Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

Neurodegeneration and demyelination in multiple sclerosis

Progressive multiple sclerosis (PMS) is an immune-initiated neurodegenerative condition that lacks effective therapies. Although peripheral immune infiltration is a hallmark of relapsing-remitting MS (RRMS), PMS is associated with chronic, tissue-restricted inflammation and disease-associated reactive glial states. The effector functions of disease-associated microglia, astrocytes, and oligodendrocyte lineage cells are beginning to be defined, and recent studies have made significant progress in uncovering their pathologic implications. In this review, we discuss the immune-glia interactions that underlie demyelination, failed remyelination, and neurodegeneration with a focus on PMS. We highlight the common and divergent immune mechanisms by which glial cells acquire disease-associated phenotypes. Finally, we discuss recent advances that have revealed promising novel therapeutic targets for the treatment of PMS and other neurodegenerative diseases.

Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis

OBJECTIVES

This study aimed to reveal critical genes regulating peri-implantitis during its development and construct a diagnostic model by using random forest (RF) and artificial neural network (ANN).

METHODS

GSE-33774, GSE106090, and GSE57631 datasets were obtained from the GEO database. The GSE33774 and GSE106090 datasets were analyzed for differential expression and functional enrichment. The protein-protein interaction networks (PPI) and RF screened vital genes. A diagnostic model for peri-implantitis was established using ANN and validated on the GSE33774 and GSE57631 datasets. A transcription factor-gene interaction network and a transcription factor-micro-RNA (miRNA) regulatory network were also established.

RESULTS

A total of 124 differentially expressed genes (DEGs) involved in the regulation of peri-implantitis were screened. Enrichment analysis showed that DEGs were mainly associated with immune receptor activity and cytokine receptor activity and were mainly involved in processes such as leukocyte and neutrophil migration. The PPI and RF screened six essential genes, namely, CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8. The receiver operating characteristic curve (ROC) indicated that the ANN model had an excellent diagnostic performance. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 may be a key miRNA.

CONCLUSIONS

The diagnostic model of peri-implantitis constructed by RF and ANN has high confidence, and CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8 are potential diagnostic markers. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 plays a vital role as a critical miRNA.

Integrated Bioinformatics Analysis Reveals Diagnostic Biomarkers and Immune Cell Infiltration Characteristics of Solar Lentigines

Background

Solar lentigines (SLs), serving as a prevalent characteristic of skin photoaging, present as cutaneous aberrant pigmentation. However, the underlying pathogenesis remains unclear and there is a dearth of reliable diagnostic biomarkers.

Objective

The aim of this study was to identify diagnostic biomarkers for SLs and reveal its immunological features.

Methods

In this study, gene expression profiling datasets (GSE192564 and GSE192565) of SLs were obtained from the GEO database. The GSE192564 was used as the training group for screening of differentially expressed genes (DEGs) and subsequent depth analysis. Gene set enrichment analysis (GSEA) was employed to explore the biological states associated with SLs. The weighted gene co-expression network analysis (WGCNA) was employed to identify the significant modules and hub genes. Then, the feature genes were further screened by the overlapping of hub genes and up-regulated differential genes. Subsequently, an artificial neural network was constructed for identifying SLs samples. The GSE192565 was used as the test group for validation of feature genes expression level and the model's classification performance. Furthermore, we conducted immune cell infiltration analysis to reveal the immune infiltration landscape of SLs.

Results

The 9 feature genes were identified as diagnostic biomarkers for SLs in this study. And an artificial neural network based on diagnostic biomarkers was successfully constructed for identification of SLs. GSEA highlighted potential role of immune system in pathogenesis of SLs. SLs samples had a higher proportion of several immune cells, including activated CD8 T cell, dendritic cell, myeloid-derived suppressor cell and so on. And diagnostic biomarkers exhibited a strong relationship with the infiltration of most immune cells.

Conclusion

Our study identified diagnostic biomarkers for SLs and explored its immunological features, enhancing the comprehension of its pathogenesis.

