Elucidating the role of MICAL1 in pan-cancer using integrated bioinformatics and experimental approaches

Molecule interacting with CasL 1 (MICAL1) is a crucial protein involved in cell motility, axon guidance, cytoskeletal dynamics, and gene transcription. This pan-cancer study analyzed MICAL1 across 33 cancer types using bioinformatics and experiments. Dysregulated expression, diagnostic potential, and prognostic value were assessed. Associations with tumor characteristics, immune factors, and drug sensitivity were explored. Enrichment analysis revealed MICAL1's involvement in metastasis, angiogenesis, metabolism, and immune pathways. Functional experiments demonstrated its impact on renal carcinoma cells. These findings position MICAL1 as a potential biomarker and therapeutic target in specific cancers, warranting further investigation into its role in cancer pathogenesis.

Thousands of oscillating LncRNAs in the mouse testis

The long noncoding RNAs (lncRNAs) are involved in numerous fundamental biological processes, including circadian regulation. Although recent studies have revealed insights into the functions of lncRNAs, how the lncRNAs regulate circadian rhythms still requires a deeper investigation. In this study, we generate two datasets of RNA-seq profiles of the mouse (Mus musculus) testis under light-dark (LD) cycle. The first dataset included 18,613 unannotated transcripts measured at 12 time points, each with duplicate samples, under LD conditions; while the second dataset included 21,414 unannotated transcripts measured at six time points, each with three replicates, under desynchronized and control conditions. We identified 5964 testicular lncRNAs in each dataset by BLASTing these transcripts against the known mouse lncRNAs from the NONCODE database. MetaCycle analyses were performed to identify 519, 475, and 494 rhythmically expressed mouse testicular lncRNAs in the 12-time-point dataset, the six-time-point control dataset, and the six-time-point desynchronized dataset, respectively. A comparison of the expression profiles of the lncRNAs under desynchronized and control conditions revealed that 427 rhythmically expressed lncRNAs from the control condition became arrhythmic under the desynchronized condition, suggesting a possible loss of rhythmicity. In contrast, 446 arrhythmic lncRNAs from the control condition became rhythmic under the desynchronized condition, suggesting a possible gain of rhythmicity. Interestingly, 48 lncRNAs were rhythmically expressed under both desynchronized and control conditions. These oscillating lncRNAs were divided into morning lncRNAs, evening lncRNAs, and night lncRNAs based on their time-course expression patterns. We interrogated the promoter regions of these rhythmically expressed mouse testicular lncRNAs to predict their possible regulation by the E-box, D-box, or RORE promoter motifs. GO and KEGG analyses were performed to identify the possible biological functions of these rhythmically expressed mouse testicular lncRNAs. Further, we conducted conservation analyses of the rhythmically expressed mouse testicular lncRNAs with lncRNAs from humans, rats, and zebrafish, and uncovered three mouse testicular lncRNAs conserved across these four species. Finally, we computationally predicted the conserved lncRNA-encoded peptides and their 3D structures from each of the four species. Taken together, our study revealed thousands of rhythmically expressed lncRNAs in the mouse testis, setting the stage for further computational and experimental validations.

Revealing the diversity of endogenous peptides and parent proteins in human colostrum and mature milk through peptidomics analysis

Endogenous peptides and their parent proteins are important nutritional components with diverse biological functions. The objective of this study was to analyze and compare endogenous peptides and parent proteins found in human colostrum (HC) and human mature milk (HM) using a 4D label-free technique. In total, 5162 and 940 endogenous peptides derived from 258 parent proteins were identified in human milk by database (DB) search and de novo, respectively. Among these peptides, 2446 differentially expressed endogenous peptides with various bioactivities were identified. The Gene Ontology analysis unveiled the cellular components, biological processes, and molecular functions associated with these parent proteins. Metabolic pathway analysis suggested that neutrophil extracellular trap formation had the greatest significance with 24 parent proteins. These findings will offer a fresh perspective on the development of infant formula powder, highlighting the potential for incorporating these changes to enhance its nutritional composition and benefits.

SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach

Objective

Using bioinformatics machine learning methods, our research aims to identify the potential key genes associated with Lung adenocarcinoma (LUAD).

Methods

We obtained two gene expression profiling microarrays (GSE68571 and GSE74706) from the public Gene Expression Omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI). The purpose was to identify Differentially Expressed Genes (DEGs) between the lung adenocarcinoma group and the healthy control group. The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover. To evaluate the performance, ROC curves were plotted. We performed immuno-infiltration analysis using CIBERSORT. Finally, we validated the key genes through qRT-PCR and Western-blot verification, then downregulated MMP17 gene expression, upregulated SH3GL2 gene expression, and performed CCK8 experiments.

Results

A total of 32 Differentially Expressed Genes (DEGs) were identified. Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods. In Lung adenocarcinoma cells, the expression of MMP17 was observed to be elevated compared to normal lung epithelial cells in the control group (P < 0.05). In contrast, a down-regulation of SH3GL2 was found in Lung adenocarcinoma cells (P < 0.05). Finally, we downregulated MMP17 and upregulated SH3GL2 gene expression, then the CCK8 showed that the proliferation of both lung cancer cells was inhibited.

Conclusion

SH3GL2 and MMP17 are expected to be potential biomarkers for Lung adenocarcinoma.

A prognostic four-gene signature and a therapeutic strategy for hepatocellular carcinoma: Construction and analysis of a circRNA-mediated competing endogenous RNA network

BACKGROUND

Hepatocellular carcinoma (HCC) has a poor long-term prognosis. The competition of circular RNAs (circRNAs) with endogenous RNA is a novel tool for predicting HCC prognosis. Based on the alterations of circRNA regulatory networks, the analysis of gene modules related to HCC is feasible.

METHODS

Multiple expression datasets and RNA element targeting prediction tools were used to construct a circRNA-microRNA-mRNA network in HCC. Gene function, pathway, and protein interaction analyses were performed for the differentially expressed genes (DEGs) in this regulatory network. In the protein-protein interaction network, hub genes were identified and subjected to regression analysis, producing an optimized four-gene signature for prognostic risk stratification in HCC patients. Anti-HCC drugs were excavated by assessing the DEGs between the low- and high-risk groups. A circRNA-microRNA-hub gene subnetwork was constructed, in which three hallmark genes, KIF4A, CCNA2, and PBK, were subjected to functional enrichment analysis.

RESULTS

A four-gene signature (KIF4A, CCNA2, PBK, and ZWINT) that effectively estimated the overall survival and aided in prognostic risk assessment in the The Cancer Genome Atlas (TCGA) cohort and International Cancer Genome Consortium (ICGC) cohort was developed. CDK inhibitors, PI3K inhibitors, HDAC inhibitors, and EGFR inhibitors were predicted as four potential mechanisms of drug action (MOA) in high-risk HCC patients. Subsequent analysis has revealed that PBK, CCNA2, and KIF4A play a crucial role in regulating the tumor microenvironment by promoting immune cell invasion, regulating microsatellite instability (MSI), and exerting an impact on HCC progression.

CONCLUSIONS

The present study highlights the role of the circRNA-related regulatory network, identifies a four-gene prognostic signature and biomarkers, and further identifies novel therapy for HCC.

Structural and functional analysis of Cyanovirin-N homologs: Carbohydrate binding affinities and antiviral potential of cyanobacterial peptides

Cyanobacteria, a group of photosynthetic prokaryotes, can sinthesize several substances due to their secondary metabolism, with notable properties, such as Cyanovirin-N(CVN), a carbohydrate-binding lectin, that exhibits antiviral activity against several pathogens, due to its ability to bind viral surface carbohydrates such as mannose, thus interfering with the viral entry on the cell. CVN has been described in several cyanobacterial strains and shows biotechnological potential for the development of drugs of pharmaceutical interest. This study focuses on the genomic exploration and characterization of Cyanovirin-N homologs to assess the conservation of carbohydrate-binding affinity within the group. The analysis of their antiviral properties was carried out using bioinformatics tools to study protein models through an in silico pipeline, following the steps of genomic prospection on public databases, homology modeling, docking, molecular dynamics and energetic analysis. Mannose served as the reference ligand, and the lectins' binding affinity with mannose was assessed across Cyanovirin-N homologs. Genomic mining identified 33 cyanobacterial lectin sequences, which underwent structural and functional characterization. The results obtained from this work indicate strong carbohydrate affinity on several homologs, pointing to the conservation of antiviral properties alongside the group. However, this affinity was not uniformly distributed among sequences, exhibiting significant heterogeneity in binding site residues, suggesting potential multi-ligand binding capabilities on the Cyanovirin-N homologs group. Studies focused on the properties involved in these molecules and the investigation of the genetic diversity of Cyanovirin-N homologs could provide valuable insights into the discovery of new drug candidates, harvesting the potential of bioinformatics for large-scale functional and structural analysis.

