NCBI GEO: archive for functional genomics data sets--update

CelEst: a unified gene regulatory network for estimating transcription factor activities in C. elegans

Transcription factors (TFs) play a pivotal role in orchestrating critical intricate patterns of gene regulation. Although gene expression is complex, differential expression of hundreds of genes is often due to regulation by just a handful of TFs. Despite extensive efforts to elucidate TF-target regulatory relationships in Caenorhabditis elegans, existing experimental datasets cover distinct subsets of TFs and leave data integration challenging. Here, I introduce CelEst, a unified gene regulatory network designed to estimate the activity of 487 distinct C. elegans TFs-∼58% of the total-from gene expression data. To integrate data from ChIP-seq, DNA-binding motifs, and eY1H screens, optimal processing of each data type was benchmarked against a set of TF perturbation RNA-seq experiments. Moreover, I showcase how leveraging TF motif conservation in target promoters across genomes of related species can distinguish highly informative interactions, a strategy which can be applied to many model organisms. Integrated analyses of data from commonly studied conditions including heat shock, bacterial infection, and sex differences validates CelEst's performance and highlights overlooked TFs that likely play major roles in coordinating the transcriptional response to these conditions. CelEst can infer TF activity on a standard laptop computer within minutes. Furthermore, an R Shiny app with a step-by-step guide is provided for the community to perform rapid analysis with minimal coding required. I anticipate that widespread adoption of CelEsT will significantly enhance the interpretive power of transcriptomic experiments, both present and retrospective, thereby advancing our understanding of gene regulation in C. elegans and beyond.

ESM1 suppresses LncRNA GAS5/miR-23a-3p/PTEN axis to promote the cisplatin-chemotherapy resistance of ovarian cancer cells via activating the PI3K/AKT pathway

BACKGROUND

Cisplatin chemotherapy is an important treatment for advanced ovarian cancer (OC). However, the development of cisplatin resistance greatly limits the survival time of OC patients. Endothelial cell-specific molecule 1 (ESM1) has been found to be an important proto-oncogene promoting OC, but its mediating OC cisplatin resistance remains unknown.

METHODS

We used quantitative polymerase chain reaction (qPCR) to measure transcription levels of ESM1, Growth arrest specific transcript 5 (GAS5), miR-23a-3p, and Phosphatase And Tensin Homolog (PTEN). A double luciferase reporter gene assay confirmed the direct binding of GAS5 to miR-23a-3p and miR-23a-3p to PTEN mRNA. The effects of ESM1, GAS5, miR-23a-3p, and PTEN on OC cisplatin resistance were tested with an Half Maximal Inhibitory Concentration (IC50) assay. Flow cytometry was used to assess the effects of ESM1, GAS5, and miR-23a-3p on cisplatin-induced OC apoptosis. Changes in apoptosis-related proteins and PI3K/AKT-related proteins were analyzed with western blot (WB).

RESULTS

ESM1 inhibits the levels of GAS5 and PTEN but increases miR-23a-3p. ESM1 and miR-23a-3p promote OC cisplatin resistance. GAS5 and miR-23a-3p promote cisplatin sensitivity for OC cells. Moreover, the main molecular mechanism is the ESM1/GAS5/miR-23a-3p/PTEN/PI3K/Akt signaling axis.

CONCLUSION

ESM1 promotes OC cisplatin resistance by activating the Phosphoinositide-3-Kinase (PI3K)/AKT Serine/Threonine Kinase (Akt) signaling pathway through the GAS5/miR-23a-3p/PTEN signaling axis. This suggests that prescriptive ESM1 regulates key downstream molecular mechanisms via non-coding RNA and can be used before neoadjuvant chemotherapy in OC is initiated.

Bioinformatics identification and validation of pyroptosis-related gene for ischemic stroke

BACKGROUND

Ischemic stroke (IS) is one of the common and frequent diseases with extremely high lethality and disability in the world, and there is no effective treatment at present. This study aimed to screen hub genes involved in cerebral ischemia/reperfusion injury (CIRI) and pyroptosis, and explore promising intervention targets.

METHODS

CIRI-related genes (GSE202659 and GSE131193) and pyroptosis-related genes (PRGs) in mice were obtained from the Gene Expression Omnibus (GEO) and GeneCards database. We screened for LASSO regression to construct a prognostic model of GSE131193 and PRGs and examined by GSE137482. The functional enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on pyroptosis-related differentially expressed genes (PRDEGs) of GSE202659.The key modules for CIRI and pyroptosis were identified by Weight Gene Co-expression Network Analysis (WGCNA). Subsequently, Protein-protein Interaction (PPI) network and the Cytoscape was constructed to screen out hub genes. Used the starBase to predict miRNA interacting with hub genes and constructed mRNA-miRNA-lncRNA interaction networks. CIRI-related Molecular Subtypes were constructed for hub genes. The relationship between immune cells and hub genes was verified via CIBERSORT. Finally, we selected C57BL/6 mice to construct models to confirm hub genes by enzyme linked immunosorbent assay (ELISA), reverse transcription-polymerase chain reaction (RT-PCR), western blot, and Immunofluorescence.

RESULTS

A total of 272 PRGs and 35 PRDEGs were screened. An eight-gene risk prediction models were established (AUC = 0.868). GO, KEGG, GSEA and GSVA analyses revealed that PRDEGs were mainly involved in positive regulation of cytokine production, and NOD-like receptor signaling pathway. And then, seven hub genes (Irf1, Icam1, Tlr2, Tnf, Cebpb, Il1rn, and Casp8) were identified by PPI. Icam1, Tnf, Cebpb, Il1rn, and Casp8 had high expression profiles in Cluster2 by hierarchical clustering. The immune infiltration analysis results showed that among the hub genes, Cebpb, Il1rn, and Casp8, showed a significant positive correlation with the degree of NK.Actived, and Icam1 showed a significant negative correlation with B.Cells.Memory. The results of animal experiments significantly demonstrated an upregulation of Irf1, Icam1, Tlr2, Cebpb, and Il1rn.

CONCLUSION

Our finding indicated that Irf1, Icam1, Tlr2, Cebpb, and Il1rn are hub genes associated with pyroptosis, and these genes are all associated with different immune cells, so as to provide new targets for the prevention and treatment of IS from the perspective of pyroptosis.

Novel Identification of CD74 as a Biomarker for Diagnosing and Prognosing Sepsis Patients

Purpose

Sepsis, a life-threatening inflammatory condition due to an imbalanced response to infections, has been a major concern. Necroptosis, a newly discovered programmed cell death form, plays a crucial role in various inflammatory diseases. Our study aims to identify necroptosis - related genes (NRGs) and explore their potential for sepsis diagnosis.

Patients and methods

We used weighted gene co-expression network analysis to identify gene modules associated with sepsis. Cox regression and Kaplan-Meier methods were employed to assess the diagnostic and prognostic value of these genes. Single-cell and immune infiltration analyses were carried out to explore the immune environment in sepsis. Plasma CD74 protein levels were quantified in our samples, and relevant clinical data from electronic patient records were analyzed for correlation.

Results

CD74 was identified through the intersection of the hub genes of sepsis and NRGs related modules. Septic patients had lower CD74 expression compared to healthy controls. The CD74-based diagnostic model showed better performance in the training dataset (AUC, 0.79 [95% CI, 0.75-0.84]), was cross-validated in external datasets, and demonstrated better performances than other published diagnostic models. Pathway analysis and single-cell profiling supported further exploration of CD74-related inflammation and immune response in sepsis.

Conclusion

This study presents the first quantitative assessment of human plasma CD74 in sepsis patients. CD74 levels were significantly lower in the sepsis cohort. CD74 warrants further exploration as a potential prognostic and therapeutic target for sepsis.

Developing a gene expression classifier for breast cancer diagnosis

Breast cancer (BC) is the most common type of cancer in women worldwide. Solid tumors are complex structures composed of many cell types and extracellular matrix components. Understanding solid tumors is crucial for developing effective treatments. This study aimed to develop a gene expression classifier to predict BC with high accuracy. The study first identified the most important genes for cancer through differential expression analysis (DEA) between breast cancer and adjacent normal breast samples. The R package STRINGdb was then used to create a protein-protein interaction network (PPI) to examine upregulated genes and find clusters. Enrichment analyses were performed to identify overrepresented biological functions and pathways. A logistic regression prediction model was developed using a breast cancer dataset from TCGA and evaluated using discrimination and calibration measures. BUB1 expression in breast cancer was also investigated using quantitative analysis. Two significant clusters were identified, with cell cycle checkpoints and M phase key pathways in one cluster and extracellular matrix organization in the other. A prediction model using the hub gene set (COMP, FN1, SDC1, BUB1, TTK, and NUSAP1) showed high sensitivity (97.2%) and specificity (96.1%), and an AUC of 0.994. Three hub genes (COMP, FN1, and SDC1) were identified through the PPI network, strongly linked to extracellular matrix organization (BUB1, TTK, and NUSAP1) as hub genes involved in M phase and cell cycle checkpoints. Overall, the study identified hub pathways and genes that accurately distinguish between cancer and normal samples, presenting promising new possibilities for early cancer detection and improved BC therapy.

From Gene to Intervention: NLRC4 and WIPI1 Regulate Septic Acute Lung Injury Through Autophagy

Background

Septic Acute Lung Injury (SALI)-induced severe respiratory dysfunction has been established to significantly increase patient mortality rates and socioeconomic costs. To mitigate cellular damage, autophagy -a conserved biological process in organisms -degrades damaged cellular components, such as proteins and organelles. Although autophagy is crucially involved in the inflammatory response, its precise molecular mechanisms in SALI remain unclear, forming the basis of this study.

