Immune Cell Infiltration Analysis Research Articles

A study on construction of a prognosis model for liver cancer based on analgesic targets and screening therapeutic drugs.

Liver cancer is one of the most malignant liver diseases in the world, and the 5-year survival rate of such patients is low. Analgesics are often used to cure pain prevalent in liver cancer. The expression changes and clinical significance of the analgesic targets (ATs) in liver cancer have not been deeply understood. The purpose of this study is to clarify the expression pattern of ATs gene in liver cancer and its clinical significance. Through the comprehensive analysis of transcriptome data and clinical parameters, the prognosis model related to ATs gene is established, and the drug information sensitive to ATs is mined. The study primarily utilized transcriptomic data and clinical information from liver cancer patients sourced from The Cancer Genome Atlas (TCGA) database. These data were employed to analyze the expression of ATs, conduct survival analysis, gene set variation analysis (GSVA), immune cell infiltration analysis, establish a prognostic model, and perform other bioinformatic analyses. Additionally, data from liver cancer patients in the International Cancer Genome Consortium (ICGC) were utilized to validate the accuracy of the model. Furthermore, the impact of analgesics on key genes in the prognostic model was assessed using data from the Comparative Toxicogenomics Database (CTD). The study investigated the differential expression of 58 ATs genes in liver cancer compared to normal tissues. Patients were stratified based on ATs expression, revealing varied survival outcomes. Functional enrichment analysis highlighted distinctions in spindle organization, centrosome, and spindle microtubule functions. Prognostic modeling identified low TP53 expression as protective, while elevated CCNA2, NEU1, and HTR2C levels posed risks. Commonly used analgesics, including acetaminophen and others, were found to influence the expression of these genes. These findings provide insights into potential therapeutic strategies for liver cancer and shed light on the molecular mechanisms underlying its progression. The collective analysis of gene signatures associated with ATs suggests their potential as prognostic predictors in hepatocellular carcinoma patients. These findings not only offer insights into cancer therapy but also provide novel avenues for the development of indications for analgesics.

Exploring the molecular mechanisms of ferroptosis-related genes in periodontitis: a multi-dataset analysis

PurposeThis study aims to elucidate the biological functions of ferroptosis-related genes in periodontitis, along with their correlation to tumor microenvironment (TME) features such as immune infiltration. It aims to provide potential diagnostic markers of ferroptosis for clinical management of periodontitis.MethodsUtilizing the periodontitis-related microarray dataset GSE16134 from the Gene Expression Omnibus (GEO) and a set of 528 ferroptosis-related genes identified in prior studies, this research unveils differentially expressed ferroptosis-related genes in periodontitis. Subsequently, a protein–protein interaction network was constructed. Subtyping of periodontitis was explored, followed by validation through immune cell infiltration and gene set enrichment analyses. Two algorithms, randomForest and SVM(Support Vector Machine), were employed to reveal potential ferroptosis diagnostic markers for periodontitis. The diagnostic efficacy, immune correlation, and potential transcriptional regulatory networks of these markers were further assessed. Finally, potential targeted drugs for differentially expressed ferroptosis markers in periodontitis were predicted.ResultsA total of 36 ferroptosis-related genes (30 upregulated, 6 downregulated) were identified from 829 differentially expressed genes between 9 periodontitis samples and the control group. Subsequent machine learning algorithm screening highlighted 4 key genes: SLC1A5(Solute Carrier Family 1 Member 5), SLC2A14(Solute Carrier Family 1 Member 14), LURAP1L(Leucine Rich Adaptor Protein 1 Like), and HERPUD1(Homocysteine Inducible ER Protein With Ubiquitin Like Domain 1). Exploration of these 4 key genes, supported by time-correlated ROC analysis, demonstrated reliability, while immune infiltration results indicated a strong correlation between key genes and immune factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was conducted for the four key genes, revealing enrichment in GO/KEGG pathways that have a significant impact on periodontitis. Finally, the study predicted potential transcriptional regulatory networks and targeted drugs associated with these key genes in periodontitis.ConclusionsThe ferroptosis-related genes identified in this study, including SLC1A5, SLC2A14, LURAP1L, and HERPUD1, may serve as novel diagnostic and therapeutic targets for periodontitis. They are likely involved in the occurrence and development of periodontitis through mechanisms such as immune infiltration, cellular metabolism, and inflammatory chemotaxis, potentially linking the ferroptosis pathway to the progression of periodontitis. Targeted drugs such as flurofamide, L-733060, memantine, tetrabenazine, and WAY-213613 hold promise for potential therapeutic interventions in periodontitis associated with these ferroptosis-related genes.

Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study.

Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE is unclear. It is helpful to improve the diagnosis and treatment of PE using new biomarkers related to PE in the blood. Three PE-related microarray datasets were obtained from the Gene Expression Synthesis database. The limma software package was used to identify differentially expressed genes (DEGs) between PE and control groups. Least absolute shrinkage and selection operator regression, support vector machine, random forest, and multivariate logistic regression analyses were used to determine key diagnostic biomarkers, which were verified using clinical samples. Subsequently, functional enrichment analysis was performed. In addition, the datasets were combined for immune cell infiltration analysis and to determine their relationships with core diagnostic biomarkers. The diagnostic performance of key genes was evaluated using the receiver operating characteristic (ROC) curve, C-index, and GiViTi calibration band. Genes with potential clinical applications were evaluated using decision curve analysis (DCA). Seventeen DEGs were identified, and 6 key genes (FN1, MYADM, CA6, PADI4, SLC4A10, and PPP4R1L) were obtained using 3 types of machine learning methods and logistic regression. High diagnostic performance was found for PE through evaluation of the ROC, C-index, GiViti calibration band, and DCA. The 2 types of immune cells (M0 macrophages and activated mast cells) were significantly different between patients with PE and controls. All of these genes except SLC4A10 showed significant differences in expression levels between the 2 groups using quantitative reverse transcription-polymerase chain reaction. This model used 6 maternal blood markers to predict the occurrence of PE. The findings may stimulate ideas for the treatment and prevention of PE.

#803 Identification and validation of immune and cuproptosis-related genes for diabetic nephropathy by WGCNA and machine learning

Abstract Background and Aims As the leading cause of chronic kidney disease, diabetic kidney disease (DKD) is an enormous burden for all healthcare systems around the world. However, its early diagnosis has no effective methods. Method First, gene expression data in GEO database were extracted, and the differential genes of diabetic tubulopathy were obtained. Immune-related gene sets were generated by WGCNA and immune cell infiltration analyses. Then, differentially expressed immune-related cuproptosis genes (DEICGs) were derived by the intersection of differential genes and genes related to cuproptosis and immune. To investigate the functions of DEICGs, volcano plots and GO term enrichment analysis was performed. Machine learning and protein-protein interaction (PPI) network analysis helped to finally screen out hub genes. The diagnostic efficacy of them was evaluated by GSEA analysis, receiver operating characteristic (ROC) curve, single-cell RNA sequencing and the Nephroseq website. The expression of hub genes at the animal level by STZ -induced and db/db DKD mouse models was further verified. Results Finally, three hub genes, including FSTL1, CX3CR1 and AGR2 that were up-regulated in both the test set GSE30122 and the validation set GSE30529, were screened. The areas under the curve (AUCs) of ROC curves of hub genes were 0.911, 0.935 and 0.922, respectively, and 0.946 when taking as a whole. Correlation analysis showed that the expression level of three hub genes demonstrated their negative relationship with GFR, while those of FSTL1 displayed a positive correlation with the level of serum creatinine. GSEA was enriched in inflammatory and immune-related pathways. Single-nucleus RNA sequencing indicated the main distribution of FSTL1 in podocyte and mesangial cells, the high expression of CX3CR1 in leukocytes and the main localization of AGR2 in the loop of Henle. In mouse models, all three hub genes were increased in both STZ-induced and db/db DKD models. Conclusion Machine learning was combined with WGCNA, immune cell infiltration and PPI analyses to identify three hub genes associated with cuproptosis, immunity and diabetic nephropathy, which all have great potential as diagnostic markers for DKD and even predict disease progression.

