Application of ggplot2 to Pharmacometric Graphics

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.

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.

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.

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.

Tryptophan metabolism-related gene CYP1B1 serves as a shared biomarker for both Parkinson's disease and insomnia

Parkinson's disease (PD) and insomnia are prevalent neurological disorders, with emerging evidence implicating tryptophan (TRP) metabolism in their pathogenesis. However, the precise mechanisms by which TRP metabolism contributes to these conditions remain insufficiently elucidated. This study explores shared tryptophan metabolism-related genes (TMRGs) and molecular mechanisms underlying PD and insomnia, aiming to provide insights into their shared pathogenesis. We analyzed datasets for PD (GSE100054) and insomnia (GSE208668) obtained from the Gene Expression Omnibus (GEO) database. TMRGs were obtained from the Molecular Signatures Database (MSigDB) and the Genecards database. Tryptophan metabolism-related differentially expressed genes (TM-DEGs) were identified by intersecting TMRGs with shared differentially expressed genes (DEGs) from these datasets. Through Protein-Protein Interaction (PPI) network analysis, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) , and Extreme Gradient Boosting (XGBoost) machine learning, we identified Cytochrome P4501B1 (CYP1B1) and Electron Transfer Flavoprotein Alpha (ETFA) as key hub genes. Subsequently, we employed CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) to further investigate the association between hub genes and peripheral immune activation and inflammatory response. Additionally, gene interaction, Drug-mRNA, Transcription Factor (TF)-mRNA, and competing endogenous RNA (ceRNA) networks centered on these hub genes were constructed to explore regulatory mechanisms and potential drug interactions. Finally, validation through bioinformatics and animal experiments identified CYP1B1 as a promising biomarker associated with both PD and insomnia.

Clinical, pathological and gene expression profiling of estrogen receptor discordance in breast cancer

BACKGROUND

Breast cancer (BC) is the world's largest tumor species in which hormone receptor-positive patients have relatively good prognosis. However, majority of patients will develop late resistance, one of the important factors is due to the loss of the original estrogen receptor (ER) expression.

METHODS

We conducted this study in 115 patients with BC who experienced second biopsy at Jiangsu Province Hospital (JSPH) and divided patients into two subgroups ER + to - and ER + to + . First, clinicopathological characteristics between two groups were evaluated. Second, we explored candidate genes related to BC ER intratumor heterogeneity by applying next-generation sequencing (NGS) in 42 patients. Multi-omics integrative analysis of tumor transcriptomic, cancer-related pathway, diagnostic and prognostic value and immune profile were conducted. Besides, preliminary assay were also used to evaluate the correlation between KMT2C and ERα (ESR1) expression. The CCK-8, 5-Ethynyl-2'-deoxyuridine (EdU) assays, Transwell assays and the wound scratch tests were applied to explore the cellular interactions between KMT2C and BC.

RESULTS

We find the histological type (p = 0.008) and disease-free survival (DFS) (p = 0.004) were significantly different in two subgroups. In Cox survival analysis, metastasis (Hazard ratio (HR) > 1, p = 0.007) and neo-adjuvant (HR < 1, p < 0.001) are independent prognostic factors of DFS. Besides, by analyzing NGS results, we found four genes KMT2C, FGFR19, FGF1 and FGF4 were highly mutated genes in ER + to - subgroup. Furthermore, the gene KMT2C displayed significant diagnostic value and prognostic value in BC and pan-cancer. In addition, a positive correlation between KMT2C expression and immune infiltrating levels of T cell CD4 + , macrophage and neutrophil was found. In the end, Western blot and RT-qPCR assay were used and found KMT2C and ERα (ESR1) expressions are strongly positive correlated in mRNA and protein level. Inhibition of KMT2C significantly reduced proliferation, invasion, and migration of MCF7 cells.

