A Diagnostic Model for Alzheimer’s Disease Based on Blood Levels of Autophagy-Related Genes

Identification of Autophagy-Related Genes in Patients with Acute Spinal Cord Injury and Analysis of Potential Therapeutic Targets

Autophagy has been implicated in the pathogenesis and progression of spinal cord injury (SCI); however, its specific mechanisms remain unclear. This study is aimed at identifying potential molecular biomarkers related to autophagy in SCI through bioinformatics analysis and exploring potential therapeutic targets. The mRNA expression profile dataset GSE151371 was obtained from the GEO database, and R software was used to screen for differentially expressed autophagy-related genes (DE-ARGs) in SCI. A total of 39 DE-ARGs were detected in this study. Enrichment analysis, protein-protein interaction (PPI) network, TF-mRNA-miRNA regulatory network analysis, and the DSigDB database were used to investigate the regulatory mechanisms between DE-ARGs and identify potential drugs for SCI. Enrichment analysis revealed associations with autophagy, apoptosis, and cell death. PPI analysis identified the highest-scoring module and selected 10 hub genes to construct the TF-mRNA-miRNA network, revealing regulatory mechanisms. Analysis of the DSigDB database indicated that 1,9-Pyrazoloanthrone may be a potential therapeutic drug. Machine learning algorithms identified 3 key genes as candidate biomarkers. Additionally, immune cell infiltration results revealed significant correlations between PINK1, NLRC4, VAMP3, and immune cell accumulation. Molecular docking simulations revealed that imatinib can exert relatively strong regulatory effects on the three key proteins. Finally, in vivo experimental data revealed that the overall biological process of autophagy was disrupted. In summary, this study successfully identified 39 DE-ARGs and discovered several promising biomarkers, significantly contributing to our understanding of the underlying mechanisms of autophagy in SCI. These findings offer valuable insights for the development of novel therapeutic strategies.

Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. Its accurate pathogenic mechanisms are incompletely clarified, and effective therapeutic treatments are still inadequate. Autophagy is closely associated with AD and plays multiple roles in eliminating harmful aggregated proteins and maintaining cell homeostasis. This study identified 1191 differentially expressed genes (DEGs) based on the GSE5281 dataset from the GEO database, intersected them with 325 autophagy-related genes from GeneCards, and screened 26 differentially expressed autophagy-related genes (DEAGs). Subsequently, GO and KEGG enrichment analysis was performed and indicated that these DEAGs were primarily involved in autophagy-lysosomal biological process. Further, eight hub genes were determined by PPI construction, and experimental validation was performed by qRT-PCR on a SH-SY5Y cell model. Finally, three hub genes (TFEB, TOMM20, GABARAPL1) were confirmed to have potential application for biomarkers. A multigenic prediction model with good predictability (AUC = 0.871) was constructed in GSE5281 and validated in the GSE132903 dataset. Hub gene-targeted miRNAs closely associated with AD were also retrieved through the miRDB and HDMM database, predicting potential therapeutic agents for AD. This study provides new insights into autophagy-related genes in brain tissues of AD patients and offers more candidate biomarkers for AD mechanistic research as well as clinical diagnosis.

A Diagnostic Model for Parkinson's Disease Based on Anoikis-Related Genes

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, and its pathological mechanisms are thought to be closely linked to apoptosis. Anoikis, a specific type of apoptosis, has recently been suggested to play a role in the progression of Parkinson's disease; however, the underlying mechanisms are not well understood. To explore the potential mechanisms involved in PD, we selected genes from the GSE28894 dataset and compared their expression in PD patients and healthy controls to identify differentially expressed genes (DEGs), and selected anoikis-related genes (ANRGs) from the DEGs. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression approach and multivariate logistic regression highlighted five key genes-GSK3B, PCNA, CDC42, DAPK2, and SRC-as biomarker candidates. Subsequently, we developed a nomogram model incorporating these 5 genes along with age and sex to predict and diagnose PD. To evaluate the model's coherence, clinical applicability, and distinguishability, we utilized receiver operating characteristic (ROC) curves, the C-index, and calibration curves and validated it in both the GSE20295 dataset and our center's external clinical data. In addition, we confirmed the differential expression of the 5 model genes in human blood samples through qRT-PCR and Western blotting. Our constructed anoikis-related PD diagnostic model exhibits satisfactory predictive accuracy and offers novel insights into both diagnosis and treatment strategies for Parkinson's disease while facilitating its implementation in clinical practice.

