Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm

FAM83A-AS1 predicts severe development of non-small cell lung cancer and adverse postoperative prognosis of thoracotomy

BACKGROUND

Thoracotomy is a common treatment for non-small cell lung cancer (NSCLC). However, the significant trauma from this procedure can limit patients' postoperative prognosis. Therefore, it's crucial to find an easily detected indicator that can predict the prognosis of NSCLC patients undergoing thoracotomy. FAM83A-AS1 was hypothesized as a predictor for the therapeutic effectiveness of thoracotomy. We evaluated its correlation with patient outcomes and its significance in predicting postoperative prognosis, with the aim of providing a reference to improve postoperative prognosis of thoracotomy.

MATERIALS AND METHODS

The study enrolled patients with NSCLC who underwent thoracotomy, and tissue samples were collected during surgery. Blood samples were collected preoperatively and three days postoperatively. PCR was used to analyze plasma FAM83A-AS1 levels. The significance of these levels in the patients' postoperative prognosis was evaluated via logistic regression and ROC analyses, with a follow-up period of six months.

RESULTS

FAM83A-AS1 was significantly upregulated in NSCLC and correlated with severe progression in patients. Thoracotomy suppressed FAM83A-AS1 expression and reduced CA50, CEA, and CYFRA21-1 levels. Postoperative plasma levels of FAM83A-AS1 positively correlated with CA50, CEA, and CYFRA21-1. Patients with worse prognoses had higher plasma FAM83A-AS1 levels. FAM83A-AS1 was identified as a risk factor for poor postoperative outcomes in NSCLC patients undergoing thoracotomy and could be used to identify patients at risk of worse prognosis.

CONCLUSION

An increase in FAM83A-AS1 in NSCL indicates severe disease development and can serve as a biomarker associated with thoracotomy, predicting a poor prognosis. It provides a potential indicator for patient outcomes.

A bibliometric analysis of abdominal aortic aneurysm (2014-2024)

Background

Abdominal aortic aneurysm (AAA) is a localized bulge of the abdominal aorta, which mainly manifests as a pulsatile mass in the abdomen. Once an abdominal aortic aneurysm ruptures, the patient's life is seriously endangered. Surgery is the preferred treatment for abdominal aortic aneurysm. At present, there has been no comprehensive review of the current status of abdominal aortic aneurysm research. Therefore, this study aimed to identify global trends in abdominal aortic aneurysm research over the last 10 years through bibliometric analysis and to inform clinical practice, research funding allocation, and decision-making.

Methods

We downloaded research articles and reviews on abdominal aortic aneurysm from 1 January 2014, to 1 March 2024, from the Web of Science core collection. CiteSpace (version 6.2.1), RStudio and VOSviewer (version 1.6.18) were used for visual analysis of regional distribution, institutions, authors, keywords and other information.

Results

The number of documents on abdominal aortic aneurysm research increased continuously and has stabilized in recent years. A total of 9,905 publications from 67 countries were published from 1 January 2014, to 1 March 2024. A total of 2,142 (29.52%) studies were from the United States, 1,293 (13.05%) were from China, and 919 (9.28%) were from the United Kingdom. A total of 205 studies were conducted at Stanford University, 172 were conducted at Harvard Medical School, and 165 were conducted at the Mayo Clinic. The top three coauthorship authors were Schermerhorn, Marc L (114); Golledge, Jonathan (102); and De Vries, Jean Paul P.M. (74). The most cocited reference was Chaikof EL, 2018, J Vasc Surg, v67, p. 2; the most cocited journal was the Journal of Vascular Surgery; and the most cocited author was Lederle, FA. "Abdominal aortic aneurysm" was the most frequently used author keyword (2,492). Twenty-five references with strong citation bursts were identified by "CiteSpace". "Artificial intelligence", "clinical outcomes" and "bridging stent" were the primary keywords of emerging research hotspots.

Conclusion

This is the first bibliometric study to comprehensively summarize the research trends in abdominal aortic aneurysm research. This information can help us to identify the current research hotspots and directions. This study will provide extensive help for future research.

Integration of bulk/scRNA-seq and multiple machine learning algorithms identifies PIM1 as a biomarker associated with cuproptosis and ferroptosis in abdominal aortic aneurysm

Background

Abdominal aortic aneurysm (AAA) is a serious life-threatening vascular disease, and its ferroptosis/cuproptosis markers have not yet been characterized. This study was aiming to identify markers associated with ferroptosis/cuproptosis in AAA by bioinformatics analysis combined with machine learning models and to perform experimental validation.

