Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction

Investigating the risk factors for nonadherence to analgesic medications in cancer patients: Establishing a nomogram model

Objective

The substantial prevalence of nonadherence to analgesic medication among individuals diagnosed with cancer imposes a significant strain on both patients and healthcare resources. The objective of this study is to develop and authenticate a nomogram model for assessing nonadherence to analgesic medication in cancer patients.

Methods

Clinical information, demographic data, and medication adherence records of cancer pain patients were gathered from the Affiliated Hospital of Chengde Medical University between April 2020 and March 2023. The risk factors associated with analgesic medication nonadherence in cancer patients were analyzed using the least absolute selection operator (LASSO) regression model and multivariate logistic regression. Additionally, a nomogram model was developed. The bootstrap method was employed to internally verify the model. Discrimination and accuracy of the nomogram model were evaluated using the Concordance index (C-index), area under the receiver Operating characteristic (ROC) curve (AUC), and calibration curve. The potential clinical value of the nomogram model was established through decision curve analysis (DCA) and clinical impact curve.

Results

The study included a total of 450 patients, with a nonadherence rate of 43.33%. The model incorporated seven factors: age, address, smoking history, number of comorbidities, use of nonsteroidal antiinflammatory drugs (NSAIDs), use of opioids, and PHQ-8. The C-index of the model was found to be 0.93 (95% CI: 0.907-0.953), and the ROC curve demonstrated an AUC of 0.929. Furthermore, the DCA and clinical impact curves indicate that the built model can accurately predict cancer pain patients' medication adherence performance.

Conclusions

A nomogram model based on 7 risk factors has been successfully developed and validated for long-term analgesic management of cancer patients.

Utilizing systematic Mendelian randomization to identify potential therapeutic targets for mania

Background

Mania has caused incalculable economic losses for patients, their families, and even society, but there is currently no effective treatment plan for this disease without side effects.

Methods

Using bioinformatics and Mendelian randomization methods, potential drug target genes and key substances associated with mania were explored at the mRNA level. We used the chip expression profile from the GEO database to screen differential genes and used the eQTL and mania GWAS data from the IEU database for two-sample Mendelian randomization (MR) to determine core genes by colocalization. Next, we utilized bioinformatics analysis to identify key substances involved in the mechanism of action and determined related gene targets as drug targets.

Results

After differential expression analysis and MR, a causal relationship between the expression of 46 genes and mania was found. Colocalization analysis yielded six core genes. Five key substances were identified via enrichment analysis, immune-related analysis, and single-gene GSVA analysis of the core genes. MR revealed phenylalanine to be the only key substance that has a unidirectional causal relationship with mania. In the end, SBNO2, PBX2, RAMP3, and QPCT, which are significantly associated with the phenylalanine metabolism pathway, were identified as drug target genes.

Conclusion

SBNO2, PBX2, RAMP3, and QPCT could serve as potential target genes for mania treatment and deserve further basic and clinical research. Medicinal target genes regulate the phenylalanine metabolism pathway to achieve the treatment of mania. Phenylalanine is an important intermediate substance in the treatment of mania that is regulated by drug target genes.

Identification of Molecular Correlations of GSDMD with Pyroptosis inAlzheimer's Disease

AIM

An analysis of bioinformatics and cell experiments was performed to verify the relationship between gasdermin D (GSDMD), an executive protein of pyroptosis, and Alzheimer's disease (AD).

METHODS

The training set GSE33000 was utilized to identify differentially expressed genes (DEGs) in both the AD group and control group, as well as in the GSDMD protein high/low expression group. Subsequently, the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) regression analysis were conducted, followed by the selection of the key genes for the subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The association between GSDMD and AD was assessed and confirmed in the training set GSE33000, as well as in the validation sets GSE5281 and GSE48350. Immunofluorescence (IF) was employed to detect the myelin basic protein (MBP), a distinctive protein found in the rat oligodendrocytes (OLN-93 cells). A range of concentrations (1-15 μmol/L) of β-amyloid 1-42 (Aβ1-42) were exposed to the cells, and the subsequent observations were made regarding cell morphology. Additionally, the assessments were conducted to evaluate the cell viability, the lactate dehydrogenase (LDH) release, the cell membrane permeability, and the GSDMD protein expression.