Methods

Herein, two microarray datasets (GSE33118 and GSE131761) and three single-cell sequencing datasets (SCP43, SCP548, and SCP2156) derived from human samples were used to ascertain the interrelationship between Differentially Expressed Autophagy-Related Genes (DEARGs) and SALI. The relationship between key DEARGs and SALI was validated both in vitro and in vivo using various techniques, including flow cytometry, Immunofluorescence (IF), Quantitative Polymerase Chain Reaction (qPCR), Western Blotting (WB), and small interfering RNA (siRNA).

Results

Herein, we found that autophagy activation attenuated SALI, with NLRC4 and WIPI1 as the two key DEARGs involved. Specifically, NLRC4 and WIPI1 downregulation mitigated SALI via autophagy activation. Compared to NLRC4, WIPI1 was more closely associated with noncanonical autophagic flux in SALI. Furthermore, immune infiltration analysis and single-cell data showed a close relationship between NLRC4, WIPI1, and immune cells.

Conclusion

Our findings revealed that SALI correlated strongly with autophagy, with the downregulation of the two key DEARGs, NLRC4 and WIPI1, attenuating sepsis lung injury via autophagy regulation, highlighting their therapeutic significance in SALI.

FAIR data for optical tweezers experiments

The single-molecule biophysics community has delivered significant impacts to our understanding of fundamental biological processes, yet the field is also siloed and has fragmented data structures, which impede data sharing and limit the ability to conduct comprehensive meta-analyses. To advance the field of optical tweezers in single-molecule biophysics, it is important that the field adopts open and collaborative data sharing that facilitate meta-analyses that combine diverse resources and supports more advanced analyses, akin to those seen in projects such as the Protein Data Bank and the 1000 Genomes Project. Here, we assess the state of data findability, accessibility, interoperability, and reusability (the FAIR principles) within the single-molecule optical tweezers field. By combining a qualitative review with quantitative tools from bibliometrics, our analysis suggests that the field has significant room for improvement in terms of FAIR adherence. Finally, we discuss the potential of compulsory data deposition and a minimal set of metadata standards to ensure reproducibility and interoperability between systems. While implementing these measures may not be straightforward, they are key steps that will enhance the integration of optical tweezers biophysics with the broader biomedical literature.

A step-by-step protocol based on data mining to explore purinergic signaling in glioblastoma

Over the past few years, transcriptomics has emerged as a pillar for modern scientific research, enabling the comprehensive profiling of gene expression. The availability of large-scale public datasets, such as NCBI Gene Expression Omnibus, International Cancer Genome Consortium, and The Cancer Genome Atlas, has significantly boosted many scientific discoveries. However, to analyze and interpret these vast datasets, sophisticated bioinformatic tools are often necessary. Phantasus is a user-friendly web application designed to streamline gene expression analysis. By integrating data loading, normalization, filtering, enrichment pathways analysis, and principal component analysis, Phantasus enables researchers to promptly investigate and evaluate complex gene expression patterns. This tool simplifies the identification of differentially expressed genes and the discovery of novel biological insights. Here, we demonstrate how Phantasus can be utilized for gene expression analysis in glioblastoma (GBM), the most common primary malignant brain tumour in adults. Specifically, we focus on the role of purinergic signaling, with particular emphasis on the P2RX7 mRNA coding for the P2X7 receptor (P2RX7). To illustrate our proposal, we analyzed the expression of genes related to purinergic signaling in GBM patients stratified by high and low levels of P2RX7 expression. By harnessing Phantasus, researchers can further explore and navigate the nuances of gene regulation and its impact on human health and diseases.

Identification and validation of hub genes associated with biotic and abiotic stresses by modular gene co-expression analysis in Oryza sativa L

Rice, a staple food consumed by half of the world's population, is severely affected by the combined impact of abiotic and biotic stresses, with the former causing increased susceptibility of the plant to pathogens. Four microarray datasets for drought, salinity, tungro virus, and blast pathogen were retrieved from the Gene Expression Omnibus database. A modular gene co-expression (mGCE) analysis was conducted, followed by gene set enrichment analysis to evaluate the upregulation of module activity across different stress conditions. Over-representation analysis was conducted to determine the functional association of each gene module with stress-related processes and pathways. The protein-protein interaction network of mGCE hub genes was constructed, and the Maximal Clique Centrality (MCC) algorithm was applied to enhance precision in identifying key genes. Finally, genes implicated in both abiotic and biotic stress responses were validated using RT-qPCR. A total of 11, 12, 46, and 14 modules containing 85, 106, 253, and 143 hub genes were detected in drought, salinity, tungro virus, and blast. Modular genes in drought were primarily enriched in response to heat stimulus and water deprivation, while salinity-related genes were enriched in response to external stimuli. For the tungro virus and blast pathogen, enrichment was mainly observed in the defence and stress responses. Interestingly, RPS5, PKG, HSP90, HSP70, and MCM were consistently present in abiotic and biotic stresses. The DEG analysis revealed the upregulation of MCM under the tungro virus and downregulation under blast and drought in resistant rice, indicating its role in viral resistance. HSP70 showed no changes, while HSP90 was upregulated in susceptible rice during blast and drought. PKG increased during drought but decreased in japonica rice under salinity. RPS5 was highly upregulated during blast in both resistant and susceptible rice. The RT-qPCR analysis showed that all five hub genes were upregulated in all treatments, indicating their role in stress responses and potential for crop improvement.

Widespread mono- and oligoadenylation direct small noncoding RNA maturation versus degradation fates

Small non-coding RNAs (sncRNAs) are subject to 3' end trimming and tailing activities that impact maturation versus degradation decisions during biogenesis. To investigate the dynamics of human sncRNA 3' end processing at a global level we performed genome-wide 3' end sequencing of nascently-transcribed and steady-state sncRNAs. This revealed widespread post-transcriptional adenylation of nascent sncRNAs, which came in two distinct varieties. One is characterized by oligoadenylation, which is transient, promoted by TENT4A/4B polymerases, and most commonly observed on unstable snoRNAs that are not fully processed at their 3' ends. The other is characterized by monoadenylation, which is broadly catalyzed by TENT2 and, in contrast to oligoadenylation, stably accumulates at the 3'-end of sncRNAs, including Polymerase-III-transcribed (Pol-III) RNAs and a subset of small nuclear RNAs. Monoadenylation inhibits Pol-III RNA post-transcriptional 3' uridine trimming and extension and, in the case of 7SL RNAs, prevents their accumulation with nuclear La protein and promotes their biogenesis towards assembly into cytoplasmic signal recognition particles. Thus, the biogenesis of human sncRNAs involves widespread mono- or oligo-adenylation with divergent impacts on sncRNA fates.

Zinc-limited Mycobacterium tuberculosis stimulate distinct responses in macrophages compared with standard zinc-replete bacteria

Tuberculosis (TB) is notoriously difficult to treat, likely due to the complex host-pathogen interactions driven by pathogen heterogeneity. An understudied area of TB pathogenesis is host responses to Mycobacterium tuberculosis bacteria (Mtb) that are limited in zinc ions. This distinct population resides in necrotic granulomas and sputum and could be the key player in tuberculosis pathogenicity. In this study, we tested the hypothesis that macrophages differentiate between Mtb grown under zinc limitation or in the standard zinc-replete medium. Using several macrophage infection models, such as murine RAW 264.7 and murine bone marrow-derived macrophages (BMDMs), as well as human THP-1-derived macrophages, we show that macrophages infected with zinc-limited Mtb have increased bacterial burden compared with macrophages infected with zinc-replete Mtb. We further demonstrate that macrophage infection with zinc-limited Mtb trigger higher production of reactive oxygen species (ROS) and cause more macrophage death. Furthermore, the increased ROS production is linked to the increased phagocytosis of zinc-limited Mtb, whereas cell death is not. Finally, transcriptional analysis of RAW 264.7 macrophages demonstrates that macrophages have more robust pro-inflammatory responses when infected with zinc-limited Mtb than zinc-replete Mtb. Together, our findings suggest that Mtb's access to zinc affects their interaction with macrophages and that zinc-limited Mtb may be influencing TB progression. Therefore, zinc availability in bacterial growth medium should be considered in TB drug and vaccine developments.

Integrated computational analysis identifies therapeutic targets with dual action in cancer cells and T cells

Many cancer drugs that target cancer cell pathways also impair the immune system. We developed a computational target discovery platform to enable examination of both cancer and immune cells so as to identify pathways that restrain tumor progression and potentiate anti-tumor immunity. Immune-related CRISPR screen analyzer of functional targets (ICRAFT) integrates immune-related CRISPR screen datasets, single-cell RNA sequencing (scRNA-seq) data, and pre-treatment RNA-seq data from clinical trials, enabling a systems-level approach to therapeutic target discovery. Using ICRAFT, we identified numerous targets that enhance both cancer cell susceptibility to immune attack and T cell activation, including tumor necrosis factor (TNF) alpha-induced protein 3 (TNFAIP3), protein tyrosine phosphatase non-receptor type 2 (PTPN2), and suppressor of cytokine signaling 1 (SOCS1). In cancer cells, Tnfaip3 (A20) deletion activated the TNF-nuclear factor kappa-B (NF-κB) pathway, promoting chemokine expression and T cell recruitment to the tumor. T cell-mediated elimination of Tnaifp3-null cancer cells was primarily driven by TNF-induced apoptosis. Inactivation of Tnfaip3 in T cells enhanced anti-tumor efficacy. By integrating diverse functional genomics and clinical datasets, ICRAFT provides an interactive resource toward a deeper understanding of anti-tumor immunity and immuno-oncology drug development.