Methods

This study used three scRNA-seq datasets from different mouse models and a human PBMC bulk RNA-seq dataset. Candidate genes were identified by integrated analysis of scRNA-seq, cell communication analysis, monocle pseudo-time analysis, and hdWGCNA analysis. Four machine learning algorithms, LASSO, REF, RF and SVM, were used to construct a prediction model for the PBMC dataset, the above results were comprehensively analyzed, and the targets were confirmed by RT-qPCR.

Results

scRNA-seq analysis showed Mo/MF as the most sensitive cell type to AAA, and 34 cuproptosis associated ferroptosis genes were obtained. Pseudo-time series analysis, hdWGCNA and machine learning prediction model construction were performed on these genes. Subsequent comparison of the above results showed that only PIM1 appeared in all algorithms. RT-qPCR and western blot results were consistent with sequencing results, showing that PIM1 was significantly upregulated in AAA.

Conclusion

In a conclusion, PIM1 as a novel biomarker associated with cuproptosis/ferroptosis in AAA was highlighted.

Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms

Objective

Abdominal aortic aneurysm (AAA) is a life-threatening vascular condition. This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.

Methods

Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. Additionally, a separate dataset (GSE7084) was designated as the validation set. Enrichment analyses were carried out to explore the underlying biological mechanisms using Disease Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology. We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. Finally, the single sample gene set enrichment analysis algorithm was applied to probe the immune landscape in AAA and its connection to the selected feature genes.

Results

We discovered 72 differentially expressed genes (DEGs) when comparing healthy and AAA samples, including 36 upregulated and 36 downregulated genes. Functional enrichment analysis revealed that the DEGs associated with AAA are primarily involved in inflammatory regulation and immune response. By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. The diagnostic performance of all these genes was strong, as revealed by the ROC analysis. A significant increase in 15 immune cell types in AAA samples was observed, based on the analysis of immune cell infiltration. In addition, the 3 feature genes show a strong linkage with different types of immune cells.

Conclusion

Three feature genes (MRAP2, PPP1R14A, and PLN) related to the development of AAA were identified. These genes are linked to immune cell activity and the inflammatory microenvironment, providing potential biomarkers for early detection and a basis for further research into AAA progression.

Macrophage polarization and metabolic reprogramming in abdominal aortic aneurysm

BACKGROUND

Abdominal aortic aneurysm (AAA) is a macrovascular disease with high morbidity and mortality in the elderly. The limitation of the current management is that most patients can only be followed up until the AAA diameter increases to a threshold, and surgical intervention is recommended. The development of preventive and curative drugs for AAA is urgently needed. Macrophage-mediated immune inflammation is one of the key pathological links in the occurrence and development of AAA.

AIMS

This review article aims to evaluate the impact of immunometabolism on macrophage biology and its role in AAA.

METHODS

We analyze publications focusing on the polarization and metabolic reprogramming in macrophages as well as their potential impact on AAA, and summarize the potential interventions that are currently available to regulate these processes.

RESULTS

The phenotypic and functional changes in macrophages are accompanied by significant alterations in metabolic pathways. The interaction between macrophage polarization and metabolic pathways significantly influences the progression of AAA.

CONCLUSION

Macrophage polarization is a manifestation of the gross dichotomy of macrophage function into pro-inflammatory killing and tissue repair, that is, classically activated M1 macrophages and alternatively activated M2 macrophages. Macrophage functions are closely linked to metabolic changes, and the emerging field of immunometabolism is providing unique insights into the role of macrophages in AAA. It is essential to further investigate the precise metabolic changes and their functional consequences in AAA-associated macrophages.

Identifying HIF1A and HGF as two hub genes in aortic dissection and function analysis by integrating RNA sequencing and single-cell RNA sequencing data

Objective

Aortic dissection (AD) is a severe aortic disease with high mortality, and its pathogenesis remains elusive. To explore the regulatory mechanisms of AD, we integrated public RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes of AD and further analyzed their functions, which may provide references to the diagnosis and treatment of AD.