RESULTS

A total of 7,492 DEGs were screened using GSE33000. Subsequently, WGCNA analysis identified 19 genes that exhibited the strongest correlation with clinical traits in AD. Additionally, LASSO regression analysis identified 13 key genes, including GSDMD, AFF1, and ATOH8. Furthermore, the investigation revealed that the key genes were associated with cellular inflammation based on GO and KEGG analyses. Moreover, the area under the curve (AUC) values for the key genes in the training and validation sets were determined to be 0.95 and 0.70, respectively. Significantly, GSDMD demonstrated elevated levels of expression in AD across both datasets. The positivity of MBP expression in cells exceeded 95%. As the concentration of Aβ1-42 action gradually escalated, the detrimental effects on cells progressively intensified, resulting in a gradual decline in cell survival rate, accompanied by an increase in lactate dehydrogenase release, cell membrane permeability, and GSDMD protein expression.

CONCLUSION

The association between GSDMD and AD has been observed, and it has been found that Aβ1-42 can induce a significant upregulation of GSDMD in OLN-93 cells. This suggests that Aβ1-42 has the potential to induce cellular pyroptosis and can serve as a valuable cellular pyroptosis model for the study of AD.

Unfolded protein response pathways in stroke patients: a comprehensive landscape assessed through machine learning algorithms and experimental verification

BACKGROUND

The unfolding protein response is a critical biological process implicated in a variety of physiological functions and disease states across eukaryotes. Despite its significance, the role and underlying mechanisms of the response in the context of ischemic stroke remain elusive. Hence, this study endeavors to shed light on the mechanisms and role of the unfolding protein response in the context of ischemic stroke.

METHODS

In this study, mRNA expression patterns were extracted from the GSE58294 and GSE16561 datasets in the GEO database. The screening and validation of protein response-related biomarkers in stroke patients, as well as the analysis of the immune effects of the pathway, were carried out. To identify the key genes in the unfolded protein response, we constructed diagnostic models using both random forest and support vector machine-recursive feature elimination methods. The internal validation was performed using a bootstrapping approach based on a random sample of 1,000 iterations. Lastly, the target gene was validated by RT-PCR using clinical samples. We utilized two algorithms, CIBERSORT and MCPcounter, to investigate the relationship between the model genes and immune cells. Additionally, we performed uniform clustering of ischemic stroke samples based on expression of genes related to the UPR pathway and analyzed the relationship between different clusters and clinical traits. The weighted gene co-expression network analysis was conducted to identify the core genes in various clusters, followed by enrichment analysis and protein profiling for the hub genes from different clusters.

RESULTS

Our differential analysis revealed 44 genes related to the UPR pathway to be statistically significant. The integration of both machine learning algorithms resulted in the identification of 7 key genes, namely ATF6, EXOSC5, EEF2, LSM4, NOLC1, BANF1, and DNAJC3. These genes served as the foundation for a diagnostic model, with an area under the curve of 0.972. Following 1000 rounds of internal validation via randomized sampling, the model was confirmed to exhibit high levels of both specificity and sensitivity. Furthermore, the expression of these genes was found to be linked with the infiltration of immune cells such as neutrophils and CD8 T cells. The cluster analysis of ischemic stroke samples revealed three distinct groups, each with differential expression of most genes related to the UPR pathway, immune cell infiltration, and inflammatory factor secretion. The weighted gene co-expression network analysis showed that all three clusters were associated with the unfolded protein response, as evidenced by gene enrichment analysis and the protein landscape of each cluster. The results showed that the expression of the target gene in blood was consistent with the previous analysis.

CONCLUSION

The study of the relationship between UPR and ischemic stroke can help to better understand the underlying mechanisms of the disease and provide new targets for therapeutic intervention. For example, targeting the UPR pathway by blocking excessive autophagy or inducing moderate UPR could potentially reduce tissue injury and promote cell survival after ischemic stroke. In addition, the results of this study suggest that the use of UPR gene expression levels as biomarkers could improve the accuracy of early diagnosis and prognosis of ischemic stroke, leading to more personalized treatment strategies. Overall, this study highlights the importance of the UPR pathway in the pathology of ischemic stroke and provides a foundation for future studies in this field.

