Gene Expression Omnibus Datasets Research Articles

Unveiling immune tolerance pathways in preeclampsia placenta: implications for molecular targets and discovery of potential biomarkers.

During pregnancy, there is a link between disruption of maternal immune tolerance and preeclampsia, but the molecular mechanisms that regulate maternal and fetal immune tolerance remain unclear. This study employs bioinformatics to identify new markers related to placental immune tolerance and explore their potential role in predicting preeclampsia. Analyzing preeclampsia-related gene expression profiles in the Gene Expression Omnibus (GEO) dataset reveals 211 differentially expressed genes (DEGs) in the placenta, mainly influencing immune cell differentiation and response pathways. Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia. Validation using the GSE4707 dataset confirmed the diagnostic and predictive potential of these candidate genes. RT-qPCR verified up-regulation in the placenta, while ELISA showed their correlation with immune tolerance factors associated with placental immune tolerance. As a result of this study, identifies potential biomarkers associated with placental immunity and contributes to understanding the molecular mechanism of preeclampsia.

Bioinformatics Analysis Reveals HIST1H2BH as a Novel Diagnostic Biomarker for Atrial Fibrillation-Related Cardiogenic Thromboembolic Stroke.

Atrial fibrillation (AF) is a significant precursor to cerebral embolism. Our study sought to unearth new diagnostic biomarkers for atrial fibrillation-related cerebral embolism (AF-CE) by meticulously examining multiple GEO datasets and meta-analysis. The gene expression omnibus (GEO) database provided RNA sequencing data associated with AF and stroke. We began by pinpointing genes with varied expressions in AF-CE patient blood samples. A meta-analysis was subsequently undertaken using several RNA sequencing datasets to verify these genes. LASSO regression discerned key genes for AF-CE, with their diagnostic prowess verified through ROC curve examination. Active signaling pathways within stroke patients were discerned via GO and KEGG enrichment, with PPI interactions detailing gene interplay. Differential gene analysis revealed an upregulation of sixteen genes and a downregulation of four in stroke patient blood samples. Eight genes showcased varied expression in the meta-analysis. LASSO regression zeroed in on five of these, culminating in HIST1H2BH's identification as a characteristic gene. HIST1H2BH's prowess in predicting AF-CE was confirmed through ROC. Integrin signaling, platelet activation, ECM interactions, and the PI3K-Akt pathway were found active in stroke victims. HIST1H2BH's interaction with the notably upregulated ITGA2B was spotlighted by PPI. Additionally, HIST1H2BH exhibited links with NK cells and eosinophils. HIST1H2BH emerges as an insightful diagnostic beacon for AF-CE. Its presence, post AF, potentially modulates pathways, accentuating platelet activation and consequent thrombus generation, leading to cerebral embolism.

Prediction of Interactomic HUB Genes in Periodontitis With Acute Myocardial Infarction.

Acute myocardial infarction (AMI) risk correlates with C-reactive protein (CRP) levels, suggesting systemic inflammation is present well before AMI. Studying different types of periodontal disease (PD), extremely common in individuals at risk for AMI, has been one important research topic. According to recent research, AMI and PD interact via the systemic production of certain proinflammatory and anti-inflammatory cytokines, small signal molecules, and enzymes that control the onset and development of both disorders' chronic inflammatory reactions. This study uses machine learning to identify the interactome hub biomarker genes in acute myocardial infarction and periodontitis. GSE208194 and GSE222883 were chosen for our research after a thorough search using keywords related to the study's goal from the gene expression omnibus (GEO) datasets. DEGs were identified from the GEOR tool, and the hub gene was identified using Cytoscape-cytohubba. Using expression values, Random Forest, Adaptive Boosting, and Naive Bayes, widgets-generated transcriptomics data, were labelled, and divided into 80/20 training and testing data with cross-validation. ROC curve, confusion matrix, and AUC were determined. In addition, Functional Enrichment Analysis of Differentially Expressed Gene analysis was performed. Random Forest, AdaBoost, and Naive Bayes models with 99%, 100%, and 75% AUC, respectively. Compared to RF, AdaBoost, and NB classification models, AdaBoost had the highest AUC. Categorization algorithms may be better predictors than important biomarkers. Machine learning model predicts hub and non-hub genes from genomic datasets with periodontitis and acute myocardial infarction.

