Application of ggplot2 to Pharmacometric Graphics

Evolutionary dynamics and codon bias analysis of canine circovirus: Insights into global spread and host adaptability

Circoviruses are relatively new pathogens, and new circoviruses are constantly being discovered, with a growing range of hosts. Recently, canine circovirus (CanineCV) was reported to infect cats and badgers, further broadening its host range. Previous studies on the evolution and dissemination of CanineCV were fragmented. Here, when conducting a metagenomic analysis of shelter dogs, we identified a canine circovirus positivity rate of 32.4% and obtained three new viral strains. Furthermore, we integrated publicly available viral sequences and employed multiple bioinformatic software tools to analyze the evolution, codon usage bias, recombination, origin, spatiotemporal distribution and host adaptability of CanineCV. In this study, CanineCV could be divided into five distinct phylogenetic clades, named as China-I, China-II, Cosmopolitan, EA, and SEA clades. The extensive inter-clade recombination was observed, which plays an important role in viral evolution, while based on existing sequence information, CanineCV most likely originated in Norway from Vulpes vulpes in 1950.7. Notably, CanineCV exhibits greater adaptability to human hosts compared to previously documented hosts, as indicated by host adaptability indices, suggesting that this virus may possess zoonotic potential. In summary, our study elucidates the phylogeography and evolutionary dynamics of CanineCV and underscores the importance of investigating its potential for zoonotic transmission.

RNA‑seq analysis of predictive markers associated with glutamine metabolism in thyroid cancer

The incidence of thyroid cancer (TC) increases year by year. It is necessary to construct a prognostic model for risk stratification and management of TC patients. Glutamine metabolism is essential for tumor progression and the tumor microenvironment. The present study aimed to develop a predictive model for TC using a glutamine metabolism gene set. Differentially expressed genes in cells with high glutamine metabolism levels from single cell RNA‑sequencing data were compared with genes differentially expressed between normal and TC tissues from The Cancer Genome Atlas Program data. Through Boruta feature selection methods and multivariate Cox regression, six crucial genes were identified for a risk‑scoring system to develop a prognostic model. The role of each gene was verified in TC cells in vitro. A risk‑scoring system was developed according to the glutamine gene set to forecast the overall survival of TC patients. This risk score could stratify TC patients and minimize unnecessary surgeries and invasive treatments. In addition, signal induced proliferation associated 1 like 2 (SIPA1L2), an important gene in the prognostic model, knockdown in TPC‑1 and BCPAP cell lines enhanced TC cell proliferation, migration and invasion. A risk model was developed based on a glutamine metabolism gene set. The model has reference values for TC stratification.

Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning

BACKGROUND

Gastric cancer (GC), a common and deadly malignancy worldwide, is a serious burden on society and individuals. However, available diagnostic biomarkers for GC are very limited. The current study aimed to identify potential diagnostic biomarkers for GC and analyze the activity of infiltrating immune cells in this pathology.

METHODS

Microarray data for GC were acquired from the Gene Expression Omnibus (GEO) database. The limma package was utilized to normalize these data, thus identifying differentially expressed genes (DEGs). For normalized data of samples, we established a weighted gene co-expression network (WGCNA) to reveal key genes in the significant module. Afterward, we obtained overlapping genes by intersecting the DEGs and the key genes from the WGCNA module. Next, after applying the three algorithms (LASSO, RandomForest, and SVM-RFE) to analyze these overlapping genes and take the intersection, we established a GC diagnosis. The diagnostic significances of these identified genes were evaluated with receiver operating characteristic (ROC) curves and validated in the external dataset. Furthermore, ssGSEA and CIBERSORT were employed for evaluating the infiltrating immune cells and the association of the immune cells and diagnostic biomarkers.

RESULTS

Herein, we identified 49 overlapping genes, and the results of enrichment analysis demonstrated that these genes may be involved in the signaling transduction-related process. Finally, BANF1, DUSP14, and VMP1 were regarded as key biomarkers in GC patients based on the overlapping genes that we found, and these three biomarkers demonstrated great diagnostic significance. Additionally, the hub biomarkers had different levels of association with macrophages, neutrophils, memory B cells, and plasma cells.

CONCLUSIONS

BANF1, DUSP14, and VMP1 are promising diagnostic biomarkers for GC, and infiltrating immune cells may dramatically affect gastric carcinogenesis and progression.

Long non-coding RNA TMEM51-AS1 inhibits colorectal cancer progression

Colorectal cancer (CRC) is the third most common cause of death worldwide and has high mortality and a poor prognosis. Long non-coding RNAs (lncRNAs) are non-coding RNAs longer than 200 nucleotides that play roles in cancer through multiple mechanisms. TMEM51-AS1 is a newly discovered 40,650 bp lncRNA. Our results showed that TMEM51-AS1 expression was significantly downregulated in CRC tissues (fold change = 0.74, P < 0.0001). This finding was confirmed in 20 pairs of CRC carcinoma and paracancerous tissues (fold change = 0.5, P < 0.001). Additionally, TMEM51-AS1 expression was found to be significantly reduced in CRC cell lines compared to normal human intestinal epithelial cells (P < 0.001). Bioinformatic analysis revealed that TMEM51-AS1 expression was associated with immune escape, RNA methylation, and DNA damage and repair. TMEM51-AS1 may also activate energy metabolism pathways to participate in cancer development. Drug sensitivity analysis confirmed that several drugs are more effective in CRC patients with high expression of TMEM51-AS1. In conclusion, our study demonstrates that TMEM51-AS1 can suppress the progression of CRC.

The working mechanism of biomarkers related to sumoylation modification in coronary artery disease

Coronary artery disease (CAD) remains a leading global cause of mortality. The expression of small ubiquitin-like modifier 1 (SUMO-1) is reduced in heart failure. However, the mechanisms underlying its modification in CAD remain underexplored. This study sought to identify SUMOylation-related biomarkers and elucidate the potential mechanisms in CAD pathogenesis. This study analyzed three CAD datasets (GSE42148, GSE23561, and GSE121893) alongside 187 SUMOylation-related genes (SRGs). The overlap between differentially expressed genes (DEGs) and SRGs was used to identify differentially expressed SUMOylation-related genes (DE-SRGs). Biomarkers were validated through expression profiling and receiver operating characteristic (ROC) curve analysis. Enrichment and immune infiltration analyses were performed to explore the molecular mechanisms by which these biomarkers influence CAD. A drug-gene interaction network was constructed using the Drug-Gene Interaction database (DGIdb). Single-cell analysis was conducted to identify key cellular players and validate the differential expression of biomarkers across cell types. A total of 12 DE-SRGs were identified in CAD. Among them, SUMO1 and PPARG were validated as biomarkers, with their expression significantly elevated in the CAD group compared to the control group. Single-sample gene set enrichment analysis (ssGSEA) revealed distinct immune cell distributions in CAD, with central memory CD4+ T cells and memory B cells positively correlated with the biomarkers. Gene set enrichment analysis (GSEA) linked these biomarkers to ribosomal activity, olfactory transduction, and other pathways. Single-cell analysis confirmed the expression of SUMO1 and PPARG in endothelial cells, particularly in the CAD group. Additionally, SUMO1 was differentially expressed in cardiomyocytes, exhibiting higher expression in controls. SUMO1 and PPARG were identified as novel SUMOylation-related biomarkers in CAD, suggesting new therapeutic avenues for CAD management.

The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis

BACKGROUND

Thrombus formation is a severe complication in orthopedic surgery, significantly increasing mortality in patients with fractures. Therefore, identifying feature genes to determine thrombus presence in fracture surgeries is critical.

METHODS

Whole blood samples were collected from 18 patients with fractures with thrombosis (YES_thrombus) and 18 patients with fractures without thrombosis (NO_thrombus) from the Second Hospital of Shanxi Medical University, China, and used for transcriptome sequencing and quality control to generate the YES_thrombus dataset. Candidate genes were identified by overlapping differentially expressed genes (DEGs) with key module genes from weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis was then performed to explore the roles of the candidate genes. Feature genes were further refined by intersecting results from three machine learning algorithms and constructing an artificial neural network (ANN). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves. Additionally, single-gene gene set enrichment analysis (GSEA) was conducted, and correlations between feature genes and differential immune cells were analyzed. The competing endogenouse RNA (ceRNA) regulatory network for feature genes was also constructed. Finally, quantitative reverse transcriptase PCR (qRT-PCR) was used to validate gene expression.

RESULTS

Seven candidate genes were selected, with functional enrichment analysis linking them to the autophagosome and PPAR signaling pathways. Five feature genes with excellent diagnostic performance were identified. Single-gene GSEA enrichment showed that the feature genes were primarily associated with the cytosolic ribosome and oxidative phosphorylation. The correlation analysis revealed that aDC exhibited the strongest negative correlation with WDR81 and the strongest positive correlation with RGS1. The ceRNA regulatory network encompassed three feature genes, five miRNAs, and 236 lncRNAs. Expression analysis indicated that, with the exception of WDR81, other genes were significantly upregulated in the NO_thrombus group. qRT-PCR validation confirmed that the expression of AAED1, ARL4A, and WDR81 matched sequencing results.

CONCLUSIONS

In conclusion, five feature genes (RGS1, HSF2, ARL4A, AAED1, and WDR81) were identified, and functional enrichment analyses were conducted, providing a foundation for predicting the diagnosis of fractures associated with thrombosis.

WDR62 affects the progression of ovarian cancer by regulating the cell cycle

BACKGROUND

Ovarian Cancer (OC) is a gynecological malignant tumor with an extremely high mortality rate, seriously endangering women's health. Due to its insidious clinical manifestations, most patients are diagnosed in the advanced stage of the disease. The currently clinically relied CA125 has limited specificity for the early diagnosis of ovarian cancer. Hence, identifying new promising biomarkers is crucial for the early screening, diagnosis, and treatment of ovarian cancer. Based on differential expression analysis, WGCNA and survival analysis, we identified a centromere-associated gene, WDR62, which is highly expressed in ovarian cancer and highly correlated with ovarian cancer, as well as the poor prognosis of ovarian cancer patients with high expression, suggesting that WDR62 may be a potential biomarker for ovarian cancer. Previous studies have shown that WDR62 is closely associated with the occurrence, development and prognosis of a variety of tumors. However, its role in ovarian cancer has not been studied in depth.

METHODS

Using combined TCGA and GTEx datasets from the UCSC database, along with WGCNA, and survival analysis, WDR62 was identified as a potential biomarker. GEPIA2 database, GEO database, qRT-PCR, and Western blot proved the expression of WDR62. Enrichment analysis, cell transfection, Western blots and CCK8 demonstrated the regulatory mechanism of WDR62, and the detailed mechanism of WDR62 involvement in the occurrence and development of ovarian cancer was predicted by interaction analysis and correlation analysis.