Multiomics analysis demonstrated a strong correlation between lipid-mediated chronic kidney disease and stroke: Potential benefits of affected patient cohorts

OBJECTIVE

Patients with chronic kidney disease (CKD) exhibit a disproportionately elevated risk of stroke, frequently compounded by renal impairment. Therapeutic strategies for stroke based on Traditional Chinese Medicine's 'kidney-brain axis' theory demonstrate clinical efficacy, indicating that there may be a potential association between chronic kidney disease and stroke, which needs further exploration and verification.

METHODS

In this study, databases such as GEO, NHANES, and GWAS were used to collect data related to CKD and stroke. GEO gene data enrichment analysis was used to explore possible mediating factors between CKD and stroke. NHANES clinical data were used to verify the GEO data analysis results. Mendelian randomization was used to confirm the causal relationship between CKD and stroke and verify the association effect of mediating factors in these two diseases.

RESULTS

Cross-gene analysis and transcription factor analysis of GEO data revealed that lipid-related pathways may have a mediating effect on the relationship between CKD and stroke. Logistic regression analysis based on NHANES data revealed that changes in LDL-C, HDL-C, TC, and TG can affect the occurrence of stroke. Mendelian randomization analysis was used to determine the causal relationship between CKD and stroke and verified the mediating effects of lipid factors, such as LDL-C, HDL-C, TC, and TG, indicating that LDL-C, HDL-C, TC, and TG may be potential mediating factors for these two diseases. Our findings highlight the clinical relevance of lipid pathways in bridging CKD and stroke. By integrating predictive biomarkers and multi-level diagnostics, this study paves the way for AI-driven precision medicine in stroke prevention. Specifically, machine learning approaches could enhance risk stratification of high-risk CKD cohorts, enabling tailored interventions such as lipid-lowering therapies and personalized monitoring protocols. These strategies align with emerging paradigms in healthcare benefits and population-specific management.

CONCLUSION

This study provides new insights into the interactive relationship between CKD and stroke and provides a scientific basis for the process of syndrome differentiation and the treatment of stroke under the guidance of the "kidney-brain correlation". Moreover, the influence of mediating factors related to lipid metabolism on the occurrence of these two diseases was investigated, which deepened researchers' understanding of the potential association mechanism between the two diseases.

Comprehensive microarray analysis for the identification of therapeutic targets within HIF-1α signalling networks in diet-induced obesity via hypothalamic inflammation

OBJECTIVE

A high-fat diet (HFD) significantly contributes to obesity and alters the neurological function of the brain. This study explored the influence of hypoxia-inducible factor (HIF-1) and its downstream molecules on obesity progression in the context of HFD-induced hypothalamic inflammation.

MATERIALS AND METHODS

Utilizing a bioinformatics approach alongside animal models, targets and pathways related to hypothalamic obesity were identified via network analysis, gene target identification, gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and subsequent validation in animal models.

RESULTS

HIF-1α has the potential to regulate the immune response by promoting immune infiltration and increasing the population of immune cells, particularly memory CD4 T cells, in the hypothalamus, primarily through its influence on ksr2 expression. Additionally, the analysis predicted five drugs capable of enhancing HIF-1-Ksr2 signalling.

CONCLUSION

In conclusion, targeting Ksr2 with specific drugs represents a potential approach for addressing HFD-induced obesity. These novel findings lay the groundwork for developing dietary supplements and therapeutic interventions.

NAD+ prevents chronic kidney disease by activating renal tubular metabolism

Chronic kidney disease (CKD) is associated with renal metabolic disturbances, including impaired fatty acid oxidation (FAO). Nicotinamide adenine dinucleotide (NAD+) is a small molecule that participates in hundreds of metabolism-related reactions. NAD+ levels are decreased in CKD, and NAD+ supplementation is protective. However, both the mechanism of how NAD+ supplementation protects from CKD, as well as the cell types involved, are poorly understood. Using a mouse model of Alport syndrome, we show that nicotinamide riboside (NR), an NAD+ precursor, stimulated renal PPARα signaling and restored FAO in the proximal tubules, thereby protecting from CKD in both sexes. Bulk RNA-sequencing showed that renal metabolic pathways were impaired in Alport mice and activated by NR in both sexes. These transcriptional changes were confirmed by orthogonal imaging techniques and biochemical assays. Single-nuclei RNA sequencing and spatial transcriptomics, both the first of their kind to our knowledge from Alport mice, showed that NAD+ supplementation restored FAO in proximal tubule cells. Finally, we also report, for the first time to our knowledge, sex differences at the transcriptional level in this Alport model. In summary, the data herein identify a nephroprotective mechanism of NAD+ supplementation in CKD, and they demonstrate that this benefit localizes to the proximal tubule cells.

Experimental Validation of miR-4443, miR-572, and miR-150-5p in Serum and Tissue of Breast Cancer Patients as a Potential Diagnostic Biomarker: A Study Based on Bioinformatics Prediction

Breast cancer is the most common invasive cancer diagnosed in females and is also the main cause of cancer-related deaths leading to more than 500,000 deaths annually. The present study aims to identify a promising panel of microRNAs (miRNAs) using bioinformatics analysis, and to clinically validate their utility for diagnosing breast cancer patients with high accuracy in a clinical setting. First, in the in silico phase of our study, using bioinformatics analysis and the data available in the GEO database, miRNAs that were increased in the interstitial fluid of the tumor tissues (differentially expressed miRNAs), were screened and their related target genes were selected. Multimir package of R software was utilized to determine the target genes of the differentially expressed miRNAs (DEMs). The biological functions of discovered genes were analyzed using Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. In order to determine the molecular mechanisms behind important signaling pathways and cellular functions, the protein-protein interaction network was built using STRING and Cytoscape software. After that, in the laboratory phase, the expression level of three candidate miRNAs on the serum samples of 26 breast cancer patients and 26 control, as well as 14 tumor tissue samples and 14 adjacent normal tissue samples, has been investigated by Real-time PCR method. Then sensitivity and specificity of candidate miRNAs were evaluated through the ROC curve analysis. After in silico analysis, we revealed that three miRNAs including miR-4443, miR-572, and miR-150-5p were highly increased in the interstitial fluid of breast cancer patients compared to breast cancer tissues. Moreover, our results revealed that the expression level of miR-4443, miR-572, and miR-150-5p were significantly decreased in the serum of breast cancer patients compare to normal controls. Also, the expression level of miR-4443 and miR-150-5p was significantly decreased in the tumor tissue compared to the adjacent non-tumor tissue. Also, ROC curve analysis showed that these three miRNAs have high sensitivity and specificity for the diagnosis of breast cancer patients. Data analysis was conducted with GraphPad Prism software. Our findings suggest the potential utility of measuring tumor-derived miRNAs in serum as an important approach for the blood-based detection of breast cancer patients. It appears that miR-4443, miR-572, and miR-150-5p may serve as promising diagnostic biomarkers with high sensitivity and specificity. However, it's important to note that further research will be needed to definitively establish the use of these miRNAs as potential biomarkers in clinical practice.

Comprehensive bioinformatics analysis reveals key hub genes linked to prognosis in multiple myeloma with drug resistance

Multiple myeloma (MM) is an incurable hematologic malignancy, with chemotherapy being the primary treatment. However, the development of drug resistance remains a major challenge. This study aimed to identify therapeutic targets associated with drug resistance in MM and assess their prognostic significance. Gene expression data from GSE82307, GSE146649, and GSE136725 were analyzed to identify differentially expressed genes (DEGs) using the "limma" and "RobustRankAggreg" R packages. Functional enrichment analysis and protein-protein interaction (PPI) network analysis were performed, with key network modules identified using Cytoscape. The expression and prognostic relevance of DEGs were validated using MM patient samples from the GSE136725 and MMRF CoMMpass databases. A total of 4623 DEGs were identified, and robust rank aggregation analysis revealed the top 20 upregulated genes. Among them, AURKA, DLGAP5, BUB1B, and KIF20A were highly expressed in drug-resistant patients and were associated with poor prognosis. The findings suggest that AURKA, DLGAP5, BUB1B, and KIF20A are potential biomarkers linked to drug resistance and recurrence in MM. Further studies are required to elucidate the underlying molecular mechanisms and explore their potential as therapeutic targets.

Construction of T-Cell-Related Prognostic Risk Models and Prediction of Tumor Immune Microenvironment Regulation in Pancreatic Adenocarcinoma via Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq

Pancreatic adenocarcinoma (PAAD) is a fatal malignant tumor of the digestive system, and immunotherapy has currently emerged as a key therapeutic approach for treating PAAD, with its efficacy closely linked to T-cell subsets and the tumor immune microenvironment. However, reliable predictive markers to guide clinical immunotherapy for PAAD are not available. We analyzed the single-cell RNA sequencing (scRNA-seq) data focused on PAAD from the GeneExpressionOmnibus (GEO) database. Then, the information from the Cancer Genome Atlas (TCGA) database was integrated to develop and validate a prognostic risk model derived from T-cell marker genes. Subsequently, the correlation between these risk models and the effectiveness of immunotherapy was explored. Analysis of scRNA-seq data uncovered six T-cell subtypes and 1837 T-cell differentially expressed genes (DEGs). Combining these data with the TCGA dataset, we constructed a T-cell prognostic risk model containing 16 DEGs, which can effectively predict patient survival and immunotherapy outcomes. We have found that patients in the low-risk group had better prognostic outcomes, increased immune cell infiltration, and signs of immune activation compared to those in the high-risk group. Additionally, analysis of tumor mutation burden showed higher mutation rates in patients with PAAD in the high-risk group. Risk scores with immune checkpoint gene expression and drug sensitivity analysis provide patients with multiple therapeutic targets and drug options. Our study constructed a prognostic risk model for PAAD patients based on T-cell marker genes, providing valuable insights into predicting patient prognosis and the effectiveness of immunotherapy.