Contribution of FOS in neutrophils to venous thromboembolism via miR-144 based on bioinformatic prediction and validation.

The Finkel-Biskis-Jinkins Osteosarcoma (c-Fos; encoded by FOS) plays an important role in several cardiovascular diseases, including atherosclerosis and stroke. However, the relationship between FOS and venous thromboembolism (VTE) remains unknown. We identified differentially expressed genes in Gene Expression Omnibus dataset, GSE48000, comprising VTE patients and healthy individuals, and analysed them using CIBERSORT and weighted co-expression network analysis (WGCNA). FOS and CD46 expressions were significantly downregulated (FOS p = 2.26E-05, CD64 p = 8.83E-05) and strongly linked to neutrophil activity in VTE. We used GSE19151 and performed PCR to confirm that FOS and CD46 had diagnostic potential for VTE; however, only FOS showed differential expression by PCR and ELISA in whole blood samples. Moreover, we found that hsa-miR-144 which regulates FOS expression was significantly upregulated in VTE. Furthermore, FOS expression was significantly downregulated in neutrophils of VTE patients (p = 0.03). RNA sequencing performed on whole blood samples of VTE patients showed that FOS exerted its effects in VTE via the leptin-mediated adipokine signalling pathway. Our results suggest that FOS and related genes or proteins can outperform traditional clinical markers and may be used as diagnostic biomarkers for VTE.

Machine learning-based identification and immune characterization of ferroptosis-related molecular clusters in osteoarthritis and validation.

Osteoarthritis (OA), a degenerative joint disease, involves synovial inflammation, subchondral bone erosion, and cartilage degeneration. Ferroptosis, a regulated non-apoptotic programmed cell death, is associated with various diseases. This study investigates ferroptosis-related molecular subtypes in OA to comprehend underlying mechanisms. The Gene Expression Omnibus datasets GSE206848, GSE55457, GSE55235, GSE77298 and GSE82107 were used utilized. Unsupervised clustering identified the ferroptosis-related gene (FRG) subtypes, and their immune characteristics were assessed. FRG signatures were derived using LASSO and SVM-RFE algorithms, forming models to evaluate OA's ferroptosis-related immune features. Three FRG clusters were found to be immunologically heterogeneous, with cluster 1 displaying robust immune response. Models identified nine key signature genes via algorithms, demonstrating strong diagnostic and prognostic performance. Finally, qRT-PCR and Western blot validated these genes, offering consistent results. In addition, some of these genes may have implications as new therapeutic targets and can be used to guide clinical applications.

Single-nucleus analysis reveals microenvironment-specific neuron and glial cell enrichment in Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is a complicated neurodegenerative disease. Neuron-glial cell interactions are an important but not fully understood process in the progression of AD. We used bioinformatic methods to analyze single-nucleus RNA sequencing (snRNA-seq) data to investigate the cellular and molecular biological processes of AD.MethodsnRNA-seq data were downloaded from Gene Expression Omnibus (GEO) datasets and reprocessed to identify 240,804 single nuclei from healthy controls and patients with AD. The cellular composition of AD was further explored using Uniform Manifold Approximation and Projection (UMAP). Enrichment analysis for the functions of the DEGs was conducted and cell development trajectory analyses were used to reveal underlying cell fate decisions. iTALK was performed to identify ligand-receptor pairs among various cell types in the pathological ecological microenvironment of AD.ResultsSix cell types and multiple subclusters were identified based on the snRNA-seq data. A subcluster of neuron and glial cells co-expressing lncRNA-SNHG14, myocardin-related transcription factor A (MRTFA), and MRTFB was found to be more abundant in the AD group. This subcluster was enriched in mitogen-activated protein kinase (MAPK)-, immune-, and apoptosis-related pathways. Through molecular docking, we found that lncRNA-SNHG14 may bind MRTFA and MRTFB, resulting in an interaction between neurons and glial cells.ConclusionsThe findings of this study describe a regulatory relationship between lncRNA-SNHG14, MRTFA, and MRTFB in the six main cell types of AD. This relationship may contribute to microenvironment remodeling in AD and provide a theoretical basis for a more in-depth analysis of AD.