Identification of circRNA biomarkers in osteosarcoma: An updated systematic review and meta-analysis

Circular RNAs (circRNAs) play a crucial role in cancer development and progression. This study aimed to identify potential circRNA biomarkers for osteosarcoma. Articles published from January 2010 to September 2023 were searched across eight databases to compare circRNA expression profiles in osteosarcoma and control samples (human, animal and cell lines). Meta-analysis was conducted under a random effects model. Subgroup analysis of circRNAs in different samples and tissues was performed. Diagnostic value was evaluated using receiver operator characteristic curves. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis explored functions of circRNA host genes. A circRNA-miRNA-mRNA axis depicted the regulatory mechanism in osteosarcoma. Among 1356 circRNAs with differential expression were identified across 226 original studies, only 74 were reported in at least three published sub-studies. Meta-analysis identified 58 dysregulated circRNAs (52 upregulated and 6 downregulated). Eleven circRNAs consistently showed dysregulation in tissues and cell lines, with hsa_circ_0005721 showing potential as a circulating biomarker in osteosarcoma. Sensitivity analysis demonstrated 97 % consistency. The overall area under the curve was 0.87 (95 % CI, 0.83-0.89). GO and KEGG enrichment analyses revealed host gene involvement in cancer. The circRNA-miRNA-mRNA axis revealed the regulatory axis and interactions within osteosarcoma specifically. This study demonstrates circRNAs as potential diagnostic biomarkers for osteosarcoma. Consistently reported dysregulated circRNAs are potential biomarkers in osteosarcoma pathogenesis, with hsa_circ_0005721 as a potential circulating biomarker for diagnosis and treatment.

Integration of network pharmacology with experimental validation to reveal the mechanism of action of Longdan Xiegan Decoction against HSV2 infection and determine its effective components

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese Medicine (TCM) has made enormous strides recently in the discovery of anti-herpes simplex virus (HSV) drugs under the guidance of TCM theory. Longdan Xiegan Decoction (LXD), a formulation recorded in the Pharmacopoeia of the People's Republic of China, has proved to be effective against HSV infection. However, its effective components and action mechanism remain unclear.

AIM OF THE STUDY

To investigate the effective components and mechanisms of LXD in treating HSV infection based on network pharmacology and experimental validation.

MATERIALS AND METHODS

The anti-HSV activities of key compounds predicted by network analysis were detected by antiviral tests. High-performance liquid chromatography (HPLC) was applied to identify the main components of the LXD aqueous extract. Time-of-addition assay and infectivity inhibition reversibility assay were conducted to identify the potential antiviral mechanisms of licochalcone B (LCB). Additionally, we assessed the antiviral effect of LCB in vivo by use of body weight, viral load, histological analysis, and scoring of genital lesions in an HSV2-infected mouse model.

RESULTS

Our data demonstrated that some components exhibited significant anti-HSV1/2 activity in vitro, including quercetin, kaempferol, wogonin, formononetin, naringenin, baicalein, isorhamnetin, glabridin, licochalcone A, echinatin, oroxylin A, isoliquiritigenin, pinocembrin, LCB and acacetin. HPLC analysis showed that LCB was the main component of LXD aqueous extract. In vitro experiments revealed that LCB not only inactivated HSV2 particles, but also inhibited HSV2 multiplication through the inhibition of the phosphorylation of Akt and its downstream targets. In vivo experiments confirmed that LCB could significantly reduce viral titer, delay weight loss, and alleviate pathological changes in vaginal tissue in vaginal infection mouse models.

CONCLUSION

LCB acted as the main component of LXD, with significant anti-HSV2 infection effects both in vivo and in vitro. This study provides additional evidence of the healing efficacy of LXD against HSV infection and presents an efficient analytical method for further investigation of the mechanisms of TCM in prevention and treatment of various diseases.

Biodegradation of phthalate acid esters and whole-genome analysis of a novel Streptomyces sp. FZ201 isolated from natural habitats

Di-n-butyl phthalate (DBP) is one of the most extensively used phthalic acid esters (PAEs) and is considered to be an emerging, globally concerning pollutant. The genus Streptomyces holds promise as a degrader of various organic pollutants, but PAE biodegradation mechanisms by Streptomyces species remain unsolved. In this study, a novel PAE-degrading Streptomyces sp. FZ201 isolated from natural habitats efficiently degraded various PAEs. FZ201 had strong resilience against DBP and exhibited immediate degradation, with kinetics adhering to a first-order model. The comprehensive biodegradation of DBP involves de-esterification, β-oxidation, trans-esterification, and aromatic ring cleavage. FZ201 contains numerous catabolic genes that potentially facilitate PAE biodegradation. The DBP metabolic pathway was reconstructed by genome annotation and intermediate identification. Streptomyces species have an open pangenome with substantial genome expansion events during the evolutionary process, enabling extensive genetic diversity and highly plastic genomes within the Streptomyces genus. FZ201 had a diverse array of highly expressed genes associated with the degradation of PAEs, potentially contributing significantly to its adaptive advantage and efficiency of PAE degradation. Thus, FZ201 is a promising candidate for remediating highly PAE-contaminated environments. These findings enhance our preliminary understanding of the molecular mechanisms employed by Streptomyces for the removal of PAEs.

Potential targets of diosgenin for the treatment of oral squamous cell carcinoma and their bioinformatics and transcriptional profiling analyses

Diosgenin can inhibit the proliferation and cause apoptosis of various tumor cells, and its inhibitory effect on oral squamous cell carcinoma (OSCC) and its mechanism are still unclear. In this study, we predicted the targets of diosgenin for the treatment of OSCC through the database, then performed bioinformatics analysis of the targets, and further verified the effect of diosgenin on the activity of OSCC cell line HSC-3, the transcriptional profile of the targets and the molecular docking of the targets with diosgenin. The results revealed that there were 146 potential targets of diosgenin for OSCC treatment, which involved signaling pathways such as Ras, TNF, PI3K-AKT, HIF, NF-κB, and could regulate cellular activity through apoptosis, autophagy, proliferation and differentiation, inflammatory response, DNA repair, etc. Diosgenin significantly inhibited HSC-3 cell activity. The genes such as AKT1, MET1, SRC1, APP1, CCND1, MYC, PTGS2, AR, NFKB1, BIRC2, MDM2, BCL2L1, MMP2, may be important targets of its action, not only their expression was regulated by diosgenin but also their proteins had a high binding energy with diosgenin. These results suggest that diosgenin may have a therapeutic effect on OSCC through AKT1, MMP2 and other targets and multiple signaling pathways, which is of potential clinical value.

Computational methods in the analysis of SARS-CoV-2 in mammals: A systematic review of the literature

SARS-CoV-2 is an enveloped RNA virus that causes severe respiratory illness in humans and animals. It infects cells by binding the Spike protein to the host's angiotensin-converting enzyme 2 (ACE2). The bat is considered the natural host of the virus, and zoonotic transmission is a significant risk and can happen when humans come into close contact with infected animals. Therefore, understanding the interconnection between human, animal, and environmental health is important to prevent and control future coronavirus outbreaks. This work aimed to systematically review the literature to identify characteristics that make mammals suitable virus transmitters and raise the main computational methods used to evaluate SARS-CoV-2 in mammals. Based on this review, it was possible to identify the main factors related to transmissions mentioned in the literature, such as the expression of ACE2 and proximity to humans, in addition to identifying the computational methods used for its study, such as Machine Learning, Molecular Modeling, Computational Simulation, between others. The findings of the work contribute to the prevention and control of future outbreaks, provide information on transmission factors, and highlight the importance of advanced computational methods in the study of infectious diseases that allow a deeper understanding of transmission patterns and can help in the development of more effective control and intervention strategies.

Identification of MACC1 as a potential biomarker for pulmonary arterial hypertension based on bioinformatics and machine learning

BACKGROUND

Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by abnormal early activation of pulmonary arterial smooth muscle cells (PASMCs), yet the underlying mechanisms remain to be elucidated.

METHODS

Normal and PAH gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and analyzed using gene set enrichment analysis (GSEA) to uncover the underlying mechanisms. Weighted gene co-expression network analysis (WGCNA) and machine learning methods were deployed to further filter hub genes. A number of immune infiltration analysis methods were applied to explore the immune landscape of PAH. Enzyme-linked immunosorbent assay (ELISA) was employed to compare MACC1 levels between PAH and normal subjects. The important role of MACC1 in the progression of PAH was verified through Western blot and real-time qPCR, among others.