Identification and validation of mitophagy-related genes in acute myocardial infarction and ischemic cardiomyopathy and study of immune mechanisms across different risk groups

Introduction

Acute myocardial infarction (AMI) is a critical condition that can lead to ischemic cardiomyopathy (ICM), a subsequent heart failure state characterized by compromised cardiac function.

Methods

This study investigates the role of mitophagy in the transition from AMI to ICM. We analyzed AMI and ICM datasets from GEO, identifying mitophagy-related differentially expressed genes (MRDEGs) through databases like GeneCards and Molecular Signatures Database, followed by functional enrichment and Protein-Protein Interaction analyses. Logistic regression, Support Vector Machine, and LASSO (Least Absolute Shrinkage and Selection Operator) were employed to pinpoint key MRDEGs and develop diagnostic models, with risk stratification performed using LASSO scores. Subgroup analyses included functional enrichment and immune infiltration analysis, along with protein domain predictions and the integration of regulatory networks involving Transcription Factors, miRNAs, and RNA-Binding Proteins, leading to drug target identification.

Results

The TGFβ pathway showed significant differences between high- and low-risk groups in AMI and ICM. Notably, in the AMI low-risk group, MRDEGs correlated positively with activated CD4+ T cells and negatively with Type 17 T helper cells, while in the AMI high-risk group, RPS11 showed a positive correlation with natural killer cells. In ICM, MRPS5 demonstrated a negative correlation with activated CD4+ T cells in the low-risk group and with memory B cells, mast cells, and dendritic cells in the high-risk group. The diagnostic accuracy of RPS11 was validated with an area under the curve (AUC) of 0.794 across diverse experimental approaches including blood samples, animal models, and myocardial hypoxia/reoxygenation models.

Conclusions

This study underscores the critical role of mitophagy in the transition from AMI to ICM, highlighting RPS11 as a highly significant biomarker with promising diagnostic potential and therapeutic implications.

Loop Catalog: a comprehensive HiChIP database of human and mouse samples

HiChIP enables cost-effective and high-resolution profiling of chromatin loops. To leverage the increasing number of HiChIP datasets, we developed Loop Catalog (https://loopcatalog.lji.org), a web-based database featuring loop calls from 1000+ distinct human and mouse HiChIP samples from 152 studies plus 44 high-resolution Hi-C samples. We demonstrate its utility for interpreting GWAS and eQTL variants through SNP-to-gene linking, identifying enriched sequence motifs and motif pairs, and generating regulatory networks and 2D representations of chromatin structure. Our catalog spans over 4.19M unique loops, and with embedded analysis modules, constitutes an important resource for the field.

Analysis and validation of programmed cell death genes associated with spinal cord injury progression based on bioinformatics and machine learning

BACKGROUND

Spinal cord injury (SCI) is a severe condition affecting the central nervous system. It is marked by a high disability rate and potential for death. Research has demonstrated that programmed cell death (PCD) plays a significant role in the death of neuronal cells during SCI. The objective of our work was to illustrate the significant contribution of PCD genes in the progression of SCI.

METHODS

SCI-related datasets GSE5296, GSE47681, and GSE189070 from the Gene Expression Omnibus database were comprehensively analyzed using bioinformatics methods. Common differentially expressed genes were validated by post-intersection screening with PCD genes. We constructed a gene prediction model using the least absolute shrinkage and selection operator and the random forest algorithm to further screen for characteristic genes. We also performed Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis and generated a protein-protein interaction network, analyzed immune cell infiltration, and predicted upstream miRNAs and transcription factors. In animal experiments, we performed immunofluorescence staining of mouse SCI regions to verify gene expression.

RESULTS

A total of five characteristic genes (Ctsd, Abca1, Cst7, Ctsb, and Cybb) were identified in our study and showed excellent diagnostic efficacy in predicting SCI progression (areas under the curve values of the five characteristic genes were 0.976 for Ctsd, 0.993 for Abca1, 0.995 of Cst7,0.986 of Ctsb, 0.959 of Cybb). These characterized genes were highly expressed at the site of SCI. Immune cell infiltration analysis revealed that multiple immune cells were involved in SCI progression.

CONCLUSIONS

We identified five PCD genes (Ctsd, Abca1, Cst7, Ctsb, and Cybb) associated with SCI. This study helps to reveal the pathophysiologic influences of these genes on SCI and provides important insight for the development of more effective treatments.

Exploring the active ingredients and potential mechanisms of Pingchan granules in Parkinson's disease treatment through network pharmacology and transcriptomics

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, poses significant challenges to single-target therapeutic strategies due to its complex etiology. This has driven interest in multi-target approaches, particularly those leveraging natural compounds. Pingchan granules (PCG), a traditional Chinese medicine composed of plant- and animal-derived compounds, have shown efficacy in alleviating PD symptoms. Here, we identify 96 PCG-associated anti-PD targets, enriched in neuronal synaptic signaling and G protein-coupled receptor pathways. Through protein-protein interaction network analysis of anti-PD targets and random forest modeling of substantia nigra transcriptomic data from PD patients, SLC6A3 and SRC emerged as central hub targets, with Mendelian randomization further validating SRC as a potential therapeutic target. Molecular docking and single-cell sequencing reveal that dauricine, PCG's principal active compound, binds strongly to SLC6A3 and SRC, modulating glucose metabolism pathways in dopaminergic neurons. These findings illuminate the molecular basis of PCG's therapeutic effects, offer a foundation for future drug development, and underscore the potential of dauricine as a targeted treatment for PD.

A comprehensive analysis of scRNA-Seq and RNA-Seq unveils B cell immune suppression in the AAV-loaded brain

The use of AAV vectors for in vivo gene therapy has demonstrated the potential to permanently correct genetic diseases by delivering functional gene copies to the nuclei of affected tissues. AAV vectors, as tools for in vivo gene delivery, are particularly appealing and have shown safety and long-term efficacy in numerous organ-targeted experiments. Nevertheless, employing AAV vectors for gene therapy in the brain faces a notable hurdle in the shape of immune responses, chiefly instigated by the brain's resident immune cells, microglia. Additionally, lower levels of AAV vector-neutralizing antibodies have been detected in the cerebrospinal fluid compared to the circulatory system. This research, leveraging transcriptomic and single-cell RNA sequencing (scRNA-seq) data in conjunction with Mendelian randomization analysis, has identified the potential role of the XBP1 protein in mediating B-cell immunosuppression in the brain via the MDK-NCL ligand-receptor pair and associated genes. Furthermore, it paves the way for further investigation into the regulatory factors and pathways within the immune modulation network, as well as their prospective beneficial implications in immunotherapeutic treatments. By employing various innovative approaches, the study seeks to recreate the immune environment generated by AAV in the brain and preliminarily explore the immune suppression mechanisms induced by AAV vectors in the brain.

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective

The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, and are often contaminated by noise and uncertainty, obscuring the underlying biological signal. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, metabolite levels, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering approaches struggle with the complexity of biological networks, while deep learning, with its ability to handle high-dimensional data and automatically identify meaningful patterns, has shown great promise in overcoming these challenges. Besides systematically reviewing the strengths and weaknesses of advanced deep learning methods, we have curated 21 datasets from nine benchmarks to evaluate the performance of 58 computational methods. Our analysis reveals that model performance can vary significantly across different benchmark datasets and evaluation metrics, providing a useful perspective for selecting the most appropriate approach based on a specific application scenario. We highlight three key areas for future development, offering valuable insights into how deep learning can be effectively applied to transcriptomic data analysis in biological, medical, and clinical settings.

Comparison of light gradient boosting and logistic regression for interactomic hub genes in Porphyromonas gingivalis and Fusobacterium nucleatum-induced periodontitis with Alzheimer's disease

Introduction

Porphyromonas gingivalis and Treponema species have been found to invade the central nervous system through virulence factors, causing inflammation and influencing the host immune response. P. gingivalis interacts with astrocytes, microglia, and neurons, leading to neuroinflammation. Aggregatibacter actinomycetemcomitans and Fusobacterium nucleatum may also play a role in the development of Alzheimer's disease. Interactomic hub genes, central to protein-protein interaction networks, are vulnerable to perturbations, leading to diseases such as cancer, neurodegenerative disorders, and cardiovascular diseases. Machine learning can identify differentially expressed hub genes in specific conditions or diseases, providing insights into disease mechanisms and developing new therapeutic approaches. This study compares the performance of light gradient boosting and logistic regression in identifying interactomic hub genes in P. gingivalis and F. nucleatum-induced periodontitis with those in Alzheimer's disease.

Methods

Using the GSE222136 dataset, we analyzed differential gene expression in periodontitis and Alzheimer's disease. The GEO2R tool was used to identify differentially expressed genes under different conditions, providing insights into molecular mechanisms. Bioinformatics tools such as Cytoscape and CytoHubba were employed to create gene expression networks to identify hub genes. Logistic regression and light gradient boosting were used to predict interactomic hub genes, with outliers removed and machine learning algorithms applied.

Results

The data were cross-validated and divided into training and testing segments. The top hub genes identified were TNFRSF9, LZIC, TNFRSF8, SLC45A1, GPR157, and SLC25A33, which are induced by P. gingivalis and F. nucleatum and are responsible for endothelial dysfunction in brain cells. The accuracy of logistic regression and light gradient boosting was 67% and 60%, respectively.

Discussion

The logistic regression model demonstrated superior accuracy and balance compared to the light gradient boosting model, indicating its potential for future improvements in predicting hub genes in periodontal and Alzheimer's diseases.

MiNEApy: enhancing enrichment network analysis in metabolic networks

MOTIVATION

Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism.

RESULTS

Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data.

