Guanylate-Binding Protein 1 as a Potential Predictor of Immunotherapy: A Pan-Cancer Analysis

The transcription factor RIP140 regulates interferon γ signaling in breast cancer

RIP140 (receptor interacting protein of 140 kDa) is an important player in breast cancer (BC) by regulating key cellular pathways such as nuclear hormone receptor signaling. In order to identify additional genes specifically regulated by RIP140 in BC, we performed a transcriptomic analysis after silencing its expression in MCF-7 cells. We identified the interferon γ (IFNγ) signaling as being substantially repressed by RIP140 knockdown. Using the GBP1 (guanylate binding protein 1) gene as a reporter of IFNγ signaling, we demonstrated its robust induction by RIP140 through an ISRE motif, leading to a significant reduction of its induction upon IFNγ treatment. Furthermore, we showed that low levels of RIP140 amplified the IFNγ-dependent inhibition of BC cell proliferation. In line with these data, reanalysis of transcriptomic data obtained in human BC samples revealed that IFNγ levels were associated with good prognosis only for BC patients exhibiting tumors expressing low levels of RIP140, thus confirming its effect on the anti-tumor activity of IFNγ provided by our experimental data. Altogether, this study identifies RIP140 as a new regulator of IFNγ signaling in breast tumorigenesis.

Multifaceted Roles of Guanylate-Binding Proteins in Cancer

Guanylate-binding proteins (GBPs), encompassing GBP1 through GBP7 in humans, are interferon-inducible large GTPases of the dynamin superfamily, renowned for their pivotal roles in cell-autonomous immunity against intracellular pathogens such as viruses, bacteria, and protozoa. By recognizing pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs), GBPs orchestrate lysosomal targeting, regulate inflammatory cascades, and modulate apoptosis to protect host tissues from immune-mediated damage. Beyond their foundational roles in immunity, GBPs exhibit context-dependent effects in human cancer, promoting malignancy in some tumors through enhanced immune signaling, inhibition of apoptosis, and resistance to therapies, or suppressing tumor growth through immune activation and cell cycle regulation. This comprehensive review explores the structural intricacies, immune functions, and multifaceted contributions of human GBPs to cancer, delving into their molecular mechanisms, prognostic potential, and therapeutic implications. We incorporate the latest insights to highlight how understanding GBP regulation could reshape cancer treatment strategies.

Single-cell RNA sequencing of baseline PBMCs predicts ICI efficacy and irAE severity in patients with NSCLC

BACKGROUND

Immune checkpoint inhibitors (ICIs) have transformed treatment and have provided significant clinical benefits and durable responses for patients with advanced non-small cell lung cancer (NSCLC). However, only a small percentage of patients respond to ICI treatment, and immune-related adverse events (irAEs) leading to treatment discontinuation remain challenging. Despite the recognized need for biomarkers to predict both the efficacy of ICIs and the risk of irAEs, such biomarkers are yet to be clearly identified.

METHODS

In this study, we performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from 33 patients with NSCLC before ICIs treatment. To validate our findings, we reanalyzed public scRNA-seq data, conducted a cytometric bead array (CBA), and supported our findings with T-cell receptor sequencing.

RESULTS

While the immune response was more pronounced in patients with a favorable prognosis, the hypoxic pathway was more prominent in patients with primary resistance. Lymphocytes such as CD8 T cells, CD4 T cells, and natural killer cells were primarily involved in these pathways, with PRF1 and GZMB expression showing strong associations with favorable prognosis. In contrast, irAEs were mainly linked to myeloid cells, such as monocytes and macrophages. As irAE severity increased, inflammation and the TNF-NFKB1 pathway were more prominent. Specifically, increased expression of IL1B, CXCL8, and CXCL2 in monocytes and TNF in macrophages was closely associated with severe irAE through involvement in these pathways.Notably, the increase of PRF1 and GZMB expression showed a close association with both a favorable prognosis and a reduced severity of irAE, which was validated through CBA analysis. Moreover, the expression of these key markers varied according to prognosis and irAE severity regardless of patient background, such as programmed death-ligand 1 expression levels, tumor histology, or prior treatment regimens.