RESULTS

39 up-regulated and 7 down-regulated genes were identified by 'limma' and 'RRA' packages. WGCNA and machine learning further narrowed down the list to 4 hub genes, with MACC1 showing strong diagnostic capacity. In vivo and in vitro experiments revealed that MACC1 was highsly associated with malignant features of PASMCs in PAH.

CONCLUSIONS

These findings suggest that targeting MACC1 may offer a promising therapeutic strategy for treating PAH, and further clinical studies are warranted to evaluate its efficacy.

PTGS2: A potential immune regulator and therapeutic target for chronic spontaneous urticaria

AIMS

Chronic spontaneous urticaria (CSU) is a common and debilitating skin disease that is difficult to control with existing treatments, and the pathogenesis of CSU has not been fully revealed. The aim of this study was to explore the underlying mechanisms of CSU and identify potential treatments.

MATERIALS AND METHODS

Microarray datasets of CSU were obtained from Gene Expression Omnibus database. Differentially expressed genes between skin lesions of CSU and normal controls (LNS-DEGs) were identified, and the enrichment analyses of LNS-DEGs were performed. Hub genes of LNS-DEGs were selected by protein-protein interaction analysis. The co-expression and transcriptional regulatory networks of hub genes were conducted using GeneMANIA and TRRUST database, respectively. CIBERSORT was utilized for immune cell infiltration analysis. Experimental validation was performed by β-hexosaminidase release examination and passive cutaneous anaphylaxis (PCA) mouse model.

KEY FINDINGS

A total of 247 LNS-DEGs were identified, which were enriched in cell migration, cell chemotaxis, and inflammatory pathways such as TNF and interleukin (IL) -17 signaling pathway. Among LNS-DEGs, seven upregulated (PTGS2, CCL2, IL1B, CXCL1, IL6, VCAM1, ICAM1) and one downregulated hub gene (PECAM1) were selected. Immune infiltration analysis identified eight different immune cells, such as activated/resting mast cells and neutrophils. Furthermore, PTGS2, encoding cyclooxygenase 2 (COX2), was selected for further validation. COX2 inhibitor, celecoxib, significantly inhibited mast cell degranulation, and reduced vascular permeability and inflammatory cytokine expression in PCA mouse model.

SIGNIFICANCE

PTGS2 may be a potential regulator of immunity and inflammation in CSU. Targeting PTGS2 is a new perspective for CSU treatment.

Redox organization of living systems

Redox organization governs an underlying simplicity in living systems. Critically, redox reactions enable the essential characteristics of life: extraction of energy from the environment, use of energy to support metabolic and structural organization, use of dynamic redox responses to defend against environmental threats, and use of redox mechanisms to direct differentiation of cells and organ systems essential for reproduction. These processes are sustained through a redox context in which electron donor/acceptor couples are poised at substantially different steady-state redox potentials, some with relatively reducing steady states and others with relatively oxidizing steady states. Redox-sensitive thiols of the redox proteome, as well as low molecular weight redox-active molecules, are maintained individually by the kinetics of oxidation-reduction within this redox system. Recent research has revealed opposing network interactions of the metallome, redox proteome, metabolome and transcriptome, which appear to be an evolved redox response structure to maintain stability of an organism in the presence of variable oxidative environments. Considerable opportunity exists to improve human health through detailed understanding of these redox networks so that targeted interventions can be developed to support new avenues for redox medicine.

New candidate genes potentially involved in Zika virus teratogenesis

Despite efforts to elucidate Zika virus (ZIKV) teratogenesis, still several issues remain unresolved, particularly on the molecular mechanisms behind the pathogenesis of Congenital Zika Syndrome (CZS). To answer this question, we used bioinformatics tools, animal experiments and human gene expression analysis to investigate genes related to brain development potentially involved in CZS. Searches in databases for genes related to brain development and CZS were performed, and a protein interaction network was created. The expression of these genes was analyzed in a CZS animal model and secondary gene expression analysis (DGE) was performed in human cells exposed to ZIKV. A total of 2610 genes were identified in the databases, of which 1013 were connected. By applying centrality statistics of the global network, 36 candidate genes were identified, which, after selection resulted in nine genes. Gene expression analysis revealed distinctive expression patterns for PRKDC, PCNA, ATM, SMC3 as well as for FGF8 and SHH in the CZS model. Furthermore, DGE analysis altered expression of ATM, PRKDC, PCNA. In conclusion, systems biology are helpful tools to identify candidate genes to be validated in vitro and in vivo. PRKDC, PCNA, ATM, SMC3, FGF8 and SHH have altered expression in ZIKV-induced brain malformations.

DIA-based quantitative proteomics analysis of plasma exosomes in rat model of allergic rhinitis

Allergic rhinitis (AR) is a common chronic inflammatory disease characterized by symptoms such as itching, rhinorrhea, sneezing, and nasal obstruction. Despite being classified as an IgE-mediated typeⅠ allergy for many years, the complex pathophysiological mechanism of AR continues to present a challenge in clinical management. The objective of this study was to quantify the proteomics of plasma exosomes using data independent acquisition (DIA) in combination with liquid chromatography-mass spectrometry (LC-MS/MS) to identify the key proteins involved in the development and progression of AR. In the AR rat model, a total of 41 proteins demonstrated significant up-regulation, while 51 proteins were found to be significantly down-regulated. Gene ontology (GO) analysis results indicated that the altered proteins were highly enriched in cellular regulatory processes and enzymatic activity in AR rats. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network results revealed that the pivotal proteins C4b, C1qa, C1qc, and Mbl1 might be involved in the metabolic pathways of the immune system in AR through the activation of the complement and coagulation cascades pathway. These proteins could serve as diagnostic markers and therapeutic targets for AR, which is of great significance in understanding the role of exosome proteins in AR.

Identification of HK3 as a promising immunomodulatory and prognostic target in sepsis-induced acute lung injury

BACKGROUND

Sepsis is a life-threatening global disease with a significant impact on human health. Acute lung injury (ALI) has been identified as one of the primary causes of mortality in septic patients. This study aimed to identify candidate genes involved in sepsis-induced ALI through a comprehensive approach combining bioinformatics analysis and experimental validation.

METHODS

The datasets GSE65682 and GSE32707 obtained from the Gene Expression Omnibus database were merged to screen for sepsis-induced ALI related differentially expressed genes (DEGs). Functional enrichment and immune infiltration analyses were conducted on DGEs, with the construction of protein-protein interaction (PPI) networks to identify hub genes. In vitro and in vivo models of sepsis-induced ALI were used to study the expression and function of hexokinase 3 (HK3) using various techniques including Western blot, real-time PCR, immunohistochemistry, immunofluorescence, Cell Counting Kit-8, Enzyme-linked immunosorbent assay, and flow cytometry.

RESULTS

The results of bioinformatics analysis have identified HK3, MMP9, and S100A8 as hub genes with diagnostic and prognostic significance for sepsis-induced ALI. The HK3 has profound effects on sepsis-induced ALI and exhibits a correlation with immune regulation. Experimental results showed increased HK3 expression in lung tissue of septic mice, particularly in bronchial and alveolar epithelial cells. In vitro studies demonstrated upregulation of HK3 in lipopolysaccharide (LPS)-stimulated lung epithelial cells, with cytoplasmic localization around the nucleus. Interestingly, following the knockdown of HK3 expression, lung epithelial cells exhibited a significant decrease in proliferation activity and glycolytic flux, accompanied by an increase in cellular inflammatory response, oxidative stress, and cell apoptosis.

CONCLUSIONS

It was observed for the first time that HK3 plays a crucial role in the progression of sepsis-induced ALI and may be a valuable target for immunomodulation and therapy.Bioinformatics analysis identified HK3, MMP9, and S100A8 as hub genes with diagnostic and prognostic relevance in sepsis-induced ALI. Experimental findings showed increased HK3 expression in the lung tissue of septic mice, particularly in bronchial and alveolar epithelial cells. In vitro experiments demonstrated increased HK3 levels in lung epithelial cells stimulated with LPS, with cytoplasmic localization near the nucleus. Knockdown of HK3 expression resulted in decreased proliferation activity and glycolytic flux, increased inflammatory response, oxidative stress, and cell apoptosis in lung epithelial cells.

Designing a predictive Framework: Immune-Related Gene-Based nomogram and prognostic model for kidney renal papillary cell carcinoma

BACKGROUND

Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients.

AIM

The aim of this study is to employ a comprehensive analysis of data for the identification of prognosis genes, leading to the development of a nomogram with strong predictive capabilities. The objective is to provide a valuable statistical tool that, when implemented in a clinical setting, can offer patients an early opportunity for treatment and enhance their chances of ultimate recovery from this life-threatening disease.

