Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis

Immunogenic cell death biomarkers for sepsis diagnosis and mechanism via integrated bioinformatics

Immunogenic cell death (ICD) has been implicated in sepsis, a condition with high mortality, through mechanisms involving endoplasmic reticulum stress and other pathophysiological pathways. This study aimed to identify and validate ICD-related biomarkers for sepsis diagnosis and to elucidate their underlying mechanisms. Publicly available datasets (GSE65682, GSE95233 and GSE69528) and 57 ICD-related genes (ICDRGs) were collected for analysis. Candidate genes were selected using differential expression analysis and weighted gene co-expression network analysis (WGCNA). By integrating machine learning models, receiver operating characteristic (ROC) curves, and gene expression analysis, biomarkers for sepsis diagnosis were identified. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to explore the potential mechanisms by which the biomarkers influence sepsis. Additionally, immune infiltration analysis, subcellular localization, and disease association analysis were carried out. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression of the biomarkers in clinical sepsis blood samples. The biomarkers BCL2, PRF1, CXCR3, and EIF2AK3 demonstrated robust diagnostic potential for sepsis, each exhibiting an area under the curve (AUC) exceeding 0.8 in both the GSE65682 and GSE95233 datasets. These biomarkers were significantly downregulated in sepsis and were predominantly enriched in the ribosome. GSVA identified the top three activated pathways as β-alanine metabolism, citrate cycle/TCA cycle, and glyoxylate and dicarboxylate metabolism, while the most inhibited pathways included glycosphingolipid biosynthesis (lacto and neolacto series), α-linolenic acid metabolism, and linoleic acid metabolism. Immune infiltration analysis revealed reduced infiltration in sepsis, with CD8 + T cells showing the highest positive correlation with activated NK cells and PRF1. Subcellular localization analysis indicated that all four biomarkers were situated on the organelle membrane. Disease association analysis revealed correlations between these biomarkers and conditions such as hypertension and asthma. RT-qPCR analysis confirmed that the expression patterns of the biomarkers were consistent with the dataset findings, reinforcing the reliability and validity of the bioinformatic analyses. This study identified four ICD-related biomarkers (BCL2, PRF1, CXCR3, and EIF2AK3) that may help recognize early signs of sepsis, facilitate monitoring of disease progression, and have significant potential for clinical diagnosis and therapeutic strategies in sepsis.

Decoding the Tumor Microenvironment of Myoepithelial Cells in Triple-Negative Breast Cancer Through Single-Cell and Transcriptomic Sequencing and Establishing a Prognostic Model Based on Key Myoepithelial Cell Genes

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with high malignancy, rapid progression, and a poor 5-year survival rate of ~77%. Due to the lack of targeted therapies, treatment options are limited, highlighting the urgent need for novel therapeutic strategies. Myoepithelial cells (MECs) in the tumor microenvironment may significantly influence TNBC development and progression. Methods: This study used single-cell RNA sequencing to compare the MEC gene expression in the normal versus TNBC tissues. TNBC-associated MECs showed increased expression of proliferation- and immune-related genes (e.g., FDCSP, KRT14, and KRT17) and decreased expression of inflammatory and extracellular matrix-related genes (e.g., CXCL8, SRGN, and DCN). Copy number variation and pseudotime analyses revealed genomic alterations and phenotypic dynamics in MECs. A CoxBoost-based prognostic model was developed and validated across 20 survival cohorts, integrating immune profiling, pathway enrichment, and drug sensitivity analyses. Mendelian randomization identified TPD52 as a TNBC risk-associated gene. siRNA knockdown of TPD52 was performed in TNBC cell lines to evaluate its effects on proliferation and migration. Results: TNBC MECs displayed significant changes in the gene expression and genomic integrity, impacting immune responses and tumor invasion. The prognostic model effectively predicted 1-, 3-, and 5-year survival outcomes, stratifying high-risk patients with enriched cell cycle and DNA replication pathways, reduced immune checkpoint expression, and chemotherapy resistance. TPD52 was identified as a tumor-promoting gene, and its knockdown suppressed TNBC cell proliferation and migration. Conclusion: This study highlights MECs' role in TNBC progression, provides a CoxBoost prognostic model for personalized treatment, and identifies TPD52 as a potential therapeutic target for TNBC intervention.

