Identification of FPR3 as a Unique Biomarker for Targeted Therapy in the Immune Microenvironment of Breast Cancer

Differential requirement of formyl peptide receptor 1 in macrophages and neutrophils in the host defense against Mycobacterium tuberculosis Infection

Formyl peptide receptors (FPR), part of the G-protein coupled receptor superfamily, are pivotal in directing phagocyte migration towards chemotactic signals from bacteria and host tissues. Although their roles in acute bacterial infections are well-documented, their involvement in immunity against tuberculosis (TB) remains unexplored. Here, we investigate the functions of Fpr1 and Fpr2 in defense against Mycobacterium tuberculosis (Mtb), the causative agent of TB. Elevated levels of Fpr1 and Fpr2 were found in the lungs of mice, rabbits and peripheral blood of humans infected with Mtb, suggesting a crucial role in the immune response. The effects of Fpr1 and Fpr2 deletion on bacterial load, lung damage, and cellular inflammation were assessed in a murine TB model utilizing hypervirulent strain of Mtb from the W-Beijing lineage. While Fpr2 deletion had no impact on disease outcome, Fpr1-deficient mice demonstrated improved bacterial control, especially by macrophages. Bone marrow-derived macrophages from these Fpr1-/- mice exhibited an enhanced ability to contain bacterial growth over time. Contrarily, treating genetically susceptible mice with Fpr1-specific inhibitors caused impaired early bacterial control, corresponding with increased Mtb persistence in necrotic neutrophils. Furthermore, ex vivo assays revealed that Fpr1-/- neutrophils were unable to restrain Mtb growth, indicating a differential function of Fpr1 among myeloid cells. These findings highlight the distinct and complex roles of Fpr1 in myeloid cell-mediated immunity against Mtb infection, underscoring the need for further research into these mechanisms for a better understanding of TB immunity.

Pan-cancer analysis and experimental validation of FPR3 as a prognostic and immune infiltration-related biomarker for glioma

Formyl peptide receptor 3 (FPR3) is known to have implications in the progression of various cancer types. Despite this, its biological significance within pan-cancer datasets has yet to be investigated. In this investigation, we scrutinized FPR3's expression profiles, genetic alterations, prognostic significance, immune-related characteristics, methylation status, tumor mutation burden (TMB), and microsatellite instability (MSI) across different types of cancer. We utilized TISCH's single-cell data to identify immune cells closely associated with FPR3. The predictive significance of FPR3 was evaluated independently in gliomas using data from TCGA and CGGA datasets, leading to the development of a prognostic nomogram. Immunohistochemistry and Western blot analysis confirmed FPR3 expression in gliomas. Lastly, the CCK-8 and wound-healing assays were employed to assess the impact of FPR3 on the proliferation and metastasis of GBM cell lines. In numerous cancer types, heightened FPR3 expression correlated with adverse outcomes, immune cell infiltration, immune checkpoints, TMB, and MSI. In glioma, FPR3 emerged as a notable risk factor, with the prognostic model effectively forecasting patient results. The potential biological relevance of FPR3 was confirmed in glioma, and it was shown to have significant involvement in the processes of glioma growth, immune infiltration, and metastasis. Our results imply a potential association of FPR3 with tumor immunity, indicating its viability as a prognostic indicator in glioma.

Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature

Tumor-associated macrophages (TAMs) play a vital role in glioma progression and are associated with poor outcomes in glioma patients. However, the specific roles of different subpopulations of TAMs remain poorly understood. Two distinct cell types, glioma and myeloid cells, were identified through single-cell sequencing analysis in gliomas. Within the TAMs-associated weighted gene co-expression network analysis (WGCNA) module, FPR3 emerged as a hub gene and was found to be expressed on CD163+ macrophages, while also being associated with clinical outcomes. Subsequently, a comprehensive assessment was undertaken to investigate the correlation between FPR3 expression and immune characteristics, revealing that FPR3 potentially plays a role in reshaping the glioma microenvironment. We identified a macrophage subset with the nonzero expression of CD163 and FPR3 (CD163+FPR3+). Using the expression profiles of CD163+FPR3+ macrophage-related signature, we employed ten machine learning algorithms to construct a prognostic model across six glioma cohorts. Subsequently, we employed an optimal algorithm to generate an artificial intelligence-driven prognostic signature specifically for CD163+FPR3+ macrophages. The development of this model was based on the average C-index observed in the aforementioned six cohorts. The risk score of this model consistently and effectively predicted overall survival, surpassing the accuracy of conventional clinical factors and 100 previously published signatures. Consequently, the CD163+FPR3+ macrophage-related score shows potential as a prognostic biomarker for glioma patients.