METHODS

Different packages in R were used to analyze RNA-seq data from the TCGA data portal. Multivariate Cox regression analysis and Kaplan-Meier analysis were also used to investigate the prognostic values of immune-related genes and construct the predictive model and nomogram. A p-value < 0.05 was considered to be significant.

RESULTS

A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway. 9 genes were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Immunohistochemistry results show that APOH, BIRC5, CCL19, and GRN were significantly increased in kidney cancer. B cells and CD4 + T cells were positively correlated with risk score model.

CONCLUSION

A prognostic model was successfully created based on 9 immune-related genes correlated with overall survival in KIRP. This work aims to provide some insight into therapeutic approaches and prognostic predictors of KIRP.

Integrative analysis and validation of necroptosis-related molecular signature for evaluating diagnosis and immune features in Rheumatoid arthritis

OBJECTIVES

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that is characterized by persistent morning stiffness, joint pain, and swelling. However, there is a lack of reliable diagnostic markers and therapeutic targets that are both effective and trustworthy.

METHODS

In this study, gene expression profiles (GSE89408, GSE55235, GSE55457, and GSE77298) were obtained from the Gene Expression Omnibus database. Differentially expressed necroptosis-related genes were attained from intersection of necroptosis-related gene set, differentially expressed genes, and weighted gene co-expression network analysis. The LASSO, random forest, and SVM-RFE machine learning algorithms were utilized to further screen potential diagnostic genes for RA. Immune cell infiltration was analyzed using the CIBERSORT method. The expressions of diagnostic genes were validated through quantitative real-time PCR, western blotting, immunohistochemistry, and immunofluorescence staining in synovial tissues collected from three trauma controls and three RA patients.

RESULTS

Five core necroptosis-related genes (FAS, CYBB, TNFSF10, EIF2AK2, and BIRC2) were identified as potential biomarkers for RA. Two different necroptosis patterns based on these five genes were confirmed to significantly correlated with immune cells (especially macrophages). In vitro experiments showed significantly higher mRNA and protein expression levels of CYBB and EIF2AK2 in RA patients compared to normal controls, consistent with the bioinformatics analysis results.

CONCLUSION

Our study identified a novel necroptosis-related subtype and five diagnostic biomarkers of RA, revealed vital roles in the development and occurrence of RA, and offered potential targets for clinical diagnosis and immunotherapy.

Unveiling the pathophysiology of restless legs syndrome through transcriptome analysis

The aim of this study was to analyze signaling pathways associated with differentially expressed messenger RNAs in people with restless legs syndrome (RLS). Seventeen RLS patients and 18 controls were enrolled. Coding RNA expression profiling of 12,857 gene transcripts by next-generation sequencing was performed. Enrichment analysis by pathfindR tool was carried-out, with p-adjusted ≤0.001 and fold-change ≥2.5. Nine main different network groups were significantly dysregulated in RLS: infections, inflammation, immunology, neurodegeneration, cancer, neurotransmission and biological, blood and metabolic mechanisms. Genetic predisposition plays a key role in RLS and evidence indicates its inflammatory nature; the high involvement of mainly neurotropic viruses and the TORCH complex might trigger inflammatory/immune reactions in genetically predisposed subjects and activate a series of biological pathways-especially IL-17, receptor potential channels, nuclear factor kappa-light-chain-enhancer of activated B cells, NOD-like receptor, mitogen-activated protein kinase, p53, mitophagy, and ferroptosis-involved in neurotransmitter mechanisms, synaptic plasticity, axon guidance, neurodegeneration, carcinogenesis, and metabolism.

Inference of drug off-target effects on cellular signaling using interactome-based deep learning

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

From biological data to oscillator models using SINDy

Periodic changes in the concentration or activity of different molecules regulate vital cellular processes such as cell division and circadian rhythms. Developing mathematical models is essential to better understand the mechanisms underlying these oscillations. Recent data-driven methods like SINDy have fundamentally changed model identification, yet their application to experimental biological data remains limited. This study investigates SINDy's constraints by directly applying it to biological oscillatory data. We identify insufficient resolution, noise, dimensionality, and limited prior knowledge as primary limitations. Using various generic oscillator models of different complexity and/or dimensionality, we systematically analyze these factors. We then propose a comprehensive guide for inferring models from biological data, addressing these challenges step by step. Our approach is validated using glycolytic oscillation data from yeast.

Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics

Early detection of left ventricular remodeling (LVR) is crucial. While cardiac magnetic resonance (CMR) provides valuable information, it has limitations. Coronary angiography-derived fractional flow reserve (caFFR) and index of microcirculatory resistance (caIMR) offer viable alternatives. 157 patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention were prospectively included. 23.6% of patients showed LVR. Machine learning algorithms constructed three LVR prediction models: Model 1 incorporated clinical and procedural parameters, Model 2 added CMR parameters, and Model 3 included echocardiographic and functional parameters (caFFR and caIMR) with Model 1. The random forest algorithm showed robust performance, achieving AUC of 0.77, 0.84, and 0.85 for Models 1, 2, and 3. SHAP analysis identified top features in Model 2 (infarct size, microvascular obstruction, admission hemoglobin) and Model 3 (current smoking, caFFR, admission hemoglobin). Findings indicate coronary physiology and echocardiographic parameters effectively predict LVR in patients with STEMI, suggesting their potential to replace CMR.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and genetic variation. Mutations generated by APOBEC3 contribute to genetic variation and tumor evolvability. However, the influence of APOBEC3 on the evolvability of the genome and its differential impact on cancer genes versus non-cancer genes remains unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer and non-cancer genes, suggesting unique associations with cancer. Studying a bat species with numerous APOBEC3 genes, we found distinct motif patterns in orthologs of cancer genes compared to non-cancer genes, as in humans, suggesting APOBEC3 evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC3-induced heterogeneity enhances cancer evolution through bimodal patterns of mutations in certain classes of genes. Our results suggest the bimodal distribution of APOBEC-induced mutations can significantly increase cancer heterogeneity.

Evolutionary analysis reveals the role of a non-catalytic domain of peptidyl arginine deiminase 2 in transcriptional regulation

Peptidyl arginine deiminases (PADIs) catalyze protein citrullination, a post-translational conversion of arginine to citrulline. The most widely expressed member of this family, PADI2, regulates cellular processes that impact several diseases. We hypothesized that we could gain new insights into PADI2 function through a systematic evolutionary and structural analysis. Here, we identify 20 positively selected PADI2 residues, 16 of which are structurally exposed and maintain PADI2 interactions with cognate proteins. Many of these selected residues reside in non-catalytic regions of PADI2. We validate the importance of a prominent loop in the middle domain that encompasses PADI2 L162, a residue under positive selection. This site is essential for interaction with the transcription elongation factor (P-TEFb) and mediates the active transcription of the oncogenes c-MYC, and CCNB1, as well as impacting cellular proliferation. These insights could be key to understanding and addressing the role of the PADI2 c-MYC axis in cancer progression.

Shared genetic architecture and causal relationship between liver and heart disease

This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.

scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics

Gene regulatory networks (GRNs) involve complex and multi-layer regulatory interactions between regulators and their target genes. Precise knowledge of GRNs is important in understanding cellular processes and molecular functions. Recent breakthroughs in single-cell sequencing technology made it possible to infer GRNs at single-cell level. Existing methods, however, are limited by expensive computations, and sometimes simplistic assumptions. To overcome these obstacles, we propose scGREAT, a framework to infer GRN using gene embeddings and transformer from single-cell transcriptomics. scGREAT starts by constructing gene expression and gene biotext dictionaries from scRNA-seq data and gene text information. The representation of TF gene pairs is learned through optimizing embedding space by transformer-based engine. Results illustrated scGREAT outperformed other contemporary methods on benchmarks. Besides, gene representations from scGREAT provide valuable gene regulation insights, and external validation on spatial transcriptomics illuminated the mechanism behind scGREAT annotation. Moreover, scGREAT identified several TF target regulations corroborated in studies.

Pan-cancer analysis of the prognostic and immunological role of FKBP4

Objectives

Our previous studies revealed the significant roles of FK506-binding protein 4 (FKBP4) in tumorigenesis, however, there has been no pan-cancer analysis of FKBP4. Using bioinformatics, the current study reported the expression and prognostic role of FKBP4, and the correlation between FKBP4 and clinicopathological parameters, methylation, molecular network, immunological traits and drug sensitivity.

Methods

RNA sequencing data, somatic mutation, and related clinical information were obtained from TCGA using UCSC Xena. The association between FKBP4 expression and clinical features was assessed using TISIDB. The relationships between FKBP4 expression and tumour stage, OS, DSS, DFS, and PFS were analysed using univariate cox regression analysis. The radar plots for TMB and MSI were obtained using "Fmsb" R package. UALCAN was used to explore the effect of FKBP4 methylation on tumour and normal samples. CBioportal was used to analyse copy number mutations in FKBP4 Gene expression and drug sensitivity data were downloaded from the CellMiner database. GO analysis was performed for the high and the low expression of FKBP4 compared with the median level of FKBP4 using clusterProfiler4.0.

