Current Genomics - Current Issue

Lactylation is increasingly recognized to play a crucial role in human health and diseases. However, its involvement in age-related macular degeneration (AMD) remains largely unclear.

The aim of this study was to identify and characterize the pivotal lactylation-related genes and explore their underlying mechanism in AMD.

Gene expression profiles of AMD patients and control individuals were obtained and integrated from the GSE29801 and GSE50195 datasets. Differentially expressed genes (DEGs) were screened and intersected with lactylation-related genes for lactylation-related DEGs. Machine learning algorithms were used to identify hub genes associated with AMD. Subsequently, the selected hub genes were subject to correlation analysis, and reverse transcription quantitative real-time PCR (RT-qPCR) was used to detect the expression of hub genes in AMD patients and healthy control individuals.

A total of 68 lactylation-related DEGs in AMD were identified, and seven genes, including HMGN2, TOP2B, HNRNPH1, SF3A1, SRRM2, HIST1H1C, and HIST1H2BD were selected as key genes. RT-qPCR analysis validated that all 7 key genes were down-regulated in AMD patients.

We identified seven lactylation-related key genes potentially associated with the progression of AMD, which might deepen our understanding of the underlying mechanisms involved in AMD and provide clues for the targeted therapy.

Microtia, a prevalent congenital maxillofacial deformity, significantly impacts the physical and psychological health of children. Its etiology, especially in non-syndromic cases, remains a complex and partially understood domain, complicating etiological treatment. Recent studies pointed to a genetic predisposition in non-syndromic microtia, yet research on susceptible or pathogenic genes is limited.

This study focused on identifying key biomarker genes in microtia cartilage to elucidate pathogenesis and assist in prenatal diagnosis.

We first collated two bulk transcriptome datasets from the GEO database, followed by functional enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint differentially expressed genes (DEGs) and gene modules. The subsequent intersection of DEGs with WGCNA modules, aided by support vector machine-recursive feature elimination (SVM-RFE) and protein-protein interaction (PPI) networks, predicted potential susceptibility genes for microtia. Finally, we integrated bulk RNA sequencing with single-cell data via the “scissor” R package and further validated it with Real-time PCR and immunofluorescence.

We identified JAG2 as a prominent biomarker for microtia, evidenced by its significant upregulation in microtia cartilage.

Our findings implicate JAG2 in microtia development and suggest its role in chondrocyte maturation and differentiation through Notch signaling pathway activation, shedding light on the potential pathogenesis of microtia.

This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression.

Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilized bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients.

Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition.

Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC.

The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups.

Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.