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

Targeting tumor-associated macrophages: Novel insights into immunotherapy of skin cancer

BACKGROUND

The incidence of skin cancer is currently increasing, and conventional treatment options inadequately address the demands of disease management. Fortunately, the recent rapid advancement of immunotherapy, particularly immune checkpoint inhibitors (ICIs), has ushered in a new era for numerous cancer patients. However, the efficacy of immunotherapy remains suboptimal due to the impact of the tumor microenvironment (TME). Tumor-associated macrophages (TAMs), a major component of the TME, play crucial roles in tumor invasion, metastasis, angiogenesis, and immune evasion, significantly impacting tumor development. Consequently, TAMs have gained considerable attention in recent years, and their roles have been extensively studied in various tumors. However, the specific roles of TAMs and their regulatory mechanisms in skin cancer remain unclear.

AIM OF REVIEW

This paper aims to elucidate the origin and classification of TAMs, investigate the interactions between TAMs and various immune cells, comprehensively understand the precise mechanisms by which TAMs contribute to the pathogenesis of different types of skin cancer, and finally discuss current strategies for targeting TAMs in the treatment of skin cancer.

KEY SCIENTIFIC CONCEPTS OF OVERVIEW

With a specific emphasis on the interrelationship between TAMs and skin cancer, this paper posits that therapeutic modalities centered on TAMs hold promise in augmenting and harmonizing with prevailing clinical interventions for skin cancer, thereby charting a novel trajectory for advancing the landscape of immunotherapeutic approaches for skin cancer.

A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma

BRAF mutation plays an important role in the pathogenesis and progression of melanoma and is correlated to the prognosis of melanoma patients. However, fewer studies have attempted to develop a BRAF mutation-associated gene risk model for predicting the prognosis of melanoma. The current research explores BRAF mutation-related biological features in melanoma and establishes a prognostic signature. First, we identified three significantly enriched KEGG pathways (glycosphingolipid biosynthesis - ganglio series, ether lipid metabolism, and glycosaminoglycan biosynthesis - keratan sulfate) and corresponding genes in the BRAF mutant group by gene set enrichment analysis. We then developed a prognostic signature based on 7 BRAF-associated genes (PLA2G2D, FUT8, PLA2G4E, PLA2G5, PLA2G1B, B3GNT2, and ST3GAL5) and assessed its prediction accuracy using ROC curve analysis. Finally, the nomogram was established according to the prognostic signature and independent clinical characteristics to predict the survival of melanoma patients. Furthermore, we found higher proportions of naive B cells, plasma cells, CD8 T cells, CD4 memory-activated T cells, and regulatory T cells in the low-risk group. Whereas lower proportions of M0, M1, and M2 macrophages and resting NK cells were observed in the high-risk group. The analysis also showed a significantly higher expression of immune checkpoint molecules (PD-1, PD-L1, CTLA4, BTLA, CD28, CD80, CD86, HAVCR2, ICOS, LAG3, and TIGIT) in the low-risk group. Our results provide novel insights into the effect of BRAF mutation on melanoma growth and indicate a promising direction toward immunotherapy and precision medicine in melanoma patients.

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

Background

Melanoma 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.

Methods

An 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.

Results

The 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.

Conclusion

The 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.

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

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.

Impact of hepatocyte-specific deletion of staphylococcal nuclease and tudor domain containing 1 (SND1) on liver insulin resistance and acute liver failure of mice

Although our previous research shows an ameliorated high-fat diet (HFD)-induced hepatic steatosis and insulin resistance in global SND1 transgenic mice, the involvement of SND1 loss-of-function in hepatic metabolism remains elusive. Herein, we aim to explore the potential impact of hepatocyte-specific SND1 deletion on insulin-resistant mice. As SND1 is reported to be linked to inflammatory response, the pathobiological feature of acute liver failure (ALF) is also investigated. Hence, we construct the conditional liver knockout (LKO) mice of SND1 for the first time. Under the condition of HFD, the absence of hepatic SND1 affects the weight of white adipose tissue, but not the gross morphology, body weight, cholesterol level, liver weight, and hepatic steatosis of mice. Furthermore, we fail to observe significant differences in either HFD-induced insulin resistance or lipopolysaccharide/D-galactosamine-induced (LPS/D-GaIN) ALF between LKO and wild type (WT) mice in terms of inflammation and tissue damage. Compared with negative controls, there is no differential SND1 expression in various species of sample with insulin resistance or ALF, based on several gene expression omnibus datasets, including GSE23343, GSE160646, GSE120243, GSE48794, GSE13271, GSE151268, GSE62026, GSE120652, and GSE38941. Enrichment result of SND1-binding partners or related genes indicates a sequence of issues related to RNA or lipid metabolism, but not glucose homeostasis or hepatic failure. Overall, hepatic SND1 is insufficient to alter the phenotypes of hepatic insulin resistance and acute liver failure in mice. The SND1 in various organs is likely to cooperate in regulating glucose homeostasis by affecting the expression of lipid metabolism-related RNA transcripts during stress.

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

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.