Personalized identification of differentially expressed pathways in pediatric sepsis

ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES

ABSTRACT

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

Existing Drug Repurposing for Glioblastoma to Discover Candidate Drugs as a New a Approach

Aims:

Repurposing of drugs has been hypothesized as a means of identifying novel treatment methods for certain diseases.

Background:

Glioblastoma (GB) is an aggressive type of human cancer; the most effective treatment for glioblastoma is chemotherapy, whereas, when repurposing drugs, a lot of time and money can be saved.

Objective:

Repurposing of the existing drug may be used to discover candidate drugs for individualized treatments of GB.

Method:

We used the bioinformatics method to obtain the candidate drugs. In addition, the drugs were verified by MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation experiments, as well as statistical analysis.

Result:

We obtained 4 candidate drugs suitable for the treatment of glioma, camptothecin, doxorubicin, daunorubicin and mitoxantrone, by the expression spectrum data IPAS algorithm analysis and drug-pathway connectivity analysis. These validation experiments showed that camptothecin was more effective in treating the GB, such as MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation.

Conclusion:

With regard to personalized treatment, this present study may be used to guide the research of new drugs via verification experiments and tumor formation. The present study also provides a guide to systematic, individualized drug discovery for complex diseases and may contribute to the future application of individualized treatments.

Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Pediatric Sepsis

BACKGROUND Sepsis is an extremely common health issue with a considerable mortality rate in children. Our understanding about the pathogenic mechanisms of sepsis is limited. The aim of this study was to identify the differential expression genes (DEGs) in pediatric sepsis through comprehensive analysis, and to provide specific insights for the clinical sepsis therapies in children. MATERIAL AND METHODS Three pediatric gene expression profiles (GSE25504, GSE26378, GSE26440) were downloaded from the Gene Expression Omnibus (GEO) database. The difference expression genes (DEGs) between pediatric sepsis and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. RESULTS Totally, 160 overlapping upward genes and 61 downward genes were identified. In addition, 5 KEGG pathways, including hematopoietic cell lineage, Staphylococcus aureus infection, starch and sucrose metabolism, osteoclast differentiation, and tumor necrosis factor (TNF) signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 9 hub genes including ITGAM, TLR8, IL1ß, MMP9, MPO, FPR2, ELANE, SPI1, and C3AR1 were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including has-miR-204-5p, has-miR-211-5p, has-miR-590-5p, and has-miR-21-5p, were predicted as possibly the key miRNAs. CONCLUSIONS Our findings will contribute to identification of potential biomarkers and novel strategies for pediatric sepsis treatment.

Precision medicine in pediatric sepsis

PURPOSE OF REVIEW

Pediatric sepsis is a heterogeneous state associated with significant morbidity and mortality, but treatment strategies are limited. Clinical trials of immunomodulators in sepsis have shown no benefit, despite having a strong biological rationale. There is considerable interest in application of a precision medicine approach to pediatric sepsis to identify patients who are more likely to benefit from targeted therapeutic interventions.

RECENT FINDINGS

Precision medicine requires a clear understanding of the molecular basis of disease. 'Omics data' and bioinformatics tools have enabled identification of endotypes of pediatric septic shock, with corresponding biological pathways. Further, using a multibiomarker-based approach, patients at highest risk of poor outcomes can be identified at disease onset. Enrichment strategies, both predictive and prognostic, may be used to optimize patient selection in clinical trials and identify a subpopulation in whom therapy of interest may be trialed. A bedside-to-bench-to-bedside model may offer clinicians pragmatic tools to aid in decision-making.

SUMMARY

Precision medicine approaches may be used to subclassify, risk-stratify, and select pediatric patients with sepsis who may benefit from new therapies. Application of precision medicine will require robust basic and translational research, rigorous clinical trials, and infrastructure to collect and analyze big data.