Exploring the molecular mechanisms underlying intervertebral disc degeneration by analysing multiple datasets

The purpose of this study was to explore the genetic characteristics and immune cell infiltration related to intervertebral disc degeneration through multidataset analysis, predict potential therapeutic drugs, and provide a theoretical basis for clinical treatment. The gene expression profile data of the GSE70362, GSE186542, and GSE245147 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and the hub genes were identified through differentially expressed gene analysis, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) functional annotation and Mendelian randomization analysis were performed. Hub genes and immune cells were identified. Infiltration status was determined through GSEA and GSVA to clarify the specific signalling pathways associated with key genes and explore the potential molecular mechanisms by which key genes affect disease progression. The key genes were reversely predicted using miRNA grid construction and transcription factor regulation, and genes related to disease regulation were obtained from the GeneCards database. Finally, the differentially expressed genes were used for drug prediction through the Connectivity Map database to identify potential drugs for the treatment of intervertebral disc degeneration. The feasibility of the predicted drugs was tested by molecular docking technology. Real-time quantitative PCR was used to confirm the expression of key genes in the tissue samples.A total of 126 differentially expressed genes were identified in the GEO database, and 4 differentially expressed hub genes (COL6A2, DCXR, GLRX, and PDGFRB) were identified through bioinformatics methods. Immune infiltration analysis revealed that NK cells, macrophages, and eosinophils were activated during IVDD, whereas mast cells and T cells were suppressed. GO and KEGG analyses revealed that key genes are involved in the development of this disease through signalling pathways such as the glycolysis pathway, the oxidative phosphorylation pathway, the cholesterol regulatory pathway, and the haem metabolism pathway. Analysis of the constructed miRNA grid revealed that key genes are jointly regulated by multiple transcription factors, among which the most important motif is cisbp_M5578. Disease regulation-related genes were obtained through the GeneCards database, analysis of the correlation with key genes was performed, and the expression levels of the two mRNA and miRNA were significantly correlated. Finally, drug prediction performed through the Connectivity Map database revealed that drugs such as Abt-751, LY-2183240, podophyllotoxin, and vindesine can alleviate or even reverse the disease state. Finally, we collected 10 IVDD and 10 healthy disc tissue samples, and the RT‒qPCR results were consistent with the bioinformatics results. We identified COL6A2, DCXR, GLRX, and PDGFRB as key genes involved in IVDD. In addition, drugs such as Abt-751 are expected to control and reverse the progression of the disease. In the future, these key genes and predicted drugs may provide new directions for further mechanistic studies as well as new therapies for IVDD patients.

Molecular mechanisms and therapeutic targets in glioblastoma multiforme: network and single-cell analyses

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor associated with poor survival outcomes and is driven by a complex tumor microenvironment (TME) that promotes tumor progression and treatment resistance. To explore the role of the TME in GBM, we analyzed glioma-related microarray and single-cell RNA sequencing (scRNA-seq) datasets from the Gene Expression Omnibus (GEO). Functional enrichment and weighted gene coexpression network analyses revealed distinct immune profiles, metabolic alterations, and differences in chemotherapeutic drug sensitivity between the high-risk and low-risk patient groups. scRNA-seq data processed with the 'Seurat' package were used to identify differentially expressed genes in pericytes, endothelial cells, and glioma cells, particularly those involved in extracellular matrix (ECM) remodeling. A 17-gene prognostic signature developed through Cox regression and LASSO analyses revealed that key genes (COL1A1, COL4A1, and VIM) were significantly associated with survival outcomes in GBM patients. Drug sensitivity analyses using data from the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) identified potential targeted therapies for GBM, including SB-505,124, staurosporine, and AZD8186. This integrative study underscores the critical roles of the ECM and synaptic remodeling in GBM and suggests novel therapeutic targets to improve personalized treatment strategies for GBM patients.

Integrated bioinformatics analysis and biological experiments to identify key immune genes in vascular dementia

Objectives

This study aimed to identify key immune genes to provide new perspectives on the mechanisms and diagnosis of vascular dementia (VaD) based on bioinformatic methods combined with biological experiments in mice.

Methods

We obtained gene expression profiles from a Gene Expression Omnibus database (GSE186798). The gene expression data were analysed using integrated bioinformatics and machine learning techniques to pinpoint potential key immune-related genes for diagnosing VaD. Moreover, the diagnostic accuracy was evaluated through receiver operating characteristic curve analysis. The microRNA, transcription factor (TF), and drug-regulating hub genes were predicted using the database. Immune cell infiltration has been studied to investigate the dysregulation of immune cells in patients with VaD. To evaluate cognitive impairment, mice with bilateral common carotid artery stenosis (BCAS) were subjected to behavioural tests 30 d after chronic cerebral hypoperfusion. The expression of hub genes in the BCAS mice was determined using a quantitative polymerase chain reaction(qPCR).