Differential requirement of Formyl Peptide Receptor 1 in macrophages and neutrophils in the host defense against Mycobacterium tuberculosis Infection

Formyl peptide receptors (FPR), part of the G-protein coupled receptor superfamily, are pivotal in directing phagocyte migration towards chemotactic signals from bacteria and host tissues. Although their roles in acute bacterial infections are well-documented, their involvement in immunity against tuberculosis (TB) remains unexplored. This study investigates the functions of Fpr1 and Fpr2 in defense against Mycobacterium tuberculosis (Mtb), the causative agent of TB. Elevated levels of Fpr1 and Fpr2 were found in the lungs of mice, rabbits and peripheral blood of humans infected with Mtb, suggesting a crucial role in the immune response. The effects of Fpr1 and Fpr2 deletion on bacterial load, lung damage, and cellular inflammation were assessed using a TB model of hypervirulent strain of Mtb from the W-Beijing lineage. While Fpr2 deletion showed no impact on disease outcome, Fpr1-deficient mice demonstrated improved bacterial control, especially by macrophages. Bone marrow-derived macrophages from these Fpr1 -/- mice exhibited an enhanced ability to contain bacterial growth over time. Contrarily, treating genetically susceptible mice with Fpr1-specific inhibitors caused impaired early bacterial control, corresponding with increased bacterial persistence in necrotic neutrophils. Furthermore, ex vivo assays revealed that Fpr1 -/- neutrophils were unable to restrain Mtb growth, indicating a differential function of Fpr1 among myeloid cells. These findings highlight the distinct and complex roles of Fpr1 in myeloid cell-mediated immunity against Mtb infection, underscoring the need for further research into these mechanisms for a better understanding of TB immunity.

Identification of crucial genes through WGCNA in the progression of gastric cancer

Background: To explore the hub gene closely related to the progression of gastric cancer (GC), so as to provide a theoretical basis for revealing the therapeutic mechanism of GC. Methods: The gene expression profile and clinical data of GSE15459 in Gene Expression Omnibus (GEO) database were downloaded. The weighted gene co-expression network analysis (WGCNA) was used to screen the key modules related to GC progression. Survival analysis was used to assess the influence of hub genes on patients' outcomes. CIBERSORT analysis was used to predict the tissue infiltrating immune cells in patients. Immunohistochemical staining was conducted to further verify the expression of hub genes. Results: Through WGCNA, a total of 26 co-expression modules were constructed, in which salmon module and royalblue module had strong correlation with GC progression. The results of enrichment analysis showed that genes in the two modules were mainly involved in toll-like receptor signaling pathway, cholesterol metabolism and neuroactive ligand-receptor interaction. Six hub genes (C1QA, C1QB, C1QC, FCER1G, FPR3 and TYROBP) related to GC progression were screened. Survival analysis showed overall survival in the high expression group was significantly lower than that in the low expression group. CIBERSORT analysis revealed that immune characteristics difference between patients in early stage and advanced stage. Immunohistochemical results confirmed that C1QB, FCER1G, FPR3 and TYROBP were significantly associated with disease progression in GC. Conclusion: Our study identified that C1QB, FCER1G, FPR3 and TYROBP played important roles in the progression of GC, and their specific mechanisms are worth further study.

An innovative predictive model for cervical cancer constructed around a gene profile associated with cholesterol metabolism

Cholesterol metabolism is crucial for cell survival and cancer progression. The prognostic patterns of genes linked to cholesterol metabolism (CMAGs) in CESC, however, have received very little attention in research. From public databases, TCGA-CESC cohorts with mRNA expression patterns and the accompanying clinical information of patients were gathered. Consensus clustering was used to find the molecular subtype connected to cholesterol metabolism. In the TCGA-CESC cohort, a predictive risk model with 28 CMAGs was created using Lasso-Cox regression. The function enrichment analysis between groups with high-and low-risk were investigated by employing GO, KEGG, and GSVA software. The immune cell infiltration was analyzed using ESTIMATE, CIBERSORT, and MCPCOUNTER methods. Finally, we select 7 genes in risk model for further multivariate Cox analysis, and ultimately a hub gene, CHIT1, was identified. Meanwhile, the function of CHIT1 was preliminarily verified in cell and mice tumor model. In conclusion, the abundance of the CHIT1 gene might be beneficial for forecasting the prognosis of CESC, demonstrating that cholesterol metabolism could be a promising treatment target for CESC.