Methods

Four AD-related datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis and differential expression analysis were applied to identify overlapping genes in dataset GSE153434. Protein-protein interaction (PPI) network was constructed based on overlapping genes. Five methods (closeness, degree, EPC, MCC, and MNN) were used to pick hub genes. The receiver operating characteristic curve was used to evaluate the diagnostic efficiency of the hub genes in extra datasets GSE98770 and GSE52093. scRNA-seq dataset GSE213740 was used to explore the expression and function of the hub genes at the single-cell level. Quantitative real-time polymerase chain reaction was used to verify the expression of hub genes in beta-aminopropionitrile (BAPN)-induced mouse thoracic aortic aneurysm and dissection (TAAD) model.

Results

A total of 71 overlapping genes were screened by intersecting the significant genes in the pink module and the differentially expressed genes. A PPI network with 45 nodes and 74 edges was generated, and five top hub genes (HIF1A, HGF, HMOX1, ITGA5, and ITGB3) were identified. All the hub genes had area under the curve values above 0.55. scRNA-seq data analysis showed that HIF1A was significantly upregulated in macrophages and HGF was significantly upregulated in vascular smooth muscle cells (SMCs) of the ascending aortas in AD patients. HIF1A may transcriptionally regulate multiple downstream target genes involving inflammation (TLR2, ALOX5AP, and MIF), glycolysis (ENO1, LDHA, and GAPDH), tissue remodeling (PLAU), and angiogenesis (SERPIN and VEGFA). HGF may participate in the signaling among SMCs, fibroblasts, and endothelial cells through binding to different receptors (MET, EGFR, IGF1R, and KDR). The mRNA expression of Hif1a, Hgf, and their target genes, including Alox5ap, Serpine1, Tlr2, Plau, Egfr, and Igf1r, was significantly upregulated in aortic tissues of BAPN-treated mice.

Conclusion

By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD. HIF1A in macrophages may promote AD formation by promoting inflammation, glycolysis, tissue remodeling, and angiogenesis, and HGF may mediate signaling among SMCs, fibroblasts, and endothelial cells in the development of AD.

Data-driven rapid detection of Helicobacter pylori infection through machine learning with limited laboratory parameters in Chinese primary clinics

Background

Helicobacter pylori (H. pylori) is a significant global health concern, posing a high risk for gastric cancer. Conventional diagnostic and screening approaches are inaccessible, invasive, inaccurate, time-consuming, and expensive in primary clinics.

Objective

This study aims to apply machine learning (ML) models to detect H. pylori infection using limited laboratory parameters from routine blood tests and to investigate the association of these biomarkers with clinical outcomes in primary clinics.

Methods

A retrospective analysis with three ML and five ensemble models was conducted on 1409 adults from Hubei Provincial Hospital of Traditional Chinese Medicine. evaluating twenty-three blood test parameters and using theC 14 urea breath test as the gold standard for diagnosing H. pylori infection.

Results

In our comparative study employing three different feature selection strategies, Random Forest (RF) model exhibited superior performance over other ML and ensemble models. Multiple evaluation metrics underscored the optimal performance of the RF model (ROC = 0.951, sensitivity = 0.882, specificity = 0.906, F1 = 0.906, accuracy = 0.894, PPV = 0.908, NPV = 0.880) without feature selection. Key biomarkers identified through importance ranking and shapley additive Explanations (SHAP) analysis using the RF model without feature selection include White Blood Cell Count (WBC), Mean Platelet Volume (MPV), Hemoglobin (Hb), Red Blood Cell Count (RBC), Platelet Crit (PCT), and Platelet Count (PLC). These biomarkers were found to be significantly associated with the presence of H. pylori infection, reflecting the immune response and inflammation levels.

Conclusion

Abnormalities in key biomarkers could prompt clinical workers to consider H. pylori infection. The RF model effectively identifies H. pylori infection using routine blood tests, offering potential for clinical application in primary clinics. This ML approach can enhance diagnosis and screening, reducing medical burdens and reliance on invasive diagnostics.