CONCLUSION

People in two cohorts from JSPH presented different clinical characteristics and prognosis. The gene KMT2C may affect the progression of BC by regulating the molecular, epigenetic activity and immune infiltration. It may also serve as a novel prognostic biomarker for BC patients who underwent ER status converted from positive to negative.

HPV-16 E6 mutation and viral integration related host DNA methylation implicate the development and progression of cervical cancer

BACKGROUND

HPV-16 infection and viral-host integration are the most important risk factors for cervical cancer (CC). The aim of this study is to develop a new molecular strategy integrated both the viral and host genome variations identifying and monitoring CC.

METHOD

A total of 312 methylation and 538 RNA-seq datasets were collected from public databases to identify differentially methylated and expressed genes. HPV associated virus integration sites (VISs) were analysed using the ViMIC database. From September 2020 to August 2021, the 70 HPV-16 positive cases retrospectively collected from multi-centre cohorts were subjected to HPV-16 E6 deep sequencing and PCR-based host gene (ASTN1, DLX1, ITGA4, RXFP3, SOX17, ZNF671) methylation detection. RNAseq and expression validation (NNF671) were performed in C-33A cell line harbouring HPV D32E. Lasso and logistic regression algorithm were used to construct the CC diagnostic model.

RESULTS

A positive correlation was observed between the average methylation level of CC patients and their pathological features including tumour stage (p = 0.0077) and HPV subtype (p < 0.001). ZNF671 was identified as a CC-specific methylation marker, with an impressive 93% sensitivity. Both HPV-16 D32E mutation and integration of HPV-16 down-regulated the ZNF671 expression. Finally, a CC diagnostic nomogram was developed by integrating ZNF671 methylation level and HPV E6 mutation feature, yielding an exceptional AUC of 0.997 (95% CI: 0.934-1.000).

CONCLUSIONS

Our study demonstrated HPV viral mutations are closely related to host gene epigenetic alterations in CC. Integration of the viral and host genetic information might be a new promising strategy for CC screening.

Integrated Bioinformatics Analysis and Target Drug Prediction of Inflammatory Bowel Disease Co-existent Diabetes Mellitus

INTRODUCTION

Inflammatory bowel disease (IBD) has become one of the public problems worldwide and its incidence rate is increasing year by year. Its concomitant disease i.e. diabetes mellitus (DM) has attracted more and more attention due to DM altering the progression of IBD and leading to long periods of intermittent recurrence and deterioration. The common mechanism and potential target drug of IBD with comorbid chronic conditions of DM were explored.

METHODS

Gene expression profile data were downloaded from the Gene Expression Omnibus (GEO) public database. The differentially expressed genes (DEGs) were identified by R software. GO annotation and pathway enrichment were performed, a protein-protein interaction (PPI) network was constructed, associated lncRNAs were predicted and drug prediction targeting key genes was made. Additionally, the regulatory network among core genes, associated pathways, and predicted lncRNA in IBD with coexistent DM were visualized.

RESULTS

We identified the critical gene MMP3 with lncRNA CDKN2BAS involved in the PPAR pathway, which uncovered the underlying regulatory mechanism of IBD with coexistent DM. We also predicted the potential therapeutic compound ZINC05905909 acting on MMP3.

CONCLUSION

Our findings revealed the regulatory mechanism chain of critical gene MMP3, lncRNA CDKN2BAS, and PPAR pathway and provided potential therapeutic compound ZINC05905909 for drug therapy to treat comorbid IBD DM.

Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer

BACKGROUND

Lung cancer is one of the most common cancers worldwide, with the incidence increasing each year. It is crucial to improve the prognosis of patients who have lung cancer. Non-Small Cell Lung Cancer (NSCLC) accounts for the majority of lung cancer. Though its prognostic significance in NSCLC has not been often documented, Endoplasmic Reticulum (ER) stress has been identified to be implicated in tumour malignant behaviours and resistance to treatment.

OBJECTIVE

This work aimed to develop a gene profile linked to ER stress that could be applied to predictive and risk assessment for non-small cell lung cancer.

