Machine learning algorithms assisted identification of post-stroke depression associated biological features

To investigate the signature genes of fatty acid metabolism and their association with immune cells in pulmonary arterial hypertension (PAH). Fatty acid metabolism-related genes were obtained from the GeneCards database. In this retrospective study, a PAH-related dataset was downloaded from the Gene Expression Omnibus database and analyzed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify the signature genes. Diagnostic efficiency was assessed by receiver operating characteristic (ROC) curve analysis and a nomogram. Immune cell infiltration was subsequently classified using CIBERSORT. In total, 817 DEGs were screened from the GSE33463 dataset. The data were clustered into six modules via WGCNA, and the MEdarkred module was significantly related to PAH. The LASSO and random forest algorithms identified five signature genes: ARV1, KCNJ2, PEX11B, PITPNC1, and SCO1. The areas under the ROC curves of these signature genes were 0.917, 0.934, 0.947, 0.963, and 0.940, respectively. CIBERSORT suggested these signature genes may participate in immune cell infiltration. ARV1, KCNJ2, PEX11B, PITPNC1, and SCO1 show remarkable diagnostic performance in PAH and are involved in immune cell infiltration.

BackgroundDecidualization deficiency is a key pathological feature of pre-eclampsia (PE) and is closely associated with aberrant regulation of cell fate. PANoptosis is a recently characterized form of inflammatory programmed cell death that has been implicated in several pregnancy-related disorders. However, its potential involvement in decidualization deficiency in PE remains poorly understood. This study aimed to explore the association between PANoptosis-related genes and decidualization deficiency in PE, and to identify candidate biomarkers and potential therapeutic targets related to PANoptosis.MethodsDatasets containing decidual tissue samples derived from women with PE and normal controls were acquired in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. After that, PANoptosis-related genes were intersected with DEGs derived from the decidual tissue of PE, followed by protein–protein interaction (PPI) network construction and correlation analysis. Next, the immune infiltration landscape and its association with PANoptosis-related DEGs were assessed. Furthermore, three machine learning algorithms, including support vector machine-recursive feature elimination (SVM-RFE), the least absolute shrinkage and selection operator (LASSO), and the random forest (RF) algorithms, were adopted to identify potential diagnostic biomarkers for PE. Artificial neural network (ANN) and nomogram models were then constructed and evaluated in testing datasets, which included decidual stromal cell samples derived from women with PE and normal controls. Additionally, the expression of PANoptosis-related signature genes and decidualization-related markers was experimentally validated in primary human decidual stromal cells (HDSCs) derived from PE patients and healthy controls. In addition, consensus clustering analysis was conducted on the basis of signature genes, and immune infiltration landscape analysis of different subtypes of PE was performed. Ultimately, the candidate compounds targeting the signature genes were screened and then further verified in vivo and in vitro models.Results430 DEGs were determined, and enrichment analysis indicated that these DEGs were mainly involved in inflammation, apoptosis, and dysfunction of decidual tissue in PE. Then, 10 PANoptosis-related DEGs in PE were further screened. Following that, immune landscape analysis revealed an aberrant abundance of various immunocytes and the levels of immune checkpoints in the decidual tissue of PE, which were closely associated with the PANoptosis-related DEGs. Next, through machine learning, nine PANoptosis-related signature genes (MAPK3, RIPK1, RIPK3, PYCARD, BAX, TUG1, CDK1, MAPK1, and TAB2) were identified with favorable predictive performance. Besides, the ANN and nomogram models were constructed, and demonstrated high discriminative ability in the training dataset (AUC = 0.999, 95% CI: 0.995–1.000). Consistently, validation in primary HDSCs derived from PE patients and healthy controls confirmed dysregulated expression of these signature genes, accompanied by reduced decidualization markers (PRL, IGFBP1). Furthermore, on the basis of the analysis of nine signature genes, two different subtypes of PE were acquired, in which subtype B showed an immune hyperactivity state compared to subtype A. Furthermore, melatonin was identified as a candidate compound targeting PANoptosis-related genes and showed protective effects in vivo and in vitro, including improved blood pressure, reduced proteinuria, partial restoration of decidualization markers (PRL, IGFBP1, and F-actin), and declined expressions of PANoptosis-related signature genes (BAX, MAPK1, and MAPK3). Importantly, functional experiments demonstrated that MAPK3 knockdown markedly attenuated PANoptosis-associated inflammatory cytokine production, reduced BAX expression, and partially restored F-actin organization and decidualization markers under PANoptosis-inducing conditions.ConclusionThis study suggests a potential association between PANoptosis-related molecular dysregulation and decidualization deficiency in PE. The identified PANoptosis-related signature genes may serve as candidate biomarkers with predictive relevance, and melatonin may represent a potential therapeutic candidate targeting PANoptosis-related pathways. These findings provide a foundation for future mechanistic and translational studies on PE.