The Expansion of Artificial Intelligence in Modifying and Enhancing the Current Management of Abdominal Aortic Aneurysms: A Literature Review

An abdominal aortic aneurysm (AAA) is a pathological dilation that is 3 cm or greater resulting in a bulging or balloon appearance. To meet a personalized therapeutic approach for patients, artificial intelligence (AI) can exhibit an array of applications ranging from decoding patterns from large data sets to predicting new data. The review aims to discuss how AI can assist and improve the standard of care and management plans for these patients. A comprehensive non-systematic literature review was carried out for published material on the use of AI relating to AAAs. The PubMed and Google Scholar databases were used to scout for articles relating to the title of this review. The review included 54 literature papers in this study. AI is involved on a genomic level, which assists in screening, diagnosing, and identifying individual risk factors of a patient. Personalized management plans can be created with AI predictions using patient data to reduce the risk of in-hospital mortality following a repair or due to complications. AI represents a promising group of programs aimed at improving patient management and assisting surgeons in making beneficial decisions to improve the patient's prognosis.

Innovation in pathogenesis and management of aortic aneurysm

Aortic aneurysm (AA) refers to the persistent dilatation of the aorta, exceeding three centimeters. Investigating the pathophysiology of this condition is important for its prevention and management, given its responsibility for more than 25000 deaths in the United States. AAs are classified based on their location or morphology. various pathophysiologic pathways including inflammation, the immune system and atherosclerosis have been implicated in its development. Inflammatory markers such as transforming growth factor β, interleukin-1β, tumor necrosis factor-α, matrix metalloproteinase-2 and many more may contribute to this phenomenon. Several genetic disorders such as Marfan syndrome, Ehler-Danlos syndrome and Loeys-Dietz syndrome have also been associated with this disease. Recent years has seen the investigation of novel management of AA, exploring the implication of different immune suppressors, the role of radiation in shrinkage and prevention, as well as minimally invasive and newly hypothesized surgical methods. In this narrative review, we aim to present the new contributing factors involved in pathophysiology of AA. We also highlighted the novel management methods that have demonstrated promising benefits in clinical outcomes of the AA.

Comprehensive transcriptomic analysis unveils macrophage-associated genes for establishing an abdominal aortic aneurysm diagnostic model and molecular therapeutic framework

BACKGROUND

Abdominal aortic aneurysm (AAA) is a highly lethal cardiovascular disease. The aim of this research is to identify new biomarkers and therapeutic targets for the treatment of such deadly diseases.

METHODS

Single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were used to identify distinct immune cell infiltration types between AAA and normal abdominal aortas. Single-cell RNA sequencing data were used to analyse the hallmark genes of AAA-associated macrophage cell subsets. Six macrophage-related hub genes were identified through weighted gene co-expression network analysis (WGCNA) and validated for expression in clinical samples and AAA mouse models. We screened potential therapeutic drugs for AAA through online Connectivity Map databases (CMap). A network-based approach was used to explore the relationships between the candidate genes and transcription factors (TFs), lncRNAs, and miRNAs. Additionally, we also identified hub genes that can effectively identify AAA and atherosclerosis (AS) through a variety of machine learning algorithms.

RESULTS

We obtained six macrophage hub genes (IL-1B, CXCL1, SOCS3, SLC2A3, G0S2, and CCL3) that can effectively diagnose abdominal aortic aneurysm. The ROC curves and decision curve analysis (DCA) were combined to further confirm the good diagnostic efficacy of the hub genes. Further analysis revealed that the expression of the six hub genes mentioned above was significantly increased in AAA patients and mice. We also constructed TF regulatory networks and competing endogenous RNA networks (ceRNA) to reveal potential mechanisms of disease occurrence. We also obtained two key genes (ZNF652 and UBR5) through a variety of machine learning algorithms, which can effectively distinguish abdominal aortic aneurysm and atherosclerosis.

CONCLUSION

Our findings depict the molecular pharmaceutical network in AAA, providing new ideas for effective diagnosis and treatment of diseases.

Cerebral neurotoxicity of amino-modified polystyrene nanoplastics in mice and the protective effects of functional food Camellia pollen

