Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature

Yu, Yang; Long, Xuan; Li, Guiyun; Yu, Xin; Liu, Yu; Li, Kun; Tian, Xiaobin

doi:10.1080/21655979.2021.1924556

Cited by 6 publications

(7 citation statements)

References 40 publications

(40 reference statements)

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“…In our study, first, we focused on the influence of TIICs on the prognosis of melanoma patients. Although there have been previous studies exploring the predictive value of immune genes, few prognosis models focusing on the components and content of tumor-infiltrating cells have been established ( 39 – 42 ). Then, we established a cell pair score matrix generated by comparing the abundance of immune cells in pairs, and the IRRS was constructed on the basis of this matrix.…”

Section: Discussionmentioning

confidence: 99%

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma

Long²,

Bu³

et al. 2023

Front. Immunol.

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BackgroundMelanoma is among the most malignant immunologic tumor types and is associated with high mortality. However, a considerable number of melanoma patients cannot benefit from immunotherapy owing to individual differences. This study attempts to build a novel prediction model of melanoma that fully considers individual differences in the tumor microenvironment.MethodsAn immune-related risk score (IRRS) was constructed based on cutaneous melanoma data from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to calculate immune enrichment scores of 28 immune cell signatures. We performed pairwise comparisons to obtain scores for cell pairs based on the difference in the abundance of immune cells within each sample. The resulting cell pair scores, in the form of a matrix of relative values of immune cells, formed the core of the IRRS.ResultsThe area under the curve (AUC) for the IRRS was over 0.700, and when the IRRS was combined with clinical information, the AUC reached 0.785, 0.817, and 0.801 for the 1-, 3-, and 5-year survival, respectively. Differentially expressed genes between the two groups were enriched in staphylococcal infection and estrogen metabolism pathway. The low IRRS group showed a better immunotherapeutic response and exhibited more neoantigens, richer T-cell receptor and B-cell receptor diversity, and higher tumor mutation burden.ConclusionThe IRRS enables a good prediction of prognosis and immunotherapy effect, based on the difference in the relative abundance of different types of infiltrating immune cells, and could provide support for further research in melanoma.

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Section: Discussionmentioning

confidence: 99%

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma

Long²,

Bu³

et al. 2023

Front. Immunol.

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Section: Discussionmentioning

confidence: 99%

“…Currently, several studies have reported immune-related molecular markers as prognostic markers for melanoma [29][30][31], but these prognostic markers had limitations. First, molecular markers in previous studies contained various genes, which increased the workload and cost in clinical practice and constrained the clinical utility of these molecular markers to some extent [29,32,33]. Second, these studies have not further investigated the underlying mechanisms or clinical signifcance of prognostic markers [28,34], and therefore, the clinical reliability of these prognostic markers was unclarifed.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma

Tang

Wang

et al. 2022

Journal of Oncology

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Background. Melanoma development and progression are significantly influenced by ferroptosis and the immune microenvironment. However, there are no reliable biomarkers for melanoma prognosis prediction based on ferroptosis and immunological response. Methods. Ferroptosis-related genes (FRGs) were retrieved from the FerrDb website. Immune-related genes (IRGs) were collected in the ImmPort dataset. The TCGA (The Cancer Genome Atlas) and GSE65904 datasets both contained prognostic FRGs and IRGs. The model was created using multivariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and the analysis and comparison between the expression patterns of ferroptosis and immune cell infiltration were done. Last but not least, research was conducted to assess the expression and involvement of the genes in the comprehensive index of ferroptosis and immune (CIFI). Results. Two prognostic ferroptosis- and immune-related markers (PDGFRB and FOXM1) were utilized to develop a CIFI. In various datasets and patient subgroups, CIFI exhibits consistent predictive performance. The fact that CIFI is an independent prognostic factor for melanoma patients was revealed. Patients in the CIFI-high group further exhibited immune-suppressive characteristics and had elevated ferroptosis gene expression levels. The results of in vitro research point to the possibility that the PDGFRB and FOXM1 genes function as oncogenes in melanoma. Conclusion. In this study, a novel prognostic classifier for melanoma patients was developed and validated using ferroptosis and immune expression profiles.

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“…The methods used for other cancers, such as cutaneous melanoma, are similar to our methods. However, they are based on too many gene pairs, which will be relatively cumbersome in clinical applications [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

Tao

Zhang

et al. 2021

Bioengineered

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Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Currently, we lack effective risk models for the prognosis of ccRCC patients. Given the significant role of cancer immunity in ccRCC, we aimed to establish a novel united risk model including clinical stage and immune-related gene pairs (IRGPs) to assess the prognosis. The gene expression profile and clinical data of ccRCC patients from The Cancer Genome Atlas and Arrayexpress were divided into training cohort (n = 381), validation cohort 1 (n = 156), and validation cohort 2 (n = 101). Through univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator analysis, 11 IRGPs were obtained. After further analysis, it was found that clinical stage could be an independent prognostic factor; hence, we used it to construct a united prognostic model with 11 IRGPs. Based on this model, patients were divided into high-risk and low-risk groups. In Kaplan-Meier analysis, a significant difference was observed in overall survival (OS) among all three cohorts (p < 0.001). The calibration curve revealed that the signature model is in high accordance with the observed values of each data cohort. The 1-year, 3-year, and 5-year receiver operating characteristic curves of each data cohort showed better performance than only IRGP signatures. The results of immune infiltration analysis revealed significantly (p < 0.05) higher abundance of macrophages M0, T follicular helper cells, and other tumor infiltrating cells. In summary, we successfully established a united prognostic risk model, which can effectively assess the OS of ccRCC patients.

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Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature

Cited by 6 publications

References 40 publications

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma

Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma

A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

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