Circular RNAs (circRNAs) are involved in the occurrence and development of various tumors. CircRNAs can act as competing endogenous RNAs (ceRNAs), which are important regulatory networks, by regulating microRNAs (miRNAs). However, the effects of ceRNA networks on lung cancer (LC), especially the circRNA-miRNA-mRNA regulatory network, remain incompletely understood. Therefore, the aim of this study was to explore novel ceRNA networks and their function and underlying mechanisms in LC.

Six RNA expression datasets were obtained from the Gene Expression Omnibus microarray datasets (circRNA: GSE158695, GSE101684, GSE112214, and GSE101586; miRNA: GSE135918; mRNA: GSE98929). First, we constructed a circRNA-miRNA-mRNA ceRNA network in LC using Cytoscape. Second, we constructed a protein-protein interaction network using STRING and identified hub genes using CytoHubba. Functional analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to predict the potential function of the hub genes. Third, expression and survival analysis of the hub genes were performed to identify prognostic genes.

We constructed a ceRNA network including 18 circRNAs, 32 miRNAs, and 135 mRNAs, and identified 10 hub genes (VEGFA, FOS, MAD2L1, CREBBP, TYMS, EDN1, RFC5, KIF11, SLC2A1, and TOP2A). Both GO and KEGG analyses revealed that the 10 hub genes were associated with several cancer‑related biological functions and pathways, including "oxygen levels", "nuclear division", and "HIF-1 signaling pathway". Five genes (MAD2L1, TYMS, KIF11, SLC2A1, and TOP2A) were associated with the prognosis of lung adenocarcinoma (LUAD), the most common histological type of LC.

Our study provides novel insights into the pathogenesis and therapy of LC from a ceRNA network perspective.

Liquid-liquid phase separation (LLPS) may affect the therapeutic sensitivity of multiple myeloma (MM). This study aimed to identify LLPS-related genes with MM prognostic values and to confirm their effects on tumor progression.

Based on public transcriptomic data, this study screened LLPS- and immune-related genes for MM-derived plasma cells. Subtypes were identified using consensus clustering, followed by comparisons using t-test and survival analysis. Least absolute shrinkage and selection operator was implemented to screen prognostic signatures, and Kaplan-Meier and receiver operator characteristic curves were plotted to assess their prognostic values. After transfected with sh-DDX21, CCK8, flow cytometry, and Transwells were used to observe MM cell proliferation, apoptosis, migration, and invasion.

By overlapping LLPS- and immune-related genes, 103 genes were obtained to cluster MM samples into three subtypes, which had significant differences in survival and immune landscape. Cox regression analysis screened out EZH2 and DDX21 that significantly overexpressed in MM to construct a prognostic model, with superior performance in predicting MM prognostic risks. Notably, subtype2 with more adverse prognosis showed significantly elevated risk scores and was more distributed in groups with high prognostic risk. In vitro experiments confirmed that cell proliferation, invasion, and migration were significantly inhibited in MM.1S cells transfected with sh-DDX21.

LLPS-related EZH2 and DDX21 were novel markers to predict prognostic risk of MM. Among them, DDX21 was experimentally confirmed to promote MM cell proliferation, migration and invasion. These potential prognostic markers could be targeted in future personalized therapeutic strategies for MM, potentially improving patient outcomes.

Perceived discrimination, recognized as a chronic psychosocial stressor, has adverse consequences on health. DNA methylation (DNAm) may be a potential mechanism by which stressors get embedded into the human body at the molecular level and subsequently affect health outcomes. However, relatively little is known about the effects of perceived discrimination on DNAm. To identify the DNAm sites across the epigenome that are associated with discrimination, we conducted epigenome-wide association analyses (EWAS) of three discrimination measures (everyday discrimination, race-related major discrimination, and non-race-related major discrimination) in 1,151 participants, including 565 non-Hispanic White, 221 African American, and 365 Hispanic individuals, from the Multi-Ethnic Study of Atherosclerosis (MESA). We conducted both race/ethnicity-stratified analyses as well as trans-ancestry meta-analyses. At false discovery rate of 10%, 7 CpGs and 4 differentially methylated regions (DMRs) containing 11 CpGs were associated with perceived discrimination exposures in at least one racial/ethnic group or in meta-analysis. Identified CpGs and/or nearby genes have been implicated in cellular development pathways, transcription factor binding, cancer and multiple autoimmune and/or inflammatory diseases. Of the identified CpGs (7 individual CpGs and 11 within DMRs), two CpGs and one CpG within a DMR were associated with expression of cis genes NDUFS5, AK1RIN1, NCF4 and ADSSL1. Our study demonstrated the potential influence of discrimination on DNAm and subsequent gene expression.

Ankylosing spondylitis (AS) is a chronic autoimmune disease that primarily affects the axial joints. Immune cells play a key role in the pathogenesis of AS. This study integrated bioinformatics methods with experimental validation to explore the role of natural killer (NK) cells in AS.

Two microarray datasets, GSE25101 and GSE73754, were selected, and the scRNA-seq data were obtained from GSE194315 and Liu's research. Differentially expressed genes (DEGs) and functional enrichment analysis were performed respectively. Weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules of co-expressed genes and genes involved in NK cell function. The diagnostic value of the identified key genes was evaluated using ROC curves, logistic regression analysis, and a nomogram. Real-time PCR (RT-PCR) was used to quantified the expression of genes. Statistical analysis was conducted using the R software package, and a p-value of less than 0.05 was considered statistically significant.

