AIMC Topic: Gene Regulatory Networks

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Identification and validation of aryl hydrocarbon receptor-associated hub genes in ulcerative colitis via integrated bioinformatics analysis.

Human genomics
OBJECTIVE: Ulcerative colitis (UC), a chronic inflammatory bowel disease, continues to pose substantial challenges in both diagnosis and treatment. The aryl hydrocarbon receptor (AhR) plays a pivotal role in intestinal immune regulation; however, its...

Identification of biomarkers related to neutrophil extracellular traps and potential therapeutic drugs for rheumatoid arthritis using computational analysis.

European journal of medical research
BACKGROUND: Neutrophil extracellular traps (NETs) derived from neutrophils are implicated in the pathogenesis of rheumatoid arthritis (RA) pathogenicity, though the underlying mechanisms remain unclear.

Single-cell RNA sequencing and Mendelian randomization, revealing molecular mechanisms and causal correlation of immune-related genes in periodontitis.

Scientific reports
The immune system has been linked to periodontitis risk in oral inflammation and systemic consequences. Specifically, this study investigated whether hub genes were associated with immune cells via integrating single-cell RNA sequencing (scRNA-seq) a...

Evaluation of biomarkers and immune microenvironment of gestational diabetes mellitus evidence from omics data and machine learning.

Scientific reports
This study aimed to identify core genes of Gestational diabetes mellitus (GDM) and explore its immune microenvironment. Using the limma package, we were able to identify differentially expressed genes (DEGs) between GDM and normal placental tissue. W...

Identification and validation of cell senescence genes in recurrent spontaneous abortion via multiple bioinformatics algorithms.

Scientific reports
Recurrent spontaneous abortion (RSA) represents a significant challenge in reproductive obstetrics, affecting approximately 5% of couples globally. Despite various treatments, the effectiveness of these interventions remains highly contentious. Emerg...

Pan-cancer single-cell and spatial transcriptomics implicate cancer-associated fibroblasts in neutrophil immunosuppressive phenotypic transitions and immunotherapy resistance.

Functional & integrative genomics
Neutrophils are the most abundant granulocyte population and have important functions such as defense against pathogens. However, they show significant Heterogeneity and play more complex roles in tumors. The theory of two-tiered differentiation of n...

Multi-omics identification of RNASE6 as an immune regulatory RNA-binding protein associated with melanoma metastasis.

Autoimmunity
BACKGROUND: Cutaneous melanoma is a highly invasive tumor. It enhances metastasis and resistance to immunotherapy immunosuppressive mechanisms. Understanding RNA-binding proteins (RBPs) in melanoma's immune alterations is limited. This study explore...

Neutrophil extracellular trapping network-associated biomarkers in liver fibrosis: machine learning and experimental validation.

Journal of translational medicine
BACKGROUND: The diagnostic and therapeutic potential of neutrophil extracellular traps (NETs) in liver fibrosis (LF) has not been fully explored. We aim to screen and verify NETs-related liver fibrosis biomarkers through machine learning.

Characterization of SPTLC2 as a key driver promoting microglial activation and energy metabolism reprogramming after ischemic stroke through bulk and single-cell analyses combined with experimental validation.

Cell biology and toxicology
BACKGROUND: Ischemic stroke (IS) stands as a principal contributor to high rates of sickness and death. The condition's pathological development is complicated, featuring mechanisms like mitochondrial impairment and the activation of microglial cells...

Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.

Scientific reports
This study aimed to identify candidate diagnostic miRNAs from the serum of colorectal cancer (CRC) patients using Boruta, a wrapper-based feature selection technique, in combination with decision tree-based machine learning methods. We analyzed three...