Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC's ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.

Elucidating the role of TWIST1 in ulcerative colitis: a comprehensive bioinformatics and machine learning approach.

Background: Ulcerative colitis (UC) is a common and progressive inflammatory bowel disease primarily affecting the colon and rectum. Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC. Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1's potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network. Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1's regulatory mechanism. Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC's molecular mechanisms and underscores TWIST1's importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.

Identifying Crucial Biomarkers in Osteoporosis and Ulcerative Colitis Through Bioinformatics Analysis of Co-expressed Genes.

Osteoporosis (OP) and ulcerative colitis (UC), prevalent immune diseases, exert a substantial socioeconomic impact globally. This study identifies biomarkers for these diseases, paving the way for in-depth research. Initially, the Gene Expression Omnibus (GEO) database was employed to analyze datasets GSE35958 and GSE87466. This analysis aimed to pinpoint co-expression differential genes (DEGs) between OP and UC. Subsequently, the Metascape database facilitated the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEGs' co-expression. For network construction and visualization, the STRING11.5 database along with Cytoscape 3.7.2 (Cytoscape Team, USA) were utilized to create a protein-protein interaction (PPI) network. Moreover, Cytoscape's cytoHubba plugin was instrumental in identifying the central genes, known as hub genes. In the datasets GSE35958 and GSE87466, 156 co-expressed DEGs were discovered. The PPI network, constructed using STRING11.5 and Cytoscape 3.7.2, comprises 96 nodes and 222 connections. Notably, seven hub genes were identified, namely COL6A1, COL6A2, BGN, NID1, PLAU, TGFB1, and PLAUR. These DEGs were predominantly enriched in pathways such as extracellular matrix organization and collagen-containing extracellular matrix, as per GO analysis. For diagnostic model construction and hub gene validation, datasets GSE56815 and GSE107499 from the GEO database were employed. The top five hub genes were validated. In conclusion, the hub genes identified in this study played a significant role in the early diagnosis, prevention, and treatment of OP and UC. Furthermore, they provide fresh insights into the underlying mechanisms of these diseases' development and progression.

Identification and validation of ferroptosis-related genes for chronic rhinosinusitis with nasal polyps.

PURPOSE: Even though the great progress in the field of chronic rhinosinusitis with nasal polyps (CRSwNP) has been achieved, ferroptosis and its molecular mechanism in CRSwNP remain blank. We are the first to study the relationship between CRSwNP and ferroptosis, aiming to identify ferroptosis-related genes in the process of CRSwNP. METHODS: Using the GEO database and the FerrDb database, significantly differentially expressed ferroptosis-related genes (DEFGs) were selected between CRSwNP-NP and CRSwNP-IT specimens. Then, the protein-protein interaction (PPI) network of ferroptosis-related genes was constructed. Functional enrichment analyses (GSVA, GO, KEGG, and GeneCodis analyses) were introduced in our study. Besides, based on the GSE136825 data set, DEFGs between CRSwNP-NP and CS-IT specimens were also analyzed. Finally, qRT-PCR was performed to validate the selected ferroptosis-related genes with clinical samples. RESULTS: 31 significantly DEFGs were identified between CRSwNP-NP and CRSwNP-IT specimens. Functional enrichment analyses and the analysis of GeneCodis 4 pointed out that DEFGs may potentially be involved in some related KEGG pathways. 8 DEFGs were selected between CRSwNP-NP and CS-IT specimens. The experimental verification indicated that 4 genes (GPX2, CDO1, CAV1, and TP53) were the important DEFGs of CRSwNP. The Venn diagrams proved that CDO1 and GPX2 were considered as the most important DEFGs genes of CRSwNP, especially GPX2. CONCLUSIONS: Though a comprehensive bioinformatics analysis and the experimental verification, CDO1 and GPX2 were considered as the important ferroptosis-related genes of CRSwNP, especially GPX2. However, further molecular biological experiments would be still required to uncover the underlying mechanism between ferroptosis and CRSwNP.

Immune abnormalities and differential gene expression in the hippocampus and peripheral blood of patients with Alzheimer's disease.

