scholarly journals Development and Validation of a Ferroptosis-related Prognostic Model in Pancreatic Cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Analysis

Abstract Background: With the development of genomics, ferroptosis has been determined to be highly important in cancer. The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas (TCGA) database, we employed univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox analysis to establish the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus (GEO) datasets and tissue samples obtained from our center were utilized to validate the prognostic model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The KM curve indicated that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The nomogram showed that the prognostic model was the most important element. Gene set enrichment analysis (GSEA) identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. GSE57495, GSE62452 and 88 pancreatic cancer tissues from our center were utilized to validate the prognostic model. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

scholarly journals Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

scholarly journals The Pyroptosis-Related 9 LncRNA Signature and LncRNA-miRNA-mRNA Regulatory Network in Breast Cancer: A Comprehensive Analysis Based On TCGA Database

2021 ◽  
Author(s):  
Ye Tian ◽  
Yanan Zhang ◽  
Jing Dong ◽  
Lin Li
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Regulatory Network ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Tumor Immune Microenvironment ◽  
Cox Analysis

Abstract Background: Pytoproptosis has been verified to participate in various malignancies. However, studies on pyroptosis-related lncRNAs in breast cancer and its effects on tumor immune micro-environment are still limited. Consequently, it was aimed in this study to construct a pyroptosis-related lncRNAs signature for prognostic prediction and explore the effect of the pyroptosis-related LncRNAs on tumor immune microenvironment through LncRNA-miRNA-mRNA regulatory network. Methods: The pyroptosis-related differentially expressed genes (DEGs) were discovered using differential expression analysis. The differentially expressed LncRNAs (DELncRNAs) associated with DEGs were discovered using correlation analysis. The function of DEGs was analyed using GO and KEGG analyses. The LncRNAs signature used as the prognostic model of breast cancer was constructed using univariate and multivariate Cox analysis, and the effectiveness was verified by K-M analysis and ROC curve. The risk score calculated using the prognostic model was proved as an independent factor by univariate Cox analysis, multivariate Cox analysis and PCA analysis, and used to predict patient prognosis through nomogram. The pathyways enriched in High risk group and Low risk group were analyzed by GSEA. The differences in immune cell distribution (B cell memory, T cell CD4+, T cell CD8+ among others) were analyzed using ssGSEA. The immune function (type I/II IFN response among others), immune checkpoint (ADORA2A among others) and m6A-related protein expression (FTO among others) of High risk group and Low risk group were compared. The regulatory network of pyroptosis-related LncRNA-miRNA-mRNA was constructed and the core network was extracted. The functions of the target genes of miRNA associated with DELncRNAs were explored using GO and KEGG analysis. Results: A 9 LncRNAs signature (LMNTD2-AS1, AL589765.4, AC079298.3, U62317.3, LINC02446, AL645608.7, HSD11B1-AS1, AC009119.1, AC087239.1) was constructed as the prognostic model of breast cancer. Significant differences were discovered in immune cell distribution, immune function, immune checkpointand m6A-related protein expression between High risk group and Low risk group. The regulatory network of LncRNA-miRNA-mRNA was constructed and found to participate in the crosstalk among apoptosis, pyroptosis and necroptosis of breast cancer. Conclusions: The 9 lncRNAs signature was valuable for predicting breast cancer prognosis, and the pyroptosis-related lncRNAs influenced tumor immune microenvironment of breast cancer through the LncRNA-miRNA-mRNA regulatory network.

scholarly journals A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

2021 ◽  
Author(s):  
Jianyu Zhao ◽  
Bo Liu ◽  
Xiaoping Li
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Adrenocortical Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Abdominal Masses ◽  
Cox Analysis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Methods: The gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

scholarly journals Identification of N6-Methyladenosine-Associated Long Non-coding RNAs for Immunotherapeutic Response and Prognosis in Patients With Pancreatic Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinshuang Yu ◽  
Peng Dong ◽  
Yu Yan ◽  
Fengjun Liu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Messenger Rna ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Significant Difference ◽  
Selection Operator

Pancreatic cancer is a highly aggressive disease with poor prognosis. N6-methyladenosine (m6A) is critical for post-transcriptional modification of messenger RNA (mRNA) and long non-coding RNA (lncRNA). However, the m6A-associated lncRNAs (m6A-lncRNA) and their values in predicting clinical outcomes and immune microenvironmental status in pancreatic cancer patients remain largely unexplored. This study aimed to evaluate the importance of m6A-lncRNA and established a m6A-lncRNA signature for predicting immunotherapeutic response and prognosis of pancreatic cancer. The m6A-lncRNA co-expression networks were constructed using data from the TCGA and GTEx database. Based on the least absolute shrinkage and selection operator (LASSO) analysis, we constructed an 8 m6A-lncRNA signature risk model, and selection operator (LASSO) analysis, and stratified patients into the high- and low-risk groups with significant difference in overall survival (OS) (HR = 2.68, 95% CI = 1.74–4.14, P &lt; 0.0001). Patients in the high-risk group showed significantly reduced OS compared to patients in the low-risk group (P &lt; 0.001). The clinical characteristics and m6A-lncRNA risk scores were used to construct a nomogram which accurately predicted the OS in pancreatic cancer. TIMER 2.0 were used to investigate tumor immune infiltrating cells and its relationship with pancreatic cancer. CIBERSORT analysis revealed increased higher infiltration proportions of M0 and M2 macrophages, and lower infiltration of naive B cell, CD8+ T cell and Treg cells in the high-risk group. Compared to the low-risk group, functional annotation using ssGSEA showed that T cell infiltration and the differential immune-related check-point genes are expressed at low level in the high-risk group (P &lt; 0.05). In summary, our study constructed a novel m6A-associated lncRNAs signature to predict immunotherapeutic responses and provided a novel nomogram for the prognosis prediction of pancreatic cancer.