CONCLUSIONS

This study identified biological pathways and key biomarkers associated with ICI prognosis and irAE severity using PBMC samples before treatment. These findings provide a foundation for improved therapeutic strategies that enhance clinical outcomes while minimizing ICI treatment-associated risks.

Newcastle disease virus promotes pyroptosis in medulloblastoma cells by regulating interferon-gamma-mediated guanylate-binding protein 1 expression and activating caspase-4

Objective

The literature has reported that Newcastle disease virus (NDV) can have inhibitory effects on various tumors. This study aims to investigate the mechanism by which NDV induces pyroptosis in medulloblastoma (MB) cells.

Material and Methods

We treated MB cell lines Daoy and D283 with NDV or recombinant interferon-gamma (IFN-g) proteins. Guanylate-binding proteins (GBPs) were measured using real-time quantitative polymerase chain reaction. Small interfering RNA-specific targeting GBP1 was transfected into MB cells. Apoptosis was assessed using Terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nucleoside nick end labeling and flow cytometry assays. Pyroptosis-related proteins, including caspase-4, caspase-1, and gasdermin D (GSDMD), were detected using Western blotting.

Results

Bioinformatics analysis revealed that GBP family genes and interferon-related genes might be responsive to NDV stimulation in MB cells. Treatment with NDV resulted in increased IFN-g levels and upregulated GBP expression, particularly GBP1. In addition, IFN-g treatment induced GBP1 expression and enhanced cell apoptosis. GBP1 knockdown attenuated the decreased cell proliferation and increased cell apoptosis induced by NDV in MB cells. GBP1 overexpression upregulated the expression of pyroptosis-related proteins, including caspase-4, caspase-1, and GSDMD, subsequently leading to inhibition of cell proliferation and an increase in cell apoptosis levels. The silencing of caspase-4 confirmed the regulatory role of GBP1 in MB cell pyroptosis.

Conclusion

Our findings suggest that NDV elevates IFN-g and GBP1 expression in MB cells, potentially contributing to caspase-4-mediated pyroptosis activation.

The Large GTPase Guanylate-Binding Protein-1 (GBP-1) Promotes Mitochondrial Fission in Glioblastoma

Glioblastomas (aka Glioblastoma multiformes (GBMs)) are the most deadly of the adult brain tumors. Even with aggressive treatment, the prognosis is extremely poor. The large GTPase Guanylate-Binding Protein-1 (GBP-1) contributes to the poor prognosis of GBM by promoting migration and invasion. GBP-1 is substantially localized to the cytosolic side of the outer membrane of mitochondria in GBM cells. Because mitochondrial dynamics, particularly mitochondrial fission, can drive cell migration and invasion, the potential interactions between GBP-1 and mitochondrial dynamin-related protein 1 (Drp1) were explored. Drp1 is the major driver of mitochondrial fission. While GBP-1 and Drp1 both had punctate distributions within the cytoplasm and localized to regions of the cytoplasmic side of the plasma membrane of GBM cells, the proteins were only molecularly co-localized at the mitochondria. Subcellular fractionation showed that the presence of elevated GBP-1 promoted the movement of Drp1 from the cytosol to the mitochondria. The migration of U251 cells treated with the Drp1 inhibitor, Mdivi-1, was less inhibited in the cells with elevated GBP-1. Elevated GBP-1 in GBM cells resulted in shorter and wider mitochondria, most likely from mitochondrial fission. Mitochondrial fission can drive several important cellular processes, including cell migration, invasion, and metastasis.

Integrative analysis of the immunological significances of guanylate binding protein family genes in microsatellite stability colorectal cancer

Background

Microsatellite stability (MSS) colorectal cancer (CRC) has poor sensitivity to immunotherapy and its underlying mechanisms are still unclear. Guanylate binding proteins (GBPs) are a family of GTPase involving innate immune responses by providing defense against invading microbes and pathogens. However, the immunological significances of GBPs in MSS CRC remain unknown.