Accumulating evidence suggests that nanoplastics contribute to an increased risk of brain damage, however, the precise underlying mechanisms remain unclear. Here, we subjected mice to long-term exposure to amino-modified polystyrene nanoplastics (APS-NPs). These nanoplastics were detected in the mouse brain; coupled with the observed upregulation of Alzheimer's disease-associated genes (APP and MAPT). To further explore nanoplastic damage mechanisms and the corresponding protective strategies against these mechanisms in vitro, we used hCMEC/D3 and HT22 cells. Results showed that APS-NPs disrupted tight junction proteins (Occludin and ZO-1) via TLR2/MMP9 axis, resulting in blood-brain barrier permeation; this was significantly mitigated by functional food Camellia pollen treatment. APS-NPs initiated iNOS and nNOS upregulation within neurons resulting in Sirtuin 1 deacetylase inactivation and CBP acetyltransferase stimulation, ultimately leading to Ac-Tau formation. This process was attenuated by Camellia pollen, which also ameliorated the APS-NPs-induced neuronal apoptosis mediated by the p53/Bax/Bcl-2 axis. Network pharmacology analysis of Camellia pollen offered a further theoretical understanding of its potential applications in preventing and treating nervous system disorders, such as Alzheimer's disease. This study established that Camellia pollen protects the brain against APS-NPs-mediated blood-brain barrier damage and alleviates neuronal apoptosis and Alzheimer's disease-like neurotoxicity. This study elucidates the mechanisms underlying polystyrene-induced brain damage and can be used to inform future prevention and treatment strategies.

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes

BACKGROUND

Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.

METHODS

In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.

RESULTS

38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.

CONCLUSION

We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.

Identification of SV2C and DENR as Key Biomarkers for Parkinson's Disease Based on Bioinformatics, Machine Learning, and Experimental Verification

The objective of this study is to investigate the potential biomarkers and therapeutic target genes for Parkinson's disease (PD). We analyzed four datasets (GSE8397, GSE20292, GSE20163, GSE20164) from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and differential expression analysis to select genes and perform functional analysis. We applied three algorithms, namely, random forest, support vector machine recursive feature elimination, and least absolute shrinkage and selection operator, to identify hub genes, perform functional analysis, and assess their clinical diagnostic potential using receiver operating characteristic (ROC) curve analysis. We employed the xCell website to evaluate differences in the composition patterns of immune cells in the GEO datasets. We also collected serum samples from PD patients and established PD cell model to validate the expression of hub genes using enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction. Our findings identified SV2C and DENR as two hub genes for PD and decreased in PD brain tissue compared with controls. ROC analysis showed effectively value of SV2C and DENR to diagnose PD, and they were downregulated in the serum of PD patients and cell model. Functional analysis revealed that dopamine vesicle transport and synaptic vesicle recycling are crucial pathways in PD. Besides, the differences in the composition of immune cells, especially basophils and T cells, were discovered between PD and controls. In summary, our study identifies SV2C and DENR as potential biomarkers for diagnosing PD and provides a new perspective for exploring the molecular mechanisms of PD.

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models

AIMS

The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

BACKGROUND

Glaucoma has afflicted many patients for many years, with excessive pressure in the eye continuously damaging the nervous system and leading to severe blindness. An effective molecular diagnostic method is currently lacking.

OBJECTIVE

The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes.

METHODS

A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database. The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Least absolute shrinkage and selection operator regression model (LASSO), and Support Vector Machines model (SVM). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on these genes, we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples by applying CIBERSORT method.

RESULTS

8 hub genes were determined: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158 and GSN. The diagnostic model showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma.

CONCLUSION

Our results systematically determined 8 hub genes and established a molecular diagnostic model that allowed the diagnosis of Glaucoma. Our study provided a basis for future systematic studies of Glaucoma pathogenesis.

Integrative Analysis of Machine Learning and Molecule Docking Simulations for Ischemic Stroke Diagnosis and Therapy

Due to the narrow therapeutic window and high mortality of ischemic stroke, it is of great significance to investigate its diagnosis and therapy. We employed weighted gene coexpression network analysis (WGCNA) to ascertain gene modules related to stroke and used the maSigPro R package to seek the time-dependent genes in the progression of stroke. Three machine learning algorithms were further employed to identify the feature genes of stroke. A nomogram model was built and applied to evaluate the stroke patients. We analyzed single-cell RNA sequencing (scRNA-seq) data to discern microglia subclusters in ischemic stroke. The RNA velocity, pseudo time, and gene set enrichment analysis (GSEA) were performed to investigate the relationship of microglia subclusters. Connectivity map (CMap) analysis and molecule docking were used to screen a therapeutic agent for stroke. A nomogram model based on the feature genes showed a clinical net benefit and enabled an accurate evaluation of stroke patients. The RNA velocity and pseudo time analysis showed that microglia subcluster 0 would develop toward subcluster 2 within 24 h from stroke onset. The GSEA showed that the function of microglia subcluster 0 was opposite to that of subcluster 2. AZ_628, which screened from CMap analysis, was found to have lower binding energy with Mmp12, Lgals3, Fam20c, Capg, Pkm2, Sdc4, and Itga5 in microglia subcluster 2 and maybe a therapeutic agent for the poor development of microglia subcluster 2 after stroke. Our study presents a nomogram model for stroke diagnosis and provides a potential molecule agent for stroke therapy.