From odor to oncology: non-canonical odorant receptors in cancer

Odorant receptors, traditionally associated with olfaction as chemoreceptors, have been increasingly recognized for their presence and diverse functions in various non-nasal tissues throughout the body. Beyond their roles in sensory perception, emerging evidence suggests a compelling interplay between odorant receptors and cancer progression as well. Alongside the canonical GPCR odorant receptors, dysregulation of non-canonical odorant receptors such as trace amine-associated receptors (TAARs), formyl peptide receptors (FPRs), and membrane-spanning 4A family (MS4As) has been observed in various cancer types, suggesting their contributions to cancer progression. The roles of these non-canonical chemoreceptors in cancer are complex, with some receptors promoting tumorigenesis and others acting as tumor-suppressing factors upon activation, depending on the cancer type. These findings shed light on the potential of non-canonical odorant receptors as therapeutic targets and prognostic markers in cancer, inviting further exploration to unravel their precise mechanisms of action and implications in cancer biology. In this review, we provide a comprehensive overview of the intricate relationships between these chemoreceptors and various types of cancer, potentially paving the way for innovative odor-based therapeutics. Ultimately, this review discusses the potential development of novel therapeutic strategies targeting these non-canonical chemoreceptors.

Deciphering the immune landscape of head and neck squamous cell carcinoma: A single-cell transcriptomic analysis of regulatory T cell responses to PD-1 blockade therapy

Immunotherapy is changing the Head and Neck Squamous Cell Carcinoma (HNSCC) landscape and improving outcomes for patients with recurrent or metastatic HNSCC. A deeper understanding of the tumor microenvironment (TME) is required in light of the limitations of patients' responses to immunotherapy. Here, we aimed to examine how Nivolumab affects infiltrating Tregs in the HNSCC TME. We used single-cell RNA sequencing data from eight tissues isolated from four HNSCC donors before and after Nivolumab treatment. Interestingly, the study found that Treg counts and suppressive activity increased following Nivolumab therapy. We also discovered that changes in the CD44-SSP1 axis, NKG2C/D-HLA-E axis, and KRAS signaling may have contributed to the increase in Treg numbers. Furthermore, our study suggests that decreasing the activity of the KRAS and Notch signaling pathways, and increasing FOXP3, CTLA-4, LAG-3, and GZMA expression, may be mechanisms that enhance the killing and suppressive capacity of Tregs. Additionally, the result of pseudo-temporal analysis of the HNSCC TME indicated that after Nivolumab therapy, the expression of certain inhibitory immune checkpoints including TIGIT, ENTPD1, and CD276 and LY9, were decreased in Tregs, while LAG-3 showed an increased expression level. The study also found that Tregs had a dense communication network with cluster two, and that certain ligand-receptor pairs, including SPP1/CD44, HLA-E/KLRC2, HLA-E/KLRK1, ANXA1/FPR3, and CXCL9/FCGR2A, had notable changes after the therapy. These changes in gene expression and cell interactions may have implications for the role of Tregs in the TME and in response to Nivolumab therapy.

Immune cell dynamics deconvoluted by single-cell RNA sequencing in normothermic machine perfusion of the liver

Normothermic machine perfusion (NMP) has emerged as an innovative organ preservation technique. Developing an understanding for the donor organ immune cell composition and its dynamic changes during NMP is essential. We aimed for a comprehensive characterization of immune cell (sub)populations, cell trafficking and cytokine release during liver NMP. Single-cell transcriptome profiling of human donor livers prior to, during NMP and after transplantation shows an abundance of CXC chemokine receptor 1⁺/2⁺ (CXCR1⁺/CXCR2⁺) neutrophils, which significantly decreased during NMP. This is paralleled by a large efflux of passenger leukocytes with neutrophil predominance in the perfusate. During NMP, neutrophils shift from a pro-inflammatory state towards an aged/chronically activated/exhausted phenotype, while anti-inflammatory/tolerogenic monocytes/macrophages are increased. We herein describe the dynamics of the immune cell repertoire, phenotypic immune cell shifts and a dominance of neutrophils during liver NMP, which potentially contribute to the inflammatory response. Our findings may serve as resource to initiate future immune-interventional studies.

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Background

Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment.

Methods

Gene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC).

Results

Consensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses.

Conclusions

The molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03393-9.