Results

The results of gene set enrichment and gene set variation analyses indicated that immune-related pathways were upregulated in patients with VaD. A total of 1620 immune genes were included in the combined immune dataset, and 323 differentially expressed genes were examined using the GSE186798 dataset. Thirteen potential genes were identified using differential gene analysis. Protein-protein interaction network design and functional enrichment analysis were performed using the immune system as the main subject. To evaluate the diagnostic value, two potential core genes were selected using machine learning. Two putative hub genes, Rac family small GTPase 1(RAC1) and CKLF-like MARVEL transmembrane domain containing 5 (CMTM5) exhibit good diagnostic value. Their high confidence levels were confirmed by validating each biomarker using a different dataset. According to GeneMANIA, VaD pathophysiology is strongly associated with immune and inflammatory responses. The data were used to construct miRNA hub gene, TFs-hub gene, and drug-hub gene networks. Varying levels of immune cell dysregulation were also observed. In the animal experiments, a BCAS mouse model was employed to mimic VaD in humans, further confirmed using the Morris water maze test. The mRNA expression of RAC1 and CMTM5 was significantly reduced in the BCAS group, which was consistent with the results of the integrated bioinformatics analysis.

Conclusions

RAC1 and CMTM5 are differentially expressed in the frontal lobes of BCAS mice, suggesting their potential as biomarkers for diagnosing and prognosis of VaD. These findings pave the way for exploring novel molecular mechanisms aimed at preventing or treating VaD.

Identification of potential biomarkers and therapeutic targets in psoriasis based on NF-κB pathway-related genes

Psoriasis, a common chronic and relapsing inflammatory skin disease, requires the identification of new potential biomarkers to improve its diagnosis and treatment. Dysregulation of the Nuclear factor kappa B (NF-κB) signaling pathway is a hallmark of chronic inflammatory diseases. Accordingly, this study aims to identify NF-κB-related biomarkers for psoriasis diagnosis and treatment. Psoriasis-related datasets were downloaded from the Gene Expression Omnibus to screen for differentially expressed genes (DEGs). The intersection of DEGs with NF-κB-related genes yielded NF-κB_DEGs. A protein-protein interaction network was constructed for NF-κB_DEGs, and hub genes were identified using the MCODE plugin. Through LASSO regression, NF-κB-related characteristic genes for psoriasis were identified and a diagnostic model was developed. This model was validated using the GSE13355 and GSE30999 datasets. Disparities in immune cell infiltration between control and psoriasis groups were assessed via single-sample gene set enrichment analysis. Lastly, we predicted functionally similar genes, related pathways, transcription factors (TFs), microRNAs (miRNAs), and potential therapeutic drugs for the characteristic genes. We uncovered four NF-κB-associated genes-LYN, MALT1, MYD88, and PTGS2-that hold significant diagnostic value for psoriasis. A diagnostic model leveraging these genes exhibited high reliability and effectiveness in both training and validation datasets. Immune profiling revealed marked differences between psoriasis and control groups, with substantial enrichment of dendritic cells (DCs), macrophages, B cells, and T cells in psoriasis samples. We also identified 20 genes functionally related to our characteristic genes, which were primarily involved in pathways such as IκB kinase/NF-κB signaling and Toll-like receptor signaling. Furthermore, we predicted 70 TFs (including SP1, FOS, and JUN) and 17 miRNAs (including hsa-let-7b, hsa-miR-30a, and hsa-miR-155) associated with these genes. Among potential therapeutic candidates, Quercetin emerged as the most promising, scoring highest in our comprehensive evaluation. Our study identified four potential biomarkers-LYN, MALT1, MYD88, and PTGS2-which hold considerable value for the diagnosis and therapy of psoriasis.

A network toxicology and machine learning approach to investigate the mechanism of kidney injury from melamine and cyanuric acid co-exposure

BACKGROUND

Within the past two decades, high-profile cases of melamine (MA) exposure have raised significant toxicological concerns, particularly regarding food adulteration. While widely used as a fundamental organic chemical intermediate in various household products, MA's potential for unexpected toxicological synergy with its homolog, cyanuric acid (CA), remains a concern. This study aimed to investigate the nephrotoxicity of combined melamine and cyanuric acid (MC) exposure and its underlying mechanisms in rats through an integrative approach, combining network toxicology (NT), bioinformatics, and experimental validation.

MATERIALS AND METHODS

Rats were exposed to MC at doses of 0/0 mg/kg/day (Control) and 63/63 mg/kg/day (MC) for four weeks. Kidney pathology, injury markers, and RNA sequencing (RNA-seq) data were analyzed to identify differentially expressed genes between the two groups. Bioinformatics analysis, including pathway enrichment and immune microenvironment analysis, was conducted to elucidate the underlying mechanisms of MC-induced kidney injury. Potential target proteins were identified using ChEMBL, STITCH, and GeneCards databases, and hub genes were screened using three machine learning algorithms: LASSO regression, Random Forest, and Molecular Complex Detection. Molecular docking simulations were performed to assess the interactions between MC and the identified hub genes.