Integrative Multi-omics Analysis to Characterize Herpes Virus Infection Increases the Risk of Alzheimer's Disease

Evidence suggests that herpes virus infection is associated with an increased risk of Alzheimer's disease (AD), and innate and adaptive immunity plays an important role in the association. Although there have been many studies, the mechanism of the association is still unclear. This study aims to reveal the underlying molecular and immune regulatory network through multi-omics data and provide support for the study of the mechanism of infection and AD in the future. Here, we found that the herpes virus infection significantly increased the risk of AD. Genes associated with the occurrence and development of AD and genetically regulated by herpes virus infection are mainly enrichment in immune-related pathways. The 22 key regulatory genes identified by machine learning are mainly immune genes. They are also significantly related to the infiltration changes of 3 immune cell in AD. Furthermore, many of these genes have previously been reported to be linked, or potentially linked, to the pathological mechanisms of both herpes virus infection and AD. In conclusion, this study contributes to the study of the mechanisms related to herpes virus infection and AD, and indicates that the regulation of innate and adaptive immunity may be an effective strategy for preventing and treating herpes virus infection and AD. Additionally, the identified key regulatory genes, whether previously studied or newly discovered, may serve as valuable targets for prevention and treatment strategies.

Identification of aging-related genes in diagnosing osteoarthritis via integrating bioinformatics analysis and machine learning

BACKGROUND

Osteoarthritis (OA) is one of the main causes of pain and disability in the world, it may be caused by many factors. Aging plays a significant role in the onset and progression of OA. However, the mechanisms underlying it remain unknown. Our research aimed to uncover the role of aging-related genes in the progression of OA.

METHODS

In Human OA datasets and aging-related genes were obtained from the GEO database and the HAGR website, respectively. Bioinformatics methods including Gene Ontology (GO), Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment, and Protein-protein interaction (PPI) network analysis were used to analyze differentially expressed aging-related genes (DEARGs) in the normal control group and the OA group. And then weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) regression, and the Random Forest (RF) machine learning algorithms were used to find the hub genes.

RESULTS

Four overlapping hub genes: HMGB2, CDKN1A, JUN, and DDIT3 were identified. According to the nomogram model and receiver operating characteristic (ROC) curve analysis, four hub DEARGs had good diagnostic value in distinguishing normal from OA. Furthermore, the qRT-PCR test demonstrated that HMGB2, CDKN1A, JUN, and DDIT3 mRNA expression levels were lower in OA group than in normal group.

CONCLUSION

Finally, these four-hub aging-related genes may help us understand the underlying mechanism of aging in osteoarthritis and could be used as possible diagnostic and therapeutic targets.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Background

The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Background

Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.

Methods

Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.

Results

The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.

Conclusions

The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 "kidney transplant"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms-LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs-were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO.

Revealing a potential necroptosis-related axis (RP11-138A9.1/hsa-miR-98-5p/ZBP1) in periodontitis by construction of the ceRNA network

BACKGROUND AND OBJECTIVES

Periodontitis, a prevalent chronic inflammatory condition, poses a significant risk of tooth loosening and subsequent tooth loss. Within the realm of programmed cell death, a recently recognized process known as necroptosis has garnered attention for its involvement in numerous inflammatory diseases. Nevertheless, its correlation with periodontitis is indistinct. Our study aimed to identify necroptosis-related lncRNAs and crucial lncRNA-miRNA-mRNA regulatory axes in periodontitis to further understand the pathogenesis of periodontitis.

MATERIALS AND METHODS

Gene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus (GEO) database. Selecting hub necroptosis-related lncRNA and extracting the key lncRNA-miRNA-mRNA axes based on the ceRNA network by adding novel machine-learning models based on conventional analysis and combining qRT-PCR validation. Then, an artificial neural network (ANN) model was constructed for lncRNA in regulatory axes, and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Weighted correlation network analysis (WGCNA) and single-sample gene set enrichment analysis (ssGSEA) was performed to explore how these lncRNAs work in periodontitis.