Pathways enrichment analysis revealed the involvement of NK cell-mediated immune pathways and regulation of the innate immune response, indicating the crucial role of innate immunity, especially NK cells, in AS pathogenesis. The construction of a co-expression network revealed that the MElightyellow module was most relevant to the NK cell-mediated immune pathway. IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2 from the MElightyellow module were identified as key genes involved in NK cell-mediated immune response and served as potential diagnostic biomarkers for AS, with moderate to high diagnostic values based on AUC values. Further analysis using scRNA-seq profiling revealed the higher expression level of IL2RB, CD247, PLEKHF1, S1PR5, FGFBP2 in NK cells compared to that in other cell types. CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 were reduced expressed in AS patients as compare to control group verified by scRNA-seq data, CD247, EOMES, FGFBP2, IL2RB and S1PR5 were reduced expressed verified by RT-PCR, and PLEKHF1, S1PR5, and FGFBP2 was upregulated after TNF-α blocker therapy.

The study revealed the potential role of NK cells and identified IL2RB, CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 as key genes associated with NK cells in the pathogenesis of AS.

This study aimed to develop a prognostic model based on extracellular trap-related genes (NETRGs) for patients with cALL.

Data from the TARGET-ALL-P2 and TARGET-ALL-P3 cohorts in the Genomic Data Commons database, the transcriptome dataset GSE26713, the single-cell transcriptome dataset GSE130116 from the Gene Expression Omnibus database and 306 NETRGs identified were analysed. Differentially expressed genes (DEGs) were identified from GSE26713 and differentially expressed NETRGs (DE-NETRGs) were obtained by overlapping DEGs with NETRGs. Functional analyses were conducted. Key feature genes were identified through univariate and least absolute shrinkage and selection operator (LASSO) regression. Prognostic genes were determined via multivariate Cox regression analysis, followed by the construction and validation of a risk model and nomogram. Additional analyses included immune profiling, drug sensitivity, functional differences, cell-type-specific expression, enrichment analysis and RT-qPCR.

A total of 1,270 DEGs were identified in GSE26713, of which 74 overlapped with NETRGs. Seven prognostic genes were identified using univariate, LASSO and multivariate Cox regression analyses. Survival analysis revealed lower survival rates in the high-risk group. Independent prognostic analysis identified risk scores and primary diagnosis as independent predictors of prognosis. Immune cell profiling showed significant differences in cell populations such as aDCs, eosinophils and Th2 cells between risk groups. Six cell subtypes were annotated, with prognostic genes predominantly expressed in myeloid cells. RT-qPCR revealed that PTAFR, FCGR2A, RETN and CAT were significantly downregulated, while TLR2 and S100A12 were upregulated in cALL.

TLR2, PTAFR, FCGR2A, RETN, S100A12 and CAT may serve as potential therapeutic targets.

Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC.

The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene LBH were verified by in vitro experiments.

Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. In vitro experiments indicated that overexpression of LBH enhanced the radiosensitivity of A549 cells, and knockdown LBH reversed the cytotoxicity induced by X-rays.

Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets.

The relationship between bacteria, cognitive function and obesity is well established, yet the role of archaeal species remains underexplored. We used shotgun metagenomics and neuropsychological tests to identify microbial species associated with cognition in a discovery cohort (IRONMET, n = 125). Interestingly, methanogen archaeas exhibited the strongest positive associations with cognition, particularly Methanobrevibacter smithii (M. smithii). Stratifying individuals by median-centered log ratios (CLR) of M. smithii (low and high M. smithii groups: LMs and HMs) revealed that HMs exhibited better cognition and distinct gut bacterial profiles (PERMANOVA p = 0.001), characterized by increased levels of Verrucomicrobia, Synergistetes and Lentisphaerae species and reduced levels of Bacteroidetes and Proteobacteria. Several of these species were linked to the cognitive test scores. These findings were replicated in a large-scale validation cohort (Aging Imageomics, n = 942). Functional analyses revealed an enrichment of energy, butyrate, and bile acid metabolism in HMs in both cohorts. Global plasma metabolomics by CIL LC-MS in IRONMET identified an enrichment of methylhistidine, phenylacetate, alpha-linolenic and linoleic acid, and secondary bile acid metabolism associated with increased levels of 3-methylhistidine, phenylacetylgluamine, adrenic acid, and isolithocholic acid in the HMs group. Phenylacetate and linoleic acid metabolism also emerged in the Aging Imageomics cohort performing untargeted HPLC-ESI-MS/MS metabolic profiling, while a targeted bile acid profiling identified again isolithocholic acid as one of the most significant bile acid increased in the HMs. 3-Methylhistidine levels were also associated with intense physical activity in a second validation cohort (IRONMET-CGM, n = 116). Finally, FMT from HMs donors improved cognitive flexibility, reduced weight, and altered SCFAs, histidine-, linoleic acid- and phenylalanine-related metabolites in the dorsal striatum of recipient mice. M. smithii seems to interact with the bacterial ecosystem affecting butyrate, histidine, phenylalanine, and linoleic acid metabolism with a positive impact on cognition, constituting a promising therapeutic target to enhance cognitive performance, especially in subjects with obesity.