Bioinformatics analysis of genes related to iron death in diabetic nephropathy through network and pathway levels based approaches

Diabetic nephropathy is one of the common microvascular complications of diabetes. Iron death is a recently reported way of cell death. To explore the effects of iron death on diabetic nephropathy, iron death score of diabetic nephropathy was analyzed based on the network and pathway levels. Furthermore, markers related to iron death were screened. Using RNA-seq data of diabetic nephropathy, samples were clustered uniformly and the disease was classified. Differentially expressed gene analysis was conducted on the typed disease samples, and the WGCNA algorithm was used to obtain key modules. String database was used to perform protein interaction analysis on key module genes for the selection of Hub genes. Moreover, principal component analysis method was applied to get transcription factors and non-coding genes, which interact with the Hub gene. All samples can be divided into two categories and principal component analysis shows that the two categories are significantly different. Hub genes (FPR3, C3AR1, CD14, ITGB2, RAC2 and ITGAM) related to iron death in diabetic nephropathy were obtained through gene expression differential analysis between different subtypes. Non-coding genes that interact with Hub genes, including hsa-miR-572, hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-208a-3p, hsa-miR-153-3p and hsa-miR-29c-3p, may be related to diabetic nephropathy. Transcription factors HIF1α, KLF4, KLF5, RUNX1, SP1, VDR and WT1 may be related to diabetic nephropathy. The above factors and Hub genes are collectively involved in the occurrence and development of diabetic nephropathy, which can be further studied in the future. Moreover, these factors and genes may be potential target for therapeutic drugs.

A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

Background

Breast cancer (BRCA) is the most common tumor in women, and lipid metabolism involvement has been demonstrated in its tumorigenesis and development. However, the role of lipid metabolism-associated genes (LMAGs) in the immune microenvironment and prognosis of BRCA remains unclear.

Methods

A total of 1076 patients with BRCA were extracted from The Cancer Genome Atlas database and randomly assigned to the training cohort (n = 760) or validation cohort (n = 316). Kaplan–Meier analysis was used to assess differences in survival. Consensus clustering was performed to categorize the patients with BRCA into subtypes. Using multivariate Cox regression analysis, an LMAG-based prognostic risk model was constructed from the training cohort and validated using the validation cohort. The immune microenvironment was evaluated using the ESTIMATE and tumor immune estimation resource algorithms, CIBERSORT, and single sample gene set enrichment analyses.

Results

Consensus clustering classified the patients with BRCA into two subgroups with significantly different overall survival rates and immune microenvironments. Better prognosis was associated with high immune infiltration. The prognostic risk model, based on four LMAGs (MED10, PLA2G2D, CYP4F11, and GPS2), successfully stratified the patients into high- and low-risk groups in both the training and validation sets. High risk scores predicted poor prognosis and indicated low immune status. Subgroup analysis suggested that the risk model was an independent predictor of prognosis in BRCA.

Conclusion

This study demonstrated, for the first time, that LMAG expression plays a crucial role in BRCA. The LMAG-based risk model successfully predicted the prognosis and indicated the immune microenvironment of patients with BRCA. Our study may provide inspiration for further research on BRCA pathomechanisms.

Integrated Profiling Identifies CCNA2 as a Potential Biomarker of Immunotherapy in Breast Cancer

Introduction

Breast cancer is the main reason for cancer-related deaths in women and the most common malignant cancer among women. In recent years, immunosuppressive factors have become a new type of treatment for cancer. However, there are no effective biomarkers for breast cancer immunotherapy. Therefore, exploring immune-related biomarkers is presently an important topic in breast cancer.

Methods

Gene expression profile data of breast cancer from The Cancer Genome Atlas (TCGA) was downloaded. Scale-free gene co-expression networks were built with weighted gene co-expression network analysis. The correlation of genes was performed with Pearson’s correlation values. The potential associations between clinical features and gene sets were studied, and the hub genes were screened out. Gene Ontology and gene set enrichment analysis were used to reveal the function of hub gene in breast cancer. The gene expression profiles of GSE15852, downloaded from the Gene Expression Omnibus database, were used for hub gene verification. In addition, candidate biomarkers expression in breast cancer was studied. Survival analysis was performed using Log rank test and Kaplan–Meier. Immunohistochemistry was used to analyze the expression of CCNA2.

Results

A total of 6 modules related to immune cell infiltration were identified via the average linkage hierarchical clustering. According to the threshold criteria (module membership >0.9 and gene significance >0.35), a significant module consisting of 13 genes associated with immune cells infiltration were identified as candidate hub genes after performed with the human protein interaction network. And 3 genes with high correlation to clinical traits were identified as hub genes, which were negatively associated with the overall survival. Among them, the expression of CCNA2 was increased in metastatic breast cancer compare with non-metastatic breast cancer, who underwent immunotherapy. Immunohistochemistry results showed that CCNA2 expression in carcinoma tissues was elevated compared with normal control.

Discussion

CCNA2 identified as a potential immune therapy marker in breast cancer, which were first reported here and deserved further research.