Background The genetic factors and pathogenesis of idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) have not been understood thoroughly; there is a lack of specific diagnostic markers and treatment methods for the disease. Hence, we aimed to identify the mechanisms of action at the molecular level and potential molecular markers for this disease. Methods Gene expression profiles of IDCM-HF and non-heart failure (NF) specimens were acquired from the database of Gene Expression Omnibus (GEO). We then identified the differentially expressed genes (DEGs) and analyzed their functions and related pathways by using “Metascape”. Weighted gene co-expression network analysis (WGCNA) was utilized to search for key module genes. Candidate genes were identified by intersecting the key module genes identified via WGCNA with DEGs and further screened via the support vector machine-recursive feature elimination (SVM-RFE) method and the least absolute shrinkage and selection operator (LASSO) algorithm. At last, the biomarkers were validated and evaluated the diagnostic efficacy by the area under curve (AUC) value and further confirmed the differential expression in the IDCM-HF and NF groups using an external database. Results We detected 490 genes exhibiting differential expression between IDCM-HF and NF specimens from the GSE57338 dataset, with most of them being concentrated in the extracellular matrix (ECM) of cells related to biological processes and pathways. After screening, 13 candidate genes were identified. Aquaporin 3 (AQP3) and cytochrome P450 2J2 (CYP2J2) showed high diagnostic efficacy in the GSE57338 and GSE6406 datasets, respectively. In comparison to the NF group, AQP3 was significantly down-regulated in the IDCM-HF group, while CYP2J2 was significantly up-regulated. Conclusion As far as we know, this is the first study that combines WGCNA and machine learning algorithms to screen for potential biomarkers of IDCM-HF. Our findings suggest that AQP3 and CYP2J2 could be used as novel diagnostic markers and treatment targets of IDCM-HF.

BackgroundImmune system dysregulation plays a critical role in aortic valve calcification (AVC) and metabolic syndrome (MS) pathogenesis. The study aimed to identify pivotal diagnostic candidate genes for AVC patients with MS.MethodsWe obtained three AVC and one MS dataset from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module gene via Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify candidate immune-associated hub genes for diagnosing AVC with MS. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC.ResultsThe merged AVC dataset included 587 DEGs, and 1,438 module genes were screened out in MS. MS DEGs were primarily enriched in immune regulation. The intersection of DEGs for AVC and module genes for MS was 50, which were mainly enriched in the immune system as well. Following the development of the PPI network, 26 node genes were filtered, and five candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all five candidate hub genes had high diagnostic values (area under the curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well.ConclusionFive immune-associated candidate hub genes (BEX2, SPRY2, CXCL16, ITGAL, and MORF4L2) were identified, and the nomogram was constructed for AVC with MS diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for AVC in MS patients.

Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis.Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs.Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment.Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.

