The prognostic value of immune-related genes AZGP1, SLCO5A1, and CTF1 in Uveal melanoma

Quantitative Proteomics and Molecular Mechanisms of Non-Hodgkin Lymphoma Mice Treated with Incomptine A, Part II

Background/Objectives: Incomptine A (IA) has cytotoxic activity in non-Hodgkin lymphoma (NHL) cancer cell lines. Its effects on U-937 cells include induction of apoptosis, production of reactive oxygen species, and inhibition of glycolytic enzymes. We examined the altered protein levels present in the lymph nodes of an in vivo mouse model. Methods: We induced an in vivo model with Balb/c mice with U-937 cells and treated it with IA or methotrexate, as well as healthy mice. We determined expressed proteins by TMT based on the LC-MS/MS method (Data are available via ProteomeXchange with identifier PXD060392) and a molecular docking study targeting 15 deregulated proteins. We developed analyses through the KEGG, Reactome, and Gene Ontology databases. Results: A total of 2717 proteins from the axillary and inguinal lymph nodes were analyzed and compared with healthy mice. Of 412 differentially expressed proteins, 132 were overexpressed (FC ≥ 1.5) and 117 were underexpressed (FC ≤ 0.67). This altered expression was associated with 20 significantly enriched processes, including chromatin remodeling, transcription, translation, metabolic and energetic processes, oxidative phosphorylation, glycolysis/gluconeogenesis, cell proliferation, cytoskeletal organization, and with cell death with necroptosis. Conclusions: We confirmed the previously observed dose-dependent effect of IA as a secondary metabolite with important potential as an anticancer agent for the treatment of NHL, showing that the type of drug or the anatomical location influences the response to treatment. The IA promises to be a likely safer and more effective treatment to improve outcomes, reduce toxicities, and improve survival in patients with NHL, initially targeting histones and transcription factors that will affect cell death proteins.

Relationship between CTF1 gene expression and prognosis and tumor immune microenvironment in glioma

OBJECTIVE

This study aimed to evaluate CTF1 expression in glioma, its relationship to patient prognosis and the tumor immune microenvironment, and effects on glioma phenotypes to identify a new therapeutic target for treating glioma precisely.

METHODS

We initially assessed the expression of CTF1, a member of the IL-6 family, in glioma, using bioinformatics tools and publicly available databases. Furthermore, we examined the correlation between CTF1 expression and tumor prognosis, DNA methylation patterns, m6A-related genes, potential biological functions, the immune microenvironment, and genes associated with immune checkpoints. We also explored potential associations with drug sensitivity. To assess the impact on glioma cell proliferation and apoptosis, we employed various assays, including the Cell Counting Kit-8, colony formation assay, and flow cytometry.

RESULTS

CTF1 gene and protein expression were significantly elevated in glioma tissues, and correlated with malignancy and poor prognosis. CTF1 was an independent prognostic factor and negatively associated with DNA methylation. The involvement of CTF1 in m6A modifications contributed to glioma progression. Enrichment analysis revealed immune response pathways linked with CTF1 in glioma, including natural killer cell cytotoxicity, NOD-like receptor signaling, Toll-like receptor signaling, antigen processing, chemokine signaling, and cytokine receptor interactions. CTF1 expression correlated positively with pathways related to apoptosis, inflammation, proliferation, and epithelial-mesenchymal transition, and PI3K-AKT-mTOR signaling. Additionally, CTF1 expression was positively associated with macrophage, eosinophil, and neutrophil contents and immune checkpoint-related genes, but negatively associated with sensitivity to 14 drugs. In vitro experiments confirmed that CTF1 knockdown inhibited glioma cell proliferation and promoted apoptosis.

CONCLUSION

This study identifies CTF1 as a significant independent prognostic factor that is closely associated with the tumor immune microenvironment in glioma. Additionally, reduced expression of CTF1 suppresses the proliferation and induces apoptosis of glioma cells in vitro. Consequently, CTF1 is a potentially promising novel therapeutic target for glioma treatment.