Results

FKBP4 expression is significantly upregulated in various types of cancers. Cox regression analysis showed that high FKBP4 levels were correlated with poor OS, DSS, DFS, and PFS in most patients with cancer. Methylation of FKBP4 DNA was upregulated in most cancers, and FKBP4 expression is positively associated with transmethylase expression. FKBP4 and its copy were significantly associated with the expression of immune-infiltrating cells, immune checkpoint genes, immune modulators, TMB, MMR, and MSI. FKBP4 expression levels significantly correlated with 16 different drug sensitivities (all p < 0.05).

Conclusions

Our pan-cancer bioinformatic analysis revealed a potential mechanism underlying the effects of FKBP4 on the prognosis and progression of various cancers.

Molecular network mechanism in cerebral ischemia-reperfusion rats treated with human urine stem cells

Objective

To explore the effect of human urine-derived stem cells (husc) in improving the neurological function of rats with cerebral ischemia-reperfusion (CIR), and report new molecular network by bioinformatics, combined with experiment validation.

Methods

After CIR model was established, and husc were transplanted into the lateral ventricle of rats，neurological severe score (NSS) andgene network analysis were performed. Firstly, we input the keywords "Cerebral reperfusion" and "human urine stem cells" into Genecard database and merged data with findings from PubMed so as to get their targets genes, and downloaded them to make Venny intersection plot. Then, Gene ontology (GO) analysis, kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and protein-protein interaction (PPI) were performed to construct molecular network of core genes. Lastly, the expressional level of core genes was validated via quantitative real-time polymerase chain reaction (qRT-PCR), and localized by immunofluorescence.

Results

Compared with the Sham group, the neurological function of CIR rats was significantly improved after the injection of husc into the lateral ventricle; at 14 days, P = 0.028, which was statistically significant. There were 258 overlapping genes between CIR and husc, and integrated with 252 genes screened from PubMed and CNKI. GO enrichment analysis were mainly involved neutrophil degranulation, neutrophil activation in immune response and platelet positive regulation of degranulation, Hemostasis, blood coagulation, coagulation, etc. KEGG pathway analysis was mainly involved in complement and coagulation cascades, ECM-receptor. Hub genes screened by Cytoscape consist ofCD44, ACTB, FN1, ITGB1, PLG, CASP3, ALB, HSP90AA1, EGF, GAPDH. Lastly, qRT-PCR results showed statistic significance (P < 0.05) in ALB, CD44 and EGF before and after treatment, and EGF immunostaining was localized in neuron of cortex.

Conclusion

husc transplantation showed a positive effect in improving neural function of CIR rats, and underlying mechanism is involved in CD44, ALB, and EGF network.

Bioinformatics-driven identification and validation of diagnostic biomarkers for cerebral ischemia reperfusion injury

Objective

This article aims to identify genetic features associated with immune cell infiltration in cerebral ischemia-reperfusion injury (CIRI) development through bioinformatics, with the goal of discovering diagnostic biomarkers and potential therapeutic targets.

Methods

We obtained two datasets from the Gene Expression Omnibus (GEO) database to identify immune-related differentially expressed genes (IRDEGs). These genes' functions were analyzed via Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Tools such as CIBERSORT and ssGSEA assessed immune cell infiltration. The Starbase and miRDB databases predicted miRNAs interacting with hub genes, and Cytoscape software mapped mRNA-miRNA interaction networks. The ENCORI database was employed to predict RNA binding proteins interacting with hub genes. Key genes were identified using a random forest algorithm and constructing a Support Vector Machine (SVM) model. LASSO regression analysis constructed a diagnostic model for hub genes to determine their diagnostic value, and PCR analysis validated their expression in cerebral ischemia-reperfusion.

Results

We identified 10 IRDEGs (C1qa, Ccl4, Cd74, Cd8a, Cxcl10, Gmfg, Grp, Lgals3bp, Timp1, Vim). The random forest algorithm, and SVM model intersection revealed three key genes (Ccl4, Gmfg, C1qa) as diagnostic biomarkers for CIRI. LASSO regression analysis, further refined this to two key genes (Ccl4 and C1qa), With ROC curve, analysis confirming their diagnostic efficacy (C1qa AUC = 0.75, Ccl4 AUC = 0.939). PCR analysis corroborated these findings.

Conclusions

Our study elucidates immune and metabolic response mechanisms in CIRI, identifying two immune-related genes as key biomarkers and potential therapeutic targets in response to cerebral ischemia-reperfusion injury.

Identification and validation of mutual hub genes in idiopathic pulmonary fibrosis and rheumatoid arthritis-associated usual interstitial pneumonia

Objectives

The study aims at exploring common hub genes and pathways in idiopathic pulmonary fibrosis (IPF) and rheumatoid arthritis-associated usual interstitial pneumonia (RA-UIP) through integrated bioinformatics analyses.

Methods

The GSE199152 dataset containing lung tissue samples from IPF and RA-UIP patients was acquired from the Gene Expression Omnibus (GEO) database. The identification of overlapping differentially expressed genes (DEGs) in IPF and RA-UIP was carried out through R language. Protein-protein interaction (PPI) network analysis and module analysis were applied to filter mutual hub genes in the two diseases. Enrichment analyses were also conducted to analyze the possible biological functions and pathways of the overlapped DEGs and hub genes. The diagnostic value of key genes was assessed with R language, and the expressions of these genes in pulmonary cells of IPF and rheumatoid arthritis-associated interstitial lung disease (RA-ILD) patients were analyzed with single cell RNA-sequencing (scRNA-seq) datasets. The expression levels of hub genes were validated in blood samples from patients, specimens of human lung fibroblasts, lung tissue samples from mice, as well as external GEO datasets.

Results

Four common hub genes (THBS2, TIMP1, POSTN, and CD19) were screened. Enrichment analyses showed that the abnormal expressions of DEGs and hub genes may be connected with the onset of IPF and RA-UIP by regulating the progression of fibrosis. ScRNA-seq analyses illustrated that for both IPF and RA-ILD patients, THBS2, TIMP1, and POSTN were mainly expressed in lung fibroblasts, while CD19 was uniquely high-expressed in B cells. The qRT-PCR and immunohistochemistry (IHC) results verified that the expression levels of hub genes were mostly in accordance with the findings obtained from the bioinformatics analyses.

Conclusion

Though IPF and RA-UIP are distinct diseases, they may to some extent have mutual pathogenesis in the development of fibrosis. THBS2, TIMP1, POSTN, and CD19 may be the potential biomarkers of IPF and RA-UIP, and intervention on related pathways of these genes could offer new strategies for the precision treatment of IPF and RA-UIP.

Identification and validation of immune-related genes in osteoarthritic synovial fibroblasts

Objective

OA was generally considered as a non-inflammatory disease dominated by articular cartilage degeneration. However, the role of synovitis in OA pathogenesis has received increasing attention. Recent studies support that OA patients have a pro-inflammatory/catabolic synovial environment similar to RA patients, promoting the occurrence and development of OA. Therefore, we investigated the co-immune-related genes and pathways of OA and RA to explore whether part of the pathogenesis of RA synovitis can be used to explain OA synovitis.

Methods

Data of GSE29746 and GSE12021 were downloaded from the Gene Expression Omnibus (GEO) database. Compared with control group, differentially expressed genes (DEGs) of OA and RA groups were screened separately by R software, Venny website was used to screen co-DEGs. Metascape was used to screen the common enriched terms and pathways between OA and RA. STRING website and Cytoscape software were used to map protein-protein interaction (PPI) networks and screen co-hub genes. GSE29746 was selected as the test dataset, and GSE12021 as the validation dataset for validate the co-hub genes. The results were validated by western blotting (WB) and real-time quantitative polymerase chain reaction (qPCR) of clinical synovial samples.

Results

We identified 573 OA-related DEGs, 148 RA-related DEGs, and 52 co-DEGs, revealing 14 common enriched terms, most of which were related to immune inflammation. IL7R was the only upregulated co-hub gene between OA and RA in the PPI network, consistent with the validation dataset. IL7R was highly expressed in clinical osteoarthritic synovial samples (P < 0.001).

Conclusion

Our findings suggested that IL7R is a critical co-DEG in OA and RA and confirmed the involvement of immune inflammation in disease pathogenesis. Furthermore, it confirms the role of IL7R in synovial inflammation in RA and OA synovitis and provides evidence for further investigation of OA immune inflammation.