A neutrophil extracellular traps-related classification predicts prognosis and response to immunotherapy in colon cancer

Neutrophil extracellular traps (NETs) have been categorized as a form of inflammatory cell death mode of neutrophils (NETosis) involved in natural immunity and the regulation of adaptive immunity. More and more studies revealed the ability of NETs to reshape the tumor immune microenvironment (TIME) by limiting antitumor effector cells, which may impair the efficacy of immunotherapy. To explore whether NETs-related genes make vital impacts on Colon carcinoma (COAD), we have carried out a systematic analysis and showed several findings in the present work. First, we obtained the patient's data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset, aiming to detect two NETs-associated subtypes by consensus clustering. For the purpose of annotating the roles of NETs-related pathways, gene ontology enrichment analyses were adopted. Next, we constructed a 6 novel NETs-related genes score using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model. We found that the NETs risk score was notably upregulated in COAD patient samples, and its levels were notably correlated with tumor clinicopathological and immune traits. Then, according to NETs-associated molecular subtypes and the risk signature, this study compared immune cell infiltration calculated through the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumor immune dysfunction, as well as exclusion (TIDE). Furthermore, we confirm that MPO(myeloperoxidase) was significantly upregulated in COAD patient samples, and its levels were significantly linked to tumor malignancy and clinic outcome. Moreover, multiplex immunohistochemistry (mIHC) spatial analysis confirmed that MPO was closely related to Treg and PD-1 + Treg in spatial location which suggested MPO may paly an important role in TIME formation. Altogether, the obtained results indicated that a six NETs-related genes prognostic signature was conducive to estimating the prognosis and response of chemo-/immuno-therapy of COAD patients.

Renal interferon-inducible protein 16 expression is associated with disease activity and prognosis in lupus nephritis

BACKGROUND

Lupus nephritis (LN) is one of the most severe complications of systemic lupus erythematosus (SLE). However, the current management of LN remains unsatisfactory due to sneaky symptoms during early stages and lack of reliable predictors of disease progression.

METHODS

Bioinformatics and machine learning algorithms were initially used to explore the potential biomarkers for LN development. Identified biomarker expression was evaluated by immunohistochemistry (IHC) and multiplex immunofluorescence (IF) in 104 LN patients, 12 diabetic kidney disease (DKD) patients, 12 minimal change disease (MCD) patients, 12 IgA nephropathy (IgAN) patients and 14 normal controls (NC). The association of biomarker expression with clinicopathologic indices and prognosis was analyzed. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were utilized to explore potential mechanisms.

RESULTS

Interferon-inducible protein 16 (IFI16) was identified as a potential biomarker for LN. IFI16 was highly expressed in the kidneys of LN patients compared to those with MCD, DKD, IgAN or NC. IFI16 co-localized with certain renal and inflammatory cells. Glomerular IFI16 expression was correlated with pathological activity indices of LN, while tubulointerstitial IFI16 expression was correlated with pathological chronicity indices. Renal IFI16 expression was positively associated with systemic lupus erythematosus disease activity index (SLEDAI) and serum creatinine while negatively related to baseline eGFR and serum complement C3. Additionally, higher IFI16 expression was closely related to poorer prognosis of LN patients. GSEA and GSVA suggested that IFI16 expression was involved in adaptive immune-related processes of LN.

CONCLUSION

Renal IFI16 expression is a potential biomarker for disease activity and clinical prognosis in LN patients. Renal IFI16 levels may be used to shed light on predicting the renal response and develop precise therapy for LN.

Single-cell RNA sequencing provides novel insights to pathologic pathways in abdominal aortic aneurysm

There is gaining popularity in the use of single-cell technology and analysis in studying the pathogenesis of abdominal aortic aneurysm (AAA). As there are no current pharmacologic therapies for impeding aneurysm growth or preventing AAA rupture, identifying key pathways involved in AAA formation is critical for the development of future therapies. Single-cell RNA sequencing (scRNA-seq) technology provides an unbiased and global view of transcriptomic characteristics within each of the major cell types in aneurysmal tissues. In this brief review, we examine the current literature utilizing scRNA-seq for the analysis of AAA and discuss trends and future utility of this technology.

Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

BACKGROUND

Atherosclerosis is the common pathological basis for many cardiovascular and cerebrovascular diseases. The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm.

METHODS

Clinicopathological parameters and transcriptomics data were obtained from 4 datasets (GSE21545, GSE20129, GSE43292, GSE100927). A nonnegative matrix factorization algorithm was used to classify arteriosclerosis patients in GSE21545 dataset. Then, we identified prognosis-related differentially expressed genes (DEGs) between the subtypes. Multiple machine learning methods to detect pivotal markers. Discrimination, calibration and clinical usefulness of the predicting model were assessed using area under curve, calibration plot and decision curve analysis respectively. The expression level of the feature genes was validated in GSE20129, GSE43292, GSE100927.

RESULTS

2 molecular subtypes of atherosclerosis was identified, and 223 prognosis-related DEGs between the 2 subtypes were identified. These genes are not only related to epithelial cell proliferation, mitochondrial dysfunction, but also to immune related pathways. Least absolute shrinkage and selection operator, random forest, support vector machine- recursive feature elimination show that IL17C and ACOXL were identified as diagnostic markers of atherosclerosis. The prediction model displayed good discrimination and good calibration. Decision curve analysis showed that this model was clinically useful. Moreover, IL17C and ACOXL were verified in other 3 GEO datasets, and also have good predictive performance.

CONCLUSION

IL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events.

Signaling pathways in vascular function and hypertension: molecular mechanisms and therapeutic interventions

Hypertension is a global public health issue and the leading cause of premature death in humans. Despite more than a century of research, hypertension remains difficult to cure due to its complex mechanisms involving multiple interactive factors and our limited understanding of it. Hypertension is a condition that is named after its clinical features. Vascular function is a factor that affects blood pressure directly, and it is a main strategy for clinically controlling BP to regulate constriction/relaxation function of blood vessels. Vascular elasticity, caliber, and reactivity are all characteristic indicators reflecting vascular function. Blood vessels are composed of three distinct layers, out of which the endothelial cells in intima and the smooth muscle cells in media are the main performers of vascular function. The alterations in signaling pathways in these cells are the key molecular mechanisms underlying vascular dysfunction and hypertension development. In this manuscript, we will comprehensively review the signaling pathways involved in vascular function regulation and hypertension progression, including calcium pathway, NO-NOsGC-cGMP pathway, various vascular remodeling pathways and some important upstream pathways such as renin-angiotensin-aldosterone system, oxidative stress-related signaling pathway, immunity/inflammation pathway, etc. Meanwhile, we will also summarize the treatment methods of hypertension that targets vascular function regulation and discuss the possibility of these signaling pathways being applied to clinical work.

Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach

Background: Traditionally, cancer prognosis was determined by tumours size, lymph node spread and presence of metastasis (TNM staging). Radiomics of tumour volume has recently been used for prognosis prediction. In the present study, we evaluated the effect of various sizes of tumour volume. A voted ensemble approach with a combination of multiple machine learning algorithms is proposed for prognosis prediction for head and neck squamous cell carcinoma (HNSCC). Methods: A total of 215 HNSCC CT image sets with radiotherapy structure sets were acquired from The Cancer Imaging Archive (TCIA). Six tumour volumes, including gross tumour volume (GTV), diminished GTV, extended GTV, planning target volume (PTV), diminished PTV and extended PTV were delineated. The extracted radiomics features were analysed by decision tree, random forest, extreme boost, support vector machine and generalized linear algorithms. A voted ensemble machine learning (VEML) model that optimizes the above algorithms was used. The receiver operating characteristic area under the curve (ROC-AUC) were used to compare the performance of machine learning methods, including accuracy, sensitivity and specificity. Results: The VEML model demonstrated good prognosis prediction ability for all sizes of tumour volumes with reference to GTV and PTV with high accuracy of up to 88.3%, sensitivity of up to 79.9% and specificity of up to 96.6%. There was no significant difference between the various target volumes for the prognostic prediction of HNSCC patients (chi-square test, p > 0.05). Conclusions: Our study demonstrates that the proposed VEML model can accurately predict the prognosis of HNSCC patients using radiomics features from various tumour volumes.