Bioinformatic Identification of Neuroblastoma Microenvironment-Associated Biomarkers with Prognostic Value

Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes

Motivation

Large datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery.

Model

We propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints.

Results

In simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation.

Availability

Source code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR. The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR.

Contact

Stanley.Pounds@stjude.org.

Supplementary Information

Supplementary data are available at the journal's website.

Investigation of differentially expressed genes related to cellular senescence between high-risk and non-high-risk groups in neuroblastoma

Object

This study aims to identify differentially expressed genes (DEGs) between high-risk and non-high-risk groups in neuroblastoma (NB), construct a prognostic model, and establish a risk score formula.

Materials and methods

The NB dataset GSE49710 (n = 498) from the GEO database served as the training cohort to select DEGs between high-risk and non-high-risk NB groups. Cellular senescence-related genes were obtained from the Aging Atlas database. Intersection genes from both datasets were identified as key genes of cellular senescence-related genes (SRGs). A prognostic model was constructed using Univariate Cox regression analysis and the Lasso algorithm with SRGs. Validation was performed using the E-MTAB-8248 cohort (n = 223). The expression levels of AURKA and CENPA were evaluated via RT-qPCR in two clinical NB sample groups.

Results

Eight SRGs were identified, and a prognostic model comprising five genes related to cellular senescence was constructed. AURKA and CENPA showed significant expression in clinical samples and were closely associated with cellular senescence.

Conclusion

The prognostic model consisted with five cellular senescence related genes effectively predicts the prognosis of NB patients. AURKA and CENPA represent promising targets in NB for predicting cellular senescence, offering potential insights for NB therapy.

Dendritic Cell-Related Immune Marker CD1C for Predicting Prognosis and Immunotherapy Opportunities of Lung Adenocarcinoma Patients

Lung adenocarcinoma (LUAD) is the most frequent type of lung cancer with a high mortality rate. Here, we aim to explore novel immune-related biomarkers for LUAD patients. Datasets, mRNA expression profiles, and clinical data concerned with LUAD were obtained from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA), respectively. Differential expression analysis was performed to obtain differentially expressed genes (DEGs). Based on DEGs, we conducted functional enrichment analyses. Subsequently, Kaplan‑Meier (KM) was performed to analyze survival differences among different groups. Furthermore, immune cell infiltration proportion was calculated by CIBERSORT and TIMER. The relationship between gene and immune response was analyzed using Tumor Immune System Interactions (TISIDB) database. Finally, Pearson correlation analysis was performed between CD1C and six immune checkpoints. We identified dendritic cells (DCs)-related expression profiles from four LUAD samples. DCs' immune marker CD1C in LUAD was selected by univariate Cox regression analysis. Low CD1C expression patients had a poor prognosis. A total of 332 DEGs were identified in high and low CD1C expression groups, which primarily enriched in 348 GO terms and 30 KEGG pathways. There were significant differences in the infiltration proportion of 17 immune cells between high and low CD1C expression groups. Most immunomodulators, chemokines, and chemokine receptors were positively associated with CD1C expression. Six immune checkpoints were also positively correlated with CD1C expression. DCs related immunomarker CD1C probably plays a pivotal part in prognosis and immunotherapy of LUAD via a joint analysis of single-cell and bulk sequencing data.

Analysis of Zinc and Stromal Immunity in Disuse Osteoporosis: Mendelian Randomization and Transcriptomic Analysis

OBJECTIVE

Disuse osteoporosis is known to be primarily caused by a lack of exercise. However, the causal relationships between zinc and immunity and disuse osteoporosis remain unknown. This study investigated these relationships and their potential mechanisms.

METHODS

This study was an integrative study combining genome-wide association studies and transcriptomics. Two-sample Mendelian randomization analysis (MR) was used to analyze the causal relationships between exposures (zinc, immunity, physical activity) and the outcome (osteoporosis) with the aid of single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). Four models, MR-Egger, inverse variance weighted, weighted median and MR-Pleiotrophy RESidual Sum and Outlier (MRPRESSO), were used to calculate odds ratio values. Sensitivity and heterogeneity analyses were also performed using MRPRESSO and MR-Egger methods. The mRNA transcriptomic analysis was subsequently conducted. Zinc metabolism scores were acquired through single-sample Gene Set Enrichment Analysis algorithms. Stromal scores were obtained using the R Package "estimate" algorithms. Important Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathways were also derived through gene set variation analysis. Cytoscape software helped construct the transcription factor (TF)-mRNA-microRNA (miRNA) network. Virtual screening and molecular docking were performed. Polymerase chain reaction validation was also carried out in vivo.