A High-quality Genome of the convergent lady beetle, Hippodamia convergens

Here we describe a high quality genome assembly and annotation of the convergent lady beetle, Hippodamia convergens (Coleoptera: Coccinellidae). The highest quality unmasked genome comprises 619 Megabases (Mb) of chromosomal DNA, organized into 899 contigs, with a contig N50 score of 89 Mbps. The genome was assessed to be 96% complete (BUSCO). Reconstruction of a whole genome phylogeny resolved H. convergens as sister to the Harlequin lady beetle, Harmonia axyridis, and nested within a clade of several known agricultural pests.

Identification and verification of AK4 as a protective immune-related biomarker in adipose-derived stem cells and breast cancer

Background

Breast cancer (BC) remains the most common cancer among women, and novel post-surgical reconstruction techniques, including autologous fat transplantation, have emerged. While Adipose-derived stem cells (ADSCs) are known to impact the viability of fat grafts, their influence on breast cancer progression remains unclear. This study aims to elucidate the genetic interplay between ADSCs and breast cancer, focusing on potential therapeutic targets.

Methods

Using the GEO and TCGA databases, we pinpointed differentially expressed (DE) mRNAs, miRNAs, lncRNAs, and pseudogenes of ADSCs and BC. We performed functional enrichment analysis and constructed protein-protein interaction (PPI), RNA binding protein (RBP)-pseudogene-mRNA, and lncRNA-miRNA-transcription factor (TF)-gene networks. Our study delved into the correlation of AK4 expression with 33 different malignancies and examined its impact on prognostic outcomes across a pan-cancer cohort. Additionally, we scrutinized immune infiltration, microsatellite instability, and tumor mutational burden, and conducted single-cell analysis to further understand the implications of AK4 expression. We identified novel sample subtypes based on hub genes using the ConsensusClusterPlus package and examined their association with immune infiltration. The random forest algorithm was used to screen DE mRNAs between subtypes to validate the powerful prognostic prediction ability of the artificial neural network.

Results

Our analysis identified 395 DE mRNAs, 3 DE miRNAs, 84 DE lncRNAs, and 26 DE pseudogenes associated with ADSCs and BC. Of these, 173 mRNAs were commonly regulated in both ADSCs and breast cancer, and 222 exhibited differential regulation. The PPI, RBP-pseudogene-mRNA, and lncRNA-miRNA-TF-gene networks suggested AK4 as a key regulator. Our findings support AK4 as a promising immune-related therapeutic target for a wide range of malignancies. We identified 14 characteristic genes based on the AK4-related cluster using the random forest algorithm. Our artificial neural network yielded excellent diagnostic performance in the testing cohort with AUC values of 0.994, 0.973, and 0.995, indicating its ability to distinguish between breast cancer and non-breast cancer cases.

Conclusions

Our research sheds light on the dual role of ADSCs in BC at the genetic level and identifies AK4 as a key protective mRNA in breast cancer. We found that AK4 significantly predicts cancer prognosis and immunotherapy, indicating its potential as a therapeutic target.

Identification of cuproptosis-related miRNAs in triple-negative breast cancer and analysis of the miRNA-mRNA regulatory network

Introduction

The close association between cuproptosis and tumor immunity in triple-negative breast cancer (TNBC) allows its monitoring for predicting the prognosis of patients with TNBC. Nevertheless, the biological function and prognostic value of cuproptosis-related miRNAs and their target genes have not been reported.

Purpose

To construct the miRNA and mRNA-based risk models associated with cuproptosis for patients with TNBC.

Methods

Comparison of expression levels for genes associated with cuproptosis was executed between patients in the normal individuals and the TCGA-TNBC cohort. Conducting differential analysis resulted in the identification of differentially expressed miRNA (DE-miRNAs) and differentially expressed genes (DEGs) between the TNBC and Control samples. Screening for prognostic miRNAs and biomarkers involved employing univariate Cox analysis and least absolute shrinkage and selection operator regression analyses. These methods were utilized to construct risk models aimed at predicting the survival of patients with TNBC. Based on the median value of risk scores, patients were then stratified into low- and high-risk groups. Functional enrichment analysis was employed to explore the potential function and pathways of prognostic genes. Additionally, independent prognostic analysis was performed through univariate and multivariate Cox regression. Immune infiltration analysis was performed to examine disparities in the infiltration of immune cells between the two risk groups. Finally, the prognostic gene expression was mined in key cell types of TNBC.

Results

We obtained 5213 DEGs and 204 DE-miRNAs related to cuproptosis between TNBC and Control samples. Five prognostic miRNAs (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were closely associated with TNBC. Significant differences in the functions of prognostic genes between the two risk groups were observed, encompassing adipogenesis, inflammatory response, and hormone metabolic process. The prognostic gene regulatory network revealed that miR200C-3p regulated ZFPM2 and CFL2, and miR-1277-3p regulated BMP2 and RORA. A nomogram was created based on riskScore, cancer status, and pathologic stage to predict 1/3/5-year survival of patients with TNBC. Immune infiltration analysis indicated that the immune microenvironment may be associated with the progression of TNBC. Interestingly, prognostic genes exhibited higher expression levels in T cells, fibroblasts, endothelial cells, and monocytes compared to other cells.

Conclusions

Five prognostic miRNA (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were significantly associated with TNBC, it provides new therapeutic targets for the treatment and prognosis of TNBC.

Pan-cancer analyses reveal genomics and clinical outcome association of the fatty acid oxidation regulators in cancer

Background

Fatty acid oxidation (FAO) is considered to play a vital part in tumor metabolic reprogramming. But the comprehensive description of FAO dysregulation in tumors has not been unknown.

Methods

We obtained FAO genes, RNA-seq data and clinical information from the Msigdb, TCGA and GTEx databases. We assessed their prognosis value using univariate cox analysis, survival analysis and Kaplan-Meier curve. We determined the function of FAO genes using gene set variation analysis. The correlation analysis was calculated by corrplot R package. Immunotherapy response was assessed through TIDE scores. The protein expression levels of FAO genes were validated using immunohistochemistry (IHC).

Results

The FAO scores were highest in COAD but lowest in PCPG. FAO scores were significantly associated with the prognosis of some cancers in OS, DSS, DFI and PFI. Besides, gene set variation analysis identified that FAO scores were related to immune-related pathways, and immune infiltration analysis showed FAO scores were positively related to cancer-associated fibroblasts and various immune-related genes. TIDE scores were significantly decreased in ACC, CHOL, ESCA, GBM, LAML, SARC, SKCM and THCA compared with normal samples, while it was significantly increased in BLCA, LUAD, LUSC, PCPG, PRAD and STAD. Besides, most FAO genes were downregulated in pan-cancer compared with normal samples. Moreover, we found copy number variation (CNV) of FAO genes played a positive role in their mRNA expression, while methylation was negative. We determined FAO genes were closely related to some drugs in pan-cancer.

Conclusions

FAO score is a novel and promising factor for predicting outcomes.

Designing and delivering bioinformatics project-based learning in East Africa

BACKGROUND

The Eastern Africa Network for Bioinformatics Training (EANBiT) has matured through continuous evaluation, feedback, and codesign. We highlight how the program has evolved to meet challenges and achieve its goals and how experiential learning through mini projects enhances the acquisition of skills and collaboration. We continued to learn and grow through honest feedback and evaluation of the program, trainers, and modules, enabling us to provide robust training even during the Coronavirus disease 2019 (COVID-19) pandemic, when we had to redesign the program due to restricted travel and in person group meetings.

RESULTS

In response to the pandemic, we developed a program to maintain "residential" training experiences and benefits remotely. We had to answer the following questions: What must change to still achieve the RT goals? What optimal platforms should be used? How would we manage connectivity and data challenges? How could we avoid online fatigue? Going virtual presented an opportunity to reflect on the essence and uniqueness of the program and its ability to meet the objective of strengthening bioinformatics skills among the cohorts of students using different delivery approaches. It allowed an increase in the number of participants. Evaluating each program component is critical for improvement, primarily when feedback feeds into the program's continuous amendment. Initially, the participants noted that there were too many modules, insufficient time, and a lack of hands-on training as a result of too much focus on theory. In the subsequent iterations, we reduced the number of modules from 27 to five, created a harmonized repository for the materials on GitHub, and introduced project-based learning through the mini projects.

CONCLUSION

We demonstrate that implementing a program design through detailed monitoring and evaluation leads to success, especially when participants who are the best fit for the program are selected on an appropriate level of skills, motivation, and commitment.