Background: Despite decades of research, no precise mechanisms of Alzheimer's disease (AD) development have been elucidated. This study aimed to investigate novel diagnostic biomarkers in both peripheral blood cells and hippocampus tissue, and the pathogenesis of memory impairment in AD. Methods: mRNA microarray data, including hippocampus samples (GSE1297 and GSE5281) and peripheral blood mononuclear cells (PBMCs) (GSE63060 and GSE63061), associated with AD were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AD and normal-aging samples were screened through a comprehensive analysis of multiple gene expression spectra after gene reannotation and batch normalization. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to analyze hub genes and to discover potential biomarkers related to AD. Protein-protein interaction (PPI) network maps were constructed to visualize the correlation between possible genes. The CIBERSORT algorithm was built to explore the patterns of PBMC infiltration to investigate the role of inflammation in the pathogenesis of AD. Results: The bioinformatics analysis indicated 1,261 DEGs in the hippocampal samples and 290 in PBMCs when comparing patients with AD with normal-aging individuals. We selected 28 genes co-expressed in the hippocampus and PBMCs. A functional analysis of differential genes revealed that they were primarily involved in neuronal death, immune response, and mitochondrial function. Further, immune cell infiltration patterns demonstrated that the levels of naive CD4+ T cells, resting natural killer cells, M0 macrophages, and activated mast cells were higher in the peripheral blood of patients with AD, while resting memory CD4+ T cells were significantly lower. Conclusions: The key gene changes present in both the hippocampus and PBMCs highly suggest their utility as an AD biomarker. In addition, according to our present results, immune abnormalities may have an important role in AD pathophysiology. When patients display these peripheral blood immune abnormalities, they may be recognized as being at high risk of developing AD.

Comprehensive analysis of ubiquitin-proteasome system genes related to prognosis and immunosuppression in head and neck squamous cell carcinoma.

The ubiquitin-proteasome system (UPS) with a capacity of degrading multiple intracellular proteins is an essential regulator in tumor immunosurveillance. Tumor cells that escape from recognition and destruction of immune system have been consistently characterized an important hallmark in the setting of tumor progression. Little know about the exact functions of UPS-related genes (UPSGs) and their relationships with antitumor immunity in head and neck squamous cell carcinoma (HNSCC) patients. In this study, for the first time, we comprehensively identified 114 differentially expressed UPSGs (DEUPSGs) and constructed a prognostic risk model based on the eight DEUPSGs (BRCA1, OSTM1, PCGF2, PSMD2, SOCS1, UCHL1, UHRF1, and USP54) in the TCGA-HNSCC database. This risk model was validated using multiple data sets (all P < 0.05). The high-risk score was found to be an independently prognostic factor in HNSCC patients and was significantly correlated with T cells suppression. Accordingly, our risk model can act as a prognostic signature and provide a novel concept for improving the precise immunotherapy for patients with HNSCC.

Pathogenesis and therapeutic targets of inflammatory cardiomyopathy based on bioinformatics analysis / 西安交通大学学报(医学版)

【Objective】 To make bioinformatics analysis of inflammatory cardiomyopathy so as to screen out hub genes related to etiology and therapeutic targets. 【Methods】 Differential expression analysis of inflammatory cardiomyopathy gene chip data from Gene Expression Omnibus （GEO） Database was carried out via GEO2R tool. Protein-protein interaction（PPI）network and hub genes identification were realized by String database and CytoHubba. GO and KEGG enrichment analysis for functional annotation and pathway analysis of hub genes were conducted by R language. Web-based enrichment analysis platform Enrichr and Drug Signatures database were applied to screen out candidate drugs targeting hub genes for inflammatory cardiomyopathy. 【Results】 The 149 DEGs were statistically significant， among which 44 were upregulated and 105 were downregulated. To identify hub genes， PPI network consisting of 37 nodes and 116 edges was constructed， and 16 hub genes were NDUFB7， POLR2L， NDUFS7， UQCR11， NDUFA13， NDUFA2， PHPT1， NDUFB10， UBA52， ATP5D， NDUFA3， COX6B1， POLR2J， COX4I2， AURKAIP1 and MRPL41. Hub genes were enriched to 113 different GO terms， and the most significant terms were mitochondrial ATP synthesis coupled electron transport， respiratory electron transport chain， oxidative phosphorylation， respiratory chain， mitochondrial inner membrane， NADH dehydrogenase activity and oxidoreductase activity. DEGs were enriched to 13 different signal pathways， including oxidative phosphorylation， non-alcoholic fatty liver disease， diabetic cardiomyopathy， and cardiac muscle contraction. We screened out candidate drugs targeting hub genes， namely， metformin hydrochloride， clindamycin， and hydralazine. 【Conclusion】 Hub genes screened out by decoding the expression profiles are convolved in the etiology and mechanism of inflammatory cardiomyopathy， which might serve as latent therapeutic targets and benefit patients with inflammatory cardiomyopathy.

