The risk score was positively relevant with ICB key targets (HAVCR2, IDO1, and PDCD1) and the infiltration of T cells, MDSC, and activated B cells. We provided novel and effective indices, including a risk score and clinical nomogram, for prognostic prediction in UM and discussed the potential relationship between survival-related AS events and immune cell infiltration, which is crucial for developing immune-targeted therapy to improve prognosis.