Identification of cuproptosis-related gene clusters and immune cell infiltration in major burns based on machine learning models and experimental validation

Introduction

Burns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient's prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models.

Methods

We analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR.

Results

In both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury.

Conclusion

This study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated.

Identification of novel biomarkers based on lipid metabolism-related molecular subtypes for moderately severe and severe acute pancreatitis

BACKGROUND

Acute pancreatitis (AP) is an unpredictable and potentially fatal disorder. A derailed or unbalanced immune response may be the root of the disease's severe course. Disorders of lipid metabolism are highly correlated with the occurrence and severity of AP. We aimed to characterize the contribution and immunological characteristics of lipid metabolism-related genes (LMRGs) in non-mild acute pancreatitis (NMAP) and identify a robust subtype and biomarker for NMAP.

METHODS

The expression mode of LMRGs and immune characteristics in NMAP were examined. Then LMRG-derived subtypes were identified using consensus clustering. The weighted gene co-expression network analysis (WGCNA) was utilized to determine hub genes and perform functional enrichment analyses. Multiple machine learning methods were used to build the diagnostic model for NMAP patients. To validate the predictive effectiveness, nomograms, receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) were used. Using gene set variation analysis (GSVA) and single-cell analysis to study the biological roles of model genes.

RESULTS

Dysregulated LMRGs and immunological responses were identified between NMAP and normal individuals. NMAP individuals were divided into two LMRG-related subtypes with significant differences in biological function. The cluster-specific genes are primarily engaged in the regulation of defense response, T cell activation, and positive regulation of cytokine production. Moreover, we constructed a two-gene prediction model with good performance. The expression of CARD16 and MSGT1 was significantly increased in NMAP samples and positively correlated with neutrophil and mast cell infiltration. GSVA results showed that they are mainly upregulated in the T cell receptor complex, immunoglobulin complex circulating, and some immune-related routes. Single-cell analysis indicated that CARD16 was mainly distributed in mixed immune cells and macrophages, and MGST1 was mainly distributed in exocrine glandular cells.

CONCLUSIONS

This study presents a novel approach to categorizing NMAP into different clusters based on LMRGs and developing a reliable two-gene biomarker for NMAP.

A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics

Alzheimer's disease (AD) is a neurodegenerative disease with an annual incidence increase that poses significant health risks to people. However, the pathogenesis of AD is still unclear. Autophagy, as an intracellular mechanism can degrade damaged cellular components and abnormal proteins, which is closely related to AD pathology. The goal of this work is to uncover the intimate association between autophagy and AD, and to mine potential autophagy-related AD biomarkers by identifying key differentially expressed autophagy genes (DEAGs) and exploring the potential functions of these genes. GSE63061 and GSE140831 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of AD expression profiles. A total of 259 autophagy-related genes were discovered through the autophagy gene databases ATD and HADb. The differential genes of AD and autophagy genes were integrated and analyzed to screen out DEAGs. Then the potential biological functions of DEAGs were predicted, and Cytoscape software was used to detect the key DEAGs. There were ten DEAGs associated with the AD development, including nine up-regulated genes (CAPNS1, GAPDH, IKBKB, LAMP1, LAMP2, MAPK1, PRKCD, RAB24, RAF1) and one down-regulated gene (CASP1). The correlation analysis reveals the potential correlation among 10 core DEAGs. Finally, the significance of the detected DEAGs expression was verified, and the value of DEAGs in AD pathology was detected by the receiver operating characteristic curve. The area under the curve values indicated that ten DEAGs are potentially valuable for the study of the pathological mechanism and may become biomarkers of AD. This pathway analysis and DEAG screening in this study found a strong association between autophagy-related genes and AD, providing new insights into the pathological progression of AD. Exploring the relationship between autophagy and AD: analysis of genes associated with autophagy in pathological mechanisms of AD using bioinformatics. 10 autophagy-related genes play an important role in the pathological mechanisms of AD.

Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis

Background

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) infection. Cuproptosis is a novel cell death mechanism correlated with various diseases. This study sought to elucidate the role of cuproptosis-related genes (CRGs) in TB.

Methods

Based on the GSE83456 dataset, we analyzed the expression profiles of CRGs and immune cell infiltration in TB. Based on CRGs, the molecular clusters and related immune cell infiltration were explored using 92 TB samples. The Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was utilized to identify the co-expression modules and cluster-specific differentially expressed genes. Subsequently, the optimal machine learning model was determined by comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB). The predictive performance of the machine learning model was assessed by generating calibration curves and decision curve analysis and validated in an external dataset.

Results

11 CRGs were identified as differentially expressed cuproptosis genes. Significant differences in immune cells were observed in TB patients. Two cuproptosis-related molecular clusters expressed genes were identified. Distinct clusters were identified based on the differential expression of CRGs and immune cells. Besides, significant differences in biological functions and pathway activities were observed between the two clusters. A nomogram was generated to facilitate clinical implementation. Next, calibration curves were generated, and decision curve analysis was conducted to validate the accuracy of our model in predicting TB subtypes. XGB machine learning model yielded the best performance in distinguishing TB patients with different clusters. The top five genes from the XGB model were selected as predictor genes. The XGB model exhibited satisfactory performance during validation in an external dataset. Further analysis revealed that these five model-related genes were significantly associated with latent and active TB.

Conclusion

Our study provided hitherto undocumented evidence of the relationship between cuproptosis and TB and established an optimal machine learning model to evaluate the TB subtypes and latent and active TB patients.

Identification of lipid metabolism-related biomarkers for diagnosis and molecular classification of atherosclerosis

BACKGROUND

Atherosclerosis is now the main cause of cardiac-cerebral vascular diseases around the world. Disturbances in lipid metabolism have an essential role in the development and progression of atherosclerosis. Thus, we aimed to investigate lipid metabolism-related molecular clusters and develop a diagnostic model for atherosclerosis.

METHODS

First, we used the GSE100927 and GSE43292 datasets to screen differentially expressed lipid metabolism-related genes (LMRGs). Subsequent enrichment analysis of these key genes was performed using the Metascape database. Using 101 atherosclerosis samples, we investigated the LMRG-based molecular clusters and the corresponding immune cell infiltration. After that, a diagnostic model for atherosclerosis was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Finally, a series of bioinformatics techniques, including CIBERSORT, gene set variation analysis, and single-cell data analysis, were used to analyze the potential mechanisms of the model genes in atherosclerosis.

RESULTS

A total of 29 LMRGs were found to be differentially expressed between atherosclerosis and normal samples. Functional and DisGeNET enrichment analyses indicated that 29 LMRGs are primarily engaged in cholesterol and lipid metabolism, the PPAR signaling pathway, and regulation of the inflammatory response and are also closely associated with atherosclerotic lesions. Two LMRG-related molecular clusters with significant biological functional differences are defined in atherosclerosis. A three-gene diagnostic model containing ADCY7, SCD, and CD36 was subsequently constructed. Receiver operating characteristic curves, decision curves, and an external validation dataset showed that our model exhibits good predictive performance. In addition, three model genes were found to be closely associated with immune cell infiltration, especially macrophage infiltration.

CONCLUSIONS

Our study comprehensively highlighted the intricate association between lipid metabolism and atherosclerosis and created a three-gene model for future clinical diagnosis.