RESULTS

WDR62 was highly expressed in ovarian cancer cells compared to normal ovarian epithelial cells, both at the RNA and protein levels. Patients with high WDR62 expression had a poor survival prognosis. Upon WDR62 knockdown, the expression of cell cycle-related proteins CDK1 and C-Myc decreased in ovarian cancer cells, and the cell proliferative capacity was decreased. Based on bioinformatic analysis, it was hypothesized that WDR62 might mediate the JNK signaling pathway by interacting with MAPK8, thus affecting ovarian cancer progression through cell cycle regulation.

CONCLUSIONS

WDR62 is overexpressed in ovarian cancer and is closely related to the prognosis of ovarian cancer patients. WDR62 promotes ovarian cancer progression by regulating the cell cycle and may influence its development through interaction with MAPK8 to mediate the JNK signaling pathway. These findings suggest that WDR62 could be a potential target for the early screening, diagnosis, and treatment of ovarian cancer.

RACGAP1 and MKI67 are potential prognostic biomarker in hepatocellular carcinoma caused by HBV/HCV via lactylation

Introduction

Hepatocellular carcinoma (HCC) is recognized as the prime and lethal form of liver cancer caused by the hepatitis B virus (HBV) and hepatitis C virus (HCV) globally. Lactate is an end product of glycolysis that influences epigenetic expression through histone lactylation. While MKI67 and RACGAP1 play crucial roles in HBV- and HCV-related HCC. However, the role of lactylation-related genes (LRGs) effects in this context remains unclear. This study innovatively explored the role of LRGs in HBV/HCV-associated HCC, identifying novel biomarkers for diagnosis and prognosis.

Methods

The present study used various online databases for analysis, and the findings were validated via immunohistochemical (IHC) analysis of HCC patient samples (n=60).

Results

We identified six signature LRGs (ALB, G6PD, HMGA1, MKI67, RACGAP1, and RFC4) possess prognostic potential, correlation with immune infiltration, and lactylation-related pathways, providing novel insights into tumor microenvironment (TME) of HCC. Moreover, MKI67 and RACGAP1 were significantly associated with HBV- and HCV-related HCC. IHC confirmed these findings, with high expression of MKI67 and RACGAP1 was significantly linked with HBV/HCV-associated HCC compared to non-viral HCC. The expression is also significantly associated with key clinical variables.

Conclusion

Our results suggest that MKI67 and RACGAP1 could serve as promising biomarkers for detecting and predicting HCC caused by HBV/HCV via lactylation, opening a new direction for immune-targeted therapies.

Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning

BACKGROUND

COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for both venous and arterial thrombosis. This heightened risk contributes to an increased probability of acute complications, including acute myocardial infarction (AMI) and acute ischemic stroke (AIS). Given the unclear relationship between COVID-19, AMI, and AIS, it is crucial to gain a deeper understanding of their associations and potential molecular mechanisms. This study aims to utilize bioinformatics to analyze gene expression data, identify potential therapeutic targets and biomarkers, and explore the role of immune cells in the disease.

METHODS

This study employed three Gene Expression Omnibus (GEO) datasets for analysis, which included data on COVID-19, AMI and AIS. We performed enrichment analysis on the co-DEGs for these three diseases to clarify gene pathways and functions, and also examined the relationship between co-DEGs and immune infiltration. Machine learning techniques and protein-protein interaction networks (PPI) were used to identify hub genes within the co-DEGs. Finally, we employed a dual validation strategy integrating independent GEO datasets and in vitro experiments with human blood samples to comprehensively assess the reliability of our experimental findings.

RESULTS

We identified 88 co-DEGs associated with COVID-19, AMI and AIS. Enrichment analysis results indicated that co-DEGs were significantly enriched in immune inflammatory responses related to leukocytes and neutrophils. Immune infiltration analysis revealed significant differences in immune cell populations between the disease group and the normal group. Finally, genes selected through machine learning methods included: CLEC4E, S100A12, and IL1R2. Based on the PPI network, the top ten most influential DEGs were identified as MMP9, TLR2, TLR4, ITGAM, S100A12, FCGR1A, CD163, FCER1G, FPR2, and CLEC4D. The integration of the protein-protein interaction (PPI) network with machine learning techniques facilitated the identification of S100A12 as a potential common biomarker for early diagnosis and a therapeutic target for all three diseases. Ultimately, validation of S100A12 showed that it was consistent with our experimental results, confirming its reliability as a biomarker. Moreover, it demonstrated good diagnostic performance for the three diseases.

CONCLUSION

We employed bioinformatics methods and machine learning to investigate common diagnostic biomarkers and immune infiltration characteristics of COVID-19, AMI and AIS. Functional and pathway analyses indicated that the co-DEGs were primarily enriched in immune inflammatory responses related to leukocytes and neutrophils. Through two machine learning approaches and the PPI network, and subsequent validation and evaluation, we identified S100A12 as a potential common therapeutic target and biomarker related to immune response that may influence these three diseases.

The complete mitochondrial genome of Sinojackia microcarpa: evolutionary insights and gene transfer

BACKGROUND

As a dicotyledonous plant within the Styracaceae family, Sinojackia microcarpa (S. microcarpa) is notable for its library-shaped fruit and sparse distribution, serving as a model system for studying the entire tree family. However, the scarcity of genomic data, particularly concerning the mitochondrial and nuclear sequences of S. microcarpa, has substantially impeded our understanding of its evolutionary traits and fundamental biological mechanisms.

RESULTS

This study presents the first complete mitochondrial genome sequence of S. microcarpa and conducts a comparative analysis of its protein-encoding genes across eight plant species. Our analysis revealed that the mitochondrial genome of S. microcarpa spans 687,378 base pairs and contains a total of 59 genes, which include 37 protein-coding genes (PCGs), 20 transfer RNA (tRNA) genes, and 2 ribosomal RNA (rRNA) genes. Sixteen plastid-derived fragments strongly linked with mitochondrial genes, including one intact plastid-related gene (rps7), were identified. Additionally, Ka/Ks ratio analysis revealed that most mitochondrial genes are under purifying selection, with a few genes, such as nad9 and ccmB, showing signs of relaxed or adaptive evolution. An analysis of twenty-nine protein-coding genes from twenty-four plant species reveals that S. microcarpa exhibits a closer evolutionary relationship with species belonging to the genus Camellia. The findings of this study provide new genomic data that enhance our understanding of S. microcarpa, and reveal its mitochondrial genome's evolutionary proximity to other dicotyledonous species.

CONCLUSIONS

Overall, this research enhances our understanding of the evolutionary and comparative genomics of S. microcarpa and other plants in the Styracaceae family and lays the foundation for future genetic studies and evolutionary analyses in the Styracaceae family.

Deciphering the mechanism of baicalein in cervical cancer via bioinformatics, machine learning and computational simulations: PIM1 and CDK2 are key target proteins

Cervical cancer is one of the leading causes of death among women worldwide. Current treatments are limited by chemoresistance and chemotherapeutic agents' adverse effects, prompting the search for better therapeutic alternatives. Baicalein, a natural compound with potent antitumor activity and low toxicity, has drawn significant attention. However, the precise mechanisms of baicalein against cervical cancer remain to be fully elucidated. In this study, bioinformatics and machine learning algorithms predicted six potential core targets of baicalein against cervical cancer. Molecular docking and molecular dynamics simulations were employed to further validate these targets, with a focus on assessing their binding affinity and stability. The molecular docking results demonstrated that five of the core targets exhibited significant binding affinity with baicalein. Notably, PIM1 and CDK2 showed stable binding conformations in molecular dynamics simulations. GO and KEGG enrichment analyses indicated baicalein might regulate cell cycle progression via histone kinase - mediated phosphorylation modifications. Thus, baicalein likely suppresses cervical cancer cells' abnormal proliferation by inhibiting PIM1 and CDK2 activity, inducing cell cycle arrest.

Identification of a novel FOXO3‑associated prognostic model in hepatocellular carcinoma

Although numerous molecular classifications are available to predict the prognosis of patients with hepatocellular carcinoma (HCC), they are still unsatisfactory. Forkhead box O3 (FOXO3) has been widely reported as a transcription factor involved in human cancers, but its role in HCC remains controversial. The present study aimed to explore the role of FOXO3 in HCC, as well as to identify biomarkers and construct prognostic models based on FOXO3. FOXO3 was highly expressed in HCC and was closely associated with poor prognosis in The Cancer Genome Atlas (the training set) and International Cancer Genome Consortium (the validation set). Subsequently, a co-expression network indicated that the red modules were closely related to FOXO3. Five key FOXO3-related genes [DEAD-box helicase 55 (DDX55), RAB10, member RAS oncogene family (RAB10), RAB7A, TATA-box binding protein associated factor, RNA polymerase I subunit B (TAF1B) and TAF3] were obtained using Cox-least absolute shrinkage and selection operator analyses. The 5-gene signature successfully predicted the prognosis of patients with HCC in both the training and validation sets. Enrichment analysis suggested marked differences in AKT and cell cycle-related (E2F targets and G2/M checkpoints) pathways between HCC subgroups. Furthermore, the tumor microenvironment analysis suggested that the difference in the distribution of M2 macrophages among various subgroups may contribute to the poor prognosis using the CIBERSORTx framework. Furthermore, the mRNA and protein expressions of DDX55, RAB10, RAB7A, TAF1B and TAF3 were found to be higher in HCC tissues compared with paracancerous tissues using RT-qPCR and western blotting. Additionally, knockdown of RAB10, RAB7A and TAF3 inhibited proliferation of Huh7 cells, assessed by a Cell Counting Kit-8 assay. In conclusion, a novel FOXO3-related model was constructed and revealed that RAB10, RAB7A and TAF3 may be potential molecular targets or biomarkers for HCC.

Identification of a PANoptosis-related gene signature reveals therapeutic potential of SFRP2 in pulmonary arterial hypertension

Background

Pulmonary arterial hypertension (PAH) is a serious condition marked by elevated pulmonary artery pressure, often progressing to right heart failure and high mortality. PANoptosis, an inflammatory form of programmed cell death, remains understudied in the context of PAH. This study aims to identify and validate PANoptosis-related signature genes in PAH using bioinformatics analysis alongside in vivo and in vitro experiments, seeking to uncover its potential role in disease progression.

Methods

PAH-related datasets and PANoptosis-associated genes were sourced from the Gene Expression Omnibus (GEO) database and prior studies. Feature genes were identified through weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and random forest (RF) algorithms, with validation performed on external datasets. The immune landscape in PAH was characterized using the CIBERSORT algorithm, providing insights into immune cell composition and its role in disease progression. Gene expression was further validated using a rat PAH model and pulmonary artery fibroblasts (PAAFs), while hub gene functions were investigated at the cellular level through Western blot, CCK-8, and flow cytometry assays.