The development of a clinical nomogram to predict medication nonadherence in patients with knee osteoarthritis

Knee osteoarthritis (KOA) is a common bone disease in older patients. Medication adherence is of great significance in the prognosis of this disease. Therefore, this study analyzed the high-risk factors that lead to medication nonadherence in patients with KOA and constructed a nomogram risk prediction model. The basic information and clinical characteristics of inpatients diagnosed with KOA at the Department of Orthopedics, The Affiliated Hospital of Chengde Medical University, were collected from January 2020 to January 2022. The Chinese version of the eight-item Morisky scale was used to evaluate medication adherence. The Kellgren-Lawrence (KL) classification was performed in combination with the imaging data of patients. Least absolute shrinkage and selection operator regression analysis and logistic multivariate regression analysis were used to analyze high-risk factors leading to medication nonadherence, and a prediction model of the nomogram was constructed. The model was internally verified using bootstrap self-sampling. The index of concordance (C-index), area under the operating characteristic curve (AUC), decision curve, correction curve, and clinical impact curve were used to evaluate the model. A total of 236 patients with KOA were included in this study, and the non-adherence rate to medication was 55.08%. Seven influencing factors were included in the nomogram prediction: age, underlying diseases, diabetes, age-adjusted Charlson comorbidity index (aCCI), payment method, painkillers, and use of traditional Chinese medicine. The C-index and AUC was 0.935. The threshold probability of the decision curve analysis was 0.02-0.98. The nomogram model can be effectively applied to predict the risk of medication adherence in patients with KOA, which is helpful for medical workers to identify and predict the risk of individualized medication adherence in patients with KOA at an early stage of treatment, and then carry out early intervention.

Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy

Introduction

Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM.

Methods

Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated).

Results

We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control.

Discussion

SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

Construction of the ceRNA network in the progression of acute myocardial infarction

Acute myocardial infarction (AMI) is a common disease that induced by sudden occlusion of a coronary artery and myocardial necrosis, which causes a great medical burden worldwide. Noncoding RNAs, such as circRNA, lncRNA and miRNA, play crucial roles in the progression of cardiovascular diseases. However, the circRNA-miRNA-mRNA network in the occurrence and development of AMI needs further investigation. In this study, we downloaded three AMI datasets, including circRNA (GSE160717), miRNA (GSE24591), and mRNA (GSE66360) from GEO database. The differentially expressed candidates, and GO and KEGG functions were analyzed by RStudio, and subsequently import to PPI and Cytoscape to obtain the hub genes. By using the starbase target prediction database, we further screen the ceRNA network of circRNA-miRNA-mRNA based on the selected differentially expressed candidates. We found 46 differential expressed mRNAs, 65 miRNAs, and five circRNAs. GO functions and KEGG enrichment of the 46 mRNAs focused on immune response and functions, involving IL-17 signaling pathway, Toll-like receptor signaling pathway, cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, and NF-kappaB signaling pathway, which may aggravate the pathologies of AMI. PPI and Cytoscape analysis showed 10 hub genes, including TLR2, IL1B, CCL4, CCL3, CCR5, TREM1, CXCL2, NLRP3, CSF3, and CCL20. By using starbase and circinteractome databases, ceRNA network construction showed that circRNA_023461 and circRNA_400027 regulate several miRNA-mRNA axes in AMI. In summary, this study uncovered the circRNA-miRNA-mRNA network based on three AMI datasets. The differentially expressed genes, including CCL20, CCL4, CSF3, and IL1B, focus on immune functions and pathways. Furthermore, circRNA_023461 and circRNA_400027 regulate several miRNA-mRNA axes, exerting important roles in AMI progression. Our founding provides new insights into AMI and improve the therapeutic strategies for AMI.

Association of Soluble IL-1 Receptor Type 2 with Recovery of Left Ventricular Function and Clinical Outcomes in Acute Myocardial Infarction

Background

The role of soluble interleukin-1 receptor type 2 (sIL-1R2) in acute myocardial infarction (AMI) remains undocumented. In the present study, we aimed to evaluate the possible associations of sIL-1R2 with left ventricular (LV) function, remodeling and future clinical events in the setting of AMI.

Methods

Circulating sIL-1R2 levels were quantified after percutaneous coronary intervention (PCI) on day 1 of hospital admission for 204 AMI patients, and upon enrollment of 204 healthy controls. Echocardiography was conducted in the acute phase and at 12-month follow-up. Adverse clinical events were registered after 12 months.