LINC00330/CCL2 axis-mediated ESCC TAM reprogramming affects tumor progression

BackgroundTumor-associated macrophages (TAMs) significantly influence the progression, metastasis, and recurrence of esophageal squamous cell carcinoma (ESCC). The aberrant expression of long noncoding RNAs (lncRNAs) in ESCC has been established, yet the role of lncRNAs in TAM reprogramming during ESCC progression remains largely unexplored.MethodsESCC TAM-related lncRNAs were identified by intersecting differentially expressed lncRNAs with immune-related lncRNAs and performing immune cell infiltration analysis. The expression profile and clinical relevance of LINC00330 were examined using the TCGA database and clinical samples. The LINC00330 overexpression and interference sequences were constructed to evaluate the effect of LINC00330 on ESCC progression. Single-cell sequencing data, CIBERSORTx, and GEPIA were utilized to analyze immune cell infiltration within the ESCC tumor microenvironment and to assess the correlation between LINC00330 and TAM infiltration. ESCC-macrophage coculture experiments were conducted to investigate the influence of LINC00330 on TAM reprogramming and its subsequent effect on ESCC progression. The interaction between LINC00330 and C–C motif ligand 2 (CCL2) was confirmed through transcriptomic sequencing, subcellular localization analysis, RNA pulldown, silver staining, RNA immunoprecipitation, and other experiments.ResultsLINC00330 is significantly downregulated in ESCC tissues and strongly associated with poor patient outcomes. Overexpression of LINC00330 inhibits ESCC progression, including proliferation, invasion, epithelial–mesenchymal transition, and tumorigenicity in vivo. LINC00330 promotes TAM reprogramming, and LINC00330-mediated TAM reprogramming inhibits ESCC progression. LINC00330 binds to the CCL2 protein and inhibits the expression of CCL2 and downstream signaling pathways. CCL2 is critical for LINC00330-mediated TAM reprogramming and ESCC progression.ConclusionsLINC00330 inhibited ESCC progression by disrupting the CCL2/CCR2 axis and its downstream signaling pathways in an autocrine fashion; and by impeding CCL2-mediated TAM reprogramming in a paracrine manner. The new mechanism of TAM reprogramming mediated by the LINC00330/CCL2 axis may provide potential strategies for targeted and immunocombination therapies for patients with ESCC.Graphical

LTF as a Potential Prognostic and Immunological Biomarker in Glioblastoma.

The lactoferrin (LTF) gene behaves like a tumor suppressor gene in diverse tumors, such as renal cancer, nasopharyngeal carcinoma and gastric cancer. However, the prognostic value of LTF expression in patients with glioblastoma remains unclear. In this study, the expression levels of LTF in patients with GBM were investigated in TCGA, GEPIA, CGGA and GEO database, and a survival analysis of LTF based on TCGA and CGGA was performed. Furthermore, the present study demonstrated the LTF gene co-expression, PPI network, KEGG/GO enrichment and immune cell infiltration analysis on TCGA and TIMER2.0 database. We found that LTF expression was significantly upregulated in GBM samples compared with normal samples and other glioma samples, and Kaplan-Meier analysis demonstrated that the overexpression of LTF were significantly associated with worse overall survival (OS) and 5-year OS in GBM patients (P < 0.05). KEGG/GO enrichment analysis demonstrated that functions of LTF concentrated in immune and inflammatory response and peptidase regulation (P < 0.05). Immune cell infiltration analysis presented that high LTF expression exhibited dysregulated immune infiltration (i.e., CD4 + T cells, neutrophils, macrophages, myeloid dendritic cells and cancer associated fibroblast). LTF was upregulated in tumors and correlated with worse OS in GBM patients, and LTF might function as an oncogene via inducing dysregulated immune infiltration in GBM.

Investigation of the Shared Biomarkers in Heterotopic Ossification Between Ossification of the Ligamentum Flavum and Ankylosing Spondylitis.