Results

Through integrated transcriptomic analysis, SFRP2 was identified as a feature gene related to PAH and PANoptosis. Experimental validation was conducted in MCT-induced rat PAH models and TGF-β1-induced PAAFs, confirming SFRP2's role in regulating fibroblast proliferation and anti-apoptotic processes. The diagnostic model derived from dataset analysis exhibited high accuracy in diagnosing PAH, while immune cell infiltration analysis highlighted immune dysregulation associated with the condition.

Conclusion

SFRP2 was identified as a potential biomarker for PAH, impacting cell proliferation and resistance to apoptosis, thus providing new insights for PAH prevention and treatment.

Thlaspi arvense suppresses gut microbiota related TNF inflammatory pathway to alleviates ulcerative colitis

Introduction

Thlaspi arvense (TA), commonly known as "Ximi" or "Subaijiang," is a traditional Chinese medicinal herb used to prevent and treat ulcerative colitis (UC). However, the precise mechanisms underlying its therapeutic effects remain unclear, necessitating further investigation to identify potential pharmaceutical applications for UC management. This study aims to elucidate the efficacy and mechanisms of TA and its active constituents in UC treatment.

Methods

This study first evaluated the effects of varying TA doses on 3% dextran sulfate sodium (DSS)-induced UC. Gut microbiota alterations in UC mice were analyzed via 16S rRNA sequencing, with correlation analyses to reveal the relationship between gut microbiota and cytokines. Then, network pharmacology was utilized to identified potential TA targets for UC treatment. Protein-protein interaction (PPI) networks, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were employed to explore TA's mechanisms. Molecular docking and dynamics simulations validated interactions between TA's active compounds and UC-related targets. Finally, TNF pathway modulation by TA and its active component, isovitexin, was verified in vitro and in vivo.

Results

TA alleviated DSS-induced weight loss in a dose-dependent manner, reduced disease activity indices, and preserved intestinal mucosal barrier integrity. Subsequently, fluorescence in situ hybridization (FISH) revealed TA suppressed microbial translocation in intestinal tissues. To further characterize inflammatory responses, ELISA demonstrated that TA modulated levels of key cytokines (TNF-α, IL-1β, IL-6, IL-10) and oxidative stress markers (SOD, MDA), indicating systemic anti-inflammatory effects. Building on these findings, 16S rRNA sequencing analyses showed that TA regulated gut microbiota alpha/beta diversity and inhibited infectious disease-related pathways. Notably, correlation heatmaps highlighted a strong association between TNF-α levels and Escherichia-Shigella abundance, with high-dose TA significantly reducing this pathogenic bacterial genus. To systematically explore molecular mechanisms, network pharmacology identified 220 potential TA targets for UC treatment. Consistent with experimental data, PPI and KEGG analyses implicated TNF-α, IL-6, and AKT as key targets, primarily through TNF signaling pathway modulation. To validate these predictions, molecular docking confirmed stable interactions between TA compounds and identified targets, while dynamics simulations specifically emphasized isovitexin's high affinity for TNF-α. Finally, experiments in vivo demonstrated TA's inhibition of TNF-α-mediated NF-κB pathway activation, and in vitro studies confirmed that isovitexin directly mitigated TNF-α-induced intestinal epithelial damage. Furthermore, TA demonstrated potent inhibition of TNF-α-mediated NF-κB inflammatory pathway activation in intestinal tissues, while its active constituent isovitexin effectively mitigated TNF-α-induced epithelial cell damage, collectively highlighting their complementary anti-inflammatory mechanisms.

Discussion

Collectively, Thlaspi arvense (TA) ameliorates ulcerative colitis through synergistic mechanisms involving gut microbiota modulation, inflammatory pathway suppression, and intestinal barrier preservation. By remodeling microbial communities to reduce Escherichia-Shigella colonization and microbial translocation. TA concurrently inhibits TNF-α/NF-κB-driven inflammation, and oxidative stress regulation. Furthermore, its active constituent isovitexin directly attenuates TNF-α-induced epithelial damage, demonstrating multi-scale therapeutic efficacy. These findings establish TA's multi-target pharmacology spanning host-microbe interactions and intracellular signaling, while providing a rationale for standardizing TA-based formulations and advancing isovitexin as a precision therapeutic agent for inflammatory bowel diseases.

ULK2 deficiency stratifies autophagy-driven molecular subtypes and exacerbates trophoblasts apoptosis in preeclampsia

INTRODUCTION

Preeclampsia (PE), a placenta-originated hypertensive disorder of pregnancy, lacks targeted therapies despite its significant contribution to maternal and fetal morbidity. Emerging evidence implicates autophagy dysregulation in PE pathogenesis, though its molecular heterogeneity remains unexplored. This study aims to investigate autophagy-related molecular subtypes in PE and identify ULK2 key regulators linking autophagic imbalance to placental dysfunction.

METHODS

Placental transcriptomic datasets were analyzed to identify autophagy-related differentially expressed genes (ARGs). Unsupervised consensus clustering stratified 80 PE patients into molecular subtypes. Protein-protein interaction (PPI) networks, machine learning algorithms, and functional enrichment analyses were employed to delineate hub genes and pathways. ULK2's role was validated via qRT-PCR, shRNA knockdown in HTR8/SVneo trophoblasts, and immunofluorescence. A murine PE model was established using the ULK1/2 inhibitor MRT68921 to assess systolic blood pressure, fetal growth, and placental autophagy-apoptosis dynamics.

RESULTS

Unsupervised clustering of 45 ARGs classified PE patients into ULK2low and ULK2high subtypes. The ULK2low subtype exhibited severe clinical features: elevated preterm delivery, systolic hypertension, HELLP syndrome incidence and increased proteinuria. ULK2-kncokdown trophoblasts showed impaired autophagy and upregulated apoptosis. Six hub genes correlated with disease severity and inversely with ULK2 expression. In mice, ULK2 inhibition induced PE-like phenotypes: sustained hypertension, fetal growth restriction and severe proteinuria.

DISCUSSION

ULK2 deficiency disrupts trophoblast homeostasis, driving placental dysfunction and severe clinical outcomes. The ULK2low subtype highlights autophagy heterogeneity in PE. While pharmacological ULK2 inhibition recapitulates PE in mice, limitations include sample size and MRT68921's dual ULK1/2 activity. Our findings propose ULK2 agonism as novel therapeutic strategies.

Revealing propionate metabolism-related genes in glioblastoma and investigating their underlying mechanisms

Background

Propionate metabolism may affect tumor growth and aggressiveness, but the role of propionate metabolism-related genes (PMRGs) in glioblastoma (GBM) remains poorly understood.

Methods

Differentially expressed PMRGs (DE-PMRGs) were identified by comparing differentially expressed genes (DEGs) between GBM and normal tissues using TCGA-GBM, GSE42669, GSE162631 datasets. Functional enrichment analysis of DE-PMRGs was performed, followed by univariate Cox regression and least absolute shrinkage with selection operator (LASSO) analysis to identify potential prognostic biomarkers. In addition, prognostic models were developed and validated using independent cohorts. Genomic enrichment analysis (GSEA) was used to assess immune-related pathways in different risk subgroups. Finally, biomarker expression was confirmed using quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results

Differential expression analysis identified a total of 180 DE-PMRGs, which were strongly associated with drug response and insulin signaling pathways. Six biomarkers (SARDH, ACHE, ADSL, PNPLA3, MAPK1 and SREBF2) were identified to be associated with prognosis. The accuracy of the prognostic model was confirmed using the GSE42669 dataset, with risk score and MGMT promoter status identified as independent prognostic factors. GSEA showed enrichment of immune response activation and cell cycle regulatory pathways. qRT-PCR validation showed up-regulation of PNPLA3 and SARDH, and down-regulation of ADSL, in tumor tissues.

Conclusions

This study identified six PMRGs (SARDH, ACHE, ADSL, PNPLA3, MAPK1 and SREBF2) as potential prognostic biomarkers for glioblastoma. These biomarkers reveal the role of propionate metabolism in the progression of glioblastoma and may serve as important indicators of patient prognosis and treatment strategies.

Identification and validation of platinum resistance signature in gastric cancer

BACKGROUND

Platinum was the first drug with proven activity against gastric cancer (GC), the combination with fluoropyrimidine is the standard first-line systemic therapy for patients of GC. However, a major cause of treatment failure still is the existence of drug resistance. The purpose of this study is to identify and validate the platinum-related genes in GC and to construct a multi-gene joint signature for predicting the prognosis of GC patients.

METHODS

Based on 326 platinum-related genes from GeneCards, GO and KEGG analysis were applied for differentially expressed genes in GC, UniCox regression analysis was used to select effective genes and Lasso-Cox regression was utilized to construct a prognosis model. Stratified analysis, CNV landscape, TMB and MSI status, HLA gene expression, GSEA and GSVA analysis, immune activities, immunotherapy sensitivities were evaluated in the resistant high and low groups. Drug resistant cell lines, PDO and PDX models were used to validate this signature.

RESULTS

GO analysis of 140 differentially expressed genes were involved in many processes and KEGG pathways were enriched in platinum resistance and cancer. UniCox regression analysis was screened out 21 genes and conducted a platinum resistance scoring model. Stratified analysis indicated that the drug resistance score had a good predictive value in subgroups divided by T-stage, age and race. CNV changes were more occurred in the score-high group, and most model genes were negatively correlated with TMB, MSI and HLA gene expression. The immune score in resistant group was significantly higher, within more mast cell, regulatory T cell and dendritic cell infiltrated in. In vitro and in vivo models showed that 21 platinum resistance genes had varying degrees of upregulation under CDDP chemotherapy pressure.

CONCLUSIONS

The 21 gene-signature for platinum was developed to predict response to platinum chemotherapy for GC patients. It is worthwhile to further evaluate the molecular biology and the clinical applications of this signature.

Integration of Transcriptomic and Single-Cell Data to Uncover Senescence- and Ferroptosis-Associated Biomarkers in Sepsis

Background: Sepsis is a life-threatening condition characterized by organ dysfunction due to an imbalanced immune response to infection, with high mortality. Ferroptosis, an iron-dependent cell death process, and cellular senescence, which exacerbates inflammation, have recently been implicated in sepsis pathophysiology. Methods: Weighted gene co-expression network analysis (WGCNA) was used to identify ferroptosis- and senescence-related gene modules in sepsis. Differentially expressed genes (DEGs) were analyzed using public datasets (GSE57065, GSE65682, and GSE26378). Receiver operating characteristic (ROC) analysis was performed to evaluate their diagnostic potential, while single-cell RNA sequencing (scRNA-seq) was used to assess their immune-cell-specific expression. Molecular docking was conducted to predict drug interactions with key proteins. Results: Five key genes (CD82, MAPK14, NEDD4, TXN, and WIPI1) were significantly upregulated in sepsis patients and highly correlated with immune cell infiltration. MAPK14 and TXN exhibited strong diagnostic potential (AUC = 0.983, 0.978). Molecular docking suggested potential therapeutic interactions with diclofenac, flurbiprofen, and N-acetyl-L-cysteine. Conclusions: This study highlights ferroptosis and senescence as critical mechanisms in sepsis and identifies promising biomarkers for diagnosis and targeted therapy. Future studies should focus on clinical validation and precision medicine applications.