RESULTS

MC exposure resulted in severe kidney morphological and histological changes, as well as elevated levels of kidney injury and fibrosis markers. RNA-seq analysis revealed significant enrichment of immuno-inflammatory and apoptosis-related pathways in the MC group. Immune microenvironment analysis confirmed the infiltration of pro-inflammatory immune cells. Network toxicology analysis identified 20 potential targets associated with MC-induced kidney injury. Two hub genes, Ren and Casp3, were identified as key regulators of the renin-angiotensin-aldosterone system (RAAS) activation and apoptosis, respectively. Further experimental validation, including Western blotting and immunofluorescence, confirmed the upregulation of these proteins. Molecular docking simulations demonstrated strong binding affinities between MC and the two hub proteins.

CONCLUSION

MC exposure induces significant kidney injury and fibrosis. The activation of the RAAS pathway and apoptosis plays a crucial role in MC-mediated nephrotoxicity. However, additional vivo experimental validation is lacking. Future studies should focus on further exploration for the mechanism of MC-induced nephrotoxicity and more rigorous experimental validation.

Bioinformatics insights into mitochondrial and immune gene regulation in Alzheimer's disease

BACKGROUND

There is growing evidence that the pathogenesis of Alzheimer's disease is closely linked to the resident innate immune cells of the central nervous system, including microglia and astrocytes. Mitochondrial dysfunction in microglia has also been reported to play an essential role in the pathogenesis of AD and other neurological diseases. Therefore, finding the mitochondrial and immune-related gene (MIRG) signatures in AD can be significant in diagnosing and treating AD.

METHODS

In this study, the intersection of the differentially expressed genes (DEGs) from the GSE109887 cohort, immune-related genes (IRGs) obtained from WGCNA analysis, and mitochondria-related genes (MRGs) was taken to identify mitochondria-immune-related genes (MIRGs). Then, using machine learning algorithms, biomarkers with good diagnostic value were selected, and a nomogram was constructed. Subsequently, we further analyzed the signaling pathways and potential biological mechanisms of the biomarkers through gene set enrichment analysis, prediction of transcription factors (TFs), miRNAs, and drug prediction.

RESULTS

Using machine learning algorithms, five biomarkers (TSPO, HIGD1A, NDUFAB1, NT5DC3, and MRPS30) were successfully identified, and a nomogram model with strong diagnostic ability and accuracy (AUC > 0.9) was constructed. In addition, single-gene enrichment analysis revealed that NDUFAB1 was significantly enriched in pathways associated with diseases, such as Alzheimer's and Parkinson's, providing valuable insights for future clinical research on Alzheimer's in the context of mitochondrial-immune interactions. Interestingly, brain tissue pathology showed neuronal atrophy and demyelination in AD mice, along with a reduction in Nissl bodies. Furthermore, the escape latency of AD mice was significantly longer than that of the control group. After platform removal, there was a notable increase in the path complexity and time required to reach the target quadrant, suggesting a reduction in spatial memory capacity in AD mice. Moreover, qRT-PCR validation confirmed that the mRNA expression of the five biomarkers was consistent with bioinformatics results. In AD mice, TSPO expression was increased, while HIGD1A, NDUFAB1, NT5DC3, and MRPS30 expressions were decreased. However, peripheral blood samples did not show expression of HIGD1A or MRPS30. These findings provide new insights for research on Alzheimer's disease in the context of mitochondrial-immune interactions, further exploring the pathogenesis of Alzheimer's disease and offering new perspectives for the clinical development of novel drugs.

CONCLUSIONS

Five mitochondrial and immune biomarkers, i.e., TSPO, HIGD1A, NDUFAB1, NT5DC3, and MRPS30, with diagnostic value in Alzheimer's disease, were screened by machine-learning algorithmic models, which will be a guide for future clinical research of Alzheimer's disease in the mitochondria-immunity-related direction.

Interactions between NAD+ metabolism and immune cell infiltration in ulcerative colitis: subtype identification and development of novel diagnostic models

Background

Ulcerative colitis (UC) is a chronic inflammatory disease of the colonic mucosa with increasing incidence worldwide. Growing evidence highlights the pivotal role of nicotinamide adenine dinucleotide (NAD+) metabolism in UC pathogenesis, prompting our investigation into the subtype-specific molecular underpinnings and diagnostic potential of NAD+ metabolism-related genes (NMRGs).

