Immune Score Indicator for the Survival of Melanoma Patients Based on Tumor Microenvironment

Ning, Xuchao; Li, Renzhi; Zhang, Bin; Wang, Yue; Zhou, Ziyi; Ji, Zanzan; Lyu, Xiajie; Chen, Zhenyu

doi:10.2147/ijgm.s336105

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Compared with the low FerrGR group, the high FerrGR group had a higher immune score and higher tumor purity. Generally, immune scores increased significantly with the malignant progression of SKCM ( Ning et al, 2021 ), while tumor purity decreased at higher grades in our analysis. Immune cells constitute a comfortable environment for tumor growth, suggesting that the poor prognosis of patients in the high FerrGR group is due to the tumor immunosuppressive environment (TIME).…”

Section: Discussionmentioning

confidence: 52%

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Huang

et al. 2023

Front. Genet.

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Background: Ferroptosis is an iron-dependent cell death mode and closely linked to various cancers, including skin cutaneous melanoma (SKCM). Although attempts have been made to construct ferroptosis-related gene (FRG) signatures for predicting the prognosis of SKCM, the prognostic impact of ferroptosis-related genetic mutations in SKCM remains lacking. This study aims to develop a prediction model to explain the relationship between ferroptosis-related genetic mutations and clinical outcomes of SKCM patients and to explore the potential value of ferroptosis in SKCM treatment.Methods: FRGs which significantly correlated with the prognosis of SKCM were firstly screened based on their single-nucleotide variant (SNV) status by univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) and Cox regressions were performed to construct a new ferroptosis-related genetic mutation risk (FerrGR) model for predicting the prognosis of SKCM. We then illustrate the survival and receiver operating characteristic (ROC) curves to evaluate the predictive power of the FerrGR model. Moreover, independent prognostic factors, genomic and clinical characteristics, immunotherapy, immune infiltration, and sensitive drugs were compared between high—and low—FerrGR groups.Results: The FerrGR model was developed with a good performance on survival and ROC analysis. It was a robust independent prognostic indicator and followed a nomogram constructed to predict prognostic outcomes for SKCM patients. Besides, FerrGR combined with tumor mutational burden (TMB) or MSI (microsatellite instability) was considered as a combined biomarker for immunotherapy response. The high FerrGR group patients were associated with an inhibitory immune microenvironment. Furthermore, potential drugs target to high FerrGR samples were predicted.Conclusion: The FerrGR model is valuable to predict prognosis and immunotherapy in SKCM patients. It offers a novel therapeutic option for SKCM.

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Section: Discussionmentioning

confidence: 52%

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Huang

et al. 2023

Front. Genet.

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“…The goal of this algorithm was to retrieve stromal, immune and estimate scores. In particular, both stromal and immune scores were positively correlated to infiltration of stromal and immune cells, while the estimate score (the sum of the stromal and immune scores) was a negative indicator of purity of tumor cells (32). Compared with the low-risk group, the high-risk group with lower immune/stromal/estimate score exhibited higher tumor purities (Figure 7B).…”

Section: Analysis Of Immunological Status and Tme Characterizationmentioning

confidence: 94%

A pyroptosis-related gene signature for prognostic and immunological evaluation in breast cancer