RESULTS

Seven hub necroptosis-related lncRNAs and three lncRNA-miRNA-mRNA regulatory axes (RP11-138A9.1/hsa-miR-98-5p/ZBP1 axis, RP11-96D1.11/hsa-miR-185-5p/EZH2 axis, and RP4-773 N10.4/hsa-miR-21-5p/TLR3 axis) were predicted. WGCNA revealed that RP11-138A9.1 was significantly correlated with the "purple module". Functional enrichment analysis and ssGSEA demonstrated that the RP11-138A9.1/hsa-miR-98-5p/ZBP1 axis is closely related to the inflammation and immune processes in periodontitis.

CONCLUSION

Our study predicted a crucial necroptosis-related regulatory axis (RP11-138A9.1/hsa-miR-98-5p/ZBP1) based on the ceRNA network, which may aid in elucidating the role and mechanism of necroptosis in periodontitis.

Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics

Background

Recently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagnostic and therapeutic tools available for the disease are insufficient, necessitating the screening of potential biomarkers to predict renal fibrosis.

Methods

Using the Gene Expression Omnibus (GEO) database, we obtained two gene array datasets (GSE76882 and GSE22459) from patients with renal fibrosis and healthy individuals. We identified differentially expressed genes (DEGs) between renal fibrosis and normal tissues and analyzed possible diagnostic biomarkers using machine learning. The diagnostic effect of the candidate markers was evaluated using receiver operating characteristic (ROC) curves and verified their expression using Reverse transcription quantitative polymerase chain reaction (RT-qPCR). The CIBERSORT algorithm was used to determine the proportions of 22 types of immune cells in patients with renal fibrosis, and the correlation between biomarker expression and the proportion of immune cells was studied. Finally, we developed an artificial neural network model of renal fibrosis.

Results

Four candidate genes namely DOCK2, SLC1A3, SOX9 and TARP were identified as biomarkers of renal fibrosis, with the area under the ROC curve (AUC) values higher than 0.75. Next, we verified the expression of these genes by RT-qPCR. Subsequently, we revealed the potential disorder of immune cells in the renal fibrosis group through CIBERSORT analysis and found that immune cells were highly correlated with the expression of candidate markers.

Conclusion

DOCK2, SLC1A3, SOX9, and TARP were identified as potential diagnostic genes for renal fibrosis, and the most relevant immune cells were identified. Our findings provide potential biomarkers for the diagnosis of renal fibrosis.

An artificial neural network model to diagnose non-obstructive azoospermia based on RNA-binding protein-related genes

Non-obstructive azoospermia (NOA) is a severe form of male infertility, but its pathological mechanisms and diagnostic biomarkers remain obscure. Since the dysregulation of RNA-binding proteins (RBPs) had nonnegligible effects on spermatogenesis, we aimed to investigate the functions and diagnosis values of RBPs in NOA. 58 testicular samples (control = 11, NOA = 47) from Gene Expression Omnibus (GEO) were set as the training cohort. Three public datasets, containing GSE45885 (control = 4, NOA = 27), GSE45887 (control = 4, NOA = 16), and GSE145467 (control = 10, NOA = 10), and 44 clinical samples from the local hospital (control = 27, NOA = 17) were used for validation. Through a series of bioinformatical analyses and machine learning algorithms, including genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, and Boruta, DDX20 and NCBP2 were determined as significant predictors of NOA. Single-cell RNA sequencing of 432 testicular cell samples from NOA patients indicated that DDX20 and NCBP2 were associated with spermatogenesis (false discovery rate < 0.05). Based on the transcriptome expressions of DDX20 and NCBP2, we constructed multiple diagnosis models using logistic regression, random forest, and artificial neural network (ANN). The ANN model exhibited the most reliable predictive performance in the training cohort (AUC = 0.840), GSE45885 (AUC = 0.731), GSE45887 (AUC = 0.781), GSE145467 (AUC = 0.850), and local cohort (AUC = 0.623). Totally, an ANN diagnosis model based on RBP DDX20 and NCBP2 was developed and externally validated in NOA, functioning as a promising tool in clinical practice.