To investigate the comprehensive expression levels and possible molecular mechanisms of Anaphase Promoting Complex Subunit 1 (ANAPC1) in lung squamous cell carcinoma (LUSC).

Data from 2,031 samples were combined to evaluate ANAPC1 mRNA levels, and 118 samples were collected for immunohistochemical (IHC) analysis. High-expression co-expressed genes (HECEGs) associated with ANAPC1 were analyzed for signaling pathways. Clinical significance, immune computations, and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) validation of ANAPC1's role in LUSC were assessed. Molecular docking evaluated binding affinity with potential therapeutics.

ANAPC1 mRNA was significantly upregulated in LUSC (SMD = 1.97, 95% CI [1.26-2.67]). Protein-level analysis confirmed this upregulation (p < 0.001). Most HECEGs associated with ANAPC1 were enriched in cell cycle pathways. Higher ANAPC1 expression correlated with poorer survival in LUSC patients (HR = 1.11, 95% CI: 1-1.49). ANAPC1 expression was higher in males and N1-stage vs. females and N0-stage; lower in grade I vs. II/III. Overexpression reduces immune cell infiltration and immunotherapy effectiveness, while knockdown inhibits cell proliferation. Drug sensitivity and docking analyses identified tenovin-1, carboxyatractyloside, and phycocyanobilin as potential antitumor agents targeting ANAPC1.

The elevated expression of ANAPC1 might play a role in LUSC advancement and progression through its participation in cell growth-related pathways.

Diabetic Kidney Disease (DKD) is a common complication in patients with diabetes, and its pathogenesis remains incompletely understood. Recent studies have suggested that extracellular vesicles (EVs) may play a significant role in the initiation and progression of DKD. This study aimed to identify biomarkers associated with EVs in DKD through bioinformatics and Mendelian randomization (MR) analysis.

This study utilized two DKD-related datasets, GSE96804 and GSE30528, alongside 121 exosome-related genes (ERGs) and 200 inflammation-related genes (IRGs). Differential analysis, co-expression network construction, and MR analysis were conducted to identify candidate genes. Machine learning techniques and expression validation were then employed to determine biomarkers. Finally, the potential mechanisms of action of these biomarkers were explored through Immunohistochemistry (IHC) staining, enrichment analysis, immune infiltration analysis, and regulatory network construction.

A total of 22 candidate genes were identified as causally linked to DKD. CMAS and RGS10 were identified as biomarkers, with both showing reduced expression in DKD. IHC confirmed low RGS10 expression, providing new insights into DKD management. CMAS was involved primarily in mitochondria-related pathways, while RGS10 was enriched in the extracellular matrix and associated pathways. Significant differences were observed in neutrophils and M2 macrophages between DKD and normal groups, correlating strongly with the biomarkers.

This study identified two EV-associated biomarkers, CMAS and RGS10, linked to DKD and elucidated their potential roles in disease progression. These results offer valuable insights for further exploration of DKD pathogenesis and the development of new therapeutic targets.

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemo-radiotherapy (nCRT) followed by surgery. A minority of patients show complete response (CR) to nCRT and may avoid surgery and its functional consequences. Instead, most patients show non-complete response (non-CR) and may benefit from additional treatments to increase CR rates. Reliable predictive markers are lacking. Aim of this study was to identify novel signatures predicting nCRT responsiveness. We performed a combined analysis of tumor-associated microbiome and immune gene expression profiling of diagnostic biopsies from 70 patients undergoing nCRT followed by rectal resection, including 16 with CR and 54 with non-CR. Findings were validated by an independent cohort of 49 patients, including 7 with CR and 42 with non-CR. Intratumoral microbiota significantly differed between CR and non-CR groups at genus and species level. Colonization by bacterial species of Ruminococcus genera was consistently associated with CR, whereas abundance of Fusobacterium, Porhpyromonas, and Oscillibacter species predicted non-CR. Immune gene profiling revealed a panel of 59 differentially expressed genes and significant upregulation of IFN-gamma and -alpha response in patients with CR. Integrated microbiome and immune gene profiling analysis unraveled clustering of microbial taxa with each other and with immune cell-related genes and allowed the identification of a combined signature correctly identifying non-CRS in both cohorts. Thus, combined intratumoral microbiome-immune profiling improves the prediction of response to nCRT. Correct identification of unresponsive patients and of bacteria promoting responsiveness might lead to innovative therapeutic approaches based on gut microbiota pre-conditioning to increase nCRT effectiveness in LARC.

The gut microbiota transforms energy stored as undigestible carbohydrates into a remarkable number of metabolites that fuel intestinal bacterial communities and the host tissue. Colonic epithelial cells at the microbiota-host interface depend upon such microbiota-derived metabolites (MDMs) to satisfy their energy requisite. Microbial dysbiosis eliciting MDM loss contributes to barrier dysfunction and mucosal disease. Recent work has identified a role for microbiota-sourced purines (MSPs), notably hypoxanthine, as an MDM salvaged by the colonic epithelium for nucleotide biogenesis and energy balance. Here, we investigated the role of MSPs in mice during disease-modeled colonic energetic stress using a strain of E. coli genetically modified for enhanced purine nucleobase release (E. coli Mutant). E. coli Mutant colonization protected against DSS-induced tissue damage and permeability while promoting proliferation for wound healing. Metabolite and metagenomic analyses suggested a colonic butyrate-purine nucleobase metabolic axis, wherein the E. coli Mutant provided purine substrate for Clostridia butyrate production and host purine salvage, altogether supplying the host substrate for efficient nucleotide biogenesis and energy balance.