Methods

We utilized bioinformatic tools to comprehensively analysis the expression pattern, clinical relevance, prognostic value, biological function, and immunoregulation effect of distinct GBP members in MSS CRC.

Results

The expression of all seven GBPs in MSS samples are remarkably decreased compared to microsatellite instability-high (MSI-H) samples. Among them, GBP1/2/4/5 are obviously correlated with distant metastasis status. High expression of GBP1/4/5/6 was remarkably related to favorable overall survival (OS) and progression-free survival (PFS) in CRC patients with MSS tumor. Subsequent enrichment analysis revealed that Interferon-gamma (IFN-γ) and NOD-like receptor signaling are the most relevant functions. Besides, the expression patterns of GBPs are remarkably associated with several tumor infiltrated immune cells (e.g. regulatory T cells, CD4+ T cells, and macrophages) and diverse immunoregulatory molecules (e.g. immune checkpoint biomarkers (ICBs) and major histocompatibility complex (MHC) molecules). Moreover, high GBP1/2/4/5 expression predicted better immunotherapy responsiveness in immunotherapy cohorts.

Conclusion

These findings might provide novel insights for the identification of therapeutic targets and potential prognostic biomarkers of GBP family in CRC with MSS samples.

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma

Background

Immunotherapy has revolutionized skin cutaneous melanoma treatment, but response variability due to tumor heterogeneity necessitates robust biomarkers for predicting immunotherapy response.

Methods

We used weighted gene co-expression network analysis (WGCNA), consensus clustering, and 10 machine learning algorithms to develop the immunotherapy-related gene model (ITRGM) signature. Multi-omics analyses included bulk and single-cell RNA sequencing of melanoma patients, mouse bulk RNA sequencing, and pathology sections of melanoma patients.

Results

We identified 66 consensus immunotherapy prognostic genes (CITPGs) using WGCNA and differentially expressed genes (DEGs) from two melanoma cohorts. The CITPG-high group showed better prognosis and enriched immune activities. DEGs between CITPG-high and CITPG-low groups in the TCGA-SKCM cohort were analyzed in three additional melanoma cohorts using univariate Cox regression, resulting in 44 consensus genes. Using 101 machine learning algorithm combinations, we constructed the ITRGM signature based on seven model genes. The ITRGM outperformed 37 published signatures in predicting immunotherapy prognosis across the training cohort, three testing cohorts, and a meta-cohort. It effectively stratified patients into high-risk or low-risk groups for immunotherapy response. The low-risk group, with high levels of model genes, correlated with increased immune characteristics such as tumor mutation burden and immune cell infiltration, indicating immune-hot tumors with a better prognosis. The ITRGM's relationship with the tumor immune microenvironment was further validated in our experiments using pathology sections with GBP5, an important model gene, and CD8 IHC analysis. The ITRGM also predicted better immunotherapy response in eight cohorts, including urothelial carcinoma and stomach adenocarcinoma, indicating broad applicability.

Conclusions

The ITRGM signature is a stable and robust predictor for stratifying melanoma patients into 'immune-hot' and 'immune-cold' tumors, enhancing prognosis and response to immunotherapy.

Inflammation and Immunity Gene Expression Patterns and Machine Learning Approaches in Association with Response to Immune-Checkpoint Inhibitors-Based Treatments in Clear-Cell Renal Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Despite the rapid evolution of targeted therapies, immunotherapy with checkpoint inhibition (ICI) as well as combination therapies, the cure of metastatic ccRCC (mccRCC) is infrequent, while the optimal use of the various novel agents has not been fully clarified. With the different treatment options, there is an essential need to identify biomarkers to predict therapeutic efficacy and thus optimize therapeutic approaches. This study seeks to explore the diversity in mRNA expression profiles of inflammation and immunity-related circulating genes for the development of biomarkers that could predict the effectiveness of immunotherapy-based treatments using ICIs for individuals with mccRCC. Gene mRNA expression was tested by the RT2 profiler PCR Array on a human cancer inflammation and immunity crosstalk kit and analyzed for differential gene expression along with a machine learning approach for sample classification. A number of mRNAs were found to be differentially expressed in mccRCC with a clinical benefit from treatment compared to those who progressed. Our results indicate that gene expression can classify these samples with high accuracy and specificity.