Methods

Transcriptome data from UC patients and healthy controls were downloaded from the GEO database, specifically GSE75214 and GSE87466. We performed unsupervised clustering based on differentially expressed NAD+ metabolism-related genes (DE-NMRGs) to classify UC cases into distinct subtypes. GSEA and GSVA identified potential biological pathways active within these subtypes, while the CIBERSORT algorithm assessed differential immune cell infiltration. Weighted gene co-expression network analysis (WGCNA) combined with differential gene expression analysis was used to pinpoint specific NMRGs in UC. Robust gene features for subtyping and diagnosis were selected using two machine learning algorithms. Nomograms were constructed and their effectiveness was evaluated using receiver operating characteristic (ROC) curves. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted to verify gene expression in cell lines.

Results

In our study, UC patients were classified into two subtypes based on DE-NMRGs expression levels, with Cluster A exhibiting enhanced self-repair capabilities during inflammatory responses and Cluster B showing greater inflammation and tissue damage. Through comprehensive bioinformatics analyses, we identified four key biomarkers (AOX1, NAMPT, NNMT, PTGS2) for UC subtyping, and two (NNMT, PARP9) for its diagnosis. These biomarkers are closely linked to various immune cells within the UC microenvironment, particularly NAMPT and PTGS2, which were strongly associated with neutrophil infiltration. Nomograms developed for subtyping and diagnosis demonstrated high predictive accuracy, achieving area under curve (AUC) values up to 0.989 and 0.997 in the training set and up to 0.998 and 0.988 in validation sets. RT-qPCR validation showed a significant upregulation of NNMT and PARP9 in inflamed versus normal colonic epithelia, underscoring their diagnostic relevance.

Conclusion

Our study reveals two NAD+ subtypes in UC, identifying four biomarkers for subtyping and two for diagnosis. These findings could suggest potential therapeutic targets and contribute to advancing personalized treatment strategies for UC, potentially improving patient outcomes.

Identification of ubiquitination-related key biomarkers and immune infiltration in Crohn's disease by bioinformatics analysis and machine learning

Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD. Differentially expressed ubiquitination-related genes were identified by intersecting differentially expressed genes (DEGs) from the GSE95095 dataset in the Gene Expression Omnibus (GEO) database with a set of ubiquitination-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Key genes were selected by combining hub genes from the protein-protein interaction (PPI) network with feature genes identified by Lasso and Random Forest (RF) algorithms. Additionally, the correlation between key genes and immune infiltration was assessed, and Gene Set Enrichment Analysis (GSEA) of key genes was conducted. The efficacy of key genes was validated using ROC curves in an external dataset, and their expression was confirmed in LPS-induced Caco-2 cells through RT-qPCR. A total of 32 ubiquitination-related DEGs were identified, and two key genes (UBE2R2, NEDD4L) were selected. The infiltration of M2 macrophages was reduced in CD patients, with UBE2R2 expression negatively correlated and NEDD4L expression positively correlated with M2 macrophage infiltration. GSEA indicated that UBE2R2 was enriched in terpenoid backbone biosynthesis, regulation of autophagy, and limonene and pinene degradation, while NEDD4L was enriched in lysosome, Wnt signaling, and calcium signaling pathways. ROC curves demonstrated superior efficacy for NEDD4L. In LPS-induced Caco-2 cells, UBE2R2 expression increased, while NEDD4L expression decreased. A comprehensive analysis of the functional relationship between ubiquitination-related genes and CD can enhance understanding of CD pathogenesis and suggest potential therapeutic targets.

Integrative analysis of gut microbiome and host transcriptome reveal novel molecular signatures in Hashimoto's thyroiditis

BACKGROUND

Hashimoto's thyroiditis (HT) is an autoimmune disorder with unclear molecular mechanisms. While current diagnosis is well-established, understanding of the gut-thyroid axis in HT remains limited. This study aimed to uncover novel molecular signatures in HT by integrating gut metagenome and host transcriptome data (miRNA/mRNA), potentially elucidating disease pathogenesis and identifying new therapeutic targets.

METHODS

We recruited 31 early HT patients and 30 healthy controls in a two-stage study (discovery and validation). Blood and fecal samples underwent RNA and metagenomic sequencing, respectively. Integrative analysis included differential expression, weighted correlation network, correlation and random forest analyses. Regression models and ROC curve analysis were used to evaluate the significance of identified molecular signatures in HT.