Identification of key genes and immune infiltration modulated in insomnia by integrated bioinformatics analysis and mendelian randomization study

Insomnia is a prevalent sleep disorder in modern times, often linked to various factors. Its occurrence, in turn, can lead to the development of several diseases, such as common cardiovascular conditions like coronary heart disease and hypertension. Moreover, insomnia can significantly impact the strength of the immune system. Traditional Chinese medicine (TCM) classifies sleep deprivation as a form of sleepiness, attributing prolonged sleepiness to Yin-Yang imbalance, resulting in Qi consumption and impairment of the five Zang-organs. Both Qi consumption and damage to the five Zang-organs mentioned in TCM research are correlated with the immune system in medical studies. Consequently, current research indicates that sleep deprivation escalates the risk of immune-related diseases. However, more research and analysis concerning immune infiltration related explicitly to insomnia are needed. Through gene differential analysis of sample files obtained from the Gene Expression Omnibus (GEO) dataset GSE208668 , we identified differentially expressed genes (DEGs) between the Insomnia and control groups. After screening these DEGs, we conducted GO and KEGG pathway analyses to identify insomnia-related gene pathways. Subsequently, I identified ten hub genes (IL2, CALML3, GATA3, EOMES, PTGS2, ESCO2, CXCL2, GPT, CRT, PRF1), and using a logistic regression model, I predicted insomnia based on the expression patterns of these ten genes. The analysis revealed that CALML3 and IL2, which showed the best performance in ROC curve analysis and had the highest degree, respectively, were selected for further investigation. Continuing the research, we further employed CIBERSORT to study immune infiltration in patients with insomnia, discovering an association between mast cells and insomnia partially correlated with the identified hub genes. In conclusion, these essential genes and immune infiltrations are relevant for treating and alleviating insomnia. Understanding these genes and immune cells could offer crucial insights for future therapeutic approaches to insomnia.

Retraction Note: Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO).

Retraction Note: Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO).

Establishment of a neutrophil extracellular trap-related prognostic signature for colorectal cancer liver metastasis and expression validation of CYP4F3

Liver metastasis stands as the primary contributor to mortality among patients diagnosed with colorectal cancer (CRC). Neutrophil extracellular traps (NETs) emerge as pivotal players in the progression and metastasis of cancer, showcasing promise as prognostic biomarkers. Our objective is to formulate a predictive model grounded in genes associated with neutrophil extracellular traps and identify novel therapeutic targets for combating CRLM. We sourced gene expression profiles from the Gene Expression Omnibus (GEO) database. Neutrophil extracellular trap-related gene set was obtained from relevant literature and cross-referenced with the GEO datasets. Differentially expressed genes (DEGs) were identified through screening via the least absolute shrinkage and selection operator regression and random forest modeling, leading to the establishment of a nomogram and subtype analysis. Subsequently, a thorough analysis of the characteristic gene CYP4F3 was undertaken, and our findings were corroborated through immunohistochemical staining. We identified seven DEGs (ATG7, CTSG, CYP4F3, F3, IL1B, PDE4B, and TNF) and established nomograms for the occurrence and prognosis of CRLM. CYP4F3 is highly expressed in CRC and colorectal liver metastasis (CRLM), exhibiting a negative correlation with CRLM prognosis. It may serve as a potential therapeutic target for CRLM. A novel prognostic signature related to NETs has been developed, with CYP4F3 identified as a risk factor and potential target for CRLM.

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

BackgroundThyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet. MethodsWe identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas. ResultsA LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170–3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue. ConclusionA potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Diagnostic value of procalcitonin and hemocyte parameters in neonates with bloodstream infection: Role of activated hemocyte‐related genes