S100A8/A9 high-expression macrophages mediate renal tubular epithelial cell damage in acute kidney injury following acute type A aortic dissection surgery

Background

Acute kidney injury (AKI) is a major complication after acute type A aortic dissection (ATAAD), with an incidence rate of 20-66.7%. Many patients with AKI after ATAAD surgery show no clear signs of ischemia-reperfusion injury. In our previous study, S100A8 and S100A9 were identified as predictive biomarkers of AKI after ATAAD surgery. These proteins are primarily expressed in neutrophils and macrophages, where they contribute to cell damage and immune cell activation. However, the roles of S100A8/A9 in ATAAD-associated AKI remain unclear.

Methods

In this study, transcriptomics sequence was applied to identify differentially expressed genes in renal tubular epithelial cells (TCMK-1), stimulated by culture supernatant of S100A8/A9 overexpressed and downregulated RAW264.7 cells. Single-cell sequencing data were used to identify cell clusters with high S100A8/A9 expression. Cross-analysis between RNA sequencing datasets was used to investigate common pathways enrichment in both in vitro and in vivo models. Molecular biology experiments were used to explore the downstream signaling pathways of S100A8/S100A9.

Results

We found that S100A8/S100A9 expression levels were increased and co-localized primarily with macrophages in the kidneys of AKI mice. Marker genes of M1-type macrophages, like Nos2 and Il1b, were upregulated in S100A8/A9 overexpressed M1-type macrophages, while the opposite was observed in the downregulated group. In transcription sequencing results, TCMK-1 cells stimulated by the supernatant from S100A8/A9 overexpressed and downregulated RAW264.7 cells can activate the TNF and PPAR pathway respectively. Cross-analysis revealed that the TNF signaling, IL-17 signaling, and other inflammatory pathways were enriched in both S100A8/A9-related renal tubular epithelial cell impairment and other AKI sequencing datasets. Finally, recombinant protein S100A8/A9 activated the TNF signaling pathway in renal tubular epithelial cells.

Conclusion

These findings suggested that S100A8/A9 were promising predictive biomarkers for AKI after surgery for ATAAD. S100A8/A9 were upregulated and primarily localized in renal macrophages, where they promoted the transformation of macrophages into the M1 phenotype. S100A8/A9 overexpressed macrophages activated the TNF signaling pathway through secretion and direct interaction with renal tubular epithelial cells, highlighting the critical role of TNF signaling in AKI after ATAAD surgery.

Endoplasmic reticulum stress-related signatures: a game-changer in prognostic stratification for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) has limited therapeutic options and a poor prognosis. The endoplasmic reticulum (ER) plays a crucial role in tumor progression and response to stress, making it a promising target for HCC stratification. This study aimed to develop a risk stratification model using ER stress-related signatures.

METHODS

We utilized transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus, which encompass whole-genome expression profiles and clinical annotations. Machine learning algorithms, including the least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination, were applied to the key genes associated with HCC prognosis. A prognostic system was developed using univariate Cox hazard analysis and least absolute shrinkage and selection operator Cox regression, followed by validation using Kaplan-Meier analysis and receiver operating characteristic curves. Tumor immune dysfunction and exclusion tools were used to predict immunotherapy responsiveness.

RESULTS

Two distinct clusters associated with ER stress were identified in HCC, each exhibiting unique clinical and biological features. Using a computational approach, a prognostic risk model, namely the ER stress-related signature, was formulated, demonstrating enhanced predictive accuracy compared with that of existing prognostic models. An effective clinical nomogram was established by integrating the risk model with clinicopathological factors. Patients with lower risk scores exhibited improved responsiveness to various chemotherapeutic, targeted, and immunotherapeutic agents.

CONCLUSION

The critical role of ER stress in HCC is highlighted. The ER stress-related signature developed in this study is a powerful tool to assess the risk and clinical treatment of HCC.

Curcumol attenuates hyperproliferation and inflammatory response in a psoriatic HaCaT keratinocyte model by inhibiting the PI3K-Akt pathway and ameliorates skin lesions and inflammatory status in psoriasis-like mice

Psoriasis, a chronic autoimmune disorder, is characterized by keratinocyte hyperproliferation and inflammatory responses. Curcumol, a bioactive terpenoid, possesses antiproliferative and anti-inflammatory properties. This study evaluates the efficacy of curcumol in treating psoriasis in both in vitro and in vivo models. In vitro, curcumol inhibits hyperproliferation and inflammatory responses in a psoriatic HaCaT keratinocyte model stimulated by M5 cytokines by inhibiting the PI3K-Akt pathway. Additionally, in vivo, curcumol ameliorates psoriasis-like skin lesions and inflammatory status in imiquimod-induced mice. Network pharmacology revealed that curcumol's beneficial effects might involve the PI3K-Akt signaling pathway. Further investigation shows that curcumol partially counteracts the activation of PI3K-Akt by recilisib in keratinocytes. These results suggest that curcumol may be a promising therapeutic option for psoriasis.

Acute exposure of perchlorate on zebrafish larvae: Neurotoxicity during development

OBJECTIVE

This study aimed to assess the acute developmental toxicity of perchlorate exposure and identify its manifestation of neurotoxicity in zebrafish (Danio rerio) model.

METHODS

Zebrafish larvae were exposed to sodium perchlorate at early developmental stages and the phenotypes of various developmental abnormalities were observed and recorded. Neurodevelopmental toxicity has been studied in particular in terms of central nervous system apoptosis, peripheral motor abnormalities, and abnormal autonomic motor behavior. Samples were processed for pathological, transcriptomic, and PCR analyses.

RESULTS

Acute exposure of zebrafish larvae to perchlorate resulted in developmental toxicity in a concentration-dependent manner. Heart rate abnormality (33 %), slow blood flow (37 %), abnormal size of liver (23 %), delayed yolk absorption (70 %), and abnormal body pigmentation (100 %) were observed in the 3.83 mg/mL group. Heart edema (10 %), heart rate abnormality (60 %), slow blood flow (67 %), abnormal size of liver (77 %), delayed yolk absorption (100 %), abnormalities in intestinal morphology (30 %), abnormal body pigmentation (100 %), and shorter body length (47 %) were observed in the 4.03 mg/mL group. Developmental neurotoxicity was characterized by brain apoptosis, damage to the central nervous system (P < 0.01) and peripheral motor neurons (P < 0.0001), and reduced autonomous motor distance (P < 0.01). Transcriptomic analysis revealed that c3a.1, c5, fga, fgb, and fgg were upregulated in the complement and coagulation cascades, and the mRNA levels of c3a.1, dusp1, slc2a6, purba, and klf9 were found upregulated in PCR.

CONCLUSION

Acute perchlorate exposure on zebrafish larvae caused various developmental toxicity in a concentration-dependent manner, notably neurotoxicity. We believed the immunological and inflammatory responses were involved.

Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that BMX (bone marrow tyrosine kinase gene on chromosome X), GRB10 (growth factor receptor bound protein 10), and GADD45A (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, GADD45A was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. BMX showed positive correlations with eosinophils, mast cells, and neutrophils, while GRB10 was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing BMX, GRB10, and GADD45A as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.

Single-cell map of innate-like lymphocyte response to Francisella tularensis infection reveals interleukin-17-dependent protection by MAIT cells

Early immune dynamics during the initiation of fatal tularemia caused by Francisella tularensis infection remain unknown. Unto that end, we generated a transcriptomic map at single-cell resolution of the innate-like lymphocyte responses to F. tularensis live vaccine strain (LVS) infection of mice. We found that both interferon-γ (IFN-γ)-producing type 1 and interleukin-17 (IL-17)-producing type 3 innate-like lymphocytes expanded in the infected lungs. Natural killer (NK) and NKT cells drove the type 1 response, whereas mucosal-associated invariant T (MAIT) and γδ T cells drove the type 3 response. Furthermore, tularemia-like disease resistant NKT cell-deficient, Cd1d -/- mice accumulated more MAIT1 cells, MAIT17 cells, and cells with a hybrid phenotype between MAIT1 and MAIT17 cells than wild-type mice. Critically, adoptive transfer of LVS-activated MAIT cells from Cd1d -/- mice, which were enriched in MAIT17 cells, was sufficient to protect LVS-susceptible, immunodeficient RAG2 -/- mice from severe LVS infection-inflicted pathology. Collectively, our findings position MAIT cells as potential mediators of IL-17-dependent protection from pulmonary tularemia-like disease.

Identification and validation of m6A RNA methylation and ferroptosis-related biomarkers in sepsis: transcriptome combined with single-cell RNA sequencing

Background

Sepsis, a systemic inflammatory response syndrome triggered by infection, is associated with high mortality rates and an increasing global incidence. While N 6-methyladenosine (m6A) RNA methylation and ferroptosis are implicated in inflammatory diseases, their specific genes and mechanisms in sepsis remain unclear.

Methods

Transcriptomic datasets of sepsis, along with m6A-related genes (m6A-RGs) and ferroptosis-related genes (FRGs), were sourced from public databases. Differentially expressed genes (DEGs) were identified between the sepsis and control groups, and m6A-RGs were analyzed through weighted gene co-expression network analysis (WGCNA) to uncover m6A module genes. These were then intersected with DEGs and FRGs to identify candidate genes. Biomarkers were identified using two machine learning methods, receiver operating characteristic (ROC) curves, and expression validation, followed by the development of a nomogram. Further in-depth analyses of the biomarkers were performed, including functional enrichment, immune infiltration, drug prediction, and molecular docking. Single-cell analysis was conducted to identify distinct cell clusters and evaluate biomarker expression at the single-cell level. Finally, reverse transcription-quantitative PCR (RT-qPCR) was employed to validate biomarker expression in clinical samples.

Results

DPP4 and TXN were identified as key biomarkers, showing higher expression in control and sepsis samples, respectively. The nomogram incorporating these biomarkers demonstrated strong diagnostic potential. Enrichment analysis highlighted their involvement in spliceosome function and antigen processing and presentation. Differential analysis of immune cell types revealed significant correlations between biomarkers and immune cells, such as macrophages and activated dendritic cells. Drug predictions identified gambogenic acid and valacyclovir as potential treatments, which were successfully docked with the biomarkers. Single-cell analysis revealed that the biomarkers were predominantly expressed in CD4+ memory cells, and CD16+ and CD14+ monocytes. The expression of DPP4 was further validated in clinical samples.

