Pan-cancer analysis combined with experimental validation revealed IL4I1 as an immunological and prognostic biomarker

Granuloma Annulare Exhibits Mixed Immune and Macrophage Polarization Profiles with Spatial Transcriptomics

Granuloma annulare (GA) is an idiopathic condition characterized by granulomatous inflammation in the skin. Prior studies have suggested that GA develops from various triggers, leading to a complex interplay involving innate and adaptive immunity, tissue remodeling, and fibrosis. Macrophages are the major immune cells comprising GA granulomas; however, the molecular drivers and inflammatory signaling cascade behind macrophage activation are poorly understood. Histologically, GA exhibits both palisaded and interstitial patterns on histology; however, the molecular composition of GA at the spatial level remains unexplored. GA is a condition without Food and Drug Administration-approved therapies despite the significant impact of GA on QOL. Spatial transcriptomics is a valuable tool for profiling localized, genome-wide gene expression changes across tissues, with emerging applications in clinical medicine. To improve our understanding of the spatially localized gene expression patterns underlying GA, we profiled the spatial gene expression landscape from 6 patients with GA. Our findings revealed mixed T helper 1 and T helper 2 signals comprising the GA microenvironment and spatially distinct M1 and M2 macrophage polarization characteristics. IFN-γ and TNF signals emerged as important regulators of GA granulomatous inflammation, and IL-32 emerged as a key driver of granulomatous inflammation. Overall, our spatial transcriptomics data indicate that GA exhibits mixed immune and macrophage polarization.

Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning

Background

Chronic kidney disease (CKD) involves complex immune dysregulation and altered gene expression profiles. This study investigates immune cell infiltration, differential gene expression, and pathway enrichment in CKD patients to identify key diagnostic biomarkers through machine learning methods.

Methods

We assessed immune cell infiltration and immune microenvironment scores using the xCell algorithm. Differentially expressed genes (DEGs) were identified using the limma package. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to evaluate pathway enrichment. Machine learning techniques (LASSO and Random Forest) pinpointed diagnostic genes. A nomogram model was constructed and validated for diagnostic prediction. Spearman correlation explored associations between key genes and immune cell recruitment.

Results

The CKD group exhibited significantly altered immune cell infiltration and increased immune microenvironment scores compared to the normal group. We identified 2335 DEGs, including 124 differentially expressed immune-related genes. GSEA highlighted significant enrichment of inflammatory and immune pathways in the high immune score (HIS) subgroup, while GSVA indicated upregulation of immune responses and metabolic processes in HIS. Machine learning identified four key diagnostic genes: RGS1, IL4I1, NR4A3, and SOCS3. Validation in an independent dataset (GSE96804) and clinical samples confirmed their diagnostic potential. The nomogram model integrating these genes demonstrated high predictive accuracy. Spearman correlation revealed positive associations between the key genes and various immune cells, indicating their roles in immune modulation and CKD pathogenesis.

Conclusion

This study provides a comprehensive analysis of immune alterations and gene expression profiles in CKD. The identified diagnostic genes and the constructed nomogram model offer potent tools for CKD diagnosis. The immunomodulatory roles of RGS1, IL4I1, NR4A3, and SOCS3 warrant further investigation as potential therapeutic targets in CKD.

Targeting amino acid-metabolizing enzymes for cancer immunotherapy

Despite the immune system's role in the detection and eradication of abnormal cells, cancer cells often evade elimination by exploitation of various immune escape mechanisms. Among these mechanisms is the ability of cancer cells to upregulate amino acid-metabolizing enzymes, or to induce these enzymes in tumor-infiltrating immunosuppressive cells. Amino acids are fundamental cellular nutrients required for a variety of physiological processes, and their inadequacy can severely impact immune cell function. Amino acid-derived metabolites can additionally dampen the anti-tumor immune response by means of their immunosuppressive activities, whilst some can also promote tumor growth directly. Based on their evident role in tumor immune escape, the amino acid-metabolizing enzymes glutaminase 1 (GLS1), arginase 1 (ARG1), inducible nitric oxide synthase (iNOS), indoleamine 2,3-dioxygenase 1 (IDO1), tryptophan 2,3-dioxygenase (TDO) and interleukin 4 induced 1 (IL4I1) each serve as a promising target for immunotherapeutic intervention. This review summarizes and discusses the involvement of these enzymes in cancer, their effect on the anti-tumor immune response and the recent progress made in the preclinical and clinical evaluation of inhibitors targeting these enzymes.

A new prognostic model based on gamma-delta T cells for predicting the risk and aiding in the treatment of clear cell renal cell carcinoma

BACKGROUND

ccRCC is the prevailing form of RCC, accounting for the majority of cases. The formation of cancer and the body's ability to fight against tumors are strongly connected to Gamma delta (γδ) T cells.

METHODS

We examined and analyzed the gene expression patterns of 535 individuals diagnosed with ccRCC and 72 individuals serving as controls, all sourced from the TCGA-KIRC dataset, which were subsequently validated through molecular biology experiments.

RESULTS

In ccRCC, we discovered 304 module genes (DEGRGs) that were ex-pressed differentially and linked to γδ T cells. A risk model for ccRCC was constructed using 13 differentially DEGRGs identified through univariate Cox and LASSO regression analyses, which were found to be associated with prognosis. The risk model exhibited outstanding performance in both the training and validation datasets. The comparison of immune checkpoint inhibitors and the tumor immune microenvironment between the high- and low-risk groups indicates that immunotherapy could lead to positive results for low-risk patients. Moreover, the inhibition of ccRCC cell proliferation, migration, and invasion was observed in cell culture upon knocking down TMSB10, a gene associated with different types of cancers.