AbstractThis study aimed to evaluate the diagnostic potential of hemocyte parameters and procalcitonin (PCT) in detecting bloodstream infections (BSI) in neonates and explore the contribution of hemocyte activation‐related genes to pediatric sepsis through bioinformatics analysis. A cohort of 419 neonates, categorized as BSI (positive blood culture) and control, underwent comparative analysis of PCT and hemocyte parameters. A predictive model for neonatal BSI was established, demonstrating an impressive area under the receiver ROC curve of 0.968 with remarkable sensitivity (92%) and specificity (87.3%). Hemocyte parameters, including lymphocyte and neutrophil percentages, platelet distribution width (PDW), platelet to lymphocyte ratio (PLR), and PCT, emerged as independent predictors of neonatal BSI. Furthermore, bioinformatics analysis utilizing Gene Expression Omnibus (GEO) datasets yielded significant insights. Differential gene expression (DEGs), gene ontology (GO), pathway enrichment, gene set enrichment analysis (GSEA), and protein–protein interaction (PPI) networks were explored. The differentially expressed genes and hub genes were notably enriched in the activation of neutrophils, lymphocytes, and platelets. Notably, elevated expression levels of SPI1, TYROBP, and FCER1G were observed in pediatric sepsis or septic shock, with positive correlations between SPI1, FCER1G, and TYROBP. In summary, the combination of lymphocyte, PDW, PLR, and PCT effectively diagnosed neonatal BSI. Bioinformatics analysis underscored the pivotal role of activated hemocytes in diagnosing pediatric sepsis, with SPI1, TYROBP, and FCER1G co‐expression influencing the disease's pathophysiology by modulating neutrophil and platelet activity.

Enterotoxin-related genes PPFIA4 and SCN3B promote colorectal cancer development and progression.

To identify the role of enterotoxin-related genes in colorectal cancer (CRC) development and progression. Upregulated differentially expressed genes shared by three out of five Gene Expression Omnibus (GEO) data sets were included to screen the key enterotoxin-induced oncogenes (EIOGs) according to criteria oncogene definition, enrichment, and protein-protein interaction (PPI) network analysis, followed by prognosis survival, immune infiltration, and protential drugs analyses was performed via integration of RNA-sequencing data and The Cancer Genome Atlas-derived clinical profiles. We screened nine common key EIOGs from at least three GEO data sets. A Cox proportional hazards regression models verified that more alive cases, decreased overall survival, and highest 4-year survival prediction in CRC patients with high-risk score. Protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4 (PPFIA4), STY11, SCN3B, and SPTBN5 were shared in the same PPI network. Immune infiltration results showed that SCN3B and synaptotagmin 11 expression were obviously associated with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+ in both colon adenocarcinoma and rectal adenocarcinoma. CHIR-99021, MLN4924, and YK4-279 were identified as the potential drugs for treatment. Finally, upregulated EIOGs genes PPFIA4 and SCN3B were found in colon adenocarcinoma and PPFIA4 and SCN3B were proved to promote cell proliferation and migration in vitro. We demonstrated here that EIOGs promoting a malignancy phenotype was related with poor survival and prognosis in CRC, which might be served as novel therapeutic targets in CRC management.

Construction of competing endogenous RNA networks in systemic lupus erythematosus by integrated analysis.

Systemic lupus erythematosus (SLE) is a disease characterised by immune inflammation and damage to multiple organs. Recent investigations have linked competing endogenous RNAs (ceRNAs) to lupus. However, the exact mechanism through which the ceRNAs network affects SLE is still unclear. This study aims to investigate the regulatory functions of the ceRNAs network, which are important pathways that control the pathophysiological processes of SLE. CircRNA microarray for our tested assays were derived from bone marrow samples from three healthy individuals and three SLE patients in our hospital. The other sequencing data of circRNA, miRNA and mRNA were obtained from Gene Expression Omnibus (GEO) datasets. Using the limma package of R program, the differential expression of mRNA and miRNA in the GEO database was discovered. Then predicted miRNA-mRNA and circRNA-miRNA were established using miRMap, miRanda, miRDB, TargetScan, and miTarBase. CircRNA-miRNA-mRNA ceRNA network was constructed using Cytoscape, and hub genes were screened using a protein-protein interaction network. Immune infiltration analysis of the hub gene was also performed by CIBERSORT and GSEA. 230 overlapped circRNAs, 86 DEmiRNAs and 2083 DEmRNAs were identified in SLE patients as compared to healthy controls. We constructed a circRNA-miRNA-mRNA ceRNAs network contained 11 overlapped circRNAs, 9 miRNAs and 51 mRNAs. ESR1 and SIRT1 were the most frequently associated protein-protein interactions in the PPI network. KEGG analysis showed that DEGs was enriched in FoxO signaling pathway as well as lipids and atherosclerosis. We constructed a novel circRNA-miRNA-mRNA ceRNA network (HSA circ 0000345- HSA miR-22-3-P-ESR1/SIRT1) that may have a major impact on SLE. Through this bioinformatics and integrated analysis, we suggest a regulatory role for ceRNA network in the pathogenesis and treatment of SLE.