Conclusions

DPP4 and TXN were validated as biomarkers for sepsis, with insights into immune infiltration and therapeutic potential at the single-cell level, offering novel perspectives for sepsis treatment.

Temporal Transcriptomic Differences in Stroke Between Diabetic and Non-Diabetic Mice

Diabetes is a key risk factor for ischemic stroke and negatively impacts long-term outcomes post-stroke. However, genomic studies on diabetic stroke remain insufficient. This study aims to investigate the interaction between diabetes and stroke from the acute phase to the early recovery phase by establishing a diabetic stroke animal model and comparing transcriptome sequencing results with those of non-diabetic stroke models. The study identified a greater number of downregulated genes in the diabetic stroke group compared to the non-diabetic group at different stages post-stroke. Functional enrichment analysis revealed an enhanced immune response and a relatively lower neurodegeneration potential in the diabetic group. Post-stroke, a higher presence of CD4 + T cells, eosinophils, and M1 macrophages was observed in the diabetic group. Additionally, time-series analysis identified a set of genes with time-specific expression patterns following diabetic stroke. This study underscores the role of inflammation and immune responses as potential factors exacerbating ischemic stroke in diabetes while also identifying gene regulatory networks at different stages post-stroke. These findings provide new insights into the role of diabetes in stroke and suggest potential therapeutic targets for improving outcomes in diabetic patients.

Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease

Background

Bovine respiratory disease (BRD) is a prevalent and costly condition in the cattle industry, impacting long-term productivity, antibioticusage, and global food safety. Thus, identifying reliable biomarkers for BRD is crucial for early diagnosis, effective treatment, and monitoring therapeutic outcomes.

Methods

This study identified differentially expressed genes (DEGs) associated with BRD by analyzing a blood RNA-seq expression dataset associated with BRD, and conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) approach enrichment and Gene Ontology (GO) annotation analysis on these DEGs. Meanwhile, the key modules related to BRD were screened by weighted gene co-expression network analysis (WGCNA), and the genes in the module were intersected with DEGs. Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. Finally, gene set enrichment analysis (GSEA) was performed to explore the potential mechanisms of the identified biomarkers, and their diagnostic significance was assessed using receiver operator characteristic (ROC) curve analysis and real-time fluorescent quantitative PCR (RT-qPCR). In addition, immune cell infiltration in BRD was evaluated using the CIBERSORT algorithm and the correlation between biomarkers and immune cell infiltration was analyzed.

Results

The results showed that a total of 1,097 DEG were screened. GO and KEGG analysis showed that DEGs was mainly enriched in inflammatory response, defense response, Complement and coagulation cascades and Antigen processing and presentation pathways. WGCNA analysis determined that the cyan module had the highest correlation with BRD. A total of 833 overlapping genes were identified through Venn analysis of the differential and WGCNA results. Lasso and RF analyses identified five potential biomarkers for BRD. RT-qPCR testing and data set analysis showed that the expression levels of these five potential biomarkers in nasal mucus and blood of BRD cattle were significantly higher than those of healthy cattle. In addition, ROC curve analysis showed that potential biomarkers had high diagnostic value. GSEA analysis revealed that potential biomarkers are mainly involved in Neutrophil extracellular trap formation, Complement and coagulation cascades, T cell receptor signaling pathway, B cell receptor signaling pathway, Fc gamma R-mediated phagocytosis and IL-17 signaling pathway. The results from the CIBERSORT algorithm demonstrated a significant difference in immune cell composition between the BRD group and the healthy group, indicating that the diagnostic biomarkers were closely associated with immune cells.

Conclusion

This study identified ADGRG3, CDKN1A, CA4, GGT5, and SLC26A8 as potential diagnostic markers for BRD, providing significant insights for the development of new immunotherapy targets and improving disease prevention and treatment strategies.

Development and validation of prognostic and diagnostic models utilizing immune checkpoint-related genes in public datasets for clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cell carcinoma, and immune checkpoint regulator-based immunotherapy has emerged as an effective treatment for advanced stages of the disease. However, the expression patterns, prognostic significance, and diagnostic value of immune checkpoint-related genes (ICRGs) in ccRCC remain underexplored. This study utilized large-scale ccRCC datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) to analyze ICRGs and develop a prognostic and diagnostic model, which was validated using quantitative PCR in clinical samples from ccRCC patients.

Methods

RNA-seq data and clinical information were retrieved from TCGA, ICGC, and GEO databases. Differentially expressed genes (DEGs) were identified, and immune checkpoint-related genes (DICRGs) were selected by intersecting DEGs with ICRGs, followed by validation in independent datasets. Univariate and multivariate Cox regression analyses were used to develop the prognostic model. Protein expression of key genes was validated through immunohistochemistry (IHC) using data from the Human Protein Atlas (HPA). qRT-PCR confirmed gene expression levels in ccRCC and normal kidney tissues. Diagnostic models were constructed using machine learning, and functional enrichment and immune infiltration analyses were performed.

Results

Fourteen DICRGs were identified, with four (EGFR, TRIB3, ZAP70, and CD4) showing prognostic significance in Cox analyses. IHC revealed high expression of these genes in ccRCC tissues, and qRT-PCR confirmed increased expression of EGFR, TRIB3, and CD4, while ZAP70 expression showed no significant change. A prognostic risk score was developed based on gene expression levels. Functional analysis identified enriched pathways related to organic anion transport and metabolism, while immune infiltration analysis revealed associations between ZAP70, CD4, and risk scores.

Conclusion

This study establishes a prognostic model for ccRCC based on four ICRGs, providing valuable insights into the molecular mechanisms underlying prognosis and diagnosis in ccRCC.

Multi-omics integration analysis and association study reveal the potential of ADIPOQ function in gestational diabetes mellitus

AIM

To investigate the role of ADIPOQ gene in gestational diabetes mellitus (GDM).

METHODS

We genotyped single nucleotide polymorphisms (SNPs) rs266729 and rs1501299 within the ADIPOQ gene in a cohort of 1157 pregnant women of north Chinese Han ethnicity. This cohort comprised 560 pregnant women diagnosed with GDM and 597 pregnant women who exhibited normal oral glucose tolerance test at 24-28 weeks' gestation. All participants were recruited from the Department of Obstetrics and Gynecology at the Second Affiliated Hospital of Harbin Medical University. Additionally, we used conventional bioinformatics analysis methods to conduct multi-omics analysis (transcriptome, epigenome, and single-cell level) of ADIPOQ-regulated GDM.

RESULTS

The systolic blood flow velocity/diastolic blood flow velocity (S/D) ratio of the umbilical artery in GDM patients with CC genotype of rs266729 and GG genotype of rs1501299 was higher than control. Single-cell analysis suggested that ADIPOQ was expressed in extravillous trophoblast (EVT), T cell, monocytes, myelocyte, NK cell and syncytiotrophoblast (SCT). Functional enrichment analysis showed ADIPOQ gene was associated with response to nutrient levels, fat cell differentiation.

CONCLUSION

The findings of our study indicate a correlation between SNPs of ADIPOQ in GDM patients, and ADIPOQ is involved in the transcriptional regulation of GDM.

Construction and evaluation of a prognostic model of autophagy-related genes in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a globally prevalent disease. Our article evaluates risk models based on autophagy- and HCC-related genes and their prognostic value by bioinformatics analytical methods to provide a scientific basis for clinical treatment.

Methods

Prognostic genes were identified by univariate and multivariate Cox analyses, and risk scores were calculated. The value of risk models was analysed by receiver operating characteristic curve (ROC), immune microenvironment and drug sensitivity. Prognostic gene-related regulatory mechanisms based on network database.

Results

We screened four prognosis-related genes (SQSTM1, GABARAPL1, CDKN2A, HSPB8) for model construction. The AUC for 1-, 2- and 3-year survival was higher than 0.6 in both the training and validation sets. The nomogram constructed based on risk scores, pathologic_T predicted the outcome better. There were differences in the tumour microenvironment between the high and low risk groups, as evidenced by differences in the distribution of immune cells and differences in the expression of immune checkpoints.

Conclusion

Our results illustrate that models, nomograms and risk scores were valuable for tumour progression.

Clinical trial number

Not applicable.

Integrated UPLC-ESI-MS/MS, network pharmacology, and transcriptomics to reveal the material basis and mechanism of Schisandra chinensis Fruit Mixture against diabetic nephropathy

Backgrounds

It has been regarded as an essential treatment option for diabetic nephropathy (DN) in Traditional Chinese medicine. Previous studies have demonstrated the anti-DN efficacy of Schisandra chinensis Fruit Mixture (SM); however, a comprehensive chemical fingerprint is still uncertain, and its mechanism of action, especially the potential therapeutic targets of anti-DN, needs to be further elucidated.

Objective

Potential mechanisms of SM action on DN were explored through network pharmacology and experimental validation.

Methods

The chemical composition of SM was analyzed using UPLC-ESI-MS/MS technology. Active bioactive components and potential targets of SM were identified using TCMSP, SwissDrugDesign, and SymMap platforms. Differentially expressed genes were determined using microarray gene data from the GSE30528 dataset. Related genes for DN were obtained from online databases, which include GeneCards, OMIM and DisGeNET. PPI networks and compound-target-pathway networks were constructed using Cytoscape. Functional annotation was performed using R software for GO enrichment and KEGG pathway analysis. The DN model was built for experimental validation using a high-sugar and high-fat diet combined with STZ induction. Hub targets and critical signaling pathways were detected using qPCR, Western blotting and immunofluorescence.

Results

Utilizing the UPLC-ESI-MS/MS coupling technique, a comprehensive analysis identified 1281 chemical components of SM's ethanol extract, with 349 of these components recognized as potential bioactive compounds through network pharmacology. Through this analysis, 126 shared targets and 15 HUB targets were pinpointed. Of these, JAK2 is regarded as the most critical gene. Enrichment analysis revealed that SM primarily operates within the PI3K/AKT signaling pathway. In vivo experiments confirmed that SM improved pathological injury and renal function in rats with DN while improving mitochondrial morphology and function and modulating the expression of proteins linked to apoptosis (cleaved-caspase-3, Bax, and Bcl-2) and pro-inflammatory factors (IL-6 and TNF-α). Mechanistically, SM alleviates DN primarily by suppressing the PI3K/AKT/mTOR and JAK2/STAT3 signaling pathways to fulfill the energy needs of renal tissues. Furthermore, molecular docking analysis provided direct validation of these findings.

Conclusion

The findings of this study offer initial indications of the active component and robust anti-inflammatory and anti-apoptotic characteristics of SM in the mitigation of DN, along with its capacity to safeguard the integrity and functionality of mitochondria. This research unequivocally validates the favorable anti-DN effects of SM, indicating its potential as a viable pharmaceutical agent for the management of DN.