Results

Circulating sIL-1R2 levels were significantly higher in AMI patients than in healthy controls (medians respectively 6652.81 pg/mL, 3799.13 pg/mL, p < 0.0001). AMI patients with sIL-1R2 levels less than the median had a larger proportion of worsened LV ejection fraction [a decrease in LV ejection fraction (LVEF) of more than 10% units] and reduced LVEF (a final LVEF < 50%). After multivariate adjustment, sIL-1R2 levels less than the median were associated with an increased risk of worsened LVEF [odds ratio (OR): 3.7, 95% confidence interval (CI): 1.6-8.5, p = 0.002] and reduced LVEF at 12 months (OR: 2.1, 95% CI: 1.1-4.3, p = 0.035). Moreover, low sIL-1R2 levels were associated with an increased risk of having an adverse clinical event during the first 12 months after AMI [hazard ratio (HR): 2.5, 95% CI: 1.0-6.1, p = 0.039].

Conclusions

Low levels of circulating sIL-1R2 were associated with impaired recovery of LV function and adverse clinical outcomes in AMI patients. These findings might contribute to understanding the important role of sIL-1R2 in postinfarction inflammation.

Identifying Genes Related to Acute Myocardial Infarction Based on Network Control Capability

Identifying genes significantly related to diseases is a focus in the study of disease mechanisms. In this paper, from the perspective of integrated analysis and dynamic control, a method for identifying genes significantly related to diseases based on logic networks constructed by the LAPP method, referred to as NCCM, is proposed and applied to the study of the mechanism of acute myocardial infarction (AMI). It is found that 82.35% of 17 differential control capability genes (DCCGs) identified by NCCM are significantly correlated with AMI/MI in the literature and DISEASES database. The enrichment analysis of DCCGs shows that AMI is closely related to the positive regulation of vascular-associated smooth muscle cell proliferation and regulation of cytokine production involved in the immune response, in which HBEGF, THBS1, NR4A3, NLRP3, EDN1, and MMP9 play a crucial role. In addition, although the expression levels of CNOT6L and ACYP1 are not significantly different between the control group and the AMI group, NCCM shows that they are significantly associated with AMI. Although this result still needs further verification, it shows that the method can not only identify genes with large differences in expression but also identify genes that are associated with diseases but with small changes in expression.

Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis

Aims

This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis.

Methods

Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined.

Results

A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways.

Conclusion

This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.

Identification of a Four-Gene Signature for Diagnosing Paediatric Sepsis

Aim

Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database.

Methods

We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C-index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta-analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis.

Results

A total of 41 CGs were selected from the GSE119217 dataset. A four-gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta-analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively.

Conclusion

The four-gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.

Identification of potential biomarkers and immune infiltration in pediatric sepsis via multiple-microarray analysis

Immune adjustment has become a sepsis occurring in the development of an important mechanism that cannot be ignored. This article from the perspective of immune infiltration of pediatric sepsis screening markers, and promote the understanding of disease mechanisms. Bioinformatics integrated six data sets of pediatric sepsis by using the surrogate variable analysis package and then analyzed differentially expressed genes (DEGs), immune infiltration and weighted gene co-expression network analysis of characteristics (WGCNA) of immune infiltration between pediatric sepsis and the control. Common genes of WGCNA and DEGs were used to functional annotation, pathway enrichment analysis and protein-protein interaction network. Support vector machine (SVM), least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to confirm the key genes for the diagnosis of pediatric sepsis. Receiver operating characteristic (ROC) curve, C index, principal component analysis (PCA) and GiViTi calibration band were used to evaluate the diagnostic performance of key genes. Decision curve analysis (DCA) was used to evaluate the clinical application value of key genes. Lastly, the correlation between key genes and immune cells was analyze. NK cells Resting and NK cell activated in pediatric sepsis during immune infiltration were significantly lower than those in the control group, while M1 Macrophages were higher than those in the control group. ROC, C-index, PCA, GiViTi calibration band and DCA indicated that MCEMP1, CD177, MMP8 and OLFM4 had high diagnostic performance for pediatric sepsis. There is a negative correlation between 4 key genes and NK cells resting, NK cells activated. Except for MCEMP1, the other 3 genes were positively correlated with M1 Macrophages. This study revealed differences in immune responses in pediatric sepsis and identified four key genes as potential biomarkers. Pediatric sepsis in pathology maybe understood better by learning about how it develops.