AVAILABILITY AND IMPLEMENTATION

MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Establishment of a prediction model and immune infiltration characteristics of atherosclerosis progression based on neutrophil extracellular traps-related genes

Neutrophil extracellular traps (NETs) are a novel regulatory mechanism of neutrophils, which can promote endothelial cell inflammation through direct or indirect pathways and play a crucial role in the occurrence and development of atherosclerosis (AS). This study aimed to explore the mechanism of NETs in AS progression using bioinformatics methods. We acquired datasets from Gene Expression Omnibus (GEO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and used Weighted Gene Co-expression Network Analysis (WGCNA) to identify communal genes shared by NET-related genes. Gene Ontology (GO) and KEGG enrichment analyses were conducted. Machine learning algorithms were used to identify hub genes, then protein-protein interaction (PPI), CO-expression network construction, nomogram model building and validation, and immune infiltration analysis were performed. Data were verified by qPCR. Four datasets related to AS progression were included. Module genes shared 27 genes with NRGs. Pathways related to immune regulation, leukocyte migration, and others were identified. Machine learning revealed SLC25A4 and C5AR1 as hub genes. SLC25A4 and C5AR1 were confirmed to have predictive value for intraplaque hemorrhage (IPH), advanced AS plaques, ruptured plaques, and unstable plaques. These pathologic changes are closely related to AS progression and are the main contents of AS progression. Immune infiltration analysis revealed 4 immune cells associated with IPH, among them resting dendritic cells, which were closely related to SLC25A4. In qPCR validation, SLC25A4 and C5AR1 were shown to be consistent with the bioinformatic analysis results. These findings provided novel insights into the molecular characteristics of NRGs and potential therapies for AS progression.

Mendelian randomization analysis and molecular mechanism study of childhood asthma and obstructive sleep apnea

Childhood asthma is a common chronic respiratory disorder influenced by various factors, and obstructive sleep apnea (OSA) has emerged as a significant comorbidity. This study sought to investigate the underlying molecular mechanisms of the comorbidity between childhood asthma and OSA through Mendelian randomization (MR) analysis. Gene expression and genotype data were analyzed from public databases, and single nucleotide polymorphisms (SNPs) related to both diseases were identified. Our research findings unveiled 242 gene pairs associated with childhood asthma and 350 gene pairs related to OSA. Among them, the three hub genes, namely LRP3, BAK1, and CLIC4, exhibited significant expression alterations in both diseases. These hub genes participate in multiple signal transduction pathways and exhibit a remarkable correlation with the infiltration of immune cells, suggesting that they exert a vital role in modulating the immune microenvironment. Further analyses, encompassing gene set enrichment and transcriptional regulation, emphasized the complex interplay between these genes and non-coding RNAs as well as transcription factors. Our study results stressed the bidirectional relationship between childhood asthma and OSA and accentuated the significance of early identification and targeted intervention. This study identified potential therapeutic targets and laid a foundation for formulating treatment strategies aimed at improving the conditions of children with these interrelated diseases.

Decoding the possible mechanism of action of Paeoniflorigenone in combating Aflatoxin B1-induced liver cancer: an investigation using network pharmacology and bioinformatics analysis

Moutan cortex has demonstrated antitumor properties attributed to its bioactive compound Paeoniflorigenone (PA). Nevertheless, there is limited research on the efficacy of PA in the prevention and treatment of hepatocellular carcinoma (HCC). We aimed to investigate the potential pharmacological mechanisms of PA in the treatment of Aflatoxin B1 (AFB1)-induced hepatocarcinogenesis using network pharmacology and bioinformatics analysis approaches. Through various databases and bioinformatics analysis approaches, 34 shared targets were identified as potential candidate genes for PA in fighting liver cancer caused by AFB1. Pathway analysis revealed involvement in cell cycle, HIF-1, and Rap1 pathways. A risk assessment model was developed using LASSO regression, showing an association between the identified genes and the tumor immune microenvironment. The genes within the risk model were found to be linked to the immune response in liver cancer. Molecular docking studies indicated that PA interacts with its targets through hydrogen bonding and hydrophobic interactions. This study provides insights into the possible mechanisms of PA in liver cancer treatment and offers a predictive model for assessing the risk level of individuals with liver cancer. These findings have significant implications for the therapeutic strategies in managing liver cancer patients.

TSG-6 Protects Against Cerebral Ischemia-Reperfusion Injury via Upregulating Hsp70-1B in Astrocytes

AIMS

This study aimed to investigate the relationship between tumor necrosis factor alpha-induced protein (TNFAIP6/TSG-6) and astrocytes in cerebral ischemia/reperfusion (I/R) injury.

METHODS

Utilizing in vivo and in vitro cerebral I/R models, cerebral infarct volumes, neurobehavioral outcomes, blood-brain barrier (BBB) permeability, as well as indicators of astrocyte apoptosis, reactivity, and A1 phenotype were assessed to evaluate the effects of recombinant rattus TSG-6 (rrTSG-6) on astrocytes in acute cerebral I/R injury. Following mRNA sequencing of all astrocyte groups, astrocyte apoptosis and reactivity were analyzed through a combined intervention of rrTSG-6 and Apoptozole, a heat shock protein 70-1B (Hsp70-1B) inhibitor, in vitro.

RESULTS

The findings demonstrated that rrTSG-6 significantly reduced cerebral infarct volumes by nearly half, improved neurobehavioral outcomes, mitigated BBB damage, and suppressed the expressions of astrocyte apoptosis markers, reactivity indicators, and A1 phenotype markers. mRNA sequencing revealed that the Hsp70-1B protein level increased to approximately 1.6 times that of the rrTSG-6 non-intervention group. Furthermore, Apoptozole impeded the expressions of astrocyte apoptosis markers, reactivity indicators, and A1 phenotype markers.

CONCLUSION

TSG-6 inhibited nuclear factor kappa-B (NF-κB) phosphorylation by upregulating Hsp70-1B in oxygen-glucose deprivation/reoxygenation (OGD/R)-induced astrocytes, thereby exerting a protective effect through anti-apoptotic mechanisms and the suppression of astrocyte reactivity and A1 transformation following cerebral I/R injury.

Single-Nucleus Landscape of Glial Cells and Neurons in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease with a projected significant increase in incidence. Therefore, this study analyzed single-nucleus AD data to provide a theoretical basis for the clinical development and treatment of AD. We downloaded AD-related monocyte data from the Gene Expression Omnibus database, annotated cells, compared cell abundance between groups, and investigated glial and neuronal cell biological processes and pathways through functional enrichment analysis. Furthermore, we constructed a global regulatory network for AD based on cell communication and ecological analyses. Our findings revealed increased abundance of Capping Protein Regulator And Myosin 1 linker 1 (CARMIL1)+ astrocytes (AST), Immunoglobulin Superfamily Member 21 (IGSF21)+ microglia (MIC), SRY-Box Transcription Factor 6 (SOX6)+ inhibitory neurons (InNeu), and laminin alpha-2 chain (LAMA2)+ oligodendrocytes (OLI) cell subgroups in tissues of patients with AD, while prostaglandin D2 synthase (PTGDS)+ AST, Src Family Tyrosine Kinase (FYN)+ MIC, and Proteolipid Protein 1 (PLP1)+ InNeu subgroups specifically decreased. We found that the cell phenotype of patients with AD shifted from a simpler to a more complex state compared to the control group. Cell communication analysis revealed strong communication between MIC and NEU. Furthermore, AST, MIC, NEU, and OLI were involved in oxidative stress- and inflammation-related pathways, potentially contributing to disease development. This study provides a theoretical basis for further exploring the specific mechanisms underlying AD.

Identifying key genes, miRNAs, and pathways in keloid formation: A bioinformatics and experimental study

Keloids represent a challenging clinical problem because of their unpredictable and often refractory nature to treatment. This study aimed to identify the key changes in gene expression in the formation of keloid and provide potential biomarker candidates for clinical treatment and drug target discovery. Keloids and normal skin samples were analyzed for gene expression, and datasets from the Gene Expression Omnibus database were also analyzed. Differentially expressed genes (DEGs) were identified and analyzed using bioinformatics techniques, including gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A protein-protein interaction network of the DEGs was created using the Search Tool for the Retrieval of Interacting Genes database. The gene set enrichment analysis was performed on keloid and normal skin tissue from clinical samples. The enriched functions and pathways identified included collagen-containing extracellular matrix (ECM), ECM, and external encapsulating structure. Ten hub genes were identified, along with one differentially expressed microRNA, miR-22-5p. miRNA target gene prediction was performed using miRPathDB 2.0 and Targetscan database. Among the hub genes, RUNX2, IGF1, EGF, and PPARGC1A were predicted targets of miR-22-5p. Validation at the tissue level highlighted RUNX2 as a crucial DEG in keloid tissue. These findings shed light on the molecular mechanisms of keloid formation and offer candidate therapeutic targets, suggesting that modulation of the miR-22-5p/RUNX2 axis may be a promising avenue for keloid diagnosis and treatment, thus laying a foundation for improved clinical management of keloid disorders.

Integrated Analysis to Reveal Heterogeneity of Tumor-Associated Neutrophils in Glioma

BACKGROUND

Glioma, characterized by its cellular and molecular heterogeneity, presents formidable challenges in treatment strategy and prognostic assessment. The tumor microenvironment (TME) profoundly influences tumor behavior and treatment response, with tumor-associated neutrophils (TANs) playing a complex but understudied role. This study aimed to investigate the heterogeneity and role of TANs in glioma and to develop a prognostic model.