scholarly journals A Novel Signature to Predict Thyroid Cancer Prognosis and Immune Landscape Using Immune-Related LncRNA Pairs

2021 ◽  
Author(s):  
BO SONG ◽  
Lijun Tian ◽  
Fan Zhang ◽  
Zheyu Lin ◽  
Boshen Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
High Risk ◽  
Small Molecule ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

Abstract Background: Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. TC is still often overtreated due to a lack of reliable diagnostic biomarkers. Therefore, determining accurate prognostic predictions to stratify TC patients is important.Methods: Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, ICI-related molecules and small-molecule inhibitor efficacy. Results: We identified 30 DEirlncRNA pairs through Lasso regression, and 14 pairs served as the novel predictive signature. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature can predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further found that some commonly used small-molecule inhibitors, such as sunitinib, were related to this new signature. Conclusions: We constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

scholarly journals Construction and Validation of a Novel Immunosignature for Overall Survival in Uveal Melanoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Chufeng Gu ◽  
Xin Gu ◽  
Yujie Wang ◽  
Zhixian Yao ◽  
Chuandi Zhou
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Staging System ◽  
Risk Groups ◽  
Tnm Staging ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Tnm Staging System

ObjectivesUveal melanoma (UM) is the most common primary intraocular malignancy in adults, and immune infiltration plays a crucial role in the prognosis of UM. This study aimed to generate an immunological marker-based predictive signature for the overall survival (OS) of UM patients.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) was used to profile immune cell infiltration in 79 patients with UM from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate least absolute shrinkage and selection operator (LASSO) Cox regressions were used to determine the prognostic factors for UM and construct the predictive immunosignature. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the clinical ability and accuracy of the model. In addition, the predictive accuracy was compared between the immunosignature and the Tumor, Node, Metastasis (TNM) staging system of American Joint Committee on Cancer (AJCC). We further analyzed the differences in clinical characteristics, immune infiltrates, immune checkpoints, and therapy sensitivity between high- and low-risk groups characterized by the prognostic model.ResultsHigher levels of immune cell infiltration in UM were related to a lower survival rate. Matrix metallopeptidase 12 (MMP12), TCDD inducible poly (ADP-ribose) polymerase (TIPARP), and leucine rich repeat neuronal 3 (LRRN3) were identified as prognostic signatures, and an immunological marker-based prognostic signature was constructed with good clinical ability and accuracy. The immunosignature was developed with a concordance index (C-index) of 0.881, which is significantly better than that of the TNM staging system (p &lt; 0.001). We further identified 1,762 genes with upregulated expression and 798 genes with downregulated expression in the high-risk group, and the differences between the high- and low-risk groups were mainly in immune-related processes. In addition, the expression of most of the immune checkpoint-relevant and immune activity-relevant genes was significantly higher in the high-risk group, which was more sensitive to therapy.ConclusionWe developed a novel immunosignature constructed by MMP12, TIPARP, and LRRN3 that could effectively predict the OS of UM.

scholarly journals Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinzhi Lai ◽  
Hainan Yang ◽  
Tianwen Xu
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Regulatory Network ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

Abstract Background Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM. Methods We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan–Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs). Results A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM. Conclusion Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

scholarly journals Identification of Clinical and Tumor Microenvironment Characteristics of Hypoxia-Related Risk Signature in Lung Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Zili Dai ◽  
Taisheng Liu ◽  
Guihong Liu ◽  
Zhen Deng ◽  
Peng Yu ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Related Risk

Background: Lung cancer is the leading cause of cancer-related death globally. Hypoxia can suppress the activation of the tumor microenvironment (TME), which contributes to distant metastasis. However, the role of hypoxia-mediated TME in predicting the diagnosis and prognosis of lung adenocarcinoma (LUAD) patients remains unclear.Methods: Both RNA and clinical data from the LUAD cohort were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Both univariate and multivariate Cox regression analyses were used to further screen prognosis-related hypoxia gene clusters. Time-dependent receiver operation characteristic (ROC) curves were established to evaluate the predictive sensitivity and specificity of the hypoxia-related risk signature. The characterization of gene set enrichment analysis (GSEA) and TME immune cell infiltration were further explored to identify hypoxia-related immune infiltration.Results: Eight hypoxia-related genes (LDHA, DCN, PGK1, PFKP, FBP1, LOX, ENO3, and CXCR4) were identified and established to construct a hypoxia-related risk signature. The high-risk group showed a poor overall survival compared to that of the low-risk group in the TCGA and GSE68465 cohorts (p &lt; 0.0001). The AUCs for 1-, 3-, and 5-year overall survival were 0.736 vs. 0.741, 0.656 vs. 0.737, and 0.628 vs. 0.649, respectively. The high-risk group was associated with immunosuppression in the TME.Conclusion: The hypoxia-related risk signature may represent an independent biomarker that can differentiate the characteristics of TME immune cell infiltration and predict the prognosis of LUAD.

