scholarly journals Alternative Polyadenylation Associated with Prognosis and Therapy in Colorectal Cancer

2021 ◽  
Author(s):  
Yuzhi Wang ◽  
Yunfei Xu ◽  
Yi Zhang
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Alternative Polyadenylation ◽  
Risk Groups ◽  
Chemotherapeutic Agents ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Test Set

Abstract Colorectal cancer (CRC) is among the most widely spread cancers globally. Aberrant alternative polyadenylation (APA) plays a role in cancer onset and its progression. Consequently, this study focused on highlighting the role of APA events and signals in the prognosis of patients with CRC. The APA events, RNA sequencing (RNA-seq), somatic mutations, copy number variants (CNVs), and clinical information of the CRC cohort were obtained from The Cancer Genome Atlas (TCGA) database and UCSC (University of California-Santa Cruz) Xena database. The entire set was sorted into two sets: a training set and a test set in a ratio of 1:1. 197 prognosis-related APA events were collected by performing univariate Cox regression signature in patients with CRC. Subsequently, a signature for APA events was established by least absolute shrinkage and selection operator (LASSO) and multivariate Cox analysis. The risk scores were measured for individual patients on the basis of the signature and patients were sorted into two groups; the high-risk group and the low-risk group as per their median risk scores. Kaplan-Meier curves, principal component analysis (PCA), and time-dependent receiver operator characteristic (ROC) curves revealed that the signature was able to predict patient prognosis effectively and further validation was provided in the test set and the entire set. Both the groups highlighted various distributions of mutations and CNVs. Tumor mutation burden (TMB) alone and in combination with the signature predicted the prognosis of CRC patients, but the gene frequencies of TMBs and CNVs did not change in the low- and high-risk groups. Moreover, immunotherapy and chemotherapy treatments showed different responses to PD-1 inhibitors and 26 chemotherapeutic agents in the low and high-risk groups based on the tumor immune dysfunction and exclusion (TIDE) and genomics of drugs sensitivity in cancer (GDSC) databases. This study helped in understanding APA events during the progression of CRC in great detail, and the signature for prognosis-related APA events can work as the potential predictors for survival and treatment in patients with CRC.

scholarly journals Identification of RNA-Binding Proteins with Prognostic Prediction in Colorectal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiao-fen Bai ◽  
Jing-wen Liu
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Colorectal cancer (CRC) is one of the most common malignancies of the digestive system. Recent studies have revealed the importance of RNA-binding proteins (RBPs) in tumorigenesis, but their role in CRC remains unclear. The present study systematically analyzed the relationships between RBPs and CRC using data from The Cancer Genome Atlas. We detected 483 differentially expressed RBPs and identified a series of pathways and processes using GO (Gene Ontology) analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. Analyzing protein–protein interactions and modules identified the edges and modules of RBPs. Univariate and multivariate Cox regression analyses were then used to construct a prognostic model that included 13 RBPs. Survival analyses indicated that the overall survival (OS) was significantly lower for CRC patients in the high-risk group than for those in the low-risk group, and that high risk scores were associated with poor OS. Finally, we constructed a nomogram that included 13 RBPs for calculating the estimated survival probabilities of CRC patients at 1, 2, and 3 years. Calibration plots indicated good conformity between the predicted and observed outcomes. This study has revealed that the expression of RBPs differs between CRC and normal tissues. A prognostic model based on 13 RBP coding genes has been developed that can provide independent prognoses of CRC.

scholarly journals A Novel Signature Constructed by Immune-Related LncRNA Predicts the Immune Landscape of Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengyu Sun ◽  
Tongyue Zhang ◽  
Yijun Wang ◽  
Wenjie Huang ◽  
Limin Xia
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Cox Regression Analysis ◽  
Chemotherapy Drugs