Bioinformatics analysis of Gene Expression Omnibus (GEO). Ossification of the ligamentum flavum (OLF) and ankylosing spondylitis (AS) represent intricate conditions marked by the gradual progression of endochondral ossification. This investigation endeavors to unveil common biomarkers associated with heterotopic ossification and explore the potential molecular regulatory mechanisms. Microarray and RNA-sequencing datasets retrieved from the Gene Expression Omnibus (GEO) repository were harnessed to discern differentially expressed genes (DEGs) within the OLF and AS datasets. Subsequently, Weighted Gene Co-expression Network Analysis (WGCNA) was implemented to pinpoint co-expression modules linked to OLF and AS. Common genes were further subjected to an examination of functional pathway enrichment. Moreover, hub intersection genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by an evaluation of diagnostic performance in external OLF and AS cohorts. Lastly, an analysis of immune cell infiltration was conducted to scrutinize the correlation of immune cell presence with shared biomarkers in OLF and AS. A total of 1353 and 91 Differentially Expressed Genes (DEGs) were identified in OLF and AS, respectively. Using the Weighted Gene Co-expression Network Analysis (WGCNA), 2 modules were found to be notably significant for OLF and AS. The integrative bioinformatic analysis revealed 3 hub genes (MAB21L2, MEGF10, ISLR) as shared risk biomarkers, with MAB21L2 being the central focus. Receiver Operating Characteristic (ROC) analysis exhibited a strong diagnostic potential for these hub genes. Gene Ontology (GO) analysis indicated their involvement in the positive regulation of myoblast proliferation. Notably, MAB21L2 was singled out as the optimal common biomarker for OLF and AS. Furthermore, an analysis of immune infiltration demonstrated a correlation between MAB21L2 expression and changes in immune cells. Activated CD8 T cells were identified as shared differential immune infiltrating cells significantly linked to MAB21L2 in both OLF and AS. This study represents the first instance of identifying MAB21L2 as a prospective diagnostic marker for patients contending with OLF associated with AS. The research results indicate that the ECM-receptor interaction and the cell-cell adhesion may play a role in both disease processes. This newfound knowledge not only enhances our understanding of the pathogenesis behind spinal ligament ossification but also uncovers potential targets for therapeutic interventions.

Potential mechanism of Chai Gui Zexie Decoction for NSCLC treatment assessed using network pharmacology, bioinformatics, and molecular docking: An observational study.

To explore the potential mechanism of Chai Gui Zexie Decoction for non-small cell lung cancer (NSCLC) treatment using network pharmacology, bioinformatics, and molecular docking. The active ingredients of Chai Gui Zexie Decoction and the associated predicted targets were screened using the TCMSP database. NSCLC-related targets were obtained from GeneCards and OMIM. Potential action targets, which are intersecting drug-predicted targets and disease targets, were obtained from Venny 2.1. The protein-protein interaction network was constructed by importing potential action targets into the STRING database, and the core action targets and core ingredients were obtained via topological analysis. The core action targets were entered into the Metascape database, and Gene Ontology annotation analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. Differentially expressed genes were screened using the Gene Expression Omnibus, and the key targets were obtained by validating the core action targets. The key targets were input into The Tumor IMmune Estimation Resource for immune cell infiltration analysis. Finally, the molecular docking of key targets and core ingredients was performed. We obtained 60 active ingredients, 251 drug prediction targets, and 2133 NSCLC-related targets. Meanwhile, 147 potential action targets were obtained, and 47 core action targets and 40 core ingredients were obtained via topological analysis. We detected 175 pathways related to NSCLC pharmaceutical therapy. In total, 1249 Gene Ontology items were evaluated. Additionally, 3102 differential genes were screened, and tumor protein P53, Jun proto-oncogene, interleukin-6, and mitogen-activated protein kinase 3 were identified as the key targets. The expression of these key targets in NSCLC was correlated with macrophage, CD4+ T, CD8+ T, dendritic cell, and neutrophil infiltration. The molecular docking results revealed that the core ingredients have a potent affinity for the key targets. Chai Gui Zexie Decoction might exert its therapeutic effect on NSCLC through multiple ingredients, targets, and signaling pathways.

Identification of Genes Crucial for Biological Processes in Breast Cancer Liver Metastasis Relapse.