ObjectivesNasopharyngeal carcinoma (NPC) is an aggressive malignancy with high rates of morbidity and mortality, largely because of its late diagnosis and metastatic potential. Lactate metabolism and protein lactylation are thought to play roles in NPC pathogenesis by modulating the tumor microenvironment and immune evasion. However, research specifically linking lactate-related mechanisms to NPC remains limited. This study aimed to identify lactate-associated biomarkers in NPC and explore their underlying mechanisms, with a particular focus on immune modulation and tumor progression.MethodsTo achieve these objectives, we utilized a bioinformatics approach in which publicly available gene expression datasets related to NPC were analysed. Differential expression analysis revealed differentially expressed genes (DEGs) between NPC and normal tissues. We performed weighted gene coexpression network analysis (WGCNA) to identify module genes significantly associated with NPC. Overlaps among DEGs, key module genes and lactate-related genes (LRGs) were analysed to derive lactate-related differentially expressed genes (LR-DEGs). Machine learning algorithms can be used to predict potential biomarkers, and immune infiltration analysis can be used to examine the relationships between identified biomarkers and immune cell types, particularly M0 macrophages and B cells.ResultsA total of 1,058 DEGs were identified between the NPC and normal tissue groups. From this set, 372 key module genes associated with NPC were isolated. By intersecting the DEGs, key module genes and lactate-related genes (LRGs), 17 lactate-related DEGs (LR-DEGs) were identified. Using three machine learning algorithms, this list was further refined, resulting in three primary lactate-related biomarkers: TPPP3, MUC4 and CLIC6. These biomarkers were significantly enriched in pathways related to “immune cell activation” and the “extracellular matrix environment”. Additionally, M0 and B macrophages were found to be closely associated with these biomarkers, suggesting their involvement in shaping the NPC immune microenvironment.ConclusionIn summary, this study identified TPPP3, MUC4 and CLIC6 as lactate-associated clinical modelling indicators linked to NPC, providing a foundation for advancing diagnostic and therapeutic strategies for this malignancy.

Background: Clear cell renal cell carcinoma (ccRCC) is characteristics of resistance to chemotherapy and radiotherapy. The prognosis of ccRCC was dismay with immense diversity. Iron metabolism disturbance is a common phenomenon in ccRCC. The purpose of our study is to identify and validate the candidate prognostic gene signature of iron metabolism and methylation closely related to the poor prognosis of ccRCC through comprehensive bioinformatics analysis in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases.Methods: The prognostic iron metabolism-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. We built a prognostic model using risk score method to predict OS, each ccRCC patient's risk score was calculated, and the resulting score can divide these patients into two categories according to the cut-point risk score. The prognostic significance of the hub genes was further evaluated with the Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis was implemented to evaluate the impact of each variable on OS. Furthermore, the prediction power of the 25 gene signatures has been validated using an independent ccRCC cohort from the GEO database. The Gene Set Enrichment Analysis (GSEA) identified the characteristics of hub related oncogenes. Finally, we utilize Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the co-expression network based on these DEGs.Results: In this study, we identified and validated 25 iron metabolism-related and methylated genes as the prognostic signatures, which differentiated ccRCC patients into high and low risk subgroups. The KM analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 25 gene signatures could largely predict OS and DFS for 1, 3, and 5 years in patients with ccRCC.Conclusions: Taken together, we identified the key iron metabolism-related and methylated genes for ccRCC through a comprehensive bioinformatics analysis. This study provides a reliable and robust gene signature for the prognostic predictor of ccRCC patients and maybe provides a promising treatment strategy for this lethal disease.

Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis. Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed. A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (ARNTL2, FHL2, KRT19, MMP1, CDCA5, and KIF11), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells. This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.