Identification of Lipocalin 2 as a Ferroptosis-Related Key Gene Associated with Hypoxic-Ischemic Brain Damage via STAT3/NF-κB Signaling Pathway

Hypoxic-ischemic brain damage (HIBD) is a common cause of death or mental retardation in newborns. Ferroptosis is a novel form of iron-dependent cell death driven by lipid peroxidation, and recent studies have confirmed that ferroptosis plays an important role in the development of HIBD. However, HIBD ferroptosis-related biomarkers remain to be discovered. An artificial neural network (ANN) was established base on differentially expressed genes (DEGs) related to HIBD and ferroptosis and validated by external dataset. The protein-protein interaction (PPI) network, support vector machine-recursive feature elimination (SVM-RFE) algorithms, and random forest (RF) algorithm were utilized to identify core genes of HIBD. An in vitro model of glutamate-stimulated HT22 cell HIBD was constructed, and glutamate-induced ferroptosis and mitochondrial structure and function in HT22 cells were examined by propidium iodide (PI) staining, flow cytometry, Fe²⁺ assay, Western blot, JC-1 kit, and transmission electron microscopy (TEM). In addition, Western blot and immunofluorescence assays were used to detect the NF-κB/STAT3 pathway. An HIBD classification model was constructed and presented excellent performance. The PPI network and two machine learning algorithms indicated two hub genes in HIBD. Lipocalin 2 (LCN2) was the core gene correlated with the risk of HIBD according to the results of differential expression analysis and logistic regression diagnostics. Subsequently, we verified in an in vitro model that LCN2 is highly expressed in glutamate-induced ferroptosis in HT22 cells. More importantly, LCN2 silencing significantly inhibited glutamate-stimulated ferroptosis in HT22 cells. We also found that glutamate-stimulated HT22 cells produced mitochondrial dysfunction. Furthermore, in vitro experiments confirmed that NF-κB and STAT3 were activated and that silencing LCN2 could have the effect of inhibiting their activation. In short, our findings reveal a molecular mechanism by which LCN2 may promote ferroptosis in HIBD through activation of the NF-κB/STAT3 pathway, providing new and unique insights into LCN2 as a biomarker for HIBD and suggesting new preventive and therapeutic strategies for HIBD.

Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis

Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN). Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis. Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model's accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function. Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.

Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest

Systemic lupus erythematosus (SLE) is a latent, insidious autoimmune disease, and with the development of gene sequencing in recent years, our study aims to develop a gene-based predictive model to explore the identification of SLE at the genetic level. First, gene expression datasets of SLE whole blood samples were collected from the Gene Expression Omnibus (GEO) database. After the datasets were merged, they were divided into training and validation datasets in the ratio of 7:3, where the SLE samples and healthy samples of the training dataset were 334 and 71, respectively, and the SLE samples and healthy samples of the validation dataset were 143 and 30, respectively. The training dataset was used to build the disease risk prediction model, and the validation dataset was used to verify the model identification ability. We first analyzed differentially expressed genes (DEGs) and then used Lasso and random forest (RF) to screen out six key genes (OAS3, USP18, RTP4, SPATS2L, IFI27 and OAS1), which are essential to distinguish SLE from healthy samples. With six key genes incorporated and five iterations of 10-fold cross-validation performed into the RF model, we finally determined the RF model with optimal mtry. The mean values of area under the curve (AUC) and accuracy of the models were over 0.95. The validation dataset was then used to evaluate the AUC performance and our model had an AUC of 0.948. An external validation dataset (GSE99967) with an AUC of 0.810, an accuracy of 0.836, and a sensitivity of 0.921 was used to assess the model's performance. The external validation dataset (GSE185047) of all SLE patients yielded an SLE sensitivity of up to 0.954. The final high-throughput RF model had a mean value of AUC over 0.9, again showing good results. In conclusion, we identified key genetic biomarkers and successfully developed a novel disease risk prediction model for SLE that can be used as a new SLE disease risk prediction aid and contribute to the identification of SLE.