Breast cancer, when advancing to a metastatic stage, involves the liver, impacting over 50% of cases and significantly diminishing survival rates. Presently, a lack of tailored therapeutic protocols for breast cancer liver metastasis (BCLM) underscores the need for a deeper understanding of molecular patterns governing this complication. Therefore, by analyzing differentially expressed genes (DEGs) between primary breast tumors and BCLM lesions, we aimed to shed light on the diversities of this process. This research investigated breast cancer liver metastasis relapse by employing a comprehensive approach that integrated data filtering, gene ontology and KEGG pathway analysis, overall survival analysis, identification of the alteration in the DEGs, visualization of the protein-protein interaction network, Signor 2.0, identification of positively correlated genes, immune cell infiltration analysis, genetic alternation analysis, copy number variant analysis, gene-to-mRNA interaction, transcription factor analysis, molecular docking, and identification of potential treatment targets. This study's integrative approach unveiled metabolic reprogramming, suggesting altered PCK1 and LPL expression as key in breast cancer metastasis recurrence.

Characterization of alternative splicing events and prognostic signatures in gastric cancer

BackgroundAccumulating evidences indicate that the specific alternative splicing (AS) events are linked to the occurrence and prognosis of gastric cancer (GC). Nevertheless, the impact of AS is still unclear and needed to further elucidation.MethodsThe expression profile of GC and normal samples were downloaded from TCGA. AS events were achieved from SpliceSeq database. Cox regression together with LASSO analysis were employed to identify survival-associated AS events (SASEs) and calculate risk scores. PPI and pathway enrichment analysis were implemented to determine the function and pathways of these genes. Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic Curves were used to evaluate the clinical significance of genes of SASEs. Q-PCR were applied to validate the hub genes on the survival prognosis in 47 GC samples. Drug sensitivity and immune cell infiltration analysis were conducted.ResultsIn total, 48 140 AS events in 10 610 genes from 361 GC and 31 normal samples were analyzed. Through univariate Cox regression, 855 SASEs in 763 genes were screened out. Further, these SASEs were analyzed by PPI and 17 hub genes were identified. Meanwhile, using Lasso and multivariate Cox regression analysis, 135 SASEs in 132 genes related to 7 AS forms were further screened and a GC prognostic model was constructed. K-M curves indicates that high-risk group has poorer prognosis. And the nomogram analysis on the basis of the multivariate Cox analysis was disclosed the interrelationships between 7 AS forms and clinical parameters in the model. Five key genes were then screened out by PPI analysis and Differential Expression Gene analysis based on TCGA and Combined-dataset, namely STAT3, RAD51B, SOCS2, POLE2 and TSR1. The expression levels of AS in STAT3, RAD51B, SOCS2, POLE2 and TSR1 were all significantly correlated with survival by qPCR verification. Nineteen drugs were sensitized to high-risk patients and eight immune cells showed significantly different infiltration between the STAD and normal groups.ConclusionsIn this research, the prognostic model constructed by SASEs can be applied to predict the prognosis of GC patients and the selected key genes are expected to become new biomarkers and therapeutical targets for GC treatment.

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses.

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Dysregulation of phosphoenolpyruvate carboxykinase in cancers: A comprehensive analysis