Prognostic value of CTF1 in glioma and its role in the tumor microenvironment

Background

Within the realm of primary brain tumors, specifically glioblastoma (GBM), presents a notable obstacle due to their unfavorable prognosis and differing median survival rates contingent upon tumor grade and subtype. Despite a plethora of research connecting cardiotrophin-1 (CTF1) modifications to a range of illnesses, its correlation with glioma remains uncertain. This study investigated the clinical value of CTF1 in glioma and its potential as a biomarker of the disease.

Methods

Glioma project in The Cancer Genome Atlas (TCGA) database served as the training cohort, and CGGA 325 series in the Chinese Glioma Genome Atlas (CGGA) database served as the external independent validation cohort. First, the difference in the expression level of CTF1 between glioma tissue and normal tissue was analyzed, and the results were verified with the CGGA database. The relationship between CTF1 expression and the prognosis of glioma patients was evaluated using Univariate and Multivariate Cox analysis and the Kaplan-Meier (KM) curve. We used CIBERSOFT to explore the association between CTF1 and immune cell infiltration in GBM, as well as performing gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) analyses. Furthermore, we analyzed the relationship between CTF1 and gene mutations and drug sensitivity. Using Weighted gene co-expression network analysis (WGCNA) analysis, we pinpointed the gene set most correlated with CTF1 and conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene enrichment analyses to anticipate the pathways that could be influenced by CTF1. Finally, we constructed a nomogram using a multifactorial regression model to further predict patient prognosis.

Results

CTF1 expression was significantly elevated in glioma tissues compared to normal tissues in the TCGA dataset (P<0.001) and was associated with poorer survival in both TCGA and CGGA datasets (P<0.001). Receiver operating characteristic (ROC) analysis demonstrated the diagnostic potential of CTF1, with an area under the curve (AUC) of 0.889 [95% confidence interval (CI): 0.803-0.974] in TCGA and 0.664 (95% CI: 0.599-0.729) in CGGA. High CTF1 levels were correlated with advanced glioma grades, and Cox regression analysis identified CTF1 as an independent risk factor. A nomogram incorporating CTF1 levels, isocitrate dehydrogenase 1 (IDH1) mutation status, O6-methylguanine-DNA methyltransferase (MGMT) methylation status, age, and gender were developed and validated to predict 1- and 2-year survival probabilities. In GBM, drug sensitivity analysis revealed significant associations between CTF1 expression and responsiveness to gemcitabine, dasatinib, and other agents. CTF1 expression was also linked to immune infiltration (monocytes, neutrophils, M0 macrophages) and pathways involved in tumor progression, including IL2_STAT5, P53, and IL6_JAK_STAT3 signaling pathways.

Conclusions

CTF1 could serve as a prognostic marker for glioma. It acts as a predictive indicator and is associated with immune cell infiltration in GBM. These findings provide a foundation for further research into the molecular function of CTF1 and offer new insights for exploring the underlying mechanisms and developing treatments for glioma.

Machine Learning Methods for Gene Selection in Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Exosomal Non-coding RNAs: A New Approach to Melanoma Diagnosis and Therapeutic Strategy

Malignant melanoma (MM) is a highly aggressive cancer with a poor prognosis. Currently, although a variety of therapies are available for treating melanoma, MM is still a serious threat to the patient's life due to numerous factors, such as the recurrence of tumors, the emergence of drug resistance, and the lack of effective therapeutic agents. Exosomes are biologically active lipid-bilayer extracellular vesicles secreted by diverse cell types that mediate intercellular signal communication. Studies found that exosomes are involved in cancer by carrying multiple bioactive molecules, including non-- coding RNAs (ncRNAs). The ncRNAs have been reported to play an important role in regulating proliferation, angiogenesis, immune regulation, invasion, metastasis, and treatment resistance of tumors. However, the functional role of exosomal ncRNAs in MM remains unknown. Therefore, this review summarizes the current state of melanoma diagnosis, treatment, and the application of exosomal ncRNAs in MM patients, which may provide new insights into the mechanisms involved in melanoma progression and serve as biomarkers for diagnosis and therapeutic targets.