CONCLUSIONS

In summary, we have created a precise predictive biomarker using a risk model centered on γδ T cells, which can anticipate clinical results and provide direction for the advancement of innovative targeted therapies.

IL4I1 in M2-like macrophage promotes glioma progression and is a promising target for immunotherapy

Background

Glioma is the prevailing malignant intracranial tumor, characterized by an abundance of macrophages. Specifically, the infiltrating macrophages often display the M2 subtype and are known as tumor-associated macrophages (TAMs). They have a critical role in promoting the oncogenic properties of tumor cells. Interleukin-4-induced-1 (IL4I1) functions as an L-phenylalanine oxidase, playing a key part in regulating immune responses and the progression of various tumors. However, there is limited understanding of the IL4I1-mediated cross-talk function between TAMs and glioma cell in the glioma microenvironment.

Methods

TCGA, GTEx, and HPA databases were applied to assess the IL4I1 expression, clinical characteristics, and prognostic value of pan-cancer. The link between IL4I1 levels and the prognosis, methylation, and immune checkpoints (ICs) in gliomas were explored through Kaplan-Meier curve, Cox regression, and Spearman correlation analyses. The IL4I1 levels and their distribution were investigated by single-cell analysis and the TIMER 2 database. Additionally, validation of IL4I1 expression was performed by WB, RT-qPCR, IHC, and IF. Co-culture models between glioma cells and M2-like macrophages were used to explore the IL4I1-mediated effects on tumor growth, invasion, and migration of glioma cells. Moreover, the function of IL4I1 on macrophage polarization was evaluated by ELISA, RT-qPCR, WB, and siRNA transfection.

Results

Both transcriptome and protein levels of IL4I1 were increased obviously in various tumor types, and correlated with a dismal prognosis. Specifically, IL4I1 was implicated in aggressive progression and a dismal prognosis for patients with glioma. A negative association was noticed between the glioma grade and DNA promoter methylation of IL4I1. Enrichment analyses in glioma patients suggested that IL4I1 was linked to cytokine and immune responses, and was positively correlated with ICs. Single-cell analysis, molecular experiments, and in vitro assays showed that IL4I1 was significantly expressed in TAMs. Importantly, co-culture models proved that IL4I1 significantly promoted the invasion and migration of glioma cells, and induced the polarization of M2-like macrophages.

Conclusion

IL4I1 could be a promising immunotherapy target for selective modulation of TAMs and stands as a novel macrophage-related prognostic biomarker in glioma.

Amino Acid-Metabolizing Enzymes in Advanced High-Grade Serous Ovarian Cancer Patients: Value of Ascites as Biomarker Source and Role for IL4I1 and IDO1

The molecular mechanisms contributing to immune suppression in ovarian cancer are not well understood, hampering the successful application of immunotherapy. Amino acid-metabolizing enzymes are known to contribute to the immune-hostile environment of various tumors through depletion of amino acids and production of immunosuppressive metabolites. We aimed to collectively evaluate the activity of these enzymes in high-grade serous ovarian cancer patients by performing targeted metabolomics on plasma and ascites samples. Whereas no indication was found for enhanced l-arginine or l-glutamine metabolism by immunosuppressive enzymes in ovarian cancer patients, metabolism of l-tryptophan by indoleamine 2,3-dioxygenase 1 (IDO1) was significantly elevated compared to healthy controls. Moreover, high levels of l-phenylalanine- and l-tyrosine-derived metabolites associated with interleukin 4 induced 1 (IL4I1) activity were found in ovarian cancer ascites samples. While l-tryptophan is a major substrate of both IDO1 and IL4I1, only its enhanced conversion into l-kynurenine by IDO1 could be detected, despite the observed activity of IL4I1 on its other substrates. In ascites of ovarian cancer patients, metabolite levels were higher compared to those in plasma, demonstrating the value of utilizing this fluid for biomarker identification. Finally, elevated metabolism of l-phenylalanine and l-tyrosine by IL4I1 correlated with disease stage, pointing towards a potential role for IL4I1 in ovarian cancer progression.

The exploitation of enzyme-based cancer immunotherapy

Cancer immunotherapy utilizes the immune system and its wide-ranging components to deliver anti-tumor responses. In immune escape mechanisms, tumor microenvironment-associated soluble factors and cell surface-bound molecules are mainly accountable for the dysfunctional activity of tumor-specific CD8⁺ T cells, natural killer (NK) cells, tumor associated macrophages (TAMs) and stromal cells. The myeloid-derived suppressor cells (MDSCs) and Foxp3⁺ regulatory T cells (Tregs), are also key tumor-promoting immune cells. These potent immunosuppressive networks avert tumor rejection at various stages, affecting immunotherapies' outcomes. Numerous clinical trials have elucidated that disruption of immunosuppression could be achieved via checkpoint inhibitors. Another approach utilizes enzymes that can restore the body's potential to counter cancer by triggering the immune system inhibited by the tumor microenvironment. These immunotherapeutic enzymes can catalyze an immunostimulatory signal and modulate the tumor microenvironment via effector molecules. Herein, we have discussed the immuno-metabolic roles of various enzymes like ATP-dephosphorylating ectoenzymes, inducible Nitric Oxide Synthase, phenylamine, tryptophan, and arginine catabolizing enzymes in cancer immunotherapy. Understanding the detailed molecular mechanisms of the enzymes involved in modulating the tumor microenvironment may help find new opportunities for cancer therapeutics.