NSPlex: an efficient method to analyze non-specific peaks amplified using commercial STR kits

Commercial short tandem repeat (STR) kits exclusively contain human-specific primers; however, various non-human organisms with high homology to the STR kit's primer sequences can cause cross-reactivity. Owing to the proprietary nature of the primers in STR kits, the origins and sequences of most non-specific peaks (NSPs) remain unclear. Such NSPs can complicate data interpretation between the casework and reference samples; thus, we developed "NSPlex", an efficient method to discover the biological origins of NSPs. We used leftover STR kit amplicons after capillary electrophoresis and performed advanced bioinformatics analyses using next-generation sequencing followed by BLAST nucleotide searches. Using our method, we could successfully identify NSP generated from PCR amplicons of a sample mixture of human DNA and DNA extracted from matcha powder (finely ground powder of green tea leaves and previously known as a potential source of NSP). Our results showed our method is efficient for NSP analysis without the need for the primer information as in commercial STR kits.

In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches

Infection with the hepatitis C virus (HCV) is one of the causes of liver cancer, which is the world's sixth most prevalent and third most lethal cancer. The current treatments do not prevent reinfection; because they are expensive, their usage is limited to developed nations. Therefore, a prophylactic vaccine is essential to control this virus. Hence, in this study, an immunoinformatics method was applied to design a multi-epitope vaccine against HCV. The best B- and T-cell epitopes from conserved regions of the E2 protein of seven HCV genotypes were joined with the appropriate linkers to design a multi-epitope vaccine. In addition, cholera enterotoxin subunit B (CtxB) was included as an adjuvant in the vaccine construct. This study is the first to present this epitopes-adjuvant combination. The vaccine had acceptable physicochemical characteristics. The vaccine's 3D structure was predicted and validated. The vaccine's binding stability with Toll-like receptor 2 (TLR2) and TLR4 was confirmed using molecular docking and molecular dynamics (MD) simulation. The immune simulation revealed the vaccine's efficacy by increasing the population of B and T cells in response to vaccination. In silico expression in Escherichia coli (E. coli) was also successful.

MicroRNA-130b Suppresses Malignant Behaviours and Inhibits the Activation of the PI3K/Akt Signaling Pathway by Targeting MET in Pancreatic Cancer

There has been interested in the microRNAs' roles in pancreatic cancer (PC) cell biology, particularly in regulating pathways related to tumorigenesis. The study aimed to explore the hub miRNAs in PC and underlying mechanisms by bioinformatics and fundamental experiments. RNA datasets collected from the Gene Expression Omnibus were analysed to find out differentially expressed RNAs (DERNAs). The miRNA-mRNA and protein-protein interaction (PPI) networks were built. The clinicopathological features and expressions of hub miRNAs and hub mRNAs were explored. Dual-luciferase reporter gene assay was performed to assess the interaction between microRNA and target gene. RT-qPCR and western blot were employed to explore RNA expression. The roles of RNA were detected by CCK-8 test, wound healing, transwell, and flow cytometry experiment. We verified 40 DEmiRNAs and 1613 DEmRNAs, then detected a total of 69 final functional mRNAs (FmRNAs) and 23 DEmiRNAs. In the miRNA-mRNA networks, microRNA-130b (miR-130b) was the hub RNA with highest degrees. Clinical analysis revealed that miR-130b was considerably lower expressed in cancerous tissues than in healthy ones, and patients with higher-expressed miR-130b had a better prognosis. Mechanically, miR-130b directly targeted MET in PC cells. Cell functional experiments verified that miR-130b suppressed cell proliferation, migration, promoted apoptosis, and inhibited the PI3K/Akt pathway by targeting MET in PC cells. Our findings illustrated the specific molecular mechanism of miR-130b regulating PC progress. The miR-130b/MET axis may be an alternative target in the therapeutic intervention of PC and provide an opportunity to deepen our understanding of the pathogenesis of PC.

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways

BACKGROUND

Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data.

METHOD

We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware.

RESULTS

We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation.

CONCLUSION

The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .

Extracellular vesicle dynamics in COPD: understanding the role of miR-422a, SPP1 and IL-17 A in smoking-related pathology

BACKGROUND

Chronic obstructive pulmonary disease (COPD) induced by smoking poses a significant global health challenge. Recent findings highlight the crucial role of extracellular vesicles (EVs) in mediating miRNA regulatory networks across various diseases. This study utilizes the GEO database to uncover distinct expression patterns of miRNAs and mRNAs, offering a comprehensive understanding of the pathogenesis of smoking-induced COPD. This study aims to investigate the mechanisms by which extracellular vesicles (EVs) mediate the molecular network of miR-422a-SPP1 to delay the onset of COPD caused by smoking.

METHODS

The smoking-related miRNA chip GSE38974-GPL7723 was obtained from the GEO database, and candidate miRs were retrieved from the Vesiclepedia database. Downstream target genes of the candidate miRs were predicted using mRNA chip GSE38974-GPL4133, TargetScan, miRWalk, and RNA22 databases. This prediction was integrated with COPD-related genes from the GeneCards database, downstream target genes predicted by online databases, and key genes identified in the core module of WGCNA analysis to obtain candidate genes. The candidate genes were subjected to KEGG functional enrichment analysis using the "clusterProfiler" package in R language, and a protein interaction network was constructed. In vitro experiments involved overexpressing miRNA or extracting extracellular vesicles from bronchial epithelial cell-derived exosomes, co-culturing them with myofibroblasts to observe changes in the expression levels of the miR-422a-SPP1-IL-17 A regulatory network, and assessing protein levels of fibroblast differentiation-related factors α-SMA and collagen I using Western blot analysis.

RESULTS

The differential gene analysis of chip GSE38974-GPL7723 and the retrieval results from the Vesiclepedia database identified candidate miRs, specifically miR-422a. Subsequently, an intersection was taken among the prediction results from TargetScan, miRWalk, and RNA22 databases, the COPD-related gene retrieval results from GeneCards database, the WGCNA analysis results of chip GSE38974-GPL4133, and the differential gene analysis results. This intersection, combined with KEGG functional enrichment analysis, and protein-protein interaction analysis, led to the final screening of the target gene SPP1 and its upstream regulatory gene miR-422a. KEGG functional enrichment analysis of mRNAs correlated with SPP1 revealed the IL-17 signaling pathway involved. In vitro experiments demonstrated that miR-422a inhibition targets suppressed the expression of SPP1 in myofibroblasts, inhibiting differentiation phenotype. Bronchial epithelial cells, under cigarette smoke extract (CSE) stress, could compensate for myofibroblast differentiation phenotype by altering the content of miR-422a in their Extracellular Vesicles (EVs).

CONCLUSION

The differential gene analysis of Chip GSE38974-GPL7723 and the retrieval results from the Vesiclepedia database identified candidate miRs, specifically miR-422a. Further analysis involved the intersection of predictions from TargetScan, miRWalk, and RNA22 databases, gene search on COPD-related genes from the GeneCards database, WGCNA analysis from Chip GSE38974-GPL4133, and differential gene analysis, combined with KEGG functional enrichment analysis and protein interaction analysis. Ultimately, the target gene SPP1 and its upstream regulatory gene miR-422a were selected. KEGG functional enrichment analysis on mRNAs correlated with SPP1 revealed the involvement of the IL-17 signaling pathway. In vitro experiments showed that miR-422a targeted inhibition suppressed the expression of SPP1 in myofibroblast cells, inhibiting differentiation phenotype. Furthermore, bronchial epithelial cells could compensate for myofibroblast differentiation phenotype under cigarette smoke extract (CSE) stress by altering the miR-422a content in their extracellular vesicles (EVs).

Protocol to explain support vector machine predictions via exact Shapley value computation

Shapley values from cooperative game theory are adapted for explaining machine learning predictions. For large feature sets used in machine learning, Shapley values are approximated. We present a protocol for two techniques for explaining support vector machine predictions with exact Shapley value computation. We detail the application of these algorithms and provide ready-to-use Python scripts and custom code. The final output of the protocol includes quantitative feature analysis and mapping of important features for visualization. For complete details on the use and execution of this protocol, please refer to Feldmann and Bajorath1 and Mastropietro et al.2.

