Development and Validation of the B Cell-Associated Fc Receptor-like Molecule-Based Prognostic Signature in Skin Cutaneous Melanoma

The potential of FCRL genes as targets for cancer treatment: insights from bioinformatics and immunology

Cancer is a prevalent and dangerous disease that requires a multifaceted approach to treatment. The FCRL family gene has been linked to immune function and tumor progression. Bioinformatics may help unravel their role in cancer treatment. We conducted a comprehensive analysis of the FCRL family genes in pan-cancer using publicly available databases and online tools. Specifically, we examined gene expression, prognostic significance, mutation profiles, drug resistance, as well as biological and immunomodulatory roles. Our data were sourced from The Cancer Genome Atlas, Genotype-Tissue Expression, cBioPortal, STRING, GSCALite, Cytoscape, and R software. The expression of FCRL genes varies significantly across different tumor types and normal tissues. While high expression of most FCRL genes is associated with a protective effect in many cancers, FCRLB appears to be a risk factor in several types of cancer. Alterations in FCRL family genes, particularly through amplification and mutation, are common in cancers. These genes are closely linked to classical cancer pathways such as apoptosis, epithelial-mesenchymal transition (EMT), estrogen receptor (ER) signaling, and DNA damage response. Enrichment analysis indicates that FCRL family genes are predominantly associated with immune cell activation and differentiation. Immunological assays demonstrate a strong positive correlation between FCRL family genes and tumor-infiltrating lymphocytes (TILs), immunostimulators, and immunoinhibitors. Furthermore, FCRL family genes can enhance the sensitivity of various anticancer drugs. The FCRL family genes are vital in cancer pathogenesis and progression. Targeting these genes in conjunction with immunotherapy could enhance cancer treatment efficacy. Further research is required to determine their potential as therapeutic targets.

Construction of immunotherapy-related prognostic gene signature and small molecule drug prediction for cutaneous melanoma

Background

Cutaneous melanoma (CM), a kind of skin cancer with a high rate of advanced mortality, exhibits a wide variety of driver and transmitter gene alterations in the immunological tumor microenvironment (TME) associated with tumor cell survival and proliferation.

Methods

We analyzed the immunological infiltration of TME cells in normal and malignant tissues using 469 CM and 556 normal skin samples. We used a single sample gene set enrichment assay (ssGSEA) to quantify the relative abundance of 28 cells, then used the LASSO COX regression model to develop a riskScore prognostic model, followed by a small molecule drug screening and molecular docking validation, which was then validated using qRT-PCR and IHC.

Results

We developed a prognosis model around seven essential protective genes for the first time, dramatically elevated in tumor tissues, as did immune cell infiltration. Multivariate Cox regression results indicated that riskScore is an independent and robust prognostic indicator, and its predictive value in immunotherapy was verified. Additionally, we identified Gabapentin as a possible small molecule therapeutic for CM.

Conclusions

A riskScore model was developed in this work to analyze patient prognosis, TME cell infiltration features, and treatment responsiveness. The development of this model not only aids in predicting patient response to immunotherapy but also has significant implications for the development of novel immunotherapeutic agents and the promotion of tailored treatment regimens.

Identification and validation of an epithelial mesenchymal transition-related gene pairs signature for prediction of overall survival in patients with skin cutaneous melanoma

BACKGROUND

We aimed to construct a novel epithelial-mesenchymal transition (EMT)-related gene pairs (ERGPs) signature to predict overall survival (OS) in skin cutaneous melanoma (CM) patients.

METHODS

Expression data of the relevant genes, corresponding clinicopathological parameters, and follow-up data were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis was utilized to identify ERGPs significantly associated with OS, and LASSO analysis was used to identify the genes used for the construction of the ERGPs signature. The optimal cutoff value determined by the receiver operating characteristic curve was used to classify patients into high-risk and low-risk groups. Survival curves were generated using the Kaplan-Meier method, and differences between the two groups were estimated using the log-rank test. The independent external datasets GSE65904 and GSE19234 were used to verify the performance of the ERGPs signature using the area under the curve (AUC) values. In addition, we also integrated clinicopathological parameters and risk scores to develop a nomogram that can individually predict the prognosis of patients with CM.

RESULTS

A total of 104 ERGPs related to OS were obtained, of which 21 ERGPs were selected for the construction of the signature. All CM patients were stratified into high-and low-risk groups based on an optimal risk score cutoff value of 0.281. According to the Kaplan-Meier analysis, the mortality rate in the low-risk group was lower than that in the high-risk group in the TCGA cohort (P < 0.001), GSE65904 cohort (P = 0.006), and GSE19234 cohort (P = 0.002). Multivariate Cox regression analysis indicated that our ERGP signature was an independent risk factor for OS in CM patients in the three cohorts (for TCGA: HR, 2.560; 95% CI [1.907-3.436]; P < 0.001; for GSE65904: HR = 2.235, 95% CI [1.492-3.347], P < 0.001; for GSE19234: HR = 2.458, 95% CI [1.065-5.669], P = 0.035). The AUC value for predicting the 5-year survival rate of patients with CM of our developed model was higher than that of two previously established prognostic signatures. Both the calibration curve and the C-index (0.752, 95% CI [0.678-0.826]) indicated that the developed nomogram was highly accurate. Most importantly, the decision curve analysis results showed that the nomogram had a higher net benefit than that of the American Joint Committee on Cancer stage system.

CONCLUSION

Our study established an ERGPs signature that could be potentially used in a clinical setting as a genetic biomarker for risk stratification of CM patients. In addition, the ERGPs signature could also predict which CM patients will benefit from PD-1 and PD-L1 inhibitors.

B cell heterogeneity, plasticity, and functional diversity in cancer microenvironments

B cells constitute a major component of tumor-infiltrating leukocytes. However, the influence of these cells on malignancy is currently under debate, reflecting the heterogeneity of B cell subsets in tumors. With recent advances, it becomes apparent that this debate includes not only the evaluation of B cells themselves, but also the underlying immune microenvironment network, which scripts the highly heterogeneous B cell populations in tumors and directs the roles of those sub-populations in disease progression and clinical treatment. In this review, we summarize recent findings on the heterogeneous subset composition of B cells in both human and mouse tumor models and their different impacts on disease progression. We further describe the multidimensional interplays between B cells and other immune cells in the tumor microenvironment, which account for the regulation of B cell differentiation and function in situ. We also assess the potential influences of distinct sub-tumor locations on B cell function in primary tumors during development and those under immunotherapy treatment. Illuminating the heterogeneous nature of B cell subset composition, generation, localization, and related immune network in tumor is of immense significance for comprehensively understanding B cell response in tumor and designing more efficacious cancer immunotherapies.