Integrated Bioinformatics Analysis Reveals ceRNA Triplet Related to Apoptosis in Gastric Cancer

Apoptosis is a form of programmed cell death and evading the apoptosis is a milestone in gastric cancer (GC) tumorigenesis. Various RNAs such as miRNA (microRNA), mRNA (messenger RNA), lncRNA (long non-coding RNA), circRNA (circular RNA), play crucial roles in the apoptosis mechanism. In this present study, we aimed to understand the role played by these RNAs in apoptosis in GC. We constructed a ceRNA (competitive endogenous RNA) network using the expression profiles obtained from SRA (Sequence Read Archive) and GEO (Gene Expression Omnibus) datasets. After network construction, the differentially expressed mRNAs were used for gene prioritization. The prioritized genes and the RNAs interacting with them were analyzed to study their role in GC apoptosis. Then, we performed functional and pathway analysis to understand the role played by these genes in gastric cancer. This resulted in 37 miRNA-mRNA interactions, one miRNA-lncRNA interaction, and 17 miRNA-circRNA interactions. The binding site analysis resulted in 10 miRNAs that share common MRE (miRNA Response Elements) among mRNA, lncRNAs, circRNA. Besides, we found 33 miRNA-mRNA-circRNA and two miRNA-mRNA-lncRNA valid interactions. Integration of multiple omics datasets revealed dysregulated genes, including IGF1, MME, CCND2, CDH2, and COL1A1, implicated in GC apoptosis. Functional enrichment analysis highlighted pathways related to apoptosis, with CCND2 emerging as a key player. The integrative methodology has revealed a new potential diagnostic biomarker for the regulation of gastric cancer (GC) apoptosis: the CCND2-miR-141-3p-hsa_circ_0008035 ceRNA triplet. This discovery offers fresh perspectives into the intricate regulatory pathways governing gene expression in GC, promising significant insights into its pathology.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma.

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

Identification of circRNA–miRNA–mRNA regulatory network associated to the autism spectrum disorder in children through integrated bioinformatics analysis

BackgroundAutism spectrum disorder (ASD) is a complex neurological disability with multifactorial etiology. ASD is described by behavior, speech, language, and communication defects. CircRNA is a type of ceRNA that plays an important role in modulating microRNAs (miRNA) in several disorders. However, the potential role of the circRNA/miRNA/mRNA regulatory network in the pathogenesis of ASD is not fully understood. Therefore, this study aimed to create a circRNA/miRNA/mRNA network associated with ASD to cast light on the pathogenesis of ASD.MethodsCircRNA expression profile data were recruited from Gene Expression Omnibus datasets, and the differentially expressed circRNAs (DEcircRNAs) were identified. Then, miRNAs modulated by these circRNAs were predicted and overlapped with differentially expressed miRNAs. Next, the potentially involved genes were identified by overlapping predicted targets, and differentially expressed genes. The enrichment analysis was performed, and a PPI network was projected. Subsequently, ten key genes were selected from the network. Furthermore, a circRNA/miRNA/mRNA regulatory network was constructed, and probable molecules and drugs with potential anti-ASD effects were predicted.Results11 DEcircRNAs and 8 miRNAs regulated by 4 circRNAs were identified as being significantly involved. Subsequently, gene enrichment analysis of 71 overlapped mRNA regulated by these miRNAs showed that they are mostly associated with hippocampal synaptogenesis, neurogenesis, and axon guidance. Additionally, two high-score compounds, GSK3β inhibitor (SB216763) and dexamethasone, and three drugs (haloperidol, nystatin, paroxetine) were confirmed as potential therapeutic options for ASD.ConclusionThe results of this study may help gain deeper insight into the pathogenesis of the circRNA/miRNA/mRNA regulatory network in ASD, providing potential therapeutic management options.