BackgroundPhosphoenolpyruvate carboxykinase (PEPCK) plays a crucial role in gluconeogenesis, glycolysis, and the tricarboxylic acid cycle by converting oxaloacetate into phosphoenolpyruvate. Two distinct isoforms of PEPCK, specifically cytosolic PCK1 and mitochondrial PCK2, have been identified. Nevertheless, the comprehensive understanding of their dysregulation in pan-cancer and their potential mechanism contributing to signaling transduction pathways remains elusive. MethodsWe conducted comprehensive analyses of PEPCK gene expression across 33 diverse cancer types using data from The Cancer Genome Atlas (TCGA). Multiple public databases such as HPA, TIMER 2.0, GEPIA2, cBioPortal, UALCAN, CancerSEA, and String were used to investigate protein levels, prognostic significance, clinical associations, genetic mutations, immune cell infiltration, single-cell sequencing, and functional enrichment analysis in patients with pan-cancer. PEPCK expression was analyzed about different clinical and genetic factors of patients using data from TCGA, GEO, and CGGA databases. Furthermore, the role of PCK2 in Glioma was examined using both in vitro and in vivo experiments. ResultsThe analysis we conducted revealed that the expression of PEPCK is involved in both clinical outcomes and immune cell infiltration. Initially, we verified the high expression of PCK2 in GBM cells and its role in metabolic reprogramming and proliferation in GBM. ConclusionOur study showed a correlation between PEPCK (PCK1 and PCK2) expression with clinical prognosis, gene mutation, and immune infiltrates. These findings identified two possible predictive biomarkers across different cancer types, as well as a comprehensive analysis of PCK2 expression in various tumors, with a focus on GBM.

Comprehensive multi-omics analysis of resectable locally advanced gastric cancer: Assessing response to neoadjuvant camrelizumab and chemotherapy in a single-center, open-label, single-arm phase II trial.

The current standard of care for locally advanced gastric cancer (GC) involves neoadjuvant chemotherapy followed by radical surgery. Recently, neoadjuvant treatment for this condition has involved the exploration of immunotherapy plus chemotherapy as a potential approach. However, the efficacy remains uncertain. A single-arm, phase 2 study was conducted to evaluate the efficacy and tolerability of neoadjuvant camrelizumab combined with mFOLFOX6 and identify potential biomarkers of response through multi-omics analysis in patients with resectable locally advanced GC. The primary endpoint was the pathological complete response (pCR) rate. Secondary endpoints included the R0 rate, near pCR rate, progression-free survival (PFS), disease-free survival (DFS), and overall survival (OS). Multi-omics analysis was assessed by whole-exome sequencing, transcriptome sequencing, and multiplex immunofluorescence (mIF) using biopsies pre- and post-neoadjuvant therapy. This study involved 60 patients, of which 55 underwent gastrectomy. Among these, five (9.1%) attained a pathological complete response (pCR), and 11 (20.0%) reached near pCR. No unexpected treatment-emergent adverse events or perioperative mortality were observed, and the regimen presented a manageable safety profile. Molecular changes identified through multi-omics analysis correlated with treatment response, highlighting associations between HER2-positive and CTNNB1 mutations with treatment sensitivity and a favourable prognosis. This finding was further supported by immune cell infiltration analysis and mIF. Expression data uncovered a risk model with four genes (RALYL, SCGN, CCKBR, NTS) linked to poor response. Additionally, post-treatment infiltration of CD8+ T lymphocytes positively correlates with pathological response. The findings suggest the combination of PD-1-inhibitor and mFOLFOX6 showed efficacy and acceptable toxicity for locally advanced GC. Extended follow-up is required to determine the duration of the response. This study lays essential groundwork for developing precise neoadjuvant regimens.

Weighted Gene Co-expression Network Analysis and Machine Learning Validation for Identifying Major Genes Related to Sjogren's Syndrome.

Sjogren's syndrome (SS) is an autoimmune disorder characterized by dry mouth and dry eyes. Its pathogenic mechanism is currently unclear. This study aims to integrate weighted gene co-expression network analysis (WGCNA) and machine learning to identify key genes associated with SS. We downloaded 3 publicly available datasets from the GEO database comprising the gene expression data of 231 SS and 78 control cases, including GSE84844, GSE48378 and GSE51092, and carried out WGCNA to elucidate differences in the abundant genes. Candidate biomarkers for SS were then identified using a LASSO regression model. Totally 6 machine-learning models were subsequently utilized for validating the biological significance of major genes according to their expression. Finally, immune cell infiltration of the SS tissue was assessed using the CIBERSORT algorithm. A weighted gene co-expression network was built to divide genes into 10 modules. Among them, blue and red modules were most closely associated with SS, and showed significant enrichment in type I interferon signaling, cellular response to type I interferon and response to virus, etc. Combined machine learning identified 5 hub genes, including OAS1, EIF2AK2, IFITM3, TOP2A and STAT1. Immune cell infiltration analysis showed that SS was associated with CD8+ T cell, CD4+ T cell, gamma delta T cell, NK cell and dendritic cell activation. WGCNA was combined with machine learning to uncover genes that may be involved in SS pathogenesis, which can be utilized for developing SS biomarkers and appropriate therapeutic targets.

