Integrated machine learning methods identify FNDC3B as a potential prognostic biomarker and correlated with immune infiltrates in glioma

Limitations of nomogram models in predicting survival outcomes for glioma patients

Purpose

Glioma represents a prevalent and malignant tumor of the central nervous system (CNS), and it is essential to accurately predict the survival of glioma patients to optimize their subsequent treatment plans. This review outlines the most recent advancements and viewpoints regarding the application of nomograms in glioma prognosis research.

Design

With an emphasis on the precision and external applicability of predictive models, we carried out a comprehensive review of the literature on the application of nomograms in glioma and provided a step-by-step guide for developing and evaluating nomograms.

Results

A summary of thirty-nine articles was produced. The majority of nomogram-building research has used limited patient samples, disregarded the proportional hazards (PH) assumption in Cox regression models, and some of them have failed to incorporate external validation. Furthermore, the predictive capability of nomograms is influenced by the selection of incorporated risk factors. Overall, the current predictive accuracy of nomograms is moderately credible.

Conclusion

The development and validation of nomogram models ought to adhere to a standardized set of criteria, thereby augmenting their worth in clinical decision-making and clinician-patient communication. Prior to the clinical application of a nomogram, it is imperative to thoroughly scrutinize its statistical foundation, rigorously evaluate its accuracy, and, whenever feasible, assess its external applicability utilizing multicenter databases.

Development and validation of disulfidptosis-related genes signature for patients with glioma

BACKGROUND

Disulfidptosis has recently emerged as a novel form of regulated cell death (RCD). Evasion of cell death is a hallmark of cancer, and the resistance of many tumors to apoptosis-inducing therapies has heightened interest in exploring alternative RCD mechanisms.

METHODS

Transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA). Glioma samples were classified using non-negative matrix factorization (NMF). A predictive model was constructed using Lasso regression analysis, and its performance was evaluated through receiver operating characteristic (ROC) and Kaplan-Meier survival analyses. The relationship between the model and the tumor immune microenvironment (TIME) as well as treatment sensitivity was also assessed. Finally, we validated the expression of key signature genes in glioma.

RESULTS

Glioma samples were categorized into two distinct subtypes based on disulfidptosis-related genes, showing significant differences in overall survival (OS) and progression-free survival (PFS) between the subtypes. A genetic risk score model was then developed using these genes. A nomogram predicting OS was constructed using the risk score and clinical variables. Patients were stratified into low- and high-risk groups based on the median risk score from the TCGA cohort. Low-risk patients had significantly better outcomes compared to high-risk patients (TCGA cohort, OS: p < 0.001; PFS: p < 0.001; CGGA cohort, OS: p < 0.001). The risk score was associated with HLA expression, immune checkpoint genes, immune cell infiltration, immune function, tumor mutation burden, tumor stemness score, and drug sensitivity. Lastly, the expression of 11 signature genes was confirmed in glioma tissues.

CONCLUSIONS

The disulfidptosis-related gene-based risk score model effectively predicted glioma outcomes and highlighted the role of disulfidptosis-related genes in tumor immunity. This study offers potential new avenues for glioma treatment by targeting disulfidptosis.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning

Background

Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma.

Methods

We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents.

Results

Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients.

Conclusions

The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Extracellular Matrix Structure and Interaction with Immune Cells in Adult Astrocytic Tumors

The extracellular matrix (ECM) is a dynamic set of molecules produced by the cellular component of normal and pathological tissues of the embryo and adult. ECM acts as critical regulator in various biological processes such as differentiation, cell proliferation, angiogenesis, and immune control. The most frequent primary brain tumors are gliomas and by far the majority are adult astrocytic tumors (AATs). The prognosis for patients with these neoplasms is poor and the treatments modestly improves survival. In the literature, there is a fair number of studies concerning the composition of the ECM in AATs, while the number of studies relating the composition of the ECM with the immune regulation is smaller. Circulating ECM proteins have emerged as a promising biomarker that reflect the general immune landscape of tumor microenvironment and may represent a useful tool in assessing disease activity. Given the importance it can have for therapeutic and prognostic purposes, the aim of our study is to summarize the biological properties of ECM components and their effects on the tumor microenvironment and to provide an overview of the interactions between major ECM proteins and immune cells in AATs. As the field of immunotherapy in glioma is quickly expanding, we retain that current data together with future studies on ECM organization and functions in glioma will provide important insights into the tuning of immunotherapeutic approaches.