Bioinformatics analysis of differentially expressed genes in tumor and paracancerous tissues of patients with lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma is the main pathological type of non-small cell lung cancer (NSCLC). In this study, we analyzed the gene expression profile of lung adenocarcinoma tumor and paracancerous tissues by bioinformatics to assess the genes and signal pathways related to lung adenocarcinoma. METHODS: The expression data of GSE7670, GSE27262, and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database. The three microarray data sets were integrated to obtain common differential expression genes of lung adenocarcinoma tumor and adjacent tissues. The STRING database was used to construct the protein-protein interaction (PPI) network of lung adenocarcinoma and mine the gene modules and core genes in the network, and the online tools, GEPIA and Kaplan-Meier plotter were used to further verify and analyze the core genes. RESULTS: There were 109 pairs of lung adenocarcinoma tissues and matched paracancerous normal lung tissues in the three data sets. Eighty-three differentially expressed genes were identified, including 16 up-regulated and 67 down-regulated genes, and 60 differentially expressed genes were successfully incorporated into the PPI network complex. Eleven core genes were identified in the PPI network complex, including three up-regulated (COMP, SPP1, COL1A1) and eight down-regulated genes (CDH5, CAV1, CLDN5, LYVE1, IL6, VWF, TEK, PECAM1). These core genes were verified by the GEPIA tumor database. Survival analysis showed that expression of the core genes was significantly related to the prognosis of lung adenocarcinoma. KEGG pathway analysis of core genes showed six genes (COMP, SPP1, COL1A1, IL6, VWF, TEK) were significantly enriched in the PI3K-Akt signaling-pathway (P=1.62E-06). CONCLUSIONS: By analyzing the differential expression genes of lung adenocarcinoma and paracancerous normal tissues with bioinformatics, 11 genes with significant differential expression and significant influence on prognosis were identified. The findings may provide new concepts for developing diagnosis and treatment targets and prognosis markers for lung adenocarcinoma.

A TP53-Associated Immune Prognostic Signature for the Prediction of Overall Survival and Therapeutic Responses in Muscle-Invasive Bladder Cancer.

Background: TP53 gene mutation is one of the most common mutations in human bladder cancer (BC) and has been implicated in the progression and prognosis of BC. Methods: RNA sequencing data and TP53 mutation data in different populations and platforms were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database to determine and validate a TP53-associated immune prognostic signature (TIPS) based on differentially expressed immune-related genes (DEIGs) between muscle-invasive bladder cancer (MIBC) patients with and without TP53 mutations. Results: A total of 99 DEIGs were identified based on TP53 mutation status. TIPS including ORM1, PTHLH, and CTSE were developed and validated to identify high-risk prognostic group who had a poorer prognosis than low-risk prognostic group in TCGA and GEO database. The high-risk prognostic group were characterized by a higher abundance of regulatory T cells, myeloid-derived suppressor cells, and tumor-associated macrophages than the low-risk prognostic group. Moreover, they exhibited a lower abundance of CD56bright NK cells, higher expression of CTLA4, LAG3, PDCD1, TIGIT, and HAVCR2, as well as being more likely to respond to anti-PD-1, and neoadjuvant chemotherapy than the low-risk prognostic group. Based on TIPS and other clinical characteristics, a nomogram was constructed for clinical use. Conclusion: TIPS derived from TP53 mutation status is a potential prognostic signature or therapeutic target but additional prospective studies are necessary to confirm this potential.

Screening and bioinformatics of rheumatoid arthritis markers based on GEO chip / 西安交通大学学报(医学版)

Objective To perform bioinformatics analysis of the genetic chip data of rheumatoid arthritis (RA) in order to search for the characteristic gene expression profiles. Methods Differential expression analysis of RA Gene chip data in GEO database was performed using GEO2R, and GO and KEGG enrichment analysis of functional annotation and pathway analysis of differentially expressed genes (DEGs) were conducted by DAVID6.8 and R language. Protein-protein interaction (PPI) and target genes acquisition were realized by String-database and software Cytoscape3.7.1. Results The 1 184 DEGs in synovial tissues isolated from the knee joints of RA patients were statistically significant. Among them 664 were up-regulated and 520 were down-regulated. DEGs were enriched to 70 different GOterms, and the most significant terms were signal transduction, plasma membrane and protein binding. DEGs were enriched to 62 different signal pathways, including cytokine-cytokine receptor interaction, osteoclast differentiation, rheumatoid arthritis, Th17 cell differentiation, and IL17 signal pathway. PPI analysis screened out 19 pivotal target genes, namely, NKG7, BCL6, SEMA4D, NFIL3, RAC2, MLIP, SEL1L3, GUSBP11, IGLV1-44, IGLJ3, IGLC1, IGKV1OR2-118, IGKV1OR2-108, IGKC, IGHV4-31, IGHV3-23, IGHM, IGHD and CYAT1. Conclusion Partial DEGs screened out by analyzing the expression profiles are involved in the key links affecting the development of synovial inflammation in RA, which may provide an important theoretical basis for early diagnosis and treatment of this disease and development of targeted drugs.