A novel pyroptosis-related gene signature exhibits distinct immune cells infiltration landscape in Wilms’ tumor

BackgroundWilms’ tumor (WT) is the most common renal tumor in childhood. Pyroptosis, a type of inflammation-characterized and immune-related programmed cell death, has been extensively studied in multiple tumors. In the current study, we aim to construct a pyroptosis-related gene signature for predicting the prognosis of Wilms’ tumor.MethodsWe acquired RNA-seq data from TARGET kidney tumor projects for constructing a gene signature, and snRNA-seq data from GEO database for validating signature-constructing genes. Pyroptosis-related genes (PRGs) were collected from three online databases. We constructed the gene signature by Lasso Cox regression and then established a nomogram. Underlying mechanisms by which gene signature is related to overall survival states of patients were explored by immune cell infiltration analysis, differential expression analysis, and functional enrichment analysis.ResultsA pyroptosis-related gene signature was constructed with 14 PRGs, which has a moderate to high predicting capacity with 1-, 3-, and 5-year area under the curve (AUC) values of 0.78, 0.80, and 0.83, respectively. A prognosis-predicting nomogram was established by gender, stage, and risk score. Tumor-infiltrating immune cells were quantified by seven algorithms, and the expression of CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages are positively or negatively correlated with risk score. Two single nuclear RNA-seq samples of different histology were harnessed for validation. The distribution of signature genes was identified in various cell types.ConclusionsWe have established a pyroptosis-related 14-gene signature in WT. Moreover, the inherent roles of immune cells (CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages), functions of differentially expressed genes (tissue/organ development and intercellular communication), and status of signaling pathways (proteoglycans in cancer, signaling pathways regulating pluripotent of stem cells, and Wnt signaling pathway) have been elucidated, which might be employed as therapeutic targets in the future.

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis.

Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection. COVID-19 datasets (GSE164805 and GSE180594) and aging dataset (GSE69832) were obtained from the Gene Expression Omnibus database. The combined different expression genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Diseases Ontology functional enrichment analysis, Gene Set Enrichment Analysis, machine learning, and immune cell infiltration analysis. GO and KEGG enrichment analyses revealed that the eight DEGs (IL23A, PTGER4, PLCB1, IL1B, CXCR1, C1QB, MX2, ALOX12) were mainly involved in inflammatory mediator regulation of TRP channels, coronavirus disease-COVID-19, and cytokine activity signaling pathways. Three-degree algorithm (LASSO, SVM-RFE, KNN) and correlation analysis showed that the five DEGs up-regulated the immune cells of macrophages M0/M1, memory B cells, gamma delta T cell, dendritic cell resting, and master cell resisting. Our study identified five hallmark genes that can serve as disease-characteristic genes and target immune cells infiltrated in severe COVID-19 patients among the elderly population, which may contribute to the study of pathogenesis and the evaluation of diagnosis and prognosis in aging patients infected with severe COVID-19.

Machine-learning prediction of a novel diagnostic model using mitochondria-related genes for patients with bladder cancer

Bladder cancer (BC) is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Mitochondrial Dysfunction is involved in the progression of BC. This study aimed to developed a novel diagnostic model based on mitochondria-related genes (MRGs) for BC patients using Machine Learning. In this study, we analyzed GSE13507 datasets and identified 752 DE-MRGs in BC specimens. Functional enrichment analysis uncovered the significant roles of 752 DE-MRGs in key processes such as cellular and organ development, as well as gene regulation. The analysis revealed the crucial functions of these genes in transcriptional regulation and protein-DNA interactions. Then, we performed LASSO and SVM-RFE, and identified four critical diagnostic genes including GLRX2, NMT1, OXSM and TRAF3IP3. Based on the above four genes, we developed a novel diagnostic model whose diagnostic value was confirmed in GSE13507, GSE3167 and GSE37816 datasets. Moreover, we reported the expressing pattern of GLRX2, NMT1, OXSM and TRAF3IP3 in BC samples. Immune cell infiltration analysis revealed that the four genes were associated with several immune cells. Finally, we performed RT-PCR and confirmed NMT1 was highly expressed in BC cells. Functional experiments revealed that knockdown of NMT1 suppressed the proliferation of BC cells. Overall, we have formulated a diagnostic potential that offered a comprehensive framework for delving into the underlying mechanisms of BC. Before proceeding with clinical implementation, it is essential to undertake further investigative efforts to validate its diagnostic effectiveness in BC patients.