Background Diabetic nephropathy (DN), a significant cause of chronic kidney disease (CKD), is a devastating disease worldwide. Objective The aim of this study was to reveal crucial genes closely linked to the molecular mechanism of tubulointerstitial injury in DN. Methods The Gene Expression Omnibus (GEO) database was used to download the datasets. Based on this, a weighted gene coexpression network analysis (WGCNA) network was constructed to detect DN-related modules and hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments were performed on the selected hub genes and modules. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed on the obtained gene signature. Results The WGCNA network was constructed based on 3019 genes, and nine gene coexpression modules were generated. A total of 57 genes, including 34 genes in the magenta module and 23 genes in the purple module, were adapted as hub genes. 61 significantly downregulated and 119 upregulated genes were screened as differentially expressed genes (DEGs). 25 overlapping genes between hub genes chosen from WGCNA and DEG were identified. Through LASSO analysis, a 9-gene signature may be a potential prognostic biomarker for DN. To further explore the potential mechanism of DN, the different immune cell infiltrations between tubulointerstitial samples of DN and healthy samples were estimated. Conclusions This bioinformatics study identified CX3CR1, HRG, LTF, TUBA1A, GADD45B, PDK4, CLIC5, NDNF, and SOCS2 as candidate biomarkers for the diagnosis of DN. Moreover, DN tends to own a higher proportion of memory B cell.

Apply bioinformatics combined with machine learning algorithms to screen potential biomarkers of neonatal sepsis (NS) and explore the correlation between biomarkers and immune cells. Expression profiling data containing NS samples and control samples were downloaded from the Gene Expression Omnibus (GEO) database. First, the differentially expressed genes (DEGs) were found. Then gene set enrichment analysis (GSEA) was carried out to reveal the significantly enriched pathways, and the core genes within these pathways were aggregated. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to identify the gene modules significantly correlated with NS, from which the key genes were screened. The feature genes were selected by the use of three machine learning algorithms: least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and random forest cross-validation (RF-CV), and then the potential biomarkers of NS were identified by logistic regression analyses. The dataset was randomly split into training and test sets. A logistic regression model with biomarkers was constructed using the training set, and its diagnostic value was evaluated separately on both the training and test sets. In addition, immune infiltration analysis was performed using immune cell deconvolution algorithm CIBERSORT to explore the correlation between biomarkers and immune cells. Overall, after overlapping the filtered DEGs, the core genes of GSEA, and the key genes of WGCNA, we detected 14 intersecting genes. Three machine learning algorithms further selected 4 feature genes, and logistic regression analyses identified ACSL1 and CD3D as potential biomarkers of NS. The classification accuracies of the logistic regression model were 0.883 and 0.902 on the training set and test set, respectively. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.961 and 0.966, and the calibration curves were close to the ideal curve. It should be noted that these AUC values were estimated within this discovery GEO cohort and require confirmation in independent external cohorts. Immune infiltration analysis showed significant changes (P < 0.05) in the infiltration abundance of 13 immune cell types, and a strong correlation (>0.6) existed between ACSL1, CD3D and neutrophils, naive CD4+ T cells, and CD8+ T cells. ACSL1, CD3D are potential biomarkers of NS and may play an important role in the pathogenesis of NS by modulating immune cell functions such as neutrophils, naive CD4+ T cells, and CD8+ T cells. These conclusions should be regarded as exploratory biomarker discovery rather than diagnostic readiness.

Lupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a new form of programmed cell death, has been implicated in a variety of renal diseases, but the relationship between autophagy and LN remains unelucidated. We analyzed differentially expressed genes (DEGs) in kidney tissues from 14 LN patients and 7 normal controls using the GSE112943 dataset. Key modules and their contained genes were identified utilizing weighted gene co-expression network analysis (WGCNA). Differentially expressed autophagy-related genes (DE-ARGs) among DEGs, key module genes and autophagy-related genes (ARGs) were obtained by venn plot, and subjected to protein-protein interaction network construction. Two machine learning methods were applied to identify signature genes. The area under the receiver operating characteristic (ROC) curves was used to assess the accuracy of the signature genes. We also analyzed immune cell infiltration in LN. Additionally, the association between key genes and kidney diseases was predicted. Finally, key genes expression in kidney was verified by clinical samples and animal experiments. A total of 10304 DEGs were identified in GSE1129943 and 29 modules were identified in WGCNA. Among them, the brown module and coral 2 module exhibited significant correlation with LN (cor = 0.86, -0.84, p<0.001). Machine learning techniques identified 5 signature genes, but only 2 were validated in the external dataset GSE32591, namely MAP1LC3B (AUC = 0.920) and TNFSF10 (AUC = 0.937), which are involved in autophagy and apoptosis. Immune infiltration analysis suggested that these key genes may be associated with immune cell infiltration in LN. In addition, these genes have been linked to a variety of renal diseases, and their expression was verified in kidney tissues in LN patients and lupus mice. MAP1LC3B and TNFSF10 may be key autophagy-related genes in LN. These key genes have the potential to provide new insights into the molecular diagnosis and treatment of LN.

Background: Venous thromboembolism (VTE) is a major cause of morbidity and mortality globally, accompanied with a variety of underlying diseases, and it often doesn't get enough attention for early diagnosis and treatment. VTE is associated with many risk factors, however, the genetic basis of venous thromboembolism remains poorly understood. Methods: We analyzed whole-blood gene expression profiles from 185 VTE patients and 93 normal controls obtained from the GEO database. Differential expression analysis was conducted to identify differentially expressed genes (DEGs), followed by weighted gene co-expression network analysis (WGCNA) to identify key genes through intersection analysis. Subsequently, enrichment analysis was performed on gene modules associated with key genes. Central genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest algorithms. Additionally, immune cell infiltration was analyzed using CIBERSORT, and pathway functions of key genes were assessed through Gene Set Enrichment Analysis (GSEA). Results: A total of 102 DEGs were identified in VTE. Using weighted gene co-expression network analysis (WGCNA), these DEGs were clustered into 14 modules. Among them, the black module showed significant correlation with VTE (cor=0.48, p&lt;0.0001). Machine learning techniques identified four key genes, namely TXNL1 (AUC=0.840), CD3G (AUC=0.786), MRPL15 (AUC=0.775), and MCTS1 (AUC=0.748), demonstrating high diagnostic efficacy for predicting VTE. These four hub genes were significantly associated with pathways such as TRANSLATION, SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE, and EUKARYOTIC TRANSLATION INITIATION, providing valuable insights into the potential mechanisms underlying VTE. Immunoinfiltration analysis using ssGSTEA revealed significant positive correlations between Activated CD8 T cell, Gamma delta T cell, and hub genes, suggesting the potential of these hub genes and immune cell populations for targeted immunotherapy in VTE patients, thereby opening new avenues for therapeutic intervention. Conclusion: TXNL1, CD3G, MRPL15, and MCTS1 have emerged as promising candidate driver genes for VTE. These central genes hold the potential to provide valuable insights into the molecular diagnosis and treatment of VTE.

Hepatocellular carcinoma (HCC) is a liver disease with a complex underlying mechanism, and patients with HCC have low survival rates. Iron metabolism plays a crucial role in the pathogenesis of HCC; however, the prognostic value of iron metabolism-related and methylated genes for HCC needs to be further explored. In the present study, we identified differentially expressed genes (DEGs) that play a role in iron metabolism and DNA methylation in HCC from The Cancer Genome Atlas. Four of these DEGs, whose expression levels are correlated with HCC prognosis, namely, RRM2, FTCD, CYP2C9, and ATP6V1C1, were further used to construct a prognostic model for HCC, wherein the risk score was calculated using the gene expression of the four DEGs. This could be used to predict the overall survival of HCC patients for 1, 3, and 5 years. Results of a multivariate Cox regression analysis further indicated that the risk score was an independent variable correlated with the prognosis of HCC patients. The identified gene signature was further validated using an independent cohort of HCC patients from the International Cancer Genome Consortium. Weighted gene co-expression network analysis and gene set enrichment analysis were performed to identify potential regulatory mechanisms of the gene signature in HCC. Taken together, we identified key prognostic factors of iron metabolism-related and methylated genes for HCC, providing a potential treatment strategy for HCC.