Screening of molecular markers in gastric cancer based on GEO database / 中国肿瘤生物治疗杂志

@#[Abstract] Objective: Bioinformatics combined with Gene Expression Omnibus (GEO) was used to screen key genes involved in the development of gastric cancer in order to obtain molecular markers for diagnosis, target selection and prognosis prediction of gastric cancer. Methods: The chip data sets related to gastric cancer (GC) from the GEO database were downloaded, and differentially expressed genes (DEG) were screened. Functional enrichment analysis on DEG was performed, and protein-protein interaction network (PPI) was constructed to screen key genes. Then, co-expression networks were further constructed, and survival curves were drawn and hierarchical clustering analysis was performed. Results: A total of 261 GC-related DEGs were selected, and 14 key genes were obtained through analysis, which were PLOD1, PLOD3, COL1A1, COL1A2, COL2A1, COL3A1, COL4A1, COL4A2, COL8A1, COL12A1, COL15A1, ITGA2, LUM and SERPINH1. Key genes are mainly involved in biological processes such as generation of collagen fiber tissues, extracellular matrix tissues, extracellular structure tissues, skin morphogenesis, collagen biosynthesis and vascular development. Survival curve analysis showed that the change in the expression of COL3A1 (P=0.0241) significantly reduced the overall survival rate of patients with gastric cancer; the change in the expression of ITGA2 (P=0.0679) also showed a correlation with the reduction of disease-free survival in gastric cancer patients. Compared with normal gastric tissues, hierarchical cluster analysis showed that the expressions of genes PLOD1, PLOD3, COL3A1, ITGA2, COL1A2, COL1A1, COL4A1, LUM, COL12A1, SERPINH1 and COL8A1 in GC tissues were up-regulated. Conclusion: The key genes obtained after screening can be used as potential molecular markers for early diagnosis, treatment target selection and prognosis judgment of gastric cancer, which provide reference for subsequent research.

Down-regulated hsa_circ_0067934 facilitated the progression of gastric cancer by sponging hsa-mir-4705 to downgrade the expression of BMPR1B.

BACKGROUND: Gastric cancer is the third most lethal cancer worldwide. Finding a novel marker is essential to targeted therapy and the diagnosis of gastric cancer. As newly discovered markers, circRNAs have aroused widespread attention on a global scale. Our research aims to understand the role of circRNAs in gastric cancer and to explore the underlying pathogenesis. METHODS: Raw expression data of circRNAs were obtained from the GEO database. Integrated bioinformatics analysis was used to screen differentially expressed circRNAs (DECs) by RobustRankAggreg package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to predict the functions of DECs. Then, the miRNAs and mRNAs at the downstream of DECs were predicted. Expression data of miRNAs and mRNAs were downloaded from The Cancer Genome Atlas (TCGA). The aberrantly expressed miRNAs and mRNAs were selected using the edgeR package. RESULTS: Four datasets (GSE78092, GSE83521, GSE89143, and GSE93541) were downloaded from the GEO database. Among them, two DECs (hsa_circ_0007991 and hsa_circ_0067934) were screened. The functional analyses of DECs confirmed that they were cancer-related circRNAs. Furthermore, hsa-mir-4705 (miRNA) and BMPR1B (mRNA) at the downstream of hsa_circ_0067934 were found differentially expressed in gastric cancer by expression data from TCGA database. CONCLUSIONS: Our study discovered the critical roles of hsa_circ_0007991 and hsa_circ_0067934 in the development of gastric cancer, and they could be novel markers for targeted therapy and assist the diagnosis of early-stage gastric cancer. Moreover, we discovered that the hsa_circ_0067934/hsa-mir-4705/BMPR1B axis might be involved in the carcinogenesis of gastric cancer.