Identification of miR-20a as A Potential Discerning Biomarker for Non-Invasive versus Invasive Retinoblastoma.

Intraocular retinoblastoma (RB) is common in kids. Although the cause of this disease is a mutation in the RB1 gene, the formed cancerous mass in different patients is seen in non-invasive states, limited to the ocular cavity or in invasive states distributed to other parts of the body. Because this tumor's aggressiveness cannot be predicted early, these patients receive systemic chemotherapy with multiple drugs. Treating non-invasive and invasive tumors separately reduces chemical drug side effects. The aim of this study was to identify diagnostic biomarkers by separating miRNAs in blood serum from invasive and non-invasive RB patients. In this experimental study, selected three gene expression omnibus (GEO) datasets. Two were related to serum and tumor tissue miRNAs, and one was related to non-invasive and invasive RB gene expression. Examined RB gene-miRNA relationships. Then, we performed real-time polymerase chain reaction (PCR) on candidate miRNAs in the Y79 cell line and patient blood samples in non-invasive and invasive retinoblastoma. Fourteen high-expression and 7 low-expression miRNAs resulted. MiR-181, miR-135a, miR-20a, miR-373, and miR-191 were common genes with differential genes between invasive and non-invasive retinoblastoma. Only MiR-181 was upregulated in the Y79 RB cell line. Other candidate miRNAs expressed less. Invasive retinoblastomas increased serum miR-20a and miR-191. Integrated and regular bioinformatics analyses found important miRNAs in patients' and miR-20a, miR- 191, and miR-135a can distinguish non-invasive and invasive retinoblastoma, suggesting further research.

Increased co-expression of 4-1BB with PD-1 on CD8+ tumor-infiltrating lymphocytes is associated with improved prognosis and immunotherapy response in cervical cancer.

The combination of agonistic antibodies with immune checkpoint inhibitors presents a promising avenue for cancer immunotherapy. Our objective is to explore the co-expression of 4-1BB, ICOS, CD28, with PD-1 on CD8+ T cells in the peripheral blood and tumor tissue of cervical cancer(CC) patients, with a specific focus on the association between the co-expression levels of 4-1BB with PD-1 and clinical features, prognosis as well as immunotherapy response. The goal is to offer valuable insights into cervical cancer immunotherapy. In this study, 50 treatment-naive patients diagnosed with CC were enrolled. Flow cytometry was used to detect PD-1/4-1BB, PD-1/ICOS and PD-1/CD28 co-expression on CD8+ T cells. Subsequent analysis aimed to investigate the differential co-expression between peripheral blood and cancer tissue, and also the correlation between co-expression and clinical features in these patients. Gene Expression Omnibus (GEO) datasets, The Cancer Genome Atlas (TCGA) cohort, The IMvigor210 cohort, The BMS038cohort and Immunophenoscores were utilized to investigate the correlation between PD-1/4-1BB and the immune microenvironment, prognosis, immunotherapy, and drug sensitivity in cervical cancer. The co-expression levels of PD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 on CD8+ tumor-infiltrating lymphocytes (TILs) were significantly higher in cervical cancer patients compared to those in peripheral blood. Clinical feature analysis reveals that on CD8+ TILs, the co-expression of PD-1/4-1BB is more closely correlated with clinical characteristics compared to PD-1/ICOS, PD-1/CD28, PD-1, and 4-1BB. Pseudo-time analysis and cell communication profiling reveal close associations between the subgroups harboring 4-1BB and PD-1. The prognosis, tumor mutation burden, immune landscape, and immunotherapy response exhibit statistically significant variations between the high and low co-expression groups of PD-1/4-1BB. The high co-expression group of PD-1/4-1BB is more likely to benefit from immunotherapy. PD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 exhibit elevated co-expression on CD8+TILs of cervical cancer, while demonstrating lower expression in circulating T cells. The co-expression patterns of PD-1/4-1BB significantly contributed to the prediction of immune cell infiltration characteristics, prognosis, and tailored immunotherapy tactics. PD-1/4-1BB exhibits potential as a target for combination immunotherapy in cervical cancer.