Linking Disulfide Levels and NAD+ Metabolism with Alzheimer's Disease for Diagnostic Modeling and Target Drug Analysis.

Alzheimer's disease (AD) is a condition that affects the nervous system and that requires considerably more in-depth study. Abnormal Nicotinamide Adenine Dinucleotide (NAD+) metabolism and disulfide levels have been demonstrated in AD. This study investigated novel hub genes for disulfide levels and NAD+ metabolism in relation to the diagnosis and therapy of AD. Data from the gene expression omnibus (GEO) database were analyzed. Hub genes related to disulfide levels, NAD+ metabolism, and AD were identified from overlapping genes for differentially expressed genes (DEGs), genes in the NAD+ metabolism or disulfide gene sets, and module genes obtained by weighted gene co-expression network analysis (WGCNA). Pathway analysis of these hub genes was performed by Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was constructed based on the expression level of hub genes in brain samples. CIBERSORT was used to evaluate immune cell infiltration and immune factors correlating with hub gene expression. The DrugBank database was also used to identify drugs that target the hub genes. We identified 3 hub genes related to disulfide levels in AD and 9 related to NAD+ metabolism in AD. Pathway analysis indicated these 12 genes were correlated with AD. Stepwise regression analysis revealed the area under the curve (AUC) for the predictive model based on the expression of these 12 hub genes in brain tissue was 0.935, indicating good diagnostic performance. Additionally, analysis of immune cell infiltration showed the hub genes played an important role in AD immunity. Finally, 33 drugs targeting 10 hub genes were identified using the DrugBank database. Some of these have been clinically approved and may be useful for AD therapy. Hub genes related to disulfide levels and NAD+ metabolism are promising biomarkers for the diagnosis of AD. These genes may contribute to a better understanding of the pathogenesis of AD, as well as to improved drug therapy.

Bioinformatics Approach to Identify the Pathogenetic Link of Gut Microbiota-Derived Short-Chain Fatty Acids and Ischemic Stroke.

Stroke is a life-threatening condition that impairs the arteries and causes neurological impairment. The incidence of stroke is increasing year by year with the arrival of the aging population. Thus, there is an urgent need for early stroke diagnosis. Short-chain fatty acids (SCFAs) can modulate the central nervous system and directly and indirectly impact behavioral and cognitive functions. This study aimed to investigate the connection between SCFA metabolism and stroke development via bioinformatic analysis. Initially, the Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were performed based on RNA data from stroke patients to comprehend the mechanisms governing stroke pathogenesis. The functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI), was performed based on the Differentially Expressed Gene (DEG) selected by the limma package. 1220 SCFA metabolism-related genes screened from Genecards databases were intersected with 242 genes in main modules determined by Weighted Gene Co-Expression Network Analysis (WGCNA), and the final 10 SCFA key genes were obtained. GO analysis revealed that these genes were involved in immune response processes. Through lasso regression analyses, we established a stroke early diagnosis model and selected 6 genes with diagnostic value. The genes were validated by the area under curve (AUC) values and had a relatively good diagnostic performance. Finally, 4 potential therapeutic drugs targeting these genes were predicted using the Drug Signatures Database (DSigDB) via Enrichr. In conclusion, this paper analyzes the involvement of SCFAs in the complex gut-brain axis mechanism, which contributes to developing new targets for treating central nervous system diseases and provides new ideas for early ischemic stroke diagnosis.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.