RESULTS

Causal relationships were demonstrated between zinc and exercise (95% confidence interval [CI] = 1.30-2.95, p = 0.001), exercise and immunity (95% CI = 0.36-0.80, p = 0.002), exercise and osteoporosis (95% CI = 0.97-0.99, p = 0.0007), and immunity disorder and osteoporosis (95% CI = 1.30-2.03, p = 0.00002). One hundred and seventy-nine mRNAs in important modules were screened. Combining the differential expressional genes (DEGs) and the Boruta selection, six DEGs were screened (AHNAK, CSF2, ADAMTS12, SRA1, RUNX2, and SLC39A14). TF HOXC10 and miRNA hsa-miR-204 were predicted. Then, the TF-mRNA-miRNA network was successfully constructed. RUNX2 and SLC39A14 were identified as hub mRNAs in the TF-mRNA-miRNA network. Eventually, the novel small drug C6O4NH5 was designed according to the pharmacophore structure of SLC39A14. The docking energy for the novel drug was -5.83 kcal/mol. SLC39A14 and RUNX2 were downregulated (of statistical significance p-value < 0.05) in our animal experiment.

CONCLUSION

This study revealed that zinc had a protective causal relationship with disuse osteoporosis by promoting exercise and immunity. SLC39A14 and RUNX2 mRNA participated in this zinc-related mechanism.

Impaired Antitumor Immune Response in MYCN-amplified Neuroblastoma Is Associated with Lack of CCL2 Secretion and Poor Dendritic Cell Recruitment

In neuroblastoma, MYCN amplification is associated with sparse immune infiltrate and poor prognosis. Dendritic cells (DC) are crucial immune sentinels but their involvement in neuroblastoma pathogenesis is poorly understood. We observed that the migration of monocytes, myeloid and plasmacytoid DC induced by MYCN-nonamplified neuroblastoma supernatants was abrogated by the addition of anti-CCL2 antibodies, demonstrating the involvement of the CCR2/CCL2 axis in their recruitment by these tumors. Using public RNA sequencing and microarray datasets, we describe lower level of expression of CCL2 in MYCN-amplified neuroblastoma tumors, and we propose a working model for T-cell recruitment in neuroblastoma tumors in which CCL2 produced by neuroblastoma cells initiates the recruitment of monocytes, myeloid and plasmacytoid DCs. Among these cells, the CD1c⁺ subset may recruit T cells by means of CCL19/CCL22 secretion. In vitro, supernatants from DCs cocultured with neuroblastoma cell lines and activated contain CCL22 and CCL19, and are chemotactic for both CD4⁺ and CD8⁺ T cells. We also looked at immunomodulation induced by neuroblastoma cell lines, and found MYCN-nonamplified neuroblastoma cell lines were able to create a microenvironment where DC activation is enhanced. Overall, our findings highlight a major role for CCL2/CCR2 axis in monocytes, myeloid and plasmacytoid cells recruitment toward MYCN-nonamplified neuroblastoma, allowing further immune cell recruitment, and show that these tumors present a microenvironment that can favor DC responses.

Significance

In MYCN-nonamplified neuroblastoma, CCL2 produced by neuroblastoma cells induces the recruitment of antigen-presenting cells (DCs and monocytes/macrophages), allowing infiltration by T cells, in link with CCL19 and CCL22 production, hence favoring immune responses.

Predicting amplification of MYCN using CpG methylation biomarkers in neuroblastoma

Background: Neuroblastoma is the most common extracranial solid tumor in childhood. Amplification of MYCN in neuroblastoma is a predictor of poor prognosis. Materials and methods: DNA methylation data from the TARGET data matrix were stratified into MYCN amplified and non-amplified groups. Differential methylation analysis, clustering, recursive feature elimination (RFE), machine learning (ML), Cox regression analysis and Kaplan-Meier estimates were performed. Results and Conclusion: 663 CpGs were differentially methylated between the two groups. A total of 25 CpGs were selected by RFE for clustering and ML, and a 100% clustering accuracy was obtained. ML validation on three external datasets produced high accuracy scores of 100%, 97% and 93%. Eight survival-associated CpGs were also identified. Therapeutic interventions may need to be targeted to patient subgroups.