A cuproptosis-related signature predicts prognosis and indicates cross-talk with immunocyte in ovarian cancer

PurposeCuproptosis, programmed cell death by intracellular copper-mediated lipoylated protein aggregation, is involved in various tumorigenesis and drug resistance abilities by mediating the tumor microenvironment. Previous studies have demonstrated that serum copper levels are higher in OC patients than in normal subjects. However, the exact relationship between cuproptosis and ovarian cancer progression remains to be further elucidated.MethodsThe Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) datasets were utilized to establish a cuproptosis-related prognostic signature in ovarian cancer. Subsequently, the bulk RNA-seq analysis and single-cell RNA-seq analysis were used to identify the relationship between signature with immune cell infiltration, chemotherapy, and cuproptosis-related scoring (CuRS) system. Finally, the potential biological functional roles of target genes in cuproptosis were validated in vitro.ResultsBy using LASSO-Cox regression analysis to establish the cuproptosis-related prognostic model, our works demonstrated the accuracy and efficiency of our model in the TCGA (583 OC patients) and GEO (260 OC patients) OC cohorts, and the high-scoring groups showed worse survival outcomes. Notably, there were substantial differences between the high and low-risk groups in extensive respects, such as the activating transcription factors, cell pseudotime features, cell intercommunication patterns, immunocytes infiltration, chemotherapy response, and potential drug resistance. KIF26B was selected to construct a prognostic model from the identified 33 prognosis-related genes, and high expression of KIF26B predicted poorer prognosis in ovarian cancer. Ultimately, further in vitro experiments demonstrated that KIF26B participated in the proliferation and cisplatin resistance of OC cells. Knockdown of KIF26B increased the sensitivity of OC cells to elesclomol, a cuproptosis agonists.ConclusionThis study constructed a new cuproptosis-related gene signature that has a good prognostic capacity in assessing the outcome of OC patients. This study enhances our understanding of cuproptosis associated with ovarian cancer aggressiveness, cross-talk with immunocytes, and serves as a novel chemotherapy strategy.

Comprehensive profiling of endocrine metabolism identifies a novel signature with robust predictive value in ovarian cancer.

The cell endocrine pathway is a critical physiological process composed of the endoplasmic reticulum, Golgi apparatus and associated vesicles. Loss of enzymes or proteins can cause dysfunction of endoplasmic reticulum and Golgi apparatus and affect secretion pathways leading to a variety of human diseases, including cancer. The single-cell RNA sequencing and single nucleotide variant principal component analysis data of ovarian cancer were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Eighty-four genes from SECRETORY_PATHWAYs were obtained from the gene set enrichment analysis (GSEA) website. Univariate cox regression analyses and ConsensusClusterPlus were used to identify prognostic genes and molecular subtypes, which were validated using the tumor immune dysfunction and exclusion (i.e. TIDE) analysis and gene mutation analysis. A prognosis model was established by randomForestSRC. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ssGSEA, ESTIMATE, MCP-counter and GSEA. The drug sensitive analysis was performed using pRRophetic package. Immunotherapy datasets and pan-carcinoma analysis were used to examine the performance of prognostic model. Eighteen prognostic genes from SECRETORY_PATHWAYs were found in both TCGA and GEO datasets. Next, two clusters (C1 and C2) were determined, for which C1 with a poor prognosis had higher immune infiltration. Tumor-related pathways, such as PATHWAYS_IN_CANCER and B_CELL_RECEPTOR_SIGNALING_PATHWAY, were enriched in C1. Moreover, C2 was suitable for immunotherapy. A four-gene (DNAJA1, NDRG3, LUZP1 and ZCCHC24) signature was developed and successfully validated. RiskScore of higher levels were significantly associated with worse prognoses. An enhanced immune infiltration, increased pathways score and inappropriate immunotherapy were observed in the high RiskScore group. The high- and low-RiskScore groups had different drug sensitivities. Immunotherapy datasets and pan-carcinoma analysis indicated that the low RiskScore group may benefit from immunotherapy. Based on the perspective of the secretory signaling pathway, a robust prognostic signature with great performances was determined, which may provide clues for clinical precision treatment of ovarian cancer.