Fortifying infant formula with human milk oligosaccharides, such as 2'-fucosyllactose (2'-FL), is a global trend. Previous studies have shown the inability of pathogenic gut microbes to utilize 2'-FL. However, the present study demonstrates that the type strain (JCM 1290T) of Clostridium perfringens, a pathobiont species often more prevalent and abundant in the feces of C-section-delivered infants, exhibits potentially pathogenic growth on 2'-FL. The expression of genes for α-toxin, an activator of NLRP3 inflammasome, and ethanolamine ammonia-lyase, a factor responsible for the progression of gas gangrene, was significantly upregulated during 2'-FL assimilation compared to growth on lactose. However, colony-forming unit of C. perfringens JCM 1290T markedly decreased when co-cultivated with selected strains of Bifidobacterium, a taxon frequently detected in the breastfed infant gut. Moreover, during co-cultivation, the expression of virulence-related genes, including the gene for perfringolysin O - another activator of NLRP3 inflammasome - were significantly downregulated, while the lactate oxidation genes were upregulated. This can occur through two different mechanisms: direct competition for 2'-FL between the two organisms, or cross-feeding of lactose, released from 2'-FL by C. perfringens JCM 1290T, to Bifidobacterium. Attenuation of α-toxin production by the selected Bifidobacterium strains was observed to varying extents in 2'-FL-utilizing C. perfringens strains clinically isolated from healthy infants. Our results warrant detailed in vivo studies using animal models with dysbiotic microbiota dominated by various types of C. perfringens strains to further validate the safety of 2'-FL for clinical interventions, particularly on vulnerable preterm infants.

The elderly population, who have increased susceptibility to severe outcomes, have been particularly impacted by the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), leading to a global health crisis. However, definitive parameters or mechanisms underlying the severity of COVID-19 in elderly people remain unclear. Thus, this study seeks to elucidate the mechanism behind the increased vulnerability of elderly individuals to severe COVID-19. We employed an aged mouse model with a mouse-adapted SARS-CoV-2 strain to mimic the severe symptoms observed in elderly patients with COVID-19. Comprehensive analyses of the whole lung were performed using transcriptome and proteome sequencing, comparing data from aged and young mice. For transcriptome analysis, bulk RNA sequencing was conducted using an Illumina sequencing platform. Proteomic analysis was performed using mass spectrometry following protein extraction, digestion, and peptide labelling. We analysed the transcriptome and proteome profiles of young and aged mice and discovered that aged mice exhibited elevated baseline levels of inflammation and tissue damage repair. After SARS-CoV-2 infection, aged mice showed increased antiviral and inflammatory responses; however, these responses were weaker than those in young mice, with significant complement and coagulation cascade responses. In summary, our study demonstrates that the increased vulnerability of the elderly to severe COVID-19 may be attributed to an attenuated antiviral response and the overactivation of complement and coagulation cascades. Future research on antiviral and inflammatory responses is likely to yield treatments that reduce the severity of viral respiratory diseases in the elderly.

Cell-mediated immune (CMI) responses to adeno-associated virus (AAV) can lead to tissue damage and loss of therapeutic transgene expression. Identifying robust biomarkers and mechanisms of CMI can aid clinical practice and advancement of AAV gene therapies. The present work evaluated peripheral blood mononuclear cells (PBMC) from non-human primates (NHP) before and after immunization with adenovirus 5 encoding AAV9 capsid antigen. PBMC were stimulated ex vivo with AAV9 capsid peptides to evaluate CMI responses by interferon (IFN)-γ ELISpot, intracellular cytokines/activation markers, secreted cytokines, and RNAseq. AAV peptide stimulation produced a robust IFNγ ELISpot 11 days after immunization and ≈ 4 years after cryopreservation. Flow cytometry revealed increased IFNγ, interleukin (IL)-2, or tumor necrosis factor (TNF)-positive T-cells. Increases in secreted CXCR3 ligands (IP-10, I-TAC) were detected. Robust changes and correlations to ELISpot responses were revealed by RNAseq, including IFNγ, IP-10, and I-TAC, many downstream transcripts, and several IFN-independent pathways. These data from AAV-immunized NHP identify biomarkers that could serve as robust and sensitive supplements/alternatives to ELISpot for early detection of CMI responses. Assessment of these biomarkers in non-clinical and clinical studies is a critical next step to determine the translation of this work to administration of a therapeutic AAV vector.

The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut health, and CVD. Traditional CVD risk factors (age, BMI, sex, blood pressure) explained < 10% of the proteome variance. However, unsupervised human protein-based clustering analysis revealed two distinct CVD risk clusters (low-risk and high-risk) with different blood pressure (by 9 mmHg) and sex-dependent dietary potassium and fiber intake. In the human proteome, the low-risk group had lower angiotensin-converting enzymes, inflammatory proteins associated with neutrophil extracellular trap formation and auto-immune diseases. In the microbial proteome, the low-risk group had higher expression of phosphate acetyltransferase that produces SCFAs, particularly in fiber-fermenting bacteria. This model identified severity across phenotypes in heart failure patients and long-term risk of cardiovascular events in a large population-based cohort. These findings underscore multifactorial gut-to-host mechanisms that may underlie risk factors for CVD.