METHODS

Analysis of scRNA-seq data identified cellular subpopulations and differentially expressed neutrophil-related genes (DE-NRGs). Bulk RNA-seq was obtained from four independent datasets. Molecular subtypes of glioma samples were determined by consensus clustering. WGCNA was conducted to elucidate the association between gene modules and subtypes. We developed a risk score model. Expression of selected genes was confirmed using immunohistochemistry (IHC). In vitro experiments were also performed for functional verification, including CCK8, EdU, Transwell, and TUNEL assays.

RESULTS

A total of 108 DE-NRGs for TANs were identified based on scRNA-seq data. Two molecular subtypes were characterized, showing significant differences in prognosis and clinical features. Immune-related analyses demonstrated varied immunological characteristics between subtypes. The risk score model was constructed with 7 genes, including AEBP1, CAVIN1, DCTD, DEPP1, DUSP6, FKBP9, and UGCG. It showed significant prognostic value and was validated across three external datasets. The mutation landscape highlighted higher IDH mutation prevalence in low-risk groups. Drug sensitivity analysis indicated TMZ resistance in high-risk groups. In vitro experiments showed that UGCG could promote glioma cell proliferation, migration, and invasion, while decreasing apoptosis.

CONCLUSION

This study explored the heterogeneity of TANs and developed a prognostic model, providing insights for prognostic prediction and guiding personalized treatment strategies in glioma. Declaration of Generative AI in Scientific Writing: The authors declare nonuse of generative AI and AI-assisted technologies in the writing process.

The role of miRNAs in the associations between particulate matter and ischemic stroke: A nested case-control study

Epidemiological studies have reported that atmospheric particulate matter (PM) contributes to ischemic stroke (IS). Biological studies also indicated that the pathway where PM induces IS involves several pathological processes. Moreover, exposure to PM can alter the expression of specific microRNAs (miRNAs) and ultimately accelerate the onset of IS by regulating related pathways. However, evidence on the role of miRNAs between PM and IS still needs to be fully elucidated. We used the miRNA sequencing datasets from the GEO (Gene Expression Omnibus) to screen miRNAs associated with IS. A nested case-control study was performed, including all incident ischemic stroke cases during the follow-up period and controls matched by age, sex, and entry seasons. Land use regression (LUR) models were constructed to estimate the levels of PM2.5 and PM10. The real-time quantitative PCR (RT-qPCR) assay was applied to detect the expression of candidate miRNAs in plasma samples collected at baseline to verify whether candidate miRNAs differentially expressed between cases and controls. Mediation analyses were applied to evaluate whether PM could induce IS by affecting the expression of miRNAs. We screened 23 miRNAs expressed differentially between cases and controls from the GEO database. A total of 605 incident ischemic stroke patients were finally included in the case group, and 605 healthy controls were matched. The RT-qPCR assay detected 15 differentially expressed miRNAs. Mediating effects of hsa-miR-107, hsa-miR-320b, hsa-miR-423-5p, hsa-miR-483-5p, and hsa-miR-935 were observed for the associations between PM and IS, indicating that PM could promote IS by altering the expression of those miRNAs. In this nested case-control study, PM might induce IS by affecting the expression of hsa-miR-107, hsa-miR-320b, hsa-miR-423-5p, hsa-miR-483-5p and hsa-miR-935.

The effect of rewarming ischemia on tissue transcriptome and metabolome signatures: A clinical observational study in lung transplantation

BACKGROUND

In lung transplantation (LuTx), various ischemic phases exist, yet the rewarming ischemia time (RIT) during implantation has often been overlooked. During RIT, lungs are deflated and exposed to the body temperature in the recipient's chest cavity. Our prior clinical findings demonstrated that prolonged RIT increases the risk of primary graft dysfunction. However, the molecular mechanisms of rewarming ischemic injury in this context remain unexplored. We aimed to characterize the rewarming ischemia phase during LuTx by measuring organ temperature and comparing transcriptome and metabolome profiles in tissue obtained at the end versus the start of implantation.

METHODS

In a clinical observational study, 34 double-LuTx with ice preservation were analyzed. Lung core and surface temperature (n = 65 and 55 lungs) were measured during implantation. Biopsies (n = 59 lungs) were wedged from right middle lobe and left lingula at start and end of implantation. Tissue transcriptomic and metabolomic profiling were performed.

RESULTS

Temperature increased rapidly during implantation, reaching core/surface temperatures of 21.5°C/25.4°C within 30 minutes. Transcriptomics showed increased proinflammatory signaling and oxidative stress at the end of implantation. Upregulation of NLRP3 and NFKB1 correlated with RIT. Metabolomics indicated elevated levels of amino acids, hypoxanthine, uric acid, and cysteineglutathione disulfide alongside decreased levels of glucose and carnitines. Arginine, tyrosine, and 1-carboxyethylleucine showed a correlation with incremental RIT.

CONCLUSIONS

The final rewarming ischemia phase in LuTx involves rapid organ rewarming, accompanied by transcriptomic and metabolomic changes indicating proinflammatory signaling and disturbed cell metabolism. Limiting implantation time and cooling of the lung represent potential interventions to alleviate rewarming ischemic injury.

Single-cell RNAseq reveals adverse metabolic transcriptional program in intrahepatic cholangiocarcinoma malignant cells

Intrahepatic cholangiocarcinoma (ICA) is a highly aggressive primary liver cancer, which originates from the epithelial cells of the bile ducts. The transcriptional profile of metabolic enzymes was investigated at both bulk and single-cell levels in tumor samples from distinct ICA cohorts. In a training cohort (TCGA consortium), 16 genes encoding for metabolic enzymes were found overexpressed in cases with poor survival. A computed metabolic gene expression score was significantly associated with worse ICA prognosis at the univariate level (overall survival [OS] log-rank p = 8.2e-4). After adjusting for Ishak fibrosis score and tumor staging, the metabolic expression remained an independent predictor of poor prognosis (multivariate OS log-rank p = 0.01). Seven genes encoding key enzymes (FH, MAT2B, PLOD2, PLOD1, PDE6D, ALDOC, and NT5DC3) were validated as markers of the proliferative subclass of ICA in the GSE32225 dataset, related to poor prognosis. The metabolic score was significantly different between the inflammatory and proliferative subclasses in the validation cohort (p < 2.2e-16). At the single-cell level, in the tumor microenvironment of 10 ICA patients, these seven enzymes were predominantly expressed by malignant cells. The single-cell metabolic score was thus higher in malignant cells. This study identifies a metabolic transcriptional program linked to poor prognosis in ICA, independent of fibrosis and tumor staging.

DUSP1 Promotes Osimertinib Drug-Tolerant Persistence by Inhibiting MAPK/ERK Signaling in Non-small Cell Lung Cancer

EGFR tyrosine kinase inhibitors (EGFR-TKIs) are the first-line treatment for EGFR-mutant non-small cell lung cancer (NSCLC) patients, which remarkably improve the clinical outcomes. However, drug resistance has greatly impaired the efficacy of EGFR-TKIs and contributes to cancer treatment failure. DUSP1, a negative regulator of MAPK signaling pathway, was discovered to mediate drug resistance in multiple types of cancers. Our study aimed to explore the role of DUSP1 in NSCLC cell resistance to osimertinib, a third-generation EGFR-TKI. Human NSCLC cell lines PC-9 and HCC827 were exposed to increasing concentrations of osimertinib for over 6 months to generate osimertinib resistant cells (PC-9-OR and HCC827-OR). The viabilities of osimertinib-resistant and parental sensitive NSCLC cells in response to osimertinib stimulation were detected by MTS assay and the IC50 values for osimertinib were obtained. The differentially expressed genes in osimertinib-resistant and sensitive NSCLC cells were identified by analyzing the GEO dataset GSE106765 using bioinformatic tools. DUSP1 expression was knocked down by using the short hairpin RNAs (shRNAs). Then, the effects of DUSP1 silencing on osimertinib-resistant and sensitive NSCLC cell resistance to osimertinib, viability, proliferation and apoptosis were assessed through loss-of-function experiments. The expression of key molecules (JNK, ERK, and p38 MAPK) in the MAPK signaling pathway was detected through western blotting analysis. DUSP1 was overexpressed in osimertinib-resistant NSCLC cells versus parental sensitive cells. DUSP1 silencing attenuated the resistance of NSCLC cells to osimertinib. DUSP1 silencing markedly inhibited osimertinib-resistant and sensitive NSCLC cell proliferation but enhanced cell apoptosis. Mechanically, DUSP1 knockdown increased phosphorylated-JNK, ERK, and p38 MAPK levels in NSCLC cells. Treatment with SB203580, the p38 MAPK inhibitor, reversed the effects of DUSP1 silencing on osimertinib-resistant NSCLC cell resistance to osimertinib, cell proliferation and apoptosis. DUSP1 downregulation restores the sensitivity of NSCLC cells to osimertinib via activating the MAPK signaling pathway.

Synthetic biomolecular condensates enhance translation from a target mRNA in living cells

Biomolecular condensates composed of proteins and RNA are one approach by which cells regulate post-transcriptional gene expression. Their formation typically involves the phase separation of intrinsically disordered proteins with a target mRNA, sequestering the mRNA into a liquid condensate. This sequestration regulates gene expression by modulating translation or facilitating RNA processing. Here we engineer synthetic condensates using a fusion of an RNA-binding protein, the human Pumilio2 homology domain (Pum2), and a synthetic intrinsically disordered protein, an elastin-like polypeptide (ELP), that can bind and sequester a target mRNA transcript. In protocells, sequestration of a target mRNA largely limits its translation. Conversely, in Escherichia coli, sequestration of the same target mRNA increases its translation. We characterize the Pum2-ELP condensate system using microscopy, biophysical and biochemical assays, and RNA sequencing. This approach enables the modulation of cell function via the formation of synthetic biomolecular condensates that regulate the expression of a target protein.

Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models

Training machine-learning models with synthetically generated data can alleviate the problem of data scarcity when acquiring diverse and sufficiently large datasets is costly and challenging. Here we show that cascaded diffusion models can be used to synthesize realistic whole-slide image tiles from latent representations of RNA-sequencing data from human tumours. Alterations in gene expression affected the composition of cell types in the generated synthetic image tiles, which accurately preserved the distribution of cell types and maintained the cell fraction observed in bulk RNA-sequencing data, as we show for lung adenocarcinoma, kidney renal papillary cell carcinoma, cervical squamous cell carcinoma, colon adenocarcinoma and glioblastoma. Machine-learning models pretrained with the generated synthetic data performed better than models trained from scratch. Synthetic data may accelerate the development of machine-learning models in scarce-data settings and allow for the imputation of missing data modalities.

Identification of biomarkers associated with macrophage polarization in diabetic cardiomyopathy based on bioinformatics and machine learning approaches

BACKGROUND

Numerous studies have investigated the role of macrophages in the pathogenesis of diabetic cardiomyopathy (DCM); however, the underlying mechanisms remain unclear.

METHODS

The DCM dataset (GSE62203) was downloaded from the GEO database. DEGs and WGCNA key module genes were identified. Macrophage polarization-associated genes were obtained from the GeneCards database. GO and KEGG functional enrichment were constructed. Two machine learning techniques, LASSO logistic regression and random forest, were further used to identify hub genes. The diagnostic efficiency was evaluated using ROC curves. Single-gene GSEA investigated the biological functions. Then, the relationship between hub genes and macrophage pathways was explored. Predicted Transcription factor (TF), miRNA, and lncRNA. Single cell sequencing analysis was performed. Finally, experimental validation of the hub genes using the DCM rat model.

RESULTS

Three hub genes (PGK1, LDHA, EDN1) were identified through machine learning approaches. All three hub genes were found to be associated with the HIF-1 signaling pathway. Functional enrichment analysis revealed that the HIF-1 signaling pathway and Glycolysis/Gluconeogenesis are potentially linked to DCM-induced macrophage polarization. The mRNA and protein expression levels of the hub genes were consistent with the bioinformatics analysis. Furthermore, mRNA expression of the hub genes showed a positive correlation with CD80 and CD86.

CONCLUSION

PGK1, LDHA, and EDN1 represent potential biomarkers for M1 macrophage polarization in DCM. These genes may facilitate M1 macrophage polarization in DCM. Targeting macrophage polarization could represent a novel therapeutic strategy for DCM.

The role of lnc‑MAPKAPK5‑AS1 in immune cell infiltration in hepatocellular carcinoma: Bioinformatics analysis and validation

The oncogenic and tumor suppressor roles of lnc-MAPKAPK5-AS1 in multiple cancers suggest its complexity in modulating cancer progression. The expression and promoter methylation level of lnc-MAPKAPK5-AS1 in hepatocellular carcinoma (HCC) was investigated through data mining from The Cancer Genome Atlas and Gene Expression Omnibus and its significance in prognosis and immunity was explored. lnc-MAPKAPK5-AS1 was co-expressed with its protein-coding gene MAPKAPK5 in HCC and exhibited upregulation in HCC tissues as a result of hypomethylation of its promoter region. High expression of lnc-MAPKAPK5-AS1 was associated with poor prognosis. Enrichment analysis revealed that lnc-MAPKAPK5-AS1 is involved in immune and metabolic-related pathways. Changes in the expression of lnc-MAPKAPK5-AS1 affected plasma cells, T cells CD4+ memory resting, NK cells, macrophages M0/M1, and mast cells resting in the tumor microenvironment. lnc-MAPKAPK5-AS1 was found to correlate with multiple immune checkpoints. Analysis of the Sangerbox database revealed positive relationships between expression of lnc-MAPKAPK5-AS1, tumor mutational burden and microsatellite instability, which suggested that immunotherapy may be effective in tumors with high expression of lnc-MAPKAPK5-AS1. The expression of lnc-MAPKAPK5-AS1 was verified to indicate sensitivity to 16 common targeted drugs. Immunohistochemistry confirmed the expression of MAPKAPK5 protein in HCC and its prognostic significance. Weighted gene co-expression network analysis was applied to identify hub genes related to both immunoreactive score and gene expression. These results revealed that lnc-MAPKAPK5-AS1 may be involved in the occurrence and development of HCC as an oncogene and may represent a potential therapeutic target through modulating the substance metabolism and immune response.

Transcriptomic Dysregulation in Animal Models of Attention-Deficit Hyperactivity Disorder and Nicotine Dependence Suggests Shared Neural Mechanisms

INTRODUCTION

Attention-deficit-hyperactivity disorder (ADHD) is highly heritable and increases the likelihood of nicotine dependence (ND). The self-medication hypothesis of nicotine use in ADHD proposes that ADHD patients seek nicotine for its ability to improve their symptoms, and they have less success quitting, possibly due to the worsening of ADHD symptoms in withdrawal.

METHODS

The present analysis compared transcriptomic data from the brains of rodent models of ADHD and those of ND, with a focus on striatal gene expression. Differential expression analysis, pathway enrichment analysis, and gene-network mapping identified signaling networks and candidate genes that may contribute to the high co-occurrence between ADHD and ND.

RESULTS

We identified novel differentially expressed genes (PRKAG2, MAPK1), and genes with known associations to either ADHD or ND (ANK3, CALD1, CHRNA4, CHRNA7, CMTM8, DLG4, DUSP6, GNG3, GNG11, GRIK5, GRINA2, GRM5, ICAM2, KCNJ6, PRKAB1, SNAP25, SYNPO, SYT1, VAMP2). In addition, synaptic transmission (hsa04728, R-HAS-112315, R-HSA-442755) and MAPK signaling pathways (hsa04010, hsa04014, hsa04015, R-HSA-5673001, R-HSA-5684996) were enriched in both ADHD and ND.

CONCLUSION

The signaling pathways implicated by this analysis mediate neurological mechanisms known to contribute to ND. The association of analogous differently expressed genes and common signaling pathways suggests an important causal relationship between ND and ADHD that may be clinically important.

Machine learning and experimental screening of chromatin regulator signatures and potential drugs in hepatitis B related hepatocellular carcinoma

Many evidences have confirmed that chromatin regulator factors (CRs) are involved in the progression of cancer, but its potential mechanism of affecting hepatitis B related hepatocellular carcinoma still needs to be studied. Our study detected the CRs that affect hepatitis B related hepatocellular carcinoma (HBV-HCC) through machine learning analysis, conducted the analysis of immune cells, constructed the relevant risk model and immune function infiltration, and predicted the potential therapeutic drugs. We found that these CRs were significantly related to the immune cells of Macrophages, B cells, CD8+T cells, etc., and PBK, AURKA, TOP2A and AURKB were the potential risk CRs of HBV-HCC. The expression levels of these four CRs increased in HepG2.2.15 cells and the liver of HBV-HCC patients, consistent with the predicted risk model. Subsequently, ten potential drugs closely related to the risk CRs were finally obtained, experimental research on resveratrol has shown that it can inhibit the proliferation of HepG2.2.15 cells and potentially inhibit the occurrence and development of HBV-HCC. Our study provides novel insights into the function of CRs in HBV-HCC and certain ideas for more accurate targeted therapy.

Prognostic and immunological implications of paraptosis-related genes in lung adenocarcinoma: Comprehensive analysis and functional verification of hub gene

BACKGROUND

Lung adenocarcinoma (LUAD) poses significant clinical challenges due to its inherent heterogeneity and variable response to treatment. Recent research has specifically focused on elucidating the role of Paraptosis-related genes (PRGs) in the progression of cancer and the prognosis of patients.

METHODS

We conducted a comprehensive analysis of the differential expression of PRGs in LUAD. Additionally, univariate Cox regression analysis was utilized to determine the prognostic significance of these genes. Furthermore, consensus clustering was employed to differentiate molecular subtypes within LUAD, while immune heterogeneity was assessed. To evaluate treatment outcomes, the expression of immune checkpoint inhibitors was examined, and the sensitivity of LUAD patients to chemotherapy drugs was assessed. Moreover, machine learning algorithms were employed to construct a Paraptosis-related risk score with prognostic and immunological indicators. Finally, to validate the findings, in vitro experiments were performed to verify the regulatory effect of key PRGs on Paraptosis.

RESULTS

Our analysis identified 24 PRGs that exhibited differential expression, with CDKN3, TP53, and PHB emerging as the most prominently upregulated genes in tumor tissues. Among these genes, seven were identified as prognostic markers, with HSPB8 being the sole protective factor. Notably, our analysis also revealed the existence of two distinct molecular subtypes within LUAD, each characterized by unique prognoses and immune responses. Specifically, Subtype B displayed a poorer prognosis but demonstrated increased sensitivity to both chemotherapy and immunotherapy. In addition, our development of a Paraptosis-Associated Risk Score yielded a significant prognostic value in predicting patient outcomes. Furthermore, we found regulatory effect of CDKN3 on Paraptosis in two cell lines.

CONCLUSIONS

Our study highlights the importance of PRGs in LUAD, particularly in prognosis and treatment response. The identified molecular subtypes and Paraptosis-Associated Risk Score offer valuable insights for personalized treatment strategies.

Blood transcriptomics identifies FEZ1 as a potential biomarker for inflammatory bowel disease

INTRODUCTION

While the global burden of inflammatory bowel diseases (IBD) is increasing, the identification of novel therapeutic targets and biomarkers is of significant importance. In particular, blood transcriptomes provide a non-invasive source for biomarker discovery. Therefore, this study aimed to identify potential blood markers for IBD.