Machine learning-based identification of glycosyltransferase-related mRNAs for improving outcomes and the anti-tumor therapeutic response of gliomas

Background: Glycosyltransferase participates in glycosylation modification, and glycosyltransferase alterations are involved in carcinogenesis, progression, and immune evasion, leading to poor outcomes. However, in-depth studies on the influence of glycosyltransferase on clinical outcomes and treatments are lacking. Methods: The analysis of differentially expressed genes was performed using the Gene Expression Profiling Interactive Analysis 2 database. A total of 10 machine learning algorithms were introduced, namely, random survival forest, elastic network, least absolute shrinkage and selection operator, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox, supervised principal components, generalized boosted regression modeling, and survival support vector machine. Gene Set Enrichment Analysis was performed to explore signaling pathways regulated by the signature. Cell-type identification by estimating relative subsets of RNA transcripts was used for estimating the fractions of immune cell types. Results: Here, we analyzed the genomic and expressive alterations in glycosyltransferase-related genes in gliomas. A combination of 80 machine learning algorithms was introduced to establish the glycosyltransferase-related mRNA signature (GRMS) based on 2,030 glioma samples from The Cancer Genome Atlas Program, Chinese Glioma Genome Atlas, Rembrandt, Gravendeel, and Kamoun cohorts. The GRMS was identified as an independent hazardous factor for overall survival and exhibited stable and robust performance. Notably, gliomas in the high-GRMS subgroup exhibited abundant tumor-infiltrating lymphocytes and tumor mutation burden values, increased expressive levels of hepatitis A virus cellular receptor 2 and CD274, and improved progression-free survival when subjected to anti-tumor immunotherapy. Conclusion: The GRMS may act as a powerful and promising biomarker for improving the clinical prognosis of glioma patients.

Immune gene signatures as prognostic criteria for cancer patients

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

A health condition index for assessing disease progression

INTRODUCTION

Currently immunotherapy is considered a cutting edge pharmaceutical treatment for non-small cell lung cancer. Tumor profile plays a crucial role in identifying the correct patinet group.

METHODS

Therefore initial biopsies and re-biopsies are necessary in order to identify the expression of programmed death-ligand-1.

RESULTS

This information is crucial and therefore all future immunotherapy studies have to be built upon a specific statistical model which associates the tumor profile and tumor profile expression along with treatment efficiency.

DISCUSSION

We present a novel statistical methodology for future immunotherapy studies of non-small cell lung cancer.

Unraveling the Role of Guanylate-Binding Proteins (GBPs) in Breast Cancer: A Comprehensive Literature Review and New Data on Prognosis in Breast Cancer Subtypes

At least one member of the Guanylate-Binding Protein (GBP) family of large interferon-induced GTPases has been classified as both a marker of good prognosis and as a potential drug target to treat breast cancers. However, the activity of individual GBPs appears to not just be tumor cell type–specific but dependent on the growth factor and/or cytokine environment in which the tumor cells reside. To clarify what we do and do not know about GBPs in breast cancer, the current literature on GBP-1, GBP-2, and GBP-5 in breast cancer has been assembled. In addition, we have analyzed the role of each of these GBPs in predicting recurrence-free survival (RFS), overall survival (OS), and distance metastasis-free survival (DMFS) as single gene products in different subtypes of breast cancers. When a large cohort of breast cancers of all types and stages were examined, GBP-1 correlated with poor RFS. However, it was the only GBP to do so. When smaller cohorts of breast cancer subtypes grouped into ER+, ER+/Her2-, and HER2+ tumors were analyzed, none of the GBPs influenced RFS, OS, or DMSF as single agents. The exception is GBP-5, which correlated with improved RFS in Her2+ breast cancers. All three GBPs individually predicted improved RFS, OS, and DMSF in ER- breast cancers, regardless of the PR or HER2 status, and TNBCs.