The Coronavirus Disease 2019 (COVID-19) pandemic has led to numerous cases of co-infection with SARS-CoV-2 and other viruses, including Kaposi's sarcoma-associated herpesvirus (KSHV), worldwide. This co-infection has increased patient mortality due to the lack of efficient bi-targeted drugs. Cambogin, a bioactive natural product, has been shown to effectively induce regression of KSHV-latently infected tumours in xenograft mice models; however, its impact on SARS-CoV-2 infection remains unclear. Here, we report that Cambogin targets 46 host genes commonly affected by both SARS-CoV-2 and KSHV infections, as identified through bioinformatics analysis. These genes are related with 14 key upstream signalling pathways, particularly those involved in inflammation regulation, protein phosphorylation, metabolic processes, and cellular stress response. Within the transcriptional factor (TF)-miRNA co-regulatory network, ten out of 46 hub-target genes are closely linked to Cambogin and KSHV/SARS-CoV-2. Importantly, Cambogin not only efficiently blocks the replication and virion production of SARS-CoV-2 in vitro and in vivo by reducing the expression of EGFR and Cyclin A2, but also simultaneously inhibits both SARS-CoV-2 infection and the growth of KSHV-induced tumours in vivo using a murine xenograft model. These findings provide an alternative strategy for the potential use of Cambogin in the treatment of SARS-CoV-2 patients, particularly those with KSHV co-infection.

Adipose-derived stem cells (ADSCs) are a type of stem cell found in adipose tissue with the capacity to differentiate into multiple lineages, including osteoblasts. The differentiation of ADSCs into osteoblasts underlies osteogenic and pathological cellular basis in osteoporosis, bone damage and repair.

Focused on ADSCs osteogenic differentiation, we conducted mRNA, microRNA expression and bioinformatics analysis, including gene differential expression, time series-based trend analysis, functional enrichment, and generates potential nuclear activating miRNAs (NamiRNA) regulatory network. The screened mRNAs in NamiRNA regulatory network were validated with correlation analysis.

The NamiRNA Regulatory Network reveals 4 mRNAs (C12orf61, MIR31HG, NFE2L1, and PCYOX1L) significantly downregulated in differentiated group and may be associated with ADSCs stemness. Furthermore, the significantly upregulated 10 genes (ACTA2, TAGLN, LY6E, IFITM3, NGFRAP1, TCEAL4, ATP5C1, CAV1, RPSA, and KDELR3) were significantly enriched in osteogenic-related pathways, and negatively correlated with ADSCs cell stemness in vitro.

These findings uncover potential genes related to ADSCs osteogenic differentiation, and provide theoretical basis for underlying ADSCs osteogenic differentiation and related diseases.

The human gut microbiome, crucial in various diseases, can be utilized to develop diagnostic models through machine learning (ML). The specific tools and parameters used in model construction such as data preprocessing, batch effect removal and modeling algorithms can impact model performance and generalizability. To establish an generally applicable workflow, we divided the ML process into three above-mentioned steps and optimized each sequentially using 83 gut microbiome cohorts across 20 diseases. We tested a total of 156 tool-parameter-algorithm combinations and benchmarked them according to internal- and external- AUCs. At the data preprocessing step, we identified four data preprocessing methods that performed well for regression-type algorithms and one method that excelled for non-regression-type algorithms. At the batch effect removal step, we identified the "ComBat" function from the sva R package as an effective batch effect removal method and compared the performance of various algorithms. Finally, at the ML algorithm selection step, we found that Ridge and Random Forest ranked the best. Our optimized work flow performed similarly comparing with previous exhaustive methods for disease-specific optimizations, thus is generally applicable and can provide a comprehensive guideline for constructing diagnostic models for a range of diseases, potentially serving as a powerful tool for future medical diagnostics.

Lactylation is widely involved in cellular processes and is pivotal in inflammation and immune regulation. However, the expression and clinical significance of lactylation in rheumatoid arthritis (RA) remain unclear. This study aimed to determine the role of lactylation in RA and its association with immune cell infiltration. We initially detected the levels of lactate in the plasma of RA patients and the levels of panlysine lactylation (Pan-Kla) in peripheral blood mononuclear cells (PBMCs). Next, we used differential expression analysis and weighted gene coexpression network analysis (WGCNA) to intersect with lactylation-related genes. We obtained lactylation-related differentially expressed genes (LADEGs) in RA and analyzed their functional enrichment. We subsequently used the CIBERSORT algorithm to analyze immune cell infiltration in RA synovial tissues and its correlation with LADEGs. Finally, key genes of LADEGs were validated in the Pathobiology of Early Arthritis Cohort (PEAC) study and our samples. Our study revealed elevated levels of lactate and lactylation in the peripheral blood of RA patients. IKAROS family zinc finger 1 (IKZF1), lymphocyte cytosolic protein 1 (LCP1), and WASP actin-nucleation promoting factor (WAS) may be potential biomarkers for early diagnosis and assessment of disease activity in RA.