Endometriosis (EM), a disorder driven by persistent systemic inflammation, impacts around 10% of women in their reproductive period, often diagnosed only via surgery. Metabolic alterations, particularly in lipid metabolism, may uncover novel biomarkers. We aimed to identify diagnostic markers and molecular subtypes by integrating lipid metabolism gene expression and machine learning. We downloaded gene expression datasets (GSE51981 and GSE7305) from the Gene Expression Omnibus (GEO) database. Differential expression was analyzed using limma (|log2FC|>1, p.adj<0.05); intersected with lipid genes to yield candidate genes. Weighted gene co-expression network analysis (WGCNA) demonstrated endometriosis-connected gene modules. Integrating lipid metabolism-related differentially expressed genes with WGCNA hub genes, followed by least absolute shrinkage and selection operator (LASSO) and XGBoost machine learning, identified diagnostic biomarkers. Their performance was validated using receiver operating characteristic (ROC) curves in an independent dataset. Immune infiltration, including CIBERSORT and single-sample GSEA (ssGSEA), gene set enrichment analysis (GSEA), and non-negative matrix factorization (NMF)-based subtype analyses were performed. MicroRNA (miRNA) and transcription factor (TF) regulatory networks were constructed using online databases. We identified 106 lipid metabolism-related differential genes. WGCNA revealed the turquoise module strongly correlated with endometriosis. ELOVL6 and MED20 were identified as key genes through machine learning algorithms. The two key genes emerged as robust diagnostic biomarkers, showing higharea under the ROC curves(AUCs)across both training and validation sets. Immune infiltration analysis revealed distinct immune cell patterns in endometriosis, with ELOVL6 and MED20 correlating with specific immune cells. Subtype analysis, based on lipid metabolism scores, stratified patients into high and low score groups with differential gene expression and immune cell infiltration. Regulatory networks identified miRNAs and TFs targeting ELOVL6 and MED20. Our study identified ELOVL6 and MED20 as promising lipid metabolism-related diagnostic biomarkers for endometriosis. We also uncovered distinct molecular subtypes linked to lipid metabolism, providing novel insights into endometriosis heterogeneity and potential therapeutic targets.

This study aims to elucidate the mechanisms underlying Dementia using bioinformatics analysis and machine learning algorithms, to identify novel therapeutic targets for its clinical management. Gene expression datasets related to dementia were sourced from the GEO database. Differentially expressed genes (DEGs) were identified using R, and key module genes were determined through the Weighted Gene Co-expression Network Analysis (WGCNA) method. Oligodendrocyte (OL) related targets were retrieved from the GeneCards database. The intersecting genes from DEGs, WGCNA, and OL were analyzed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Subsequently, three machine learning algorithms were employed to pinpoint core genes associated with OL in dementia. The CIBERSORT algorithm was used to evaluate the abundance of 22 immune cell types and their correlation with Dementia-related immune infiltration. Validation was carried out via quantitative reverse transcription polymerase chain reaction (RT-qPCR). Through bioinformatics and machine learning techniques, six core OL genes associated with Dementia were identified, notably C1QA, CD163, and TGFB2, which showed elevated expression in Dementia. Immune cell infiltration analysis indicated that several immune cell types may contribute to Dementia's pathogenesis, and RT-qPCR results corroborated the bioinformatics findings. The discovered genes may contribute to dementia pathogenesis through oligodendrocyte dysfunction and neuroimmune interactions. Notably, TGFB2 and complement-related genes (C1QA, CD163) suggest involvement in both myelination defects and neuroinflammation, highlighting their therapeutic potential. The six feature genes: TGFB2, C1QA, CD163, ACTG1, WIF1, and OPALIN are significantly linked to Dementia.