RESULTS

Integrative analysis revealed subtle changes in gut microbiota diversity and composition in early HT, increased abundance of Bacillota_A and Spirochaetota at the phylum level, and significant differences in 24 genera and 67 species. Ecological network analysis indicated an imbalance in the gut microbiota with reduced inhibitory interactions against pathogenic genera in HT. Functional analysis showed changes in infection- and immune-related pathways. Three characteristic species (Salaquimonas_sp002400845, Clostridium_AI_sp002297865, and Enterocloster_citroniae) were identified as most relevant to HT. Analysis of miRNA and mRNA expression profiles uncovered pathways related to immune response, inflammation, infection, metabolism, proliferation, and thyroid cancer in HT. Based on correlations with HT and interactions between them, six characteristic RNAs (hsa-miR-548aq-3p, hsa-miR-374a-5p, GADD45A, IRS2, SMAD6, WWTR1) were identified. Furthermore, our study uncovered significant gut microbiota-host transcriptome interactions in HT, revealing enrichment in metabolic, immune, and cancer-related pathways, particularly with strong associations among those 9 key molecular signatures. The validation stage confirmed improved HT classification accuracy by combining these signatures (AUC = 0.95, ACC = 0.85), suggesting their potential significance in understanding HT pathogenesis.

CONCLUSION

Our study reveals novel molecular signatures linking gut microbiome and host transcriptome in HT, providing new insights into the disease pathogenesis. These findings not only enhance our understanding of the gut-thyroid axis but also suggest potential new directions for therapeutic interventions in HT.

SPINK5 is a key regulator of eosinophil extracellular traps in head and neck squamous cell carcinoma

Enhanced infiltration of eosinophils is observed surrounding solid tumors. Some studies indicate that Eosinophil extracellular traps (EETs) play a crucial role in tumor progression and metastasis. However, its specific role in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study established a gene set associated with eosinophil differentiation, chemotaxis, and EETs release from previous research. Employing bioinformatics techniques, the expression and biological significance of these genes in HNSCC were analyzed. Briefly, unsupervised clustering based on expression patterns of 133 EETs-related genes to classify TCGA-HNSCC patients. Immune cell infiltration patterns were assessed using "ImmuCellAI" package. A prognostic model was constructed using ten algorithms, with EETs-related gene sets as input features. Here, unsupervised clustering of samples into two types revealed worse prognosis for Cluster 1 (C1) patients after the first year. Cluster 2 (C2) exhibited higher ImmuneScore, but with a distinct immune cell infiltration pattern from the C1. Additionally, high eosinophil abundance only in the C2 had a positive prognostic impact. Serine peptidase inhibitor kazal type 5 (SPINK5) emerged as a potential key gene mediating the formation of EETs in HNSCC. EETs not only exhibit a positive correlation with diverse anti-cancer pathways but also demonstrate positive associations with processes such as proliferation, migration, and other critical pathways. The random survival forest (RSF) model was identified as the optimal eosinophil-related prognostic model. Collectively, this study elucidates the potential impact and mediating pathways of EETs on tumors, providing a reference for targeted therapy based on EETs-related genes.

In Silico Analysis Uncovers FOXA1 as a Potential Biomarker for Predicting Neoadjuvant Chemotherapy Response in Fine-Needle Aspiration Biopsies

Background: The preoperative identification of neoadjuvant chemotherapy (NAC) treatment responsiveness in breast cancer (BC) patients is advantageous for tailoring treatment regimens. There is a relative scarcity in the current research exploring NAC treatment responsive biomarkers using bulk sequencing data obtained from fine-needle aspiration (FNA). Materials and Methods: Limma was employed for the selection of differentially expressed genes. Additionally, WGCNA, machine learning, and Genetic Perturbation Similarity Analysis (GPSA) were utilized to identify key genes associated with NAC treatment response. ConsensusClusterPlus was employed for unsupervised clustering. Rt-qPCR and WB were conducted to assess gene expression and protein levels in clinical tissues and cell lines. The Seahorse XF96 Extracellular Flux Analyzer was utilized to evaluate Extracellular Acidification Rate (ECAR) and Oxygen Consumption Rate (OCR). The "pRRophetic" package was used for drug sensitivity prediction, while CB-Dock2 was applied for molecular docking and optimal pose presentation. Spatial transcriptomic analysis was based on the CROST database. Results: Eleven biomarkers were identified associated with NAC treatment response in BC patients, with FOXA1 identified as a pivotal hub gene among them. The expression levels of FOXA1 showed a significant positive correlation with genomic stability and a marked negative correlation with the homologous recombination deficiency (HRD) score. Downregulation of the FOXA1 gene resulted in reduced glycolysis in MCF-7 cells.Additionally, FOXA1 were found to serve as a biomarker for both NAC and PARP inhibitor treatment sensitivity in BC patients. Spatial transcriptomic analysis indicates significantly elevated infiltration of T follicular helper (T-FH) cells and mast cells surrounding tumors exhibiting high FOXA1 expression. Conclusion: In summary, our study involved the analysis of diverse sequencing datasets derived from various FNA samples to identify biomarkers sensitive to NAC, thereby offering novel insights into resources for future personalized clinical treatment strategies.