Bladder cancer (BCa) is the most prevalent cancer of the urinary system in adults; the prognosis is dismal for BCa treated with gemcitabine (GEM) owing to intrinsic or acquired chemoresistance. This study investigated the potential of Qici Sanling decoction (QCSL), an herbal Chinese medicine, to augment the efficacy of GEM in treating GEM-resistant BCa via network pharmacology and RNA sequencing. We screened 103 active components of QCSL and their 226 targets from the TCMSP database and identified 3985 targets of GEM-resistant BCa via transcriptome sequencing. On the basis of the 69 common targets, a proteinprotein interaction (PPI) network was constructed to identify the top 7 targets. Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted to uncover key pathways. CCK-8 assays, Western blotting, flow cytometry, colony formation, and EdU assays were used to assess the apoptosis and proliferation of GEM-resistant T24 and J82 cells treated with QCSL. The BCa gene set was among the top enriched gene sets in the DO analysis; GO analysis revealed enrichment of 2020 terms linked to GEM resistance, and KEGG analysis revealed 161 enriched signalling pathways. Molecular docking indicated that PTGS2 has high affinity for targets of QCSL components. In vitro experiments demonstrated that cells treated with both QCSL and GEM had significantly reduced viability, increased levels of apoptosis, and decreased proliferative capacity. Thus, QCSL enhances the therapeutic effects of GEM in BCa by promoting cell apoptosis and inhibiting cell proliferation. These findings have significant clinical implications, highlighting a potential combined treatment strategy for GEM-resistant BCa to improve patient outcomes.

JOURNAL/nrgr/04.03/01300535-202509000-00032/figure1/v/2024-11-05T132919Z/r/image-tiff Postoperative cognitive dysfunction is a severe complication of the central nervous system that occurs after anesthesia and surgery, and has received attention for its high incidence and effect on the quality of life of patients. To date, there are no viable treatment options for postoperative cognitive dysfunction. The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research. To identify the signaling mechanisms contributing to postoperative cognitive dysfunction, we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset, which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus 3 days after tibial fracture. The dataset was enriched in genes associated with the biological process "regulation of immune cells," of which Chil1 was identified as a hub gene. Therefore, we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fracture surgery. Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 1 24 hours post-surgery, and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests. In addition, protein expression levels of proinflammatory factors (interleukin-1β and inducible nitric oxide synthase), M2-type macrophage markers (CD206 and arginase-1), and cognition-related proteins (brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B) were measured in hippocampus by western blotting. Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment, downregulated interleukin-1β and nducible nitric oxide synthase expression, and upregulated CD206, arginase-1, pNR2B, and brain-derived neurotropic factor expression compared with vehicle treatment. Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1. Collectively, our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus. Therefore, recombinant chitinase-3-like protein 1 may have therapeutic potential for postoperative cognitive dysfunction.

Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single point in time. In other words, transcriptomics as it is currently practiced is two-dimensional (2DTA). Gene expression profiling by 2DTA has proven invaluable in furthering our understanding of numerous biological processes in health and disease. That said, shortcomings including technical variability, small sample size, differential rates of transcript decay, and the lack of linearity between transcript abundance and functionality or the formation of functional proteins limit the interpretive utility and generalizability of transcriptomic data. 2DTA utility may also be constrained by its reliance on RNA extracts obtained at a single time point. In other words, much like judging a movie by a single frame, 2DTA can only provide a snapshot of the transcriptome at time of RNA extraction. Whether this perceived "temporality" problem is real and whether it has any bearing on transcriptomic data interpretation have yet to be addressed. To investigate this problem, 25 publicly available datasets relating to MCF-7 cells, where RNA extracts obtained at 12- or 48-hours post-culture were subjected to transcriptomic analysis. The individual datasets were downloaded and compiled into two separate datasets (MCF-7 U12hr and MCF-7 U48hr). To comparatively analyze the two compiled datasets, three machine learning approaches (decision trees (DT), random forests (RF), and XGBoost (Extreme Gradient Boosting)) were used as classifiers to search for genes with distinct expression patterns between the two groups. Shapley additive explanation (SHAP), an explainable AI method, was used to assess the fundamental principles of the DT, RF, and XGBoost models. Coefficient of Determination (DC), Mean Absolute Error (MAE), and Mean Squared Error (MSE) were used to evaluate the models. The results show that the two datasets exhibited very significant gene expression patterns. The XGBoost model performed better than the DT or RF models with MSE, MAE, and DC values of 0.00028, 0.00028, and 0.95778 respectively. These observations suggest that time, as a third dimension, can impact transcriptomic data interpretation and that machine learning and explainable AI are useful tools in resolving the temporality problem in transcriptomics.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impaired social communication, restricted interests, repetitive behavior and irritability. Exposure to valproic acid (VPA) during pregnancy has been shown to increase the risk of autism in children and has led to the development of the in-utero VPA rat model that elicits neurodevelopmental autistic-like features. Offspring exhibit behavioral and neurobiological alterations modelling ASD symptoms. We performed a behavioral and molecular assessment in a rat in-utero VPA model treated with a novel botanical cannabinoid, JZP541. Male offspring from dams treated with VPA were tested acutely and sub-chronically with JZP541 (10, 30, or 100 mg/kg, intraperitoneally). A behavioral testing battery was performed, and brain frontal cortex and hippocampus used for RNA sequencing. In utero exposure to VPA resulted in progeny showing behavioral phenotypes characteristic of ASD. JZP541 attenuated these deficits in social, stereotypic, hyperactivity and irritability behavior in a dose-dependent fashion. VPA exposure was associated with a substantial transcriptional dysregulation impacting multiple key biological processes in a tissue-dependent manner. The expression profiles were integrated with publicly available datasets of autism-associated genes to support the validity of the model used and to focus on the effects of treatment on known autism-relevant transcriptional targets. This approach indicated a strong and dose-dependent reduction of the autism-associated gene expression signature in brain samples from animals dosed with JZP541. Our findings demonstrate JZP541 was able to ameliorate ASD associated behavioral deficits, and this was supported by improvements in putative transcriptional biomarkers of ASD.