Colorectal cancer (CRC) has the characteristics of high morbidity and mortality. LncRNA not only participates in the progression of CRC through genes and transcription levels, but also regulates the tumor microenvironment and leads to the malignant phenotype of tumors. Therefore, we identified immune-related LncRNAs for the construction of clinical prognostic model. We searched The Cancer Genome Atlas (TCGA) database for original data. Then we identified differentially expressed irlncRNA (DEirlncRNA), which was paired and verified subsequently. Next, univariate analysis, Lasso and Cox regression analysis were performed on the DEirlncRNA pair. The ROC curve of the signature was drawn, and the optimal cut-off value was found. Then the cohort was divided into a high-risk and a low-risk group. Finally, we re-evaluated the signature from different perspectives. A total of 16 pairs of DEirlncRNA were included in the construction of the model. After regrouping according to the cut-off value of 1.275, the high-risk group showed adverse survival outcomes, progressive clinicopathological features, specific immune cell infiltration status, and high sensitivity to some chemotherapy drugs. In conclusion, we constructed a signature composed of immune-related LncRNA pair with no requirement of the specific expression level of genes, which shows promising clinical predictive value in CRC patients.

scholarly journals Identification of an Immune-Related Risk Signature Correlates With Immunophenotype and Predicts Anti-PD-L1 Efficacy of Urothelial Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Pengju Li ◽  
Shihui Hao ◽  
Yongkang Ye ◽  
Jinhuan Wei ◽  
Yiming Tang ◽  
...  
Keyword(s):  
High Risk ◽  
Urothelial Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Related Risk ◽  
Metastatic Urothelial Cancer

Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 (p < 0.0001), 2.56 (p < 0.0001), 3.36 (p < 0.0001), and 2.42 (p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.

scholarly journals Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

2021 ◽  
Author(s):  
Jianxin Li ◽  
Ting Han ◽  
Xin Wang ◽  
Yinchun Wang ◽  
Qingqiang Yang
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regression Analyses

Abstract Background Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to accurately assess the prognosis of patients with colorectal cancer (CRC). Methods Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA), and the immune-related mRNAs were extracted from immunomodulatory gene datasets IMMUNE RESPONSE and IMMUNE SYSTEM PROCESS based on the Molecular Signatures Database (MSigDB). Then, the immune-related lncRNAs were identified by a correlation analysis between immune-related mRNAs and lncRNAs. Subsequently, univariate, lasso and multivariate Cox regression were used to identify an immune-related lncRNA signature in training cohort, and the predict ability of the signature was further confirmed in the testing cohort and the entire TCGA cohort. Finally, the lncRNA-mRNA co-expression network was established to explore the biological role of the immune-related lncRNA signature. Results In total, 272 Immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature based on univariate, lasso and multivariate Cox regression analyses. The signature divided patients with CRC into low- and high-risk groups, and patients with CRC in high-risk group had poorer overall survival than those in low-risk group. Univariate and multivariate Cox regression analyses confirmed that the signature could be an independent prognostic factor in human CRC. Furthermore, functional enrichment analysis revealed that the immune-related lncRNA signature was significantly enriched in immune process and tumor classical pathways. Conclusions The present study revealed that the novel immune-related lncRNA signature could be exploited as underlying molecular biomarkers and therapeutic targets for the patients with CRC.

Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

2021 ◽  
Author(s):  
Jianxing Ma ◽  
Chen Wang
Keyword(s):  
Colorectal Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Nonnegative Matrix ◽  
Test Group ◽  
Progression Free Survival ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

A Novel Risk Classification Model Predicts Overall Survival and Locoregional Surgery Benefit in Colorectal Patients with Distant Metastasis at the Initial Diagnosis

2020 ◽  
Author(s):  
Mo Chen ◽  
Tian-en Li ◽  
Pei-zhun Du ◽  
Junjie Pan ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Distant Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Risk Classification ◽  
Low Risk ◽  
Classification Model ◽  
Intermediate Risk

Abstract Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
Yang Gao ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results Four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients. Graphical abstract

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

Reduced Intensity Conditioning with Thiotepa and Fludarabine for Allogeneic Transplantation: Evidence for Low Toxicity and Long-Lasting Disease Control in MDS with Low/Intermediate-1 IPSS Score and in AML from MDS in Complete Remission.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3285-3285
Author(s):  
Emilio Paolo Alessandrino ◽  
Luca Malcovati ◽  
Giorgio La Nasa ◽  
Paolo de Fabritiis ◽  
Massimo Bernardi ◽  
...  
Keyword(s):  
High Risk ◽  
Complete Remission ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Score ◽  
Conditioning Regimen ◽  
Disease Status ◽  
Risk Groups ◽  
High Risk Group ◽  
Valvular Stenosis