Identification of Pyroptosis-Related Molecular Subtypes and Diagnostic Model development in Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent psychological disorder associated with inflammation, with complex pathological mechanisms. Pyroptosis has been suggested to contribute to inflammation in central nervous system diseases. Little research, however, has examined what role pyroptosis played in MDD. In the present study, the differential expression pyroptosis-related genes (DE-PRGs) in MDD were identified from the GEO database (GSE98793 and GSE19738). Then, consensus clustering analysis was used to evaluate differences in MDD molecular subtypes characteristics based on PRGs. The characteristic diagnostic biomarkers for MDD were identified by Weighted Correlation Network Analysis (WGCNA) and multiple machine learning algorithms. Three intersection genes (GZMA, AKR1C3, and CD52) were obtained, which are expected to become potential biomarkers for MDD with excellent reliability and accuracy. Subsequently, the immune infiltration characteristics result indicated that the development of MDD is mediated by immune-related function, where three DE-PRGs were strongly related to the immune infiltration landscape of MDD. The biological experiments in vitro further proved that three unique PRGs are emerging as important players in MDD diagnosis. Our research aimed to provide novel ideas and biomarkers targeting MDD.

Machine Learning-based Development and Validation of a Cell Senescence Predictive and Prognostic Signature in Intrahepatic Cholangiocarcinoma

Background: Previous studies have shown that cellular senescence is strongly associated with tumorigenesis and the tumor microenvironment. Accordingly, we developed a novel prognostic signature for intrahepatic cholangiocarcinoma (ICCA) based on senescence-associated long non-coding RNAs (SR-lncRNAs) and identified a lncRNA-miRNA-mRNA axis involving in ICCA. Methods: Based on the 197 senescence-associated genes (SRGs) from Genacards and their expression in Fu-ICCA cohort, we identified 20 lncRNAs as senescence-associated lncRNAs (SR-lncRNAs) through co-expression and cox-regression analysis. According to 20 SR-lncRNAs, patients with ICCA were classified into 2 molecular subtypes using unsupervised clustering machine learning approach and to explore the prognostic and functional heterogeneity between these two subtypes. Subsequently, we integrated 113 machine learning algorithms to develop senescence-related lncRNA signature, ultimately identifying 11 lncRNAs and constructing prognostic models and risk stratification. The correlation between the signature and the immune landscape, immunotherapy response as well as drug sensitivity are explored too. Results: We developed a novel senescence related signature. The predictive model and risk score calculated by the signature exhibited favorable prognostic predictive performance, which is a suitable independent risk factor for the prognosis of patients with ICCA based on Kaplan-Meier plotter, nomogram and receiving operating characteristic (ROC) curves. The results were validated using external datasets. Estimate, ssGSEA (single sample gene set enrichment analysis), IPS (immunophenotype score) and TIDE (tumor immune dysfunction and exclusion) algorithms revealed higher immune infiltration, higher immune scores, lower immune escape potential and better response to immunotherapy in the high-risk group. In addition, signature identifies eight chemotherapeutic agents, including cisplatin for patients with different risk levels, providing guidance for clinical treatment. Finally, we identified a set of lncRNA-miRNA-mRNA axes involved in ICCA through regulation of senescence. Conclusion: SR-lncRNAs signature can favorably predict the prognosis, risk stratification, immune landscape and immunotherapy response of patients with ICCA and consequently guide individualized treatment.

Construction of a Lung Adenocarcinoma Prognostic Model Utilizing Serine and Glycine Metabolism-Related Genes

The objective of this study was to construct a prognostic model by utilizing serine/glycine metabolism-related genes (SGMGs), thus establishing a risk score for lung adenocarcinoma (LUAD). Based on the TCGA-LUAD and SGMG data set, two subtypes with different SGMG expression levels were identified by clustering analysis. Thirteen differential expression genes were used to construct RiskScore by Cox regression. GSE72094 data set was used for validation. The survival characteristics, immune features, and potential benefits of chemotherapy drugs were analyzed for two risk groups. RiskScore was constructed based on the genes ABCC12, RIC3, CYP4B1, SFTPB, CACNA2D2, IGF2BP1, NTSR1, DKK1, CREG2, PITX3, RGS20, FETUB, and IGFBP1. Patients in the low-risk (LR) group exhibited a superior overall survival. In addition, aDCs, iDSs, mast cells, neutrophils, HLA, and type II IFN were more abundant in the LR group with higher IPS scores and lower TIDE scores. In contrast, NK cells, APC coinhibition, and MHC-I were more common in the high-risk (HR) group, which may be more sensitive to chemotherapy drugs such as cisplatin, oxaliplatin, and nilotinib. RiskScore was a promising biomarker that can be used to distinguish LUAD prognosis, immune features, and sensitivity to chemotherapy drugs.