Osteoarthritis (OA) is characterized by cartilage degradation, synovial inflammation, subchondral bone remodeling, and osteophyte formation, leading to chronic pain and impaired mobility. Chondrocyte senescence, inflammation, and hypertrophic differentiation critically contribute to OA progression. Integrated analysis of four GEO datasets identified SERPINF1 as a consistently upregulated gene in both human and animal OA samples. Histopathological and immunohistochemical analyses confirmed increased SERPINF1 in OA cartilage, where chondrocytes showed elevated SERPINF1 protein alongside reduced aggrecan expression. Functional studies revealed that SERPINF1 knockdown in OA chondrocytes diminished senescence markers (p21, p16, p53) while increasing Lamin B1, and reduced levels of pro-inflammatory cytokines (IL-1β, TNF-α, and IL-6). Conversely, overexpression of SERPINF1 in normal chondrocytes induced senescence and increased inflammatory mediator expression, accompanied by altered extracellular matrix metabolism and hypertrophy marker expression. Mechanistic analysis further implicated the TNF-α/NF-κB signaling pathway in mediating these effects. In a destabilization of the medial meniscus (DMM) mouse model, intra-articular SERPINF1 knockdown attenuated cartilage destruction, reduced senescence and inflammatory markers, and restored ECM integrity. Collectively, these findings demonstrate that SERPINF1 promotes OA progression by exacerbating chondrocyte senescence, inflammation, and hypertrophy, suggesting that targeting SERPINF1 may offer a novel therapeutic strategy for OA.

Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P-NET, a biologically informed explainable artificial intelligence model, to evaluate the gene importance for sepsis. About 688 important genes were identified, and these genes were enriched in pathways involved in inflammation and immune regulation, such as the PI3K-Akt signalling pathway, necroptosis and the NF-κB signalling pathway. We further selected differentially expressed genes both at bulk and single-cell levels and found TIMP1, GSTO1 and MYL6 exhibited significant different expressions in multiple cell types. Moreover, the expression levels of these 3 genes were correlated with the abundance of important immune cells, such as M-MDSC cells. Further analysis demonstrated that these three genes were highly expressed in sepsis patients with worse outcomes, such as severe, non-survived and shock sepsis patients. Using a drug repositioning strategy, we found navitoclax, curcumin and rotenone could down-regulate and bind to these genes. In conclusion, TIMP1, GSTO1 and MYL6 may serve as promising biomarkers and targets for sepsis treatment.

Hepatocellular carcinoma (HCC) is a significant global health challenge with complex molecular underpinnings. Recent advancements in understanding the role of non-coding RNAs (ncRNAs) and exosomes in cancer biology have opened new avenues for research into potential diagnostic and therapeutic strategies.

This study utilized a comprehensive approach to analyze gene expression patterns and regulatory networks in HCC. We integrated RNA sequencing data gathered from both tissue samples and exosomes. The WGCNA and limma R packages were employed to construct co-expression networks and identify differentially expressed ncRNAs, including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs).

Our analysis demonstrated distinct expression profiles of various ncRNAs in HCC, revealing their intricate interactions with cancer-related genes. Key findings include the identification of a network of microRNAs that interact with selected lncRNAs and their potential roles as biomarkers. Moreover, exosomal RNA was shown to effectively reflect tissue-specific gene expression changes.

The results of this study highlight the significance of exosomal ncRNAs in the progression of liver cancer, suggesting their potential as both diagnostic biomarkers and therapeutic targets. Future research should focus on the functional implications of these ncRNAs to further elucidate their roles in HCC and explore their applications in clinical settings.

Multi-omics clustering has emerged as a powerful approach for understanding complex biological systems and enabling cancer subtyping by integrating diverse omics data. Existing methods primarily focus on the integration of different types of omics data, often overlooking the value of clinical context. In this study, we propose a novel framework that incorporates clinical features extracted from large language model (LLM) to enhance multi-omics clustering. Leveraging clinical data extracted from pathology reports using a BERT-based model, our framework converts unstructured medical text into structured clinical features. These features are integrated with omics data through an autoencoder, enriching the information content of each omics layer to improve feature extraction. The extracted features are then projected into a latent subspace using singular value decomposition (SVD), followed by spectral clustering to obtain the final clustering result. We evaluate the proposed framework on six cancer datasets on three omics levels, comparing it with several state-of-the-art methods. The experimental results demonstrate that the proposed framework outperforms existing methods in multi-omics clustering for cancer subtyping. Moreover, the results highlight the efficacy of integrating clinical features derived from LLM, significantly enhancing clustering performance. This work underscores the importance of clinical context in multi-omics analysis and showcases the transformative potential of LLM in advancing precision medicine.

