Genetic Research on Neuropsychiatric Disorders and Complex Human Diseases

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular Genetics and Genetic Diseases".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 590

Special Issue Editor


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Guest Editor
Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, USA
Interests: computational genetics; neuropsychiatric disorder; big data analysis; deep learning; statistical modelling; bioinformatics

Special Issue Information

Dear Colleagues,

Genetic research on neuropsychiatric disorders and other complex human diseases, such as cancer, autoimmune conditions, and aging-related diseases, is advancing rapidly, employing a diverse range of methodologies. This Special Issue aims to explore the intersection of genetic factors across these various diseases, highlighting the integration of traditional techniques and modern approaches. Neuropsychiatric disorders, including schizophrenia, bipolar disorder, and major depression, exemplify the intricate interplay of genetic, environmental, and lifestyle influences. Simultaneously, complex diseases like cancer and autoimmune disorders present unique genetic challenges and opportunities for research. By utilizing bioinformatics tools and advanced data analysis methods, researchers can effectively analyze large-scale genomic datasets. Traditional approaches, such as linkage analysis and candidate gene studies, are complemented by modern multi-omics strategies—encompassing genomics, transcriptomics, proteomics, and metabolomics—to provide a holistic understanding of disease mechanisms. This integrative approach facilitates the identification of genetic variations and their biological implications across multiple disease contexts. Additionally, sophisticated data analysis techniques, including statistical modeling and network analysis, enhance our ability to decipher complex biological interactions. Ultimately, this Special Issue seeks to illuminate the multifaceted landscape of genetic research across neuropsychiatric and other complex human diseases, paving the way for improved diagnostics and treatment strategies.

Dr. Wen Zhang
Guest Editor

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Keywords

  • neuropsychiatric disorders
  • complex human diseases
  • genetic research
  • omics data analysis
  • high-throughput sequencing
  • machine learning
  • disease mechanism

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Published Papers (1 paper)

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Research

19 pages, 9365 KiB  
Article
Integrative Multi-Omics Analysis Reveals Critical Molecular Networks Linking Intestinal-System Diseases to Colorectal Cancer Progression
by Shiliang Ji, Haoran Hu, Ruifang Zhu, Dongkai Guo, Yujing Liu, Yang Yang, Tian Li, Chen Zou, Yiguo Jiang and Guilai Liu
Biomedicines 2024, 12(12), 2656; https://doi.org/10.3390/biomedicines12122656 - 21 Nov 2024
Viewed by 230
Abstract
Background/Objectives: Colorectal cancer (CRC) frequently co-occurs with intestinal system diseases (ISDs), yet their molecular interplay remains poorly understood. We employed a comprehensive bioinformatics approach to elucidate shared genetic signatures and pathways between CRC and ISDs. Methods: We systematically analyzed 12 microarray [...] Read more.
Background/Objectives: Colorectal cancer (CRC) frequently co-occurs with intestinal system diseases (ISDs), yet their molecular interplay remains poorly understood. We employed a comprehensive bioinformatics approach to elucidate shared genetic signatures and pathways between CRC and ISDs. Methods: We systematically analyzed 12 microarray and RNA-seq datasets encompassing 989 samples across seven ISDs and CRC. Differentially expressed genes (DEGs) were identified using Limma and DESeq2. Functional enrichment analysis was performed using clusterProfiler. Protein–protein interaction networks were constructed via STRING and visualized with Cytoscape to identify hub genes. Clinical significance of shared genes was further assessed through survival analysis and validated by immunohistochemistry staining of 30 paired CRC–normal tissue samples. Results: Integrating bioinformatics and machine learning approaches, we uncovered 160 shared DEGs (87 upregulated, 73 downregulated), which predominantly enriched cell metabolism, immune homeostasis, gut–brain communication, and inflammation pathways. Network analysis revealed nine key hub proteins linking CRC and ISDs, with seven upregulated (CD44, MYC, IL17A, CXCL1, FCGR3A, SPP1, and IL1A) and two downregulated (CXCL12 and CCL5). Survival analysis demonstrated the prognostic potential of these shared genes, while immunohistochemistry confirmed their differential expression in CRC tissues. Conclusions: Our findings unveil potential biomarkers and therapeutic targets, providing insights into ISD-influenced CRC progression and offering a robust foundation for improved diagnostic and treatment strategies in ISD-associated CRC. Full article
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