METHODS

By employing an integrated transcriptomics approach, four datasets obtained from blood specimens of patients with IBD were analyzed (GSE119600, GSE94648, GSE86434, and GSE71730). After determining differentially expressed genes (DEGs) in IBD, a protein-protein interaction (PPI) network was constructed, and regulatory miRNAs targeting hub genes were identified. Weighted gene co-expression network analysis (WGCNA) was carried out to determine IBD-specific modules. Subsequently, converging results from differential expression analysis and WGCNA were subjected to random forest (RF) decision tree-based and LASSO regression methods. Lastly, the diagnostic efficacy of genes highlighted by both machine learning methods was measured using receiver operating characteristic (ROC) analysis in the integrated dataset, in each individual dataset separately, and in external datasets (GSE276395, GSE169568, GSE112057, GSE100833, GSE33943, and GSE3365).

RESULTS

Downregulated TNF was identified as the central hub gene of the PPI network, and PRF1 was the only gene identified as a hub gene in a co-expressed gene module enriched in IBD. Following the identification of FEZ1 and NLRC5 among the top 10 genes by both RF and LASSO, ROC analysis demonstrated their acceptable diagnostic efficacy in the integrated data. However, only FEZ1 was considered a potential biomarker based on replication of the results in the external datasets.

CONCLUSIONS

The results of the present study suggest FEZ1 as a potential blood biomarker for IBD. While autophagy is currently the most convincing explanation for the involvement of FEZ1 in IBD, further investigations are required to elucidate its immunological role.

Dickkopf-1 promotes tumor progression of gefitinib- resistant non-small cell lung cancer through cancer cell-fibroblast interactions

BACKGROUND

Cancer cell-secreted proteins play a critical role in tumor progression and chemoresistance by influencing intercellular interactions within the tumor microenvironment. Investigating the intratumoral functions of these secretory proteins may provide insights into understanding and treating chemoresistant cancers. This study aims to identify potential anticancer target(s) in gefitinib-resistant non-small cell lung cancer (NSCLC), with a focus on secretory proteins and their effects on intercellular interactions.

METHODS

Differentially expressed secretory proteins were identified in gefitinib-resistant human NSCLC cell lines (PC9-GR and HCC827-GR), revealing an elevation in Dickkopf-1 (DKK1) expression and secretion. To elucidate the role of DKK1 in gefitinib-resistant cancer, the anticancer effects of a neutralizing antibody against DKK1 were evaluated in tumors comprising either cancer cells alone or cancer cells co-injected with human lung fibroblasts (MRC-5). Following the confirmation of the importance of cancer cell-fibroblast interactions in the protumorigenic activity of DKK1, the fibroblast traits modulated by DKK1 were further analyzed.

RESULTS

Gefitinib-resistant NSCLC cells exhibited increased DKK1 protein expression. Although elevated DKK1 levels were linked to poor prognosis, DKK1 did not directly affect cancer cell proliferation. However, DKK1 blockade showed significant anticancer effects in gefitinib-resistant tumors containing lung fibroblasts, suggesting that DKK1's pro-tumorigenic roles are mediated through cancer cell-fibroblast interactions. DKK1 altered fibroblast characteristics, enhancing inflammatory fibroblast traits while diminishing myofibroblast traits in tumor microenvironment. These DKK1-induced changes were mediated via activation of the c-JUN pathway in fibroblasts. Moreover, DKK1 was identified as a potential anticancer target across various cancer types beyond gefitinib-resistant lung cancer.

CONCLUSIONS

This study clarifies that DKK1 mediates interactions between cancer cells and fibroblasts in gefitinib-resistant lung cancer, contributing to tumor progression. Therefore, we propose DKK1 as a promising anticancer target for the treatment of gefitinib-resistant NSCLC.

Thiazepine-Based Hybrids as Promising Anti-Colon Cancer Agents: Design, Synthesis, Computational and In Vitro Screening

Novel thiazepine-based hybrids (9 a-d) were designed and synthesized to create lead molecules with exceptional anti-colon cancer efficacy. Analytical methods, including IR, NMR, and HR-MS, characterized the synthesized compounds. The in vitro colorectal study was carried out to compare the biological activity of newly developed compounds with the computational data. The tested compounds induced cytotoxicity in HT-29 cells for both 24 h and 48 h in a dose-dependent manner. However, compound 9 a induced cytotoxicity at much higher concentrations compared to the rest of the compounds. 9 b and 9 c caused 50 % cell death (compared to the untreated cells) at a dose of ~50 μM and 40 μM in case of 24-hour exposure, respectively. On the contrary, for 48 h exposure, both 9 b and 9 c induced 50 % cell death concerning untreated cells at a dose of around ~20 μM, whereas 9 d exhibited 50 % cell death at 5 μM in the case of 48 h exposure. In silico ADMET was also carried out to understand the pharmacokinetics and safety profiles of the drug candidates. We found some of the critical targets of these compounds, which eventually will be integral to exploring the mechanistic actions of these compounds in colon cancer.

Characterization of immune landscape and prognostic value of IL-17-related signature in invasive breast cancer

Background

Recently, interleukin 17 (IL-17) has been found to play a critical role in the development of breast cancer. However, its prognostic significance in invasive breast cancer (IBC) remains unclear. This study aims to determine the role of IL-17-related signatures in IBC to identify novel therapeutic options.

Methods

IBC data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used to identify IL-17-related prognostic genes. A predictive model was developed using TCGA data and validated using METABRIC data. The relationship between IL-17 scores and immune landscape, chemotherapy drug sensitivity [half maximal inhibitory concentration (IC50)], and immune checkpoint gene expression was analyzed. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to validate key gene expression in breast tumor and normal tissue samples.

Results

The predictive model identified core IL-17-related prognostic genes and successfully estimated the prognosis of IBC patients. The model's validity was confirmed using METABRIC data. Patients with high IL-17 scores had worse overall survival (OS) compared to those with low IL-17 scores. Low IL-17 scores were associated with higher immune checkpoint gene expression and predicted enhanced responses to cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and programmed cell death protein 1 (PD-1) therapies. Patients with low IL-17 scores exhibited a higher abundance of immune microenvironment components. Furthermore, qRT-PCR confirmed the lower expression of OR51E1, NDRG2, RGS2, and TSPAN7 in breast tumors compared to normal tissue.

Conclusions

IL-17-related signatures are promising biomarkers for predicting the prognosis of IBC patients. These findings suggest that IL-17-related markers could be used to guide individualized therapeutic strategies, potentially improving outcomes for IBC patients.

Sodium Butyrate Inhibits Necroptosis by Regulating MLKL via E2F1 in Intestinal Epithelial Cells of Liver Cirrhosis

Background and Aims

Necroptosis is critical for regulating intestinal epithelial cells (IECs). Butyric acid (BA), produced during intestinal microbial metabolism, protects the intestinal epithelial barrier. However, whether necroptosis occurs in IECs during liver cirrhosis and whether sodium butyrate (NaB) can regulate necroptosis have not yet been reported. In this study, we aimed to investigate whether IECs undergo necroptosis in cirrhosis and whether NaB can regulate necroptosis and the related regulatory mechanisms.

Methods

Serum levels of RIPK3, MLKL, and Zonulin, as well as fecal BA levels, were measured and correlated in 48 patients with liver cirrhosis and 20 healthy controls. A rat model of liver cirrhosis was established, and NaB was administered. The expressions of MLKL, p-MLKL, and tight junction proteins were measured. We conducted an in vitro investigation of the effect of NaB on necroptosis in the HT29 cell line.

Results

Serum levels of RIPK3, MLKL, and Zonulin in the liver cirrhosis group were higher, while fecal BA levels were lower than those in the control group. Zonulin levels were positively correlated with RIPK3 and MLKL levels, while fecal BA levels were negatively correlated with serum MLKL levels, but not with RIPK3 levels. NaB reduced the mRNA and protein expression of MLKL but had no effect on RIPK1 and RIPK3 in vitro. Rescue experiments demonstrated that NaB inhibited necroptosis through E2F1-mediated regulation of MLKL.

Conclusions

NaB alleviates intestinal mucosal injury and reduces necroptosis in IECs in liver cirrhosis. It also inhibits the necroptosis of IECs and protects the intestinal barrier by reducing E2F1 expression and downregulating MLKL expression levels. These results can be employed to develop a novel strategy for treating complications arising from liver cirrhosis.

Itaconate: A Nexus Metabolite Fueling Leishmania Survival Through Lipid Metabolism Modulation

Leishmaniasis, caused by the Leishmania parasite, is a neglected public health issue. Leishmania mainly infects macrophages, where metabolic reprogramming shapes their plasticity (M1/M2), affecting the host's resistance or susceptibility to infection. The development of this infection is influenced by immune responses, with an excessive anti-inflammatory reaction linked to negative outcomes through the modulation of various mediators. Itaconate, produced by the Acod1 gene, is recognized for its anti-inflammatory effects, but its function in leishmaniasis is not well understood. This study aimed to investigate the potential role of itaconate in leishmaniasis. Using transcriptomic data from L. major-infected BMDMs, we assessed the expression dynamics of Il1b and Acod1 and performed pathway enrichment analysis to determine the profile of genes co-expressed with Acod1. Early Acod1 upregulation followed by later Il1b downregulation was noted, indicating a shift towards an anti-inflammatory response. Among the genes co-expressed with Acod1, Ldlr, Hadh, and Src are closely associated with lipid metabolism and the polarization of macrophages towards the M2 phenotype, thereby creating a favorable environment for the survival of Leishmania. Overall, these findings suggest that Acod1 and its co-expressed genes may affect the outcome of Leishmania infection by modulating host metabolism. Accordingly, targeting itaconate-associated pathways could provide a novel therapeutic strategy for leishmaniasis.