Identification of the entosis-related prognostic signature and tumour microenvironment in hepatocellular carcinoma on the basis of bioinformatics analysis and experimental validation

Liver cancer ranks among the deadliest cancers worldwide. Entosis, a recently uncovered method of cell death, has not yet been fully explored for its relevance to HCC. A bioinformatics analysis was performed to determine the expression and mutational landscapes of Entosis-related genes (ERGs). A subset of differentially expressed Entosis-related genes (DEERGs) was generated. A risk model for entosis was subsequently constructed employing LASSO and Cox regression methodologies. The correlations among ERGs, genes associated with risk, the developed risk model, and the immune context of the tumour were explored. Furthermore, the study investigated the varying drug sensitivities between high-risk and slight-risk patient groups. The expression patterns of four pivotal risk genes were delineated via qRT‒PCR and WB. A prognostic model comprising four DEERGs (KIF18A, SPP1, LCAT and TRIB3) was developed. The ability of this model to predict the survival outcomes of patients with HCC was confirmed through receiver operating characteristic curve analysis. Patients were grouped according to their risk assessments, revealing that the low-risk population demonstrated a more favourable survival outcome than did the high-risk population. The high-risk population presented reduced tumour stroma, immune and ESTIMATE scores, along with an increased proportion of cancer stem cells and tumour mutation burden. Additionally, a connection between the risk model and the responsiveness of various chemotherapy drugs as well as the efficacy of immunotherapies in patients was noted. These findings provide significant guidance for the development of targeted clinical treatment strategies. qRT‒PCR and WB analysis revealed that the gene expression of KIF18A and SPP1 were elevated in HCCLM3 cells compared with that in THLE2 cells; whereas, the expression level of LCAT and TIRB3 was decreased. The four genes KIF18A, SPP1, LCAT and TRIB3 could effectively predict the survival prognosis of patients with liver cancer. KIF18A and SPP1 were elevated in HCC tissues compared with that in THLE2 cells.

Pan-genome analysis and expression verification of the maize ARF gene family

Auxin transcription factors regulate auxin responses and play crucial roles in plant growth, development, and responses to abiotic stress. Utilizing the maize pan-genome data, this study identified 35 ARF family members in maize, comprising 21 core genes, 10 near-core genes, and 4 non-essential genes; no private genes were detected. The construction of a phylogenetic tree using Arabidopsis thaliana revealed that the G3 subfamily comprises the highest number of core genes, with a total of 10, and exhibits relative stability throughout the evolution of maize. The calculation of the Ka/Ks ratios for ARF family members across 26 genomes indicated that, aside from ARF8 and ARF11, which were subjected to positive selection, the remaining genes underwent purifying selection. Analysis of structural variation revealed that the expression level of the ARF4 gene significantly differed as a result of this variation. Simultaneously, the structural variation also influenced the conserved domain and cis-acting elements of the gene. Further combining the transcriptome data and RT-qPCR found that, The expression levels of ARF family members in maize were higher at the early stage of embryo and grain development, and the expression levels of each member in embryo and grain were complementary, and the ARF4 plays an important role in abiotic stress. In summary, this study utilizes the maize pan-genome and bioinformatics methods to investigate the evolutionary relationships and functional roles of ARF family members in maize, thereby providing a novel theoretical framework for further research on the maize ARF family.

Inflammatory Gene Signature Identified by Machine Algorithms Reveals Novel Biomarkers of Coronary Artery Disease

Purpose

Inflammatory activation of immune cells plays a pivotal role in the development of coronary artery diseases (CAD). This study aims to investigate the immune responses of peripheral blood mononuclear cells (PBMCs) in CAD and to identify novel signature genes and biomarkers using machine learning algorithms.

Methods

The GSE113079 dataset was analyzed and differentially expressed genes (DEGs) were identified between CAD and normal samples. The intersection of DEGs with inflammation-related genes was used to identify the differentially expressed inflammation-related genes (DIRGs). Then, the receiver operating characteristic (ROC) curves were plotted for each DIRG, and those with an area under the curve (AUC) greater than 0.9 were selected for subsequent analysis. Furthermore, machine learning algorithms were employed to identify biomarkers. A nomogram was developed based on these biomarkers. The CIBERSORT algorithm and Wilcoxon test method were used to analyze the differences in immune cells between the CAD and normal samples. The identified biomarkers were validated in PBMCs from patients with CAD and in atherosclerotic aortas from ApoE-/- mice.

Results

A total of 574 DEGs were identified between CAD and normal samples. From this intersection, 29 DIRGs were identified, of which 14 DIRGs (PTGER1, IL17RC, KLKB1, GPR32, ADM, NUPR1, SCN9A, IL17B, CX3CL1, FFAR3, PYDC2, SYT11, RORA, and GPR31) exhibited high diagnostic efficacy. Four biomarkers (ADM, NUPR1, PTGER1, and PYDC2) were identified using Support Vector Machine (SVM). Ten types of immune cells, including CD8+ T cells, regulatory T cells (Tregs), and resting NK cells, showed significant differences between the CAD and normal groups. Furthermore, increased levels of ADM, NUPR1, PTGER1, and PYDC2 were validated in PBMCs isolated from CAD patients. In addition, ADM, NUPR1, and PTGER1 were upregulated in the mouse atherosclerotic aorta.

Conclusion

Our findings revealed novel inflammatory gene signatures of CAD that could be potential biomarkers for the early diagnosis of CAD.

Identification and validation of diagnostic biomarkers for temporal lobe epilepsy related to ferroptosis and potential therapeutic targets

Ferroptosis pathway activation is potentially correlated with temporal lobe epilepsy (TLE). However, the diagnostic significance and mechanism of ferroptosis-related genes (FRGs) in TLE require further investigation. A comprehensive analysis of the GSE134697 dataset from the Gene Expression Omnibus (GEO) database using Weighted gene co-expression network analysis (WGCNA) identified 3,212 differentially expressed genes (DEGs) between temporal lobe epilepsy (TLE) and control groups, with a critical focus on the turquoise module. Through intersection of DEGs and key module genes, correlation analyses with functional-related genes (FRG), protein-protein interactions (PPI), least absolute shrinkage and selection operator (LASSO), and machine learning methods, five potential biomarkers of ferroptosis (CBS, SHMT1, RIN3, QDPR, and PLPP4) were isolated. A nomogram was constructed using these markers, and enrichment analyses revealed their links to T-cell activation, allograft rejection, and glial differentiation. Variations in 13 immune cell types were also noted. Upregulation of CBS, RIN3, QDPR, and PLPP4 in TLE was confirmed through RT-qPCR and Western blot assays. Additionally, five SHMT1-targeting and one CBS-targeting drugs were predicted using the Drug-Gene Interaction Database (DGIdb). These findings provide new insights into the potential pathogenesis of TLE and suggest new targets for future research.

RamA upregulates the ATP-binding cassette transporter mlaFEDCB to mediate resistance to tetracycline-class antibiotics and the stability of membranes in Klebsiella pneumoniae

RamA is an intrinsic regulator in Klebsiella pneumoniae, belonging to the AraC family of transcription factors and conferring a multidrug resistance phenotype, especially for tetracycline-class antibiotics. The ATP-binding cassette transporters MlaFEDCB in bacteria play essential roles in functions essential for cell survival and intrinsic resistance to many antibiotics. We found deletion of ramA resulted in a fivefold decrease in the transcriptional levels of the mlaFEDCB operon. After complementation with ramA, the transcriptional levels were comparable to those of wild-type strain. Furthermore, an electrophoretic mobility shift assay showed that RamA could bind to the promoter region of mlaEFDCB operon, which confirmed RamA is an activator of mlaEFDCB operon. The mlaEFDCB operon could mildly mediate resistance to the tetracycline family of antibiotics under RamA regulation. The MIC (minimum inhibitory concentration) of tigecycline decreased fourfold, and the MIC of doxycycline, minocycline, and eravacycline decreased twofold after mlaE-knockout. The ramA- and mlaE-knockout strains exhibited greater sensitivity to sodium dodecyl sulfate (SDS)-EDTA than the wild-type. Growth of ΔramA cells was severely compromised in 0.25/0.5% SDS and 0.55 mM EDTA, and this sensitivity was restored by complementation with ramA and mlaE. This study demonstrates that RamA can directly regulate the malEFEDCB operon, thereby mediating resistance to tetracycline-class antibiotics, contributing to the stability of bacterial membranes in K. pneumoniae. We identified a novel signal pathway in which RamA mediates multidrug resistance of K. pneumoniae, leading to new ideas for the development of novel antimicrobial therapeutics, therefore deserving further comprehensive study.

IMPORTANCE

Multidrug-resistant and extensively drug-resistant Klebsiella pneumoniae have emerged as significant global health concerns resulting in high mortality rates. Although previous research has investigated the maintenance of lipid asymmetry (Mla) pathway, the extent to which it mediates antimicrobial resistance in K. pneumoniae and the underlying upstream regulatory mechanisms remain unclear. In this study, we sought to determine at the molecular level how the AraC-type global regulator RamA directly regulates mlaFEDCB, which mediates resistance to tetracycline-class antibiotics and the stability of bacterial membranes in K. pneumoniae.

Selection Increases Mitonuclear DNA Discordance but Reconciles Incompatibility in African Cattle

Mitochondrial function relies on the coordinated interactions between genes in the mitochondrial DNA and nuclear genomes. Imperfect interactions following mitonuclear incompatibility may lead to reduced fitness. Mitochondrial DNA introgressions across species and populations are common and well documented. Various strategies may be expected to reconcile mitonuclear incompatibility in hybrids or admixed individuals. African admixed cattle (Bos taurus × B. indicus) show sex-biased admixture, with taurine (B. taurus) mitochondrial DNA and a nuclear genome predominantly of humped zebu (B. indicus). Here, we leveraged local ancestry inference approaches to identify the ancestry and distribution patterns of nuclear functional genes associated with the mitochondrial oxidative phosphorylation process in the genomes of African admixed cattle. We show that most of the nuclear genes involved in mitonuclear interactions are under selection and of humped zebu ancestry. Variations in mitochondrial DNA copy number may have contributed to the recovery of optimal mitochondrial function following admixture with the regulation of gene expression, alleviating or nullifying mitochondrial dysfunction. Interestingly, some nuclear mitochondrial genes with enrichment in taurine ancestry may have originated from ancient African aurochs (B. primigenius africanus) introgression. They may have contributed to the local adaptation of African cattle to pathogen burdens. Our study provides further support and new evidence showing that the successful settlement of cattle across the continent was a complex mechanism involving adaptive introgression, mitochondrial DNA copy number variation, regulation of gene expression, and selection of ancestral mitochondria-related genes.

Accuracy of Ultrasound Measurements of Muscle Thickness in Identifying Older Patients With Sarcopenia and Its Impact on Frailty: A Systematic Review and Meta-Analysis

OBJECTIVES

The aim of this systematic review was to assess the diagnostic test accuracy of muscle ultrasound for identifying older patients with sarcopenia and to investigate its association with frailty.