et al. 2022

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PurposePyroptosis exerts an undesirable impact on the clinical outcome of breast cancer. Since any single gene is insufficient to be an appropriate marker for pyroptosis, our aim is to develop a pyroptosis-related gene (PRG) signature to predict the survival status and immunological landscape for breast cancer patients.MethodsThe information of breast cancer patients was retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the gene expressions of this signature in breast cancer. Its prognostic value was evaluated by univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, receiver operating characteristics (ROCs), univariate/multivariate analysis, and nomogram. Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to explore its potential biological function in breast cancer. The potential correlation between this signature and tumor immunity was revealed based on single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithms.ResultsA PRG signature containing GSDMC, GZMB, IL18, and TP63 was created in a TCGA training cohort and validated in two validation GEO cohorts GSE58812 and GSE37751. Compared with a human mammary epithelial cell line MCF-10A, the expression levels of GSDMC, GZMB and IL18 were upregulated, while TP63 was found with lower expression level in breast cancer cells SK-BR-3, BT-549, MCF-7, and MDA-MB-231 using RT-qPCR assay. Based on univariate and multivariate Cox models, ROC curve, nomogram as well as calibration curve, it was revealed that this signature with high-risk score could independently predict poor clinical outcomes in breast cancer. Enrichment analyses demonstrated that the involved mechanism was tightly linked to immune-related processes. SsGSEA, ESTIMATE and CIBERSORT algorithms further pointed out that the established model might exert an impact on immune cell abundance, immune cell types and immune-checkpoint markers. Furthermore, individuals with breast cancer responded differently to these therapeutic agents based on this signature.ConclusionsOur data suggested that this PRG signature with high risk was tightly associated with impaired immune function, possibly resulting in an unfavorable outcome for breast cancer patients.

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“…Additionally, FGD3, FGD4 also been found to modulate actin cytoskeletons and exchange CdC42-Specific factors (14,15). According to FGD2, studies have shown that it affects the prognosis of melanoma patients and high expression of it in HNSCC may improve patients' prognosis (11,16). Besides, FGD2 has the ability to positively regulate immune cell infiltration in LUAD (17).…”

Section: Introductionmentioning

confidence: 99%

Comprehensively analysis of the FGD family with potential effects on prognosis and immune infiltration in lung adenocarcinoma

Zhang

Guan

et al. 2023

Preprint

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Background：The FGD family consists of six genes, namely FGD1/2/3/4/5/6. Their roles in lung adenocarcinoma have been unidentified. This research focused on determining the diagnostic efficacy, prognostic value, and immune-related functions of them in lung adenocarcinoma(LUAD). Methods：From the TCGA database, mRNA data for the FGD gene family and clinical data for the patients were obtained. Immunohistochemistry was performed to validate representative FGD gene’s expression. A relationship between the FGD genes and immune system molecules was examined using the TIMER and GEPIA databases, the ssGSEA and the MCPcounter methods. Clinical prognosis in LUAD were analyzed by searching for TCGA, KMplotter and GEPIA databases. The TIDE algorithm, TCIA, KMplotter, ROCplotter and ICBatlas databases were used to analyze the value of FGD2 in predicting the efficacy of immunotherapy. TIGER database was used to analyze single-cell RNA-sequencing data. The immune-related prognostic model was constructed using 3 machine learning algorithms: K-means clustering, LASSO regression, and WGCNA analysis. Results: All the six FGD genes’ protein and mRNA were aberrant expressed in the tissues of LUAD in contrast to healthy ones, and our external experiment confirmed FGD2’s expression pattern. Low expression of FGD2, 3, 5 resulted in a shorter OS time and were determined as independent prognostic factors via multivariate analyses. FGD2, 3, 5 were markedly linked to immune infiltration while FGD1, 4, 6 were not. Sc-RNA sequencing analysis indicating that FGD2, 3, 5 were mainly expressed in immunocytes. NSCLC patients with higher FGD2 may more responsive to ICB therapy. The functions of FGD2, 3, 5 and FGD1, 4, 6 in LUAD are heterogeneous, and patients can be separated in to two groups based on these six FGDs’ expression. A prognostic model constructed by immune-related DEGs between these two groups had good predictive value in one training set and 4 testing sets. Conclusions: FGD2, FGD3 and FGD5 can be used as diagnostic, prognostic, and immune-implicated biomarkesr for patients with LUAD and FGD2 may help to predict the ICB therapy efficacy. The immune-related prognostic model had satisfactory predictive value.

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Immune Score Indicator for the Survival of Melanoma Patients Based on Tumor Microenvironment

Cited by 3 publications

References 19 publications

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

A pyroptosis-related gene signature for prognostic and immunological evaluation in breast cancer

Comprehensively analysis of the FGD family with potential effects on prognosis and immune infiltration in lung adenocarcinoma

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