Transcriptomic investigation and biomarker discovery for zinc response in oysters Crassostrea gasar

In an era of unprecedented industrial and agricultural growth, metal contamination in marine environments is a pressing concern. Sentinel organisms such as the mangrove oyster Crassostrea gasar provide valuable insights into these environments' health. However, a comprehensive understanding of the molecular mechanisms underlying their response to metal exposure remains elusive. To address this gap, we reanalyzed the 454-sequencing data of C. gasar, utilizing an array of bioinformatics workflow of CDTA (Combined De Novo Transcriptome Assembly) to generate a more representative assembly. In parallel, C. gasar individuals were exposed to two concentrations of zinc (850 and 4500 μg L-1 Zn) for 48 h to understand their molecular responses. We utilized Trinotate workflow for the 11,684-CDTA unigenes annotation, with most transcripts aligning with the genus Crassostrea. Our analysis indicated that 67.3% of transcript sequences showed homology with Pfam, while 51.4% and 54.5%, respectively had GO and KO terms annotated. We identified potential metal pollution biomarkers, focusing on metal-related genes, such as those related to the GSH biosynthesis (CHAC1 and GCLC-like), to zinc transporters (ZNT2-like), and metallothionein (MT-like). The evolutionary conservation of these genes within the Crassostrea genus was assessed through phylogenetic analysis. Further, these genes were evaluated by qPCR in the laboratory exposed oysters. All target genes exhibited significant upregulation upon exposure to Zn at both 850 and 4500 μg L-1, except for GCLC-like, which showed upregulation only at the higher concentration of 4500 μg L-1. This result suggests distinct activation thresholds and complex interactions among these genes in response to varying Zn concentrations. Our study provides insights into the molecular responses of C. gasar to Zn, adding valuable tools for monitoring metal pollution in marine ecosystems using the mangrove oyster as a sentinel organism.

Protocol for unsupervised inference of cell-cell communication using matrix decomposition

Exploring cell-cell communication is pivotal for understanding biological processes in multicellular life forms. Here, we present a protocol that details the use of matrix decomposition to infer cell-cell communication (MDIC3) for unsupervised cell-cell communication inference. We describe steps for using the MDIC3 Python scripts to deduce cell-cell communication and identify key ligand-receptor pairs between a specific cell type pair from a single-cell gene expression dataset. This protocol has potential application in cell-cell communication inference on any species. For complete details on the use and execution of this protocol, please refer to Liu et al.1.

Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework

BACKGROUND

Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with anti-angiogenic properties have yielded encouraging outcomes, thereby creating a hopeful therapeutic avenue for the treatment of cancer. Therefore, correctly identifying the anti-angiogenic peptides is extremely important in comprehending their biophysical and biochemical traits, laying the groundwork for uncovering novel drugs to combat cancer.

METHODS

In this work, we present a novel ensemble-learning-based model, Stack-AAgP, specifically designed for the accurate identification and interpretation of anti-angiogenic peptides (AAPs). Initially, a feature representation approach is employed, generating 24 baseline models through six machine learning algorithms (random forest [RF], extra tree classifier [ETC], extreme gradient boosting [XGB], light gradient boosting machine [LGBM], CatBoost, and SVM) and four feature encodings (pseudo-amino acid composition [PAAC], amphiphilic pseudo-amino acid composition [APAAC], composition of k-spaced amino acid pairs [CKSAAP], and quasi-sequence-order [QSOrder]). Subsequently, the output (predicted probabilities) from 24 baseline models was inputted into the same six machine-learning classifiers to generate their respective meta-classifiers. Finally, the meta-classifiers were stacked together using the ensemble-learning framework to construct the final predictive model.

RESULTS

Findings from the independent test demonstrate that Stack-AAgP outperforms the state-of-the-art methods by a considerable margin. Systematic experiments were conducted to assess the influence of hyperparameters on the proposed model. Our model, Stack-AAgP, was evaluated on the independent NT15 dataset, revealing superiority over existing predictors with an accuracy improvement ranging from 5% to 7.5% and an increase in Matthews Correlation Coefficient (MCC) from 7.2% to 12.2%.

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma

BACKGROUND

Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-cell subtype, which are related to poor disease stage and immunotherapy response in various cancers.

METHODS

10 machine-learning algorithms and their combinations were applied in this work. A stable Tstr-related score (TCs) was constructed to predict the outcomes and PD-1 blockade treatment response in ccRCC patients. A nomogram based on TCs for personalized prediction of patient prognosis was constructed. Functional enrichment analysis and TimiGP algorithm were used to explore the underlying role of Tstr in ccRCC. The key TCs-related gene was identified by comprehensive analysis, and the bioinformatics results were verified by immunohistochemistry using a tissue microarray.

RESULTS

A robust TCs was constructed and validated in four independent cohorts. TCs accurately predicted the prognosis and PD-1 blockade treatment response in ccRCC patients. The novel nomogram was able to precisely predict the outcomes of ccRCC patients. The underlying biological process of Tstr was related to acute inflammatory response and acute-phase response. Mast cells were identified to be involved in the role of Tstr as a protective factor in ccRCC. TNFS13B was shown to be the key TCs-related gene, which was an independent predictor of unfavorable prognosis. The protein expression analysis of TNFSF13B was consistent with the mRNA analysis results. High expression of TNFSF13B was associated with poor response to PD-1 blockade treatment.

CONCLUSIONS

This study provides a Tstr cell-related score for predicting outcomes and PD-1 blockade therapy response in ccRCC. Tstr cells may exert their pro-tumoral role in ccRCC, acting against mast cells, in the acute inflammatory tumor microenvironment. TNFSF13B could serve as a key biomarker related to TCs.

Biomarker profiling to determine clinical impact of microRNAs in cognitive disorders

Alzheimer's disease (AD) and post-stroke cognitive impairment (PSCI) are the leading causes of progressive dementia related to neurodegenerative and cerebrovascular injuries in elderly populations. Despite decades of research, patients with these conditions still lack minimally invasive, low-cost, and effective diagnostic and treatment methods. MicroRNAs (miRNAs) play a vital role in AD and PSCI pathology. As they are easily obtained from patients, miRNAs are promising candidates for the diagnosis and treatment of these two disorders. In this study, we performed complete sequencing analysis of miRNAs from 24 participants, split evenly into the PSCI, post-stroke non-cognitive impairment (PSNCI), AD, and normal control (NC) groups. To screen for differentially expressed miRNAs (DE-miRNAs) in patients, we predicted their target genes using bioinformatics analysis. Our analyses identified miRNAs that can distinguish between the investigated disorders; several of them were novel and never previously reported. Their target genes play key roles in multiple signaling pathways that have potential to be modified as a clinical treatment. In conclusion, our study demonstrates the potential of miRNAs and their key target genes in disease management. Further in-depth investigations with larger sample sizes will contribute to the development of precise treatments for AD and PSCI.

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).

Transcriptomic analysis reveals molecular characterization and immune landscape of PANoptosis-related genes in atherosclerosis

BACKGROUND

Atherosclerosis is a chronic inflammatory disease characterized by abnormal lipid deposition in the arteries. Programmed cell death is involved in the inflammatory response of atherosclerosis, but PANoptosis, as a new form of programmed cell death, is still unclear in atherosclerosis. This study explored the key PANoptosis-related genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis.

METHODS

We evaluated differentially expressed genes (DEGs) and immune infiltration landscape in atherosclerosis using microarray datasets and bioinformatics analysis. By intersecting PANoptosis-related genes from the GeneCards database with DEGs, we obtained a set of PANoptosis-related genes in atherosclerosis (PANoDEGs). Functional enrichment analysis of PANoDEGs was performed and protein-protein interaction (PPI) network of PANoDEGs was established. The machine learning algorithms were used to identify the key PANoDEGs closely linked to atherosclerosis. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of key PANoDEGs. CIBERSORT was used to analyze the immune infiltration patterns in atherosclerosis, and the Spearman method was used to study the relationship between key PANoDEGs and immune infiltration abundance. The single gene enrichment analysis of key PANoDEGs was investigated by GSEA. The transcription factors and target miRNAs of key PANoDEGs were predicted by Cytoscape and online database, respectively. The expression of key PANoDEGs was validated through animal and cell experiments.

RESULTS

PANoDEGs in atherosclerosis were significantly enriched in apoptotic process, pyroptosis, necroptosis, cytosolic DNA-sensing pathway, NOD-like receptor signaling pathway, lipid and atherosclerosis. Four key PANoDEGs (ZBP1, SNHG6, DNM1L, and AIM2) were found to be closely related to atherosclerosis. The ROC curve analysis demonstrated that the key PANoDEGs had a strong diagnostic potential in distinguishing atherosclerotic samples from control samples. Immune cell infiltration analysis revealed that the proportion of initial B cells, plasma cells, CD4 memory resting T cells, and M1 macrophages was significantly higher in atherosclerotic tissues compared to normal tissues. Spearman analysis showed that key PANoDEGs showed strong correlations with immune cells such as T cells, macrophages, plasma cells, and mast cells. The regulatory networks of the four key PANoDEGs were established. The expression of key PANoDEGs was verified in further cell and animal experiments.

CONCLUSIONS

This study evaluated the expression changes of PANoptosis-related genes in atherosclerosis, providing a reference direction for the study of PANoptosis in atherosclerosis and offering potential new avenues for further understanding the pathogenesis and treatment strategies of atherosclerosis.