DESIGN

Systematic review and meta-analysis of observational studies. Comprehensive searches were conducted in PubMed, MEDLINE, Cochrane Library, Scopus, and Embase through October 2024.

SETTING AND PARTICIPANTS

Clinical and community settings across 7 countries, with 2537 adults aged ≥65 years.

METHODS

Two reviewers assessed study quality using QUADAS-2. Data on participant characteristics, ultrasound methods, and diagnostic outcomes were extracted. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve were calculated with a random-effects model. Sensitivity analyses ensured robustness.

RESULTS

Pooled sensitivity was 0.85 (95% CI, 0.78-0.93), specificity was 0.74 (95% CI, 0.65-0.81), DOR was 16.65 (95% CI, 4.90-96.67), and SROC-area under the curve was 0.87, indicating moderate to high diagnostic accuracy. Association with frailty yielded an odds ratio of 7.91 (95% CI, 6.15-10.17). Most studies received an "unclear" rating in several QUADAS-2 domains, especially in patient selection and reference standards, indicating limitations in study design that may impact the generalizability of results.

CONCLUSIONS AND IMPLICATIONS

Ultrasound is a reliable, noninvasive, and cost-effective tool for diagnosing sarcopenia in older patients. Further research should standardize cutoffs and explore integration with other methods.

Risk prediction model of pedicle screw loosening within 2 years after decompression and instrumented fusion surgery for degenerative lumbar disease

BACKGROUND CONTEXT

Pedicle screw loosening (PSL) after spinal fusion surgery is one of the most frequently reported complications and leads to poor clinical outcomes.

PURPOSE

This study aimed to develop and validate a risk prediction model for PSL within two years in patients undergoing lumbar instrumented fusion surgery based on their risk profiles.

STUDY DESIGN/SETTING

Retrospective, observational study.

PATIENT SAMPLE

Patients who underwent lumbar instrumented fusion surgery at a single academic institution between May 2015 and February 2019.

OUTCOME MEASURES

Risk assessment of PSL and development of a rating score based on patient characteristics.

METHODS

The demographic profiles and radiographic parameters using computed tomography were obtained. These factors were analyzed to determine possible risk factors related to postoperative PSL after 2 years. A scoring system was developed using these independent risk factors and validated using prospectively collected data from another center between May 2019 and December 2021.

RESULTS

The occurrence of PSL within 2 years postoperation was 12.7% (40/315). PSL was significantly predicted by smoking, low Hounsfield units (HU) of the pedicle tract at the index level (P), and a low psoas-lumbar vertebral index (PLVI). The risk of PSL according to the categories of the risk score was 1.1% for those with a score of 0-1, 15.1% for a score of 2-3, and 61.5% for a score of 4-6. In validation, this model demonstrated both good discrimination and calibration results. The area under the curve was 0.887 (95% CI 0.830-0.938) for the derivation cohort and 0.835 (95% CI 0.738-0.918) for the external validation cohort.

CONCLUSIONS

This PSL risk score, including smoking, Index P HU, and PLVI, is a novel approach to predict PSL 2 years post-surgery. This approach highlights the role of factors associated with osteoporosis and sarcopenia in the development of PSL and could aid in preoperative decision-making and surgical planning.

Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information

Influenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and designing antiviral drugs. In this article, we developed a sequence-based machine learning method for predicting PPI. First, we applied a new negative sample construction method to establish a high-quality IAV-human PPI dataset. Then we used conjoint triad (CT) and Moran autocorrelation (Moran) to encode biologically relevant features. The joint consideration utilizing the complementary information between contiguous and discontinuous amino acids provides a more comprehensive description of PPI information. After comparing different machine learning models, the eXtreme Gradient Boosting (XGBoost) model was determined as the final model for the prediction. The model achieved an accuracy of 96.89%, precision of 98.79%, recall of 94.85%, F1-score of 96.78%. Finally, we successfully identified 3,269 potential target proteins. Gene ontology (GO) and pathway analysis showed that these genes were highly associated with IAV infection. The analysis of the PPI network further revealed that the predicted proteins were classified as core proteins within the human protein interaction network. This study may encourage the identification of potential targets for the discovery of more effective anti-influenza drugs. The source codes and datasets are available at https://github.com/HVPPIlab/IVA-Human-PPI/.

Disulfidptosis as a key regulator of glioblastoma progression and immune cell impairment

Background

Glioblastoma, associated with poor prognosis and impaired immune function, shows potential interactions between newly identified disulfidptosis mechanisms and T cell exhaustion, yet these remain understudied.

Methods

Key genes were identified using Lasso regression, followed by multivariate analysis to develop a prognostic model. Single-cell pseudotemporal analysis explored disulfidptosis T-cell exhaustion (Tex) signaling in cell differentiation. Immune infiltration was assessed via ssGSEA, while transwell assays and immunofluorescence examined the effects of disulfidptosis-Tex genes on glioma cell behavior and immune response.

Results

Eleven disulfidptosis-Tex genes were found critical for glioblastoma survival outcomes. This gene set underpinned a model predicting patient prognosis. Single-cell analysis showed high disulfidptosis-Tex activity in endothelial cells. Memory T cell populations were linked to these genes. SMC4 inhibition reduced LN299 cell migration and increased chemotherapy sensitivity, decreasing CD4 and CD8 T cell activation.

Conclusions

Disulfidptosis-Tex genes are pivotal in glioblastoma progression and immune interactions, offering new avenues for improving anti-glioblastoma therapies through modulation of T cell exhaustion.

The role of CD101 and Tim3 in the immune microenvironment of gastric cancer and their potential as prognostic biomarkers

BACKGROUND

Gastric cancer (GC) is a prevalent malignancy. Current treatment modalities, including surgery, chemotherapy, radiotherapy, and targeted therapy, have limitations in early detection and personalized treatment, necessitating the discovery of novel biomarkers and therapeutic strategies. This study aims to elucidate the molecular mechanisms underlying GC, focusing on the differentially expressed genes (DEGs) of CD101- Tim3+ CD8+ T cells (CCT precursors) and CD101+ Tim3+ CD8+ T cells (CCT).

METHODS

Utilizing a multi-omics approach, including transcriptomic sequencing, single-cell RNA sequencing, cell communication analysis, and enrichment analysis.

RESULTS

We identified 140 genes significantly associated with overall survival in GC patients, including LYAR, ASCL2, and EMP2. A risk score model based on 14 prognostic genes was constructed, demonstrating a significant inverse correlation with survival time (p < 0.05). Immune response analysis indicated decreased infiltration of Activated B cells, CD56bright natural killer cells, and Monocytes in the high-risk group, while CD56dim natural killer cells and Gamma delta T cells were significantly increased, suggesting alterations in the immune microenvironment that influence patient prognosis. Furthermore, drug sensitivity analysis revealed potential responsiveness of high-risk patients to BI-2536, supporting personalized treatment approaches. Cell communication analysis indicated reduced intercellular interactions in PD-1 immunotherapy groups, highlighting the impact of immunotherapy on the tumor microenvironment. GSEA (Gene Set Enrichment Analysis) and GSVA (Gene Set Variation Analysis) revealed enrichment in DNA replication and proteasome pathways in high-risk groups, providing insights into the molecular mechanisms of GC.

CONCLUSIONS

This study established a foundation for future exploration of targeted therapies and personalized treatment strategies in GC.

Comprehensive Analysis of Immune Characteristics of Fluorosis and Cuprotosis-Related Genes in Fluorosis Targeted Drugs

This study aims to investigate the role of cuprotosis in fluorosis and identify potential targeted drugs for its treatment. The GSE70719 and GSE195920 datasets were merged using the inSilicoMerging package. DEGs between the exposure and control groups were found using R software. Overlapping genes of DEG and cuprotosis-related genes (CRGs) were obtained by Venn diagram and were enriched by GO and KEGG. Hub genes were identified using PPI networks and enriched by GSEA. ROC curves, the xCell algorithm, and consensus cluster analysis were utilized to evaluate diagnostic efficacy, examine immune cell infiltration, and identify cuproptosis subtypes, respectively. The GSE53937 dataset was used for external validation. The DSigDB database was used to predict small molecule drugs. Molecular docking was used to validate the relationship between small molecule drugs and hub genes. A total of 1522 DEGs (743 upregulated genes and 779 downregulated genes) and 33 overlapping genes of DEGs and CRGs were obtained. The 33 overlapping genes were enriched in ribosomal biogenesis and oxidative phosphorylation pathways. The hub genes DNTTIP2, GTPBP4, IMP4, MRPL12, MRPL13, MRPL2, MRPS2, MRPS22, NOP2, RSL1D1, and SURF6 were identified, demonstrating great diagnostic ability with AUC > 0.8. These hub genes were associated with immune response and inflammation. Two cuproptosis patterns were established based on 33 CRGs. Mepacrine was screened as a potential drug and demonstrated stability in docking with IMP4. In summary, the current study identified several CRGs that may serve as potential biomarkers for diagnosing fluorosis and are involved in fluoride-induced immune responses. Additionally, mepacrine was screened as a potential treatment for fluorosis by targeting CRGs.

Investigation of chromatin remodeling-related biomarkers and associated molecular mechanism in pulpitis

The current study aimed to identify potential chromatin remodeling-related biomarkers and the associated molecular mechanisms in pulpitis. Differentially expressed genes associated with chromatin remodeling (DECRGs) were identified using datasets from an online database. Enrichment and protein-protein interaction (PPI) network analyses were performed based on the DECRGs to identify biomarkers for pulpitis, followed by GSEA (gene set enrichment analysis). The diagnostic value of these biomarkers were evaluated by ROC (Receiver operating characteristic) and nomogram investigation. Next, microRNA(miRNA)-mRNA-TF (transcription factor), ceRNA (competing endogenous RNA), and drug prediction networks were constructed based on the biomarkers. Finally, reverse transcription-real-time quantitative PCR analysis and western blot were performed to validate the results of the bioinformatic analysis. This study identified 87 DECRGs between pulpitis and normal dental pulp samples that were mainly enriched in chromatin remodeling functions and pathways in cancer. A PPI network identified five biomarkers: TNF, STAT3, MYC, ACTB, and MAPK8. ROC and nomogram analyses demonstrated the diagnostic value of these biomarkers. GSEA of biomarkers such as STAT3 was mainly enriched in functions such as the B cell receptor signaling pathway. A biomarker-disease network and miRNA-mRNA-TF interactions were constructed using these biomarkers. A ceRNA network was constructed with interactions including chr22-38_28785274-29006793.1-miR-125b-5p-STAT3. A drug-gene network was established using 170 drugs and five biomarkers. Finally, qRT-PCR was used to validate the expression of all five biomarkers identified by the bioinformatics analysis. TNF, STAT3, MYC, ACTB, and MAPK8 are potential chromatin remodeling-related diagnostic markers for pulpitis. Moreover, long non-coding RNA (lncRNA) chr22-38_28785274-29006793.1 might function as a ceRNA to regulate the expression of the chromatin remodeling gene STAT3 by sponging miR-125b-5p in pulpitis.