Abstract This study investigated Thiotepa (TT) and Fludarabine (Fluda) as a preparative regimen for allogeneic peripheral stem cell transplantation in patients with myelodysplastic syndrome (MDS) or acute leukemia from MDS (MDS-AML) older than 50 or with comorbidities contraindicating standard conditioning. Patients were prepared with TT, given over 3 hours as an i.v. infusion at a dose of 10 mg/kg over two days (day -8 and day -7) and Fluda at the dose of 125 mg/m2 i.v. over five days ( from day -7 to day -3). Fresh or cryopreserved allogeneic peripheral stem cells were infused on day 0 or +1. Graft-versus-Host Disease (GvHD) prophylaxis consisted of cyclosporine A (CyA) at the dose of 1.5 mg/kg day as a continuous iv infusion from day -5 until engraftment. The CyA was then administered orally at the dose of 3 mg/kg twice a day. Doses were adjusted to maintain plasma level concentrations between 150–350 mg/dL. From day +60, in the absence of acute GvHD, the CyA was tapered down by 20% every 2 weeks until withdrawal. In addition, patients received methotrexate 10 mg/m2 on day +1, and 8 mg/m2 on days + 3, +6 and +11 after transplantation. At the time of transplantation, patients were classified in two risk groups (low vs high risk) according to IPSS score (low/intermediate-1 vs. intermediate-2/high) for MDS patients, and disease status (CR vs. not CR) for MDS-AML. Kaplan-Meier survival analysis was carried out to compare Overall Survival (OS), Transplant-Related Mortality (TRM) and probability of relapse. Fifty patients (29 males, 21 females) entered the study; the median age was 54 years (range 38–71). Sixteen MDS patients had a low/intermediate 1 score according to the International Prognostic Score System (IPSS), 16 had an intermediate 2/high IPSS score, 18 had MDS-AML. Thirty patients underwent transplantation as front-line therapy, 20 received one or more cycles of chemotherapy before transplant. Among the latter, nine with MDS-AML were in complete remission at the time of their transplant, while four were in a partial remission. The interval from diagnosis to transplantation ranged from 1 to 52 months (median value 11 months). Contraindications to a standard conditioning regimen were liver disease, hypertrophic cardiomyopathy secondary to hypertension or valvular stenosis, cardiac arrhythmia, diabetes mellitus, hypothyroidism, previous CNS bleeding, and a history of sepsis. All but one patient achieved engraftment, with full donor chimerism by day +30. Patients were followed up for a median time of 21 months (range 0.2–87). TRM at 1 and 2 years after transplantation was 25% and 33%; the 5-year probability of relapse was 27%. Twenty-six patients are alive in complete remission, and the 5-year OS is 50%. The 5-year OS was 73% and 28% in low- and high-risk patients respectively (p=0.002). TRM at 1 and 2 years after transplantation was 13% and 21% in the low-risk group and 39% and 45% in the high-risk group (p=0.046); the 5-year probability of relapse was 10% and 50% in the low- and high-risk group respectively (p=0.015). In a multivariate Cox regression, risk group retained a borderline significance (HR=2.6, p=0.07) when adjusted by age at transplantation (p=0.03) and interval from diagnosis to transplant (n.s.). The combination of Thiotepa and Fludarabine is an effective and well-tolerated conditioning regimen in patients with MDS or MDS-AML who are poor candidates for standard myeloablative transplantation, particularly in MDS patients with low/intermediate-1 IPSS score and MDS-AML patients in CR.

scholarly journals Prognostic Modeling of Patients with Metastatic Melanoma Based on Tumor Immune Microenvironment Characteristics

2021 ◽  
Author(s):  
Jing Liu ◽  
Ting Ye ◽  
Xue fang Zhang ◽  
Yong jian Dong ◽  
Wen feng Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Metastatic Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Microenvironment ◽  
Immune Infiltration ◽  
Tumor Immune Microenvironment

Abstract Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs.Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes (DEGs) were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic (ROC) curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT, Xcell and ssGSEA in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 were significantly different in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes (ALOX5AP, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) from the nine-IRG prognostic model, of which the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, we analyzed the prognostic ability and expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 in metastatic melanoma. Overall, a prognostic model for metastatic melanoma based on the characteristics of the tumor immune microenvironment was established, which was helpful for further studies.It could function well in helping people to understand the characteristics of the immune microenvironment in metastatic melanoma and to find possible therapeutic targets.

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