scholarly journals Development of a T-cell activation-related module with predictive value for the prognosis and immune checkpoint blockade therapy response in glioblastoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12547
Author(s):  
Zihao Yan ◽  
Siwen Chu ◽  
Chen Zhu ◽  
Yunhe Han ◽  
Qingyu Liang ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
T Cell Activation ◽  
Cell Activation ◽  
Risk Group ◽  
Immune Checkpoint Blockade ◽  
High Risk Group ◽  
Related Risk

Background Despite the rise in the use of immune checkpoint blockade drugs (ICBs) in recent years, there are no ICB drugs that are currently approved or under large-scale clinical trials for glioblastoma (GBM). T-cells, which mainly mediate adaptive immunity, are an important part of the tumor immune microenvironment. The activation of T-cells in tumors plays a key role in evaluating the sensitivity of patients to immunotherapy. Therefore, we applied bioinformatics approaches to construct a T-cell activation related risk score to study the effect of the activation of T-cells on the prognosis and ICB response of patients with GBM. Materials and Methods This study collected TCGA, CGGA, and GSE16011 glioma cohorts, as well as the IMvigor210 immunotherapy dataset, with complete mRNA expression profiles and clinical information. GraphPad Prism 8 and R 3.6.3 were used for bioinformatics analysis and plotting. Results The activation of T-cells in patients with GBM is characterized by obvious heterogeneity. We established a T-cell activation-related risk score based on five univariate Cox regression prognostic genes (CD276, IL15, SLC11A1, TNFSF4, and TREML2) in GBM. The risk score was an independent risk factor for poor prognosis. The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group. Moreover, the high-risk score was accompanied by a stronger immune response and a more complex tumor immune microenvironment. “Hot tumors” were mainly enriched in the high-risk group, and high-risk group patients highly expressed inhibitory immune checkpoints (PD1, PD-L1, TIM3 etc.). By combining the risk and priming scores we obtained the immunotherapy score, which was shown to be a good evaluation index for sensitivity to GBM immunotherapy. Conclusions As an independent risk factor for poor prognosis, the T-cell activation-related risk score, combined with other clinical characteristics, could efficiently evaluate the survival of patients with GBM. The immunotherapy score obtained by combining the risk and priming scores could evaluate the ICB response of patients with GBM, providing treatment opportunities.

scholarly journals Development And Validation of A Pyroptosis -Related Signature And Immune Microenvironment Infiltration In Cutaneous Melanoma

2021 ◽  
Author(s):  
Wei Dalong ◽  
Xiaoling Lan ◽  
Qiang Tang ◽  
Zhiqun Huang ◽  
Qianli Tang
Keyword(s):  
High Risk ◽  
Cutaneous Melanoma ◽  
Immune Cell ◽  
Risk Model ◽  
Risk Group ◽  
External Validation ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background: Cutaneous melanoma is cancer that is both malignant and aggressive, with a poor prognosis. Pyroptosis can affect the prognosis of cancer patients by controlling tumor cell growth, migration, and metastasis, as well as is closely related to the tumor immune microenvironment. The significance of pyroptosis-related genes (PRGs) in cutaneous melanoma, however, is unknown. Methods: The training set and external validation sets were cutaneous melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO), respectively. By using univariate Cox regression analysis and selection operator (Lasso) regression model, prognostic genes for overall survival (OS) were found. Candidate genes that were screened were used to calculate risk scores and construct a PRG risk model. The Kaplan Meier curve, time-dependent receiver operating characteristic (ROC) curve, and area under the curve (AUC)were used to assess the functional and prognostic usefulness of gene signatures in the risk model. Furthermore, to speculate on the activity of immune cell infiltration and immune-related pathways in the tumor immune microenvironment and calculate corresponding scores, a single sample gene set enrichment analysis (ssGSEA) was used.Results: An eight PRGs risk signature (AIM2, CASP4, CASP5, CASP8, IL18, NLRC4, NLRP6, PRKACA) were conducted and divided all cutaneous melanoma patients in the TCGA cohort into two groups: Low-risk and High-risk. Both the training and external validation sets showed that patients in the low-risk group showed a significantly higher likelihood of survival than those in the high-risk group (p < 0.001). Except for PRKACA, all the other eight PRGs in our study appeared to be longer survival times for patients. The results of ssGSEA in terms of 16 types of immune cells and the activity of 13 immune-related pathways showed that the High-risk group had lower immune pathway activity and lower levels of immune cell infiltration. In conclusion, the PRG-signature may be a significant predictor of prognosis and may play an essential role in UM patients' tumor immunity.

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