New Insights into the Role of Inflammatory Pathways and Immune Cell Infiltration in Sleep Deprivation-Induced Atrial Fibrillation: An Integrated Bioinformatics and Experimental Study

Background

The common occurrence of atrial fibrillation (AF) as a cardiac arrhythmia, along with its link to sleep deprivation (SD), is gaining more acknowledgment. Even with progress in comprehending the development of AF, the molecular connections between SD and AF are still not well-defined. The objective of this research was to pinpoint the shared molecular routes responsible for SD-induced AF and investigate possible treatment targets.

Methods

Utilizing bioinformatics, we examined two transcriptome datasets from the Gene Expression Omnibus (GEO) database to pinpoint genes with differential expression (DEGs) common to SD and AF. Analyses focusing on functional enrichment, such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were conducted to pinpoint crucial biological mechanisms and pathways. Furthermore, we utilized immunofluorescence and Western blot techniques to evaluate YBX1 expression and its role in activating NLRP3 inflammasomes in a rat model induced by SD.

Results

A total of 540 common DEGs were precisely identified between the AF and SD data collections. Studies emphasizing functional enrichment have highlighted the significance of inflammation pathways, particularly the NOD-like receptor signaling route. The application of machine learning uncovered four crucial genes-CDC5L, MAPK14, RAB5A, and YBX1-with YBX1 becoming the predominant gene in diagnostic processes. Investigating immune penetration revealed significant connections between YBX1 expression and specific immune cell types, notably CD8+ T cells and M1 macrophages. Live studies have demonstrated that SD amplifies the atrial electrical rearrangement, structural changes, the infiltration of inflammatory cells, and the heightened presence of YBX1 along with inflammasome elements.

Conclusion

The research pinpoints YBX1 as a crucial gene in SD-related AF, possibly influencing its impact via the NOD-like receptor signaling route and the invasion of immune cells. The results offer crucial understanding of the molecular processes behind AF and propose YBX1 as a possible treatment focus to reduce the risk of AF caused by SD.

Telomere-to-telomere Phragmites australis reference genome assembly with a B chromosome provides insights into its evolution and polysaccharide biosynthesis

Phragmites australis is a globally distributed grass species (Poaceae) recognized for its vast biomass and exceptional environmental adaptability, making it an ideal model for studying wetland ecosystems and plant stress resilience. However, genomic resources for this species have been limited. In this study, we assembled a chromosome-level reference genome of P. australis containing one B chromosome. An explosion of LTR-RTs, centered on the Copia family, occurred during the late Pleistocene, driving the expansion of P. australis genome size and subgenomic differentiation. Comparative genomic analysis showed that P. australis underwent two whole gene duplication events, was segregated from Cleistogenes songorica at 34.6 Mya, and that 41.26% of the gene families underwent expansion. Based on multi-tissue transcriptomic data, we identified structural genes in the biosynthetic pathway of pharmacologically active Phragmitis rhizoma polysaccharides with essential roles in rhizome development. This study deepens our understanding of Arundinoideae evolution, genome dynamics, and the genetic basis of key traits, providing essential data and a genetic foundation for wetland restoration, bioenergy development, and plant stress.

Developing a ceRNA-based lncRNA-miRNA-mRNA regulatory network to uncover roles in skeletal muscle development

The precise role of lncRNAs in skeletal muscle development and atrophy remain elusive. We conducted a bioinformatic analysis of 26 GEO datasets from mouse studies, encompassing embryonic development, postnatal growth, regeneration, cell proliferation, and differentiation, using R and relevant packages (limma et al.). LncRNA-miRNA relationships were predicted using miRcode and lncBaseV2, with miRNA-mRNA pairs identified via miRcode, miRDB, and Targetscan7. Based on the ceRNA theory, we constructed and visualized the lncRNA-miRNA-mRNA regulatory network using ggalluvial among other R packages. GO, Reactome, KEGG, and GSEA explored interactions in muscle development and regeneration. We identified five candidate lncRNAs (Xist, Gas5, Pvt1, Airn, and Meg3) as potential mediators in these processes and microgravity-induced muscle wasting. Additionally, we created a detailed lncRNA-miRNA-mRNA regulatory network, including interactions such as lncRNA Xist/miR-126/IRS1, lncRNA Xist/miR-486-5p/GAB2, lncRNA Pvt1/miR-148/RAB34, and lncRNA Gas5/miR-455-5p/SOCS3. Significant signaling pathway changes (PI3K/Akt, MAPK, NF-κB, cell cycle, AMPK, Hippo, and cAMP) were observed during muscle development, regeneration, and atrophy. Despite bioinformatics challenges, our research underscores the significant roles of lncRNAs in muscle protein synthesis, degradation, cell proliferation, differentiation, function, and metabolism under both normal and microgravity conditions. This study offers new insights into the molecular mechanisms governing skeletal muscle development and regeneration.

The systematic analysis of genes related to butyrate metabolism suggests that CDKN3 could serve as a promising therapeutic target for lung adenocarcinoma treatment

BACKGROUND

Metabolic pathways are known to significantly impact the development and advancement of lung cancer. This study sought to establish a signature related to butyrate metabolism that is specifically linked to lung adenocarcinoma (LUAD).

METHODS

For the purpose of identifying butyrate metabolism-related differentially expressed genes (BMR-DEGs) in the TCGA-LUAD dataset, we introduced transcriptome data. This was followed by the implementation of the univariate Cox and LASSO analyses in order to construct a LUAD gene signature. We performed a comprehensive analysis of gene function enrichment between the two populations at risk, thoroughly examined their immune microenvironment characteristics, and assessed the effectiveness of immunotherapy. Finally, the function of CDKN3 in LUAD was verified by in vitro experiments.

RESULTS

Through a comprehensive analysis of the TCGA-LUAD dataset, 51 significant BMR-DEGs were confirmed. Subsequently, five characteristic genes, CPS1, ABCC2, CDKN3, SLC2A1, and IGFBP1 were identified to create prognostic features for butyrate metabolism related outcomes in LUAD. Cox regression analysis determined that the pathological T stage, tumor stage, and RiskScore could serve as independent prognostic indicators. Analysis of the abundance of 22 immune infiltrating cells revealed that 15 immune cell types exhibited substantial differences and were strongly associated with risk ratings and prognosis. An important correlation exists between risk ratings and immunological checkpoints, which can be utilized to forecast the efficacy of treatment. In the high-risk group, there was an upregulation of the expression of PD-L2, PD-L1, and PD-1. Additionally, the risk score showed a positive correlation with TIDE and Exclusion score, while showing a negative correlation with Dysfunction score. Furthermore, the IC50 values for cisplatin, paclitaxel, and docetaxel were notably elevated in the high-risk group, indicating that these medications could potentially provide therapeutic advantages for this particular group. Finally, we determined that knockdown CDKN3 inhibited the proliferation and metastasis of LUAD cells.

CONCLUSION

We identify and validate a novel BMR-related prognostic signature comprising 5 DEGs for LUAD patients. Our data might provide a new molecular target for LUAD treatment.

Screening of necroptosis-related genes and evaluating the prognostic capacity, clinical value, and the effect of their copy number variations in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is an aggressive hematological neoplasm. Little improvement in survival rates has been achieved over the past few decades. Necroptosis has relationship with certain types of malignancies outcomes. Here, we evaluated the diagnostic ability, prognostic capacity of necroptosis-related genes (NRGs) and the effect of their copy number variations (CNVs) in AML.

METHODS

Necroptosis-related differentially expressed genes (NRDEGs) were identified after intersecting differentially expressed genes (DEGs) from the Gene Expression Omnibus(GEO) database with NRGs from GeneCards, the Molecular Signatures Database (MSigDB) and literatures. Machine learning was applied to obtain hub-NRDEGs. The expression levels of the hub-NRDEGs were validated in vitro. The mRNA-miRNA and mRNA-TF interaction networks with the hub-NRDEGs were screened using Cytoscape@. Single-sample gene set enrichment analysis (ssGSEA) was utilized to calculate correlations between the hub-NRDEGs and immune cells. CNV analysis of the hub-NRDEGs was carried out on the TCGA-LAML datasets from the TCGA database. Kaplan-Meier (K-M) survival analyses were utilized to evaluate the prognostic values along with Cox model.

RESULTS

Six hub-NRDEGs (SLC25A5, PARP1, CTSS, ZNF217, NFKB1, and PYGL) were obtained and their expression changes derived from CNVs in AML were visualized. In total, 65 mRNA-miRNA and 80 mRNA-TF interaction networks with hub-NRDEGs were screened. The ssGSEA result showed the expression of RAPR1 was inversely related to CD56dim natural killer cells and the expression of CTSS was positive related to Myeloid-derived suppressor cells (MDSCs) in AML. The K-M results demonstrated that ZNF217 had significant difference in the duration of survival in AML patients. Cox regression models revealed that the hub-NRDEGs had better predictive power at year-1 and year-5.

CONCLUSION

These screened NRDEGs can be exploited as clinical prognostic predictions in AML patients, as well as potential biomarkers for diagnosis and therapeutic targeting.

Exploring mosquito virome dynamics within São Paulo Zoo: insights into mosquito-virus-environment interactions

Background

Mosquito-borne diseases have a significant public health threat worldwide, with arboviruses accounting for a high proportion of infectious diseases and mortality annually. Brazil, in particular, has been suffering outbreaks of diseases transmitted by mosquito viruses, notably those of the Aedes genus, such as dengue, Zika, and chikungunya. Against this background, the São Paulo Zoo is an intriguing ecological niche to explore the virome of mosquitoes, potentially shedding light on the dynamics of arbovirus transmission within a confined setting.

Methods

In this study, we conducted a comprehensive metagenomic analysis of mosquitoes collected from diverse habitats within the zoo, focusing on the Aedes, Anopheles, and Culex genera. From 1,039 contigs of viral origin, we identified 229 viral species infecting mosquitoes, with the orders Picornavirales, Nodamuvirales and Sobelivirales being the most prevalent and abundant. The difference in virome composition was primarily driven by mosquito host species rather than specific collection sites or trap height.

Results

Despite environmental disparities, the virome remained remarkably uniform across different areas of the zoo, emphasizing the strong association between mosquito species and their viral communities. Furthermore, we identified a core virome shared among mosquito species, highlighting potential cross-species transmission events and underscoring the need for targeted surveillance and control measures.

Conclusion

These results contribute to our understanding of the interplay between mosquitoes, the environment, and viruses, providing valuable insights for disease intervention strategies in mosquito-borne diseases.