Obesity is a major health problem associated with global metabolic dysfunction and increased inflammation. It is thus critical to identify the mechanisms underlying the crosstalk between immune cells and adipose tissue that drive cardiovascular and metabolic dysfunction in obesity. Expression of the kallikrein-related serine protease 7 (KLK7) in adipose tissue is linked to inflammation and insulin resistance in high fat diet (HFD)-fed mice. Here, we engineered mice with a macrophage-specific KLK7 knockout (KLK7MKO) to investigate how KLK7 loss impacts immune cell function and obesity-related pathology. Compared to control mice, we observed lower levels of systemic inflammation, with less infiltration and activation of inflammatory macrophages in HFD-fed KLK7MKO mice, particularly in the epididymal adipose tissue. Mechanistically, we uncover that Klk7 deficiency reduces pro-inflammatory gene expression in macrophages and restricts their migration through higher cell adhesion, hallmark features of macrophages in obese conditions. Importantly, through analyses of 1143 human visceral adipose tissue samples, we uncover that KLK7 expression is associated with pathways controlling cellular migration and inflammatory gene expression. In addition, serum KLK7 levels were strongly correlated with circulating inflammatory markers in a second cohort of 60 patients with obesity and diabetes. Our work uncovers the pro-inflammatory role of KLK7 in controlling inflammatory macrophage polarization and infiltration in visceral obesity, thereby contributing to metabolic disease. Thus, targeting KLK7 to control immune cell activation may dissociate adipose dysfunction from obesity, thereby representing an alternative obesity therapy.

Recent research has established a bidirectional connection between metabolic syndrome (MetS) and dry eye disease (DED); however, the underlying mechanisms driving their co-occurrence remain poorly understood. This study employed bioinformatics and in vivo validation to investigate the shared diagnostic genes and underlying mechanisms linking MetS and DED. Differential expression analysis using Limma and weighted gene co-expression network analysis (WGCNA) identified 247 shared driver genes from MetS and DED cohorts. Functional enrichment analysis indicated that these genes are associated with immune regulation and inflammatory responses. Key diagnostic genes (Ccl5, Cxcr4, Ccl4, Spp1) were identified via PPI network analysis and validated using a receiver operating characteristic (ROC) curve. The MetS-DED mouse model further demonstrated CXCR4 overexpression in corneal epithelium and liver. These findings elucidate overlapping biomarkers and pathogenic pathways between MetS and DED, providing critical insights for advancing their diagnosis and therapeutic strategies.

RIP140 (receptor interacting protein of 140 kDa) is an important player in breast cancer (BC) by regulating key cellular pathways such as nuclear hormone receptor signaling. In order to identify additional genes specifically regulated by RIP140 in BC, we performed a transcriptomic analysis after silencing its expression in MCF-7 cells. We identified the interferon γ (IFNγ) signaling as being substantially repressed by RIP140 knockdown. Using the GBP1 (guanylate binding protein 1) gene as a reporter of IFNγ signaling, we demonstrated its robust induction by RIP140 through an ISRE motif, leading to a significant reduction of its induction upon IFNγ treatment. Furthermore, we showed that low levels of RIP140 amplified the IFNγ-dependent inhibition of BC cell proliferation. In line with these data, reanalysis of transcriptomic data obtained in human BC samples revealed that IFNγ levels were associated with good prognosis only for BC patients exhibiting tumors expressing low levels of RIP140, thus confirming its effect on the anti-tumor activity of IFNγ provided by our experimental data. Altogether, this study identifies RIP140 as a new regulator of IFNγ signaling in breast tumorigenesis.

Disulfidptosis, a newly identified regulated cell death, is linked to tumor progression, particularly in cancers with elevated SLC7A11 expression. This study investigates SLC7A11 expression in liver hepatocellular carcinoma (LIHC) and evaluates the therapeutic potential of ICG@C3F8-KL nanobubbles (NBs) combined with sonodynamic therapy (SDT) for inducing disulfidptosis. Bioinformatics analysis of TCGA datasets revealed upregulation of SLC7A11 in LIHC tissues. The synthesized ICG@C3F8-KL NBs exhibited a mean diameter of 156.46 nm and stable properties, with high encapsulation efficiencies of 51.32 % ± 0.7 % for KL and 80.15 % ± 0.21 % for ICG. In vitro, ICG@C3F8-KL NBs, under ultrasound, generated reactive oxygen species (ROS), enhancing cytotoxicity in HepG2 cells with an IC50 lower than KL alone. These NBs also inhibited cell migration and colony formation, suggesting disulfidptosis induction via altered glucose uptake and NADP+/NADPH ratio, as well as F-actin contraction. In vivo, ICG@C3F8-KL NBs accumulated in tumor tissues and suppressed growth without significant toxicity. Unsupervised clustering of disulfidptosis-related genes in TCGA LIHC cohort identified subtypes with distinct prognoses, and a predictive model based on five key genes was developed. In conclusion, ICG@C3F8-KL NBs, combined with ultrasound, effectively induce disulfidptosis, offering a promising strategy for LIHC treatment, with the potential for personalized therapy informed by disulfide-associated gene signatures.