Identification of fibronectin type III domain containing 3B as a potential prognostic and therapeutic target for pancreatic cancer: a preliminary analysis

BACKGROUND

Fibronectin type III domain containing 3B (FNDC3B), a member of the fibronectin type III domain-containing protein family, has been indicated in various malignancies. However, the precise role of FNDC3B in the progression of pancreatic cancer (PC) still remains to be elucidated.

METHODS

In this study, we integrated data from the National Center for Biotechnology Information, the Cancer Genome Atlas, Genotype-Tissue Expression database, and Gene Expression Omnibus datasets to analyze FNDC3B expression and its association with various clinicopathological parameters. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, along with Gene Set Enrichment Analysis (GSEA), single sample Gene Set Enrichment Analysis (ssGSEA) and estimate analysis were recruited to delve into the biological function and immune infiltration based on FNDC3B expression. Additionally, the prognostic estimation was conducted using Cox analysis and Kaplan-Meier analysis. Subsequently, a nomogram was constructed according to the result of Cox analysis to enhance the prognostic ability of FNDC3B. Finally, the preliminary biological function of FNDC3B in PC cells was explored.

RESULTS

The study demonstrated a significantly higher expression of FNDC3B in tumor tissues compared to normal pancreatic tissues, and this expression was significantly associated with various clinicopathological parameters. GSEA revealed the involvement of FNDC3B in biological processes and signaling pathways related to integrin signaling pathway and cell adhesion. Additionally, ssGSEA analysis indicated a positive correlation between FNDC3B expression and infiltration of Th2 cells and neutrophils, while showing a negative correlation with plasmacytoid dendritic cells and Th17 cells infiltration. Kaplan-Meier analysis further supported that high FNDC3B expression in PC patients was linked to shorter overall survival, disease-specific survival, and progression-free interval. However, although univariate analysis demonstrated a significant correlation between FNDC3B expression and prognosis in PC patients, this association did not hold true in multivariate analysis. Finally, our findings highlight the crucial role of FNDC3B expression in regulating proliferation, migration, and invasion abilities of PC cells.

CONCLUSION

Despite limitations, the findings of this study underscored the potential of FNDC3B as a prognostic biomarker and its pivotal role in driving the progression of PC, particularly in orchestrating immune responses.

Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers

Background

Mood disorders are very common among adolescents and include mainly bipolar disorder (BD) and major depressive disorder (MDD), with overlapping depressive symptoms that pose a significant challenge to realizing a rapid and accurate differential diagnosis in clinical practice. Misdiagnosis of BD as MDD can lead to inappropriate treatment and detrimental outcomes, including a poorer ultimate clinical and functional prognosis and even an increased risk of suicide. Therefore, it is of great significance for clinical management to identify clinical symptoms or features and biological markers that can accurately distinguish BD from MDD. With the aid of bibliometric analysis, we explore, visualize, and conclude the important directions of differential diagnostic studies of BD and MDD in adolescents.

Materials and methods

A literature search was performed for studies on differential diagnostic studies of BD and MDD among adolescents in the Web of Science Core Collection database. All studies considered for this article were published between 2004 and 2023. Bibliometric analysis and visualization were performed using the VOSviewer and CiteSpace software.

Results

In total, 148 publications were retrieved. The number of publications on differential diagnostic studies of BD and MDD among adolescents has been generally increasing since 2012, with the United States being an emerging hub with a growing influence in the field. Boris Birmaher is the top author in terms of the number of publications, and the Journal of Affective Disorders is the most published journal in the field. Co-occurrence analysis of keywords showed that clinical characteristics, genetic factors, and neuroimaging are current research hotspots. Ultimately, we comprehensively sorted out the current state of research in this area and proposed possible research directions in future.

Conclusion

This is the first-ever study of bibliometric and visual analyses of differential diagnostic studies of BD and MDD in adolescents to reveal the current research status and important directions in the field. Our research and analysis results might provide some practical sources for academic scholars and clinical practice.