A study on the role of Taxifolin in inducing apoptosis of pancreatic cancer cells: screening results using weighted gene co-expression network analysis

Pancreatic adenocarcinoma (PAAD) is a frequent malignant tumor in the pancreas. The incomplete understanding of cancer etiology and pathogenesis, as well as the limitations in early detection and diagnostic methods, have created an urgent need for the discovery of new therapeutic targets and drugs to control this disease. As a result, the current therapeutic options are limited. In this study, the weighted gene co-expression network analysis (WGCNA) method was employed to identify key genes associated with the progression and prognosis of pancreatic adenocarcinoma (PAAD) patients in the Gene Expression Profiling Interactive Analysis (GEPIA) database. To identify small molecule drugs with potential in the treatment of pancreatic adenocarcinoma (PAAD), we compared key genes to the reference dataset in the CMAP database. First, we analyzed the antitumor properties of small molecule drugs using cell counting kit-8 (CCK-8), AO/EB and Transwell assays. Subsequently, we integrated network pharmacology with molecular docking to explore the potential mechanisms of the identified molecules' anti-tumor effects. Our findings indicated that the progression and prognosis of PAAD patients in pancreatic cancer were associated with 11 genes, namely, DKK1, S100A2, CDA, KRT6A, ITGA3, GPR87, IL20RB, ZBED2, PMEPA1, CST6, and MUC16. These genes were filtered based on their therapeutic potential through comparing them with the reference dataset in the CMAP database. Taxifolin, a natural small molecule drug with the potential for treating PAAD, was screened by comparing it with the reference dataset in the CMAP database. Cell-based experiments have validated the potential of Taxifolin to facilitate apoptosis in pancreatic cancer cells while restraining their invasion and metastasis. This outcome is believed to be achieved via the HIF-1 signaling pathway. In conclusion, this study provided a theoretical basis for screening genes related to the progression of pancreatic cancer and discovered potentially active small molecule drugs. The experimental results confirm that Taxifolin has the ability to promote apoptosis in pancreatic cancer cells.

Mechanism Exploration on the Immunoregulation of Allogeneic Heart Transplantation Rejection in Rats With Exosome miRNA and Proteins From Overexpressed IDO1 BMSCs

Immunoregulation and indoleamine 2,3-dioxygenase 1 (IDO1) play pivotal roles in the rejection of allogeneic organ transplantation. This study aims to elucidate the immune-related functional mechanisms of exosomes (Exos) derived from bone marrow-derived mesenchymal stem cells (BMSCs) overexpressing IDO1 in the context of allogeneic heart transplantation (HTx) rejection. A rat model of allogeneic HTx was established. Exos were extracted after transfection with oe-IDO1 and oe-NC from rat BMSCs. Exos were administered via the caudal vein for treatment. The survival of rats was analyzed, and reverse transcription qualitative PCR (RT-qPCR) and immunohistochemistry (IHC) were employed to detect the expression of related genes. Histopathological examination was conducted using hematoxylin and eosin (HE) staining, and flow cytometry was utilized to analyze T-cell apoptosis. Proteomics and RNA-seq analyses were performed on Exos. The data were subjected to functional enrichment analysis using the R language. A protein interaction network was constructed using the STRING database, and miRWalk, TargetScan, and miRDB databases predicted the target genes, differentially expressed miRNAs, and transcription factors (TFs). Exos from BMSCs overexpressing IDO1 prolonged the survival time of rats undergoing allogeneic HTx. These Exos reduced inflammatory cell infiltration, mitigated myocardial damage, induced CD4 T-cell apoptosis, and alleviated transplantation rejection. The correlation between Exos from BMSCs overexpressing IDO1 and immune regulation was profound. Notably, 13 immune-related differential proteins (Anxa1, Anxa2, C3, Ctsb, Hp, Il1rap, Ntn1, Ptx3, Thbs1, Hspa1b, Vegfc, Dcn, and Ptpn11) and 10 significantly different miRNAs were identified. Finally, six key immune proteins related to IDO1 were identified through common enrichment pathways, including Thbs1, Dcn, Ptpn11, Hspa1b, Il1rap, and Vegfc. Thirteen TFs of IDO1-related key miRNAs were obtained, and a TF-miRNA-mRNA-proteins regulatory network was constructed. Exosome miRNA derived from BMSCs overexpressing IDO1 may influence T-cell activation and regulate HTx rejection by interacting with mRNA.