METHODS

Data from 1014 NSCLC patients were sourced from The Cancer Genome Atlas (TCGA) database, integrating clinical and Ribonucleic Acid (RNA) information. Diverse analytical techniques were utilized to identify ERS-associated genes associated with patients' prognoses. These techniques included Kaplan-Meier analysis, univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression analysis (LASSO) regression, and Pearson correlation analysis. Using a risk score model obtained from multivariate Cox analysis, a nomogram was created and validated to classify patients into high- and low-risk groups. The study employed the CIBERSORT algorithm and Single-Sample Gene Set Eenrichment Analysis (ssGSEA) to investigate the tumour immune microenvironment. We used the Genomics of Drug Sensitivity in Cancer (GDSC) database and R tools to identify medicines that could be responsive.

RESULTS

Four genes - FABP5, C5AR1, CTSL, and LTA4H - were chosen to create the risk model. Overall Survival (OS) was considerably lower (P< 0.05) in the high-risk group. When it came to predictive accuracy, the risk model outperformed clinical considerations. Several medication types that are sensitive to high-risk groups were chosen.

CONCLUSION

Our study has produced a gene signature associated with ER stress that may be employed to forecast the prognosis and therapeutic response of non-small cell lung cancer patients.

Molecular mechanisms of ferroptosis in renal ischemia-reperfusion injury Investigated via bioinformatics analysis and animal experiments

Kidney transplantation is a pivotal treatment for end-stage renal disease. However, renal ischemia-reperfusion injury (IRI) during surgery significantly impacts graft function. Despite unclear molecular mechanisms, no specific therapies or preventative measures are available. Gene expression profiles from renal biopsies before and after IRI were downloaded from public databases. Differentially expressed genes were identified using the Wilcoxon rank-sum test and weighted gene co-expression network analysis. Ferroptosis-associated genes were screened using the FerrDb database. The genes with the highest connectivity were identified via the protein-protein interaction (PPI) network and upstream regulatory miRNAs were found through the gene-miRNA network. A mouse renal IRI model was constructed for transcriptome sequencing and quantitative real-time polymerase chain reaction (qRT-PCR) validation to elucidate the relationship between key ferroptosis genes and regulatory miRNAs in renal IRI. Differential analysis identified 15 ferroptosis-associated genes (TNFAIP3, IL6, KLF2, EGR1, JUN, ZFP36, GDF15, CDKN1A, HSPB1, BRD2, PDK4, DUSP1, SLC2A3, DDIT3, and CXCL2) involved in renal IRI regulation. In animal experiments, ferroptosis-related genes were also upregulated in the model group. Enrichment analysis and hematoxylin-eosin pathological staining suggested these genes are primarily involved in renal inflammatory responses. PPI network analysis revealed IL6 as the gene with the highest connectivity, and the gene-miRNA network indicated IL6 might be regulated by miR-let-7a. Animal experiments revealed decreased miR-let-7a and increased IL6 levels in the model group, identifying potential therapeutic targets. MiR-let-7a regulates ferroptosis in renal IRI by targeting IL6, highlighting IL6 as a crucial gene in the ferroptosis process of renal IRI.

Bioinformatics identifies key genes and potential therapeutic targets in the pathological mechanism of oxidative stress in Randall's plaque

Randall's plaque (RP) is recognized as a precursor lesion for kidney stones, with its formation and progression potentially linked to oxidative stress. Previous studies have provided limited insights into the underlying mechanisms of RP, failing to fully elucidate its molecular pathways. To investigate the relationship between oxidative stress and RP, we employed bioinformatics approaches to identify key genes, predict associated pathways and drug molecules, analyze variations in immune cell populations, and construct diagnostic models. We initially identified three differentially expressed genes related to oxidative stress: BFSP1, LONF1, and TAF1D. These genes and their co-expressed counterparts are enriched in pathways related to oxidative phosphorylation, cellular adhesion processes, steroid hormone biosynthesis, and autophagy. Furthermore, we observed significant differences in two types of immune cells across the study groups. Ultimately, predictions from drug molecular docking suggest that BFSP1 may serve as a promising therapeutic target for RP. We propose that the formation of RP mediated by oxidative stress could be associated with BFSP1, LONF1, TAF1D along with CD56dim natural killer cells and memory B cells. Thus far, BFSP1 emerges as a pivotal therapeutic target for RP development. These findings offer new perspectives on the mechanisms underlying the pathogenesis of RP.

Mission SpaceX CRS-19 RRRM-1 space flight induced skin genomic plasticity via an epigenetic trigger

Genomic plasticity helps adapt to extreme environmental conditions. We tested the hypothesis that exposure to space environment (ESE) impacts the epigenome inducing genomic plasticity. Murine skin samples from the Rodent Research Reference Mission-1 were procured from the International Space Station (ISS) National Laboratory. Targeted RNA sequencing to test differential gene expression between the skin of ESE versus ground controls revealed upregulation of VEGF-mediated angiogenesis pathways secondary to promoter hypomethylation in responders. Methylome sequencing identified ESE-sensitive hypomethylated genes including developmental angiogenic genes Araf, Vegfb, and Vegfr1. Based on differentially expressed genes, the angiogenesis biofunction was enriched in responders. The induction of genomic plasticity in response to ESE, as reported herein, may be viewed as a mark of biological resilience that is evident in a minority of organisms, responders but not in non-responders, exposed to the same stressor. Inducible genomic plasticity may be implicated in natural resilience to ESE.

Metabolism-related proteins as biomarkers for predicting prognosis in polycystic ovary syndrome

OBJECTIVE

The study aimed to explore the role of metabolism-related proteins and their correlation with clinical data in predicting the prognosis of polycystic ovary syndrome (PCOS).

METHODS

This research involves a secondary analysis of proteomic data derived from endometrial samples collected from our study group, which includes 33 PCOS patients and 7 control subjects. A comprehensive identification and analysis of 4425 proteins were conducted to screened differentially expressed proteins (DEPs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were subsequently performed on the DEPs. To identify independent prognostic metabolism-related proteins, univariate Cox regression and LASSO regression were applied. The expression levels of these proteins were then used to develop a prognostic model, with their predictive accuracy evaluated through receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. Furthermore, we also investigate the correlation between clinical data and prognostic proteins.

RESULTS

The study identified 285 DEPs between the PCOS and control groups. GO enrichment analysis revealed significant involvement in metabolic processes, while KEGG pathway analysis highlighted pathways such as glycolysis/gluconeogenesis and glucagon signaling. Ten key metabolism-related proteins (ACSL5, ANPEP, CYB5R3, ENOPH1, GLS, GLUD1, LDHB, PLCD1, PYCR2, and PYCR3) were identified as significant predictors of PCOS prognosis. Patients were separated into high and low-risk groups according to the risk score. The ROC curves for predicting outcomes at 6, 28, and 37 weeks demonstrated excellent predictive performance, with AUC values of 0.98, 1.0, and 1.0, respectively. The nomogram constructed from these proteins provided a reliable tool for predicting pregnancy outcomes. DCA indicated a net benefit of the model across various risk thresholds, and the calibration curve confirmed the model's accuracy. Additionally, we also found BMI exhibited a significant negative correlation with the expression of GLS (r =-0.44, p = 0.01) and CHO showed a significant positive correlation with the expression of LDHB (r = 0.35, p = 0.04).

CONCLUSION

The identified metabolism-related proteins provide valuable insights into the prognosis of PCOS. The protein based prognostic model offers a robust and reliable tool for risk stratification and personalized management of PCOS patients.

Association between hypothyroidism and obstructive sleep apnea: a bidirectional Mendelian randomization study combined with the geo database

Background

The causal relationship between hypothyroidism and obstructive sleep apnea (OSA) remains controversial. Therefore, our research used a bidirectional Mendelian randomization (MR) method in an attempt to determine the causal relationship between hypothyroidism and OSA.

Methods

From the publicly accessible genome-wide association analysis (GWAS) summary database, we obtained single nucleotide polymorphism (SNPs) data pertaining to hypothyroidism and OSA. Inverse variance weighting (IVW) was the principal method of analysis utilized, with validation also conducted via weighted median, MR-Egger, simple model, and weighted model approaches. To further evaluate the robustness of the results, heterogeneity testing, pleiotropy testing, and the "leave-one-out" sensitivity analysis were performed. Differentially expressed genes (DEGs) from the OSA dataset (GSE135917) and hypothyroidism dataset (GSE176153) derived from the Gene Expression Omnibus (GEO) database were screened using the "limma" package. The "clusterProfiler" and "GO plot" packages were used for further enrichment analysis in order to validate the findings of the MR study. The Cytoscape software was utilized to build a protein-protein interaction (PPI) network of DEGs and to screen for hub genes.

Results

The MR analysis showed that genetically predicted hypothyroidism was associated with an increased risk of OSA [IVW odds ratio (OR) = 1.734; 95% confidence interval (CI) = 1.073-2.801; p = 0.025]. The trend of the outcomes of the other approaches is consistent with the trend of the IVW outcome. However, the reverse MR analysis suggested no evidence for the causal effect of OSA on hypothyroidism (IVW OR = 1.002, 95% CI: 0.996-1.009, p = 0.454). The robustness of the results was confirmed by the sensitivity analysis. Bioinformatics analysis revealed that there were DEGs that hypothyroidism and OSA have in common.

Conclusion

Our findings suggested that hypothyroidism may increase the risk of OSA, while the effect of OSA on hypothyroidism was not found in this MR study. Thus, patients with hypothyroidism should be enhanced with screening for OSA for early diagnosis and appropriate treatment.

Personalized Secukinumab Treatment in Patients with Plaque Psoriasis Using Model-Informed Precision Dosing

Background/Objectives: Patient care and control of inflammatory disorders, such as psoriasis, can be improved by model-informed precision dosing (MIPD) techniques based on population pharmacokinetic/pharmacodynamic (PK/PD) models. Clinical dose selection decisions based on MIPD strategies need to take account of the uncertainty associated with the individual PK/PD model parameters, which is determined by the quantity of individual observational data collected in clinical practice. Methods: The aim of this study was to propose an approach for personalized dosage regimens of secukinumab (SCK) in 22 Spanish patients with plaque psoriasis, whose severity level was considered moderate to severe, taking into account the uncertainty associated with individual parameters in a population-based PK/PD model. Results: The link between SCK serum concentrations and Psoriasis Area and Severity Index (PASI) scores was explained using an indirect response model. A maximum inhibition (Imax) drug effect model was applied to limit the progression of psoriatic skin lesions within the turnover PD mechanism, which explains the changes in PASI scores during treatment. A first-order remission rate constant for psoriatic lesions (kout = 0.11 day-1) was estimated. Conclusions: According to the MIPD strategy, 50% of patients would require an optimized regimen and 14% would require an intensified dosage regimen in comparison to current clinical treatment. This research has shown its usefulness as a tool for choosing individualized SCK dosage regimens in patients with long-lasting plaque psoriasis to improve the probability of achieving satisfactory response levels.

Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation

Background

Diabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therapeutic targets. This study aims to identify differentially expressed PANoptosis-related genes (DE-PRGs) in DR, offering insights into the disease's pathogenesis and potential diagnostic tools.

Methods

DR datasets were obtained from the Gene Expression Omnibus (GEO) database, while PANoptosis-related genes were sourced from the GeneCards database. Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. Three machine learning algorithms-LASSO regression, Random Forest, and SVM-RFE-were employed to identify hub genes. A diagnostic nomogram was constructed and its performance assessed via ROC analysis. The CIBERSORT algorithm analyzed immune cell infiltration. Hub genes were validated through RT-qPCR, Western blotting, immunohistochemistry, and publicly available datasets. Additionally, the impact of FASN and PLSCR3 knockdown on HUVECs behavior was validated through in vitro experiments.

Results

Differential expression analysis identified 1,418 DEGs in the GSE221521 dataset, with 39 overlapping DE-PRGs (29 upregulated, 10 downregulated). Functional enrichment indicated that DE-PRGs are involved in apoptosis, signal transduction, and inflammatory responses, with key pathways such as MAPK and TNF signaling. Machine learning algorithms identified six PANoptosis-related hub genes (BEX2, CASP2, CD36, FASN, OSMR, and PLSCR3) as potential biomarkers. A diagnostic nomogram based on these hub genes showed high diagnostic accuracy. Immune cell infiltration analysis revealed significant differences in immune cell patterns between control and DR groups, especially in Activated CD4 Memory T Cells and Monocytes. Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. Furthermore, experiments demonstrated that knocking down FASN and PLSCR3 impacted HUVECs behavior.

Conclusion

This study provides valuable insights into the molecular mechanisms of DR, particularly highlighting PANoptosis-related pathways, and identifies potential biomarkers and therapeutic targets for the disease.

A Six-Gene Signature Related to Liquid-Liquid Phase Separation for Diagnosis of Alzheimer's Disease

BACKGROUND

Liquid-liquid phase separation (LLPS) has been increasingly recognized as a crucial mechanism in the pathogenesis of various neurodegenerative disorders, including Alzheimer's disease (AD). There remains a paucity of effective diagnostic biomarkers for this condition. This study aims to develop and validate a novel LLPS-related molecular signature to enhance the diagnostic accuracy and early detection of AD.

METHODS

LLPS-related genes were identified from online databases and subjected to bioinformatic analyses, including protein-protein interaction (PPI) network analysis and least absolute shrinkage and selection operator (LASSO) regression. Based on the optimal LLPS-related genes, a diagnosis risk model was constructed, and the diagnostic ability was evaluated using a receiver operator characteristic (ROC) curve. To elucidate the biological functions of the identified LLPS-related genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted.

RESULTS

A total of 149 LLPS-related genes were screened, which were found to be involved in functions related to oxidative stress, apoptosis, and cancer progression. The 149 genes were refined to six optimal candidates through PPI network analysis and LASSO regression: Activator of HSP90 ATPase Activity 1 (AHSA1), Eukaryotic Translation Initiation Factor 2 Alpha Kinase 2 (EIF2AK2), Heat Shock Protein Family A (Hsp70) Member 4 (HSPA4), Notch Receptor 1 (NOTCH1), Superoxide Dismutase 1 (SOD1), and Thioredoxin (TXN). Based on the six optimal genes, a diagnostic risk model was constructed, and the diagnostic ability was verified to be promising in AD both in training, internal validation, and two external validation datasets, with area under ROC curve (AUC) above 0.8. Furthermore, significant correlations were observed between the expression of these genes and tumor immune cell infiltration.

CONCLUSIONS

A six-gene diagnosis model was constructed and verified to exhibit robust diagnostic ability in AD.

Differential Transcriptome Reprogramming Induced by the Soybean Cyst Nematode Type 0 and Type 1.2.5.7 During Resistant and Susceptible Interactions

Soybean cyst nematode (SCN, Heterodera glycines [Hg]) is a serious root parasite of soybean (Glycine max) that induces extensive gene expression changes associated with pleiotropic biological activities in infected cells. However, the impacts of various SCN Hg types on host transcriptome reprogramming remain largely unknown. Here, we developed and used two recombinant inbred lines (RIL; RIL-72 and RIL-137) to profile transcriptome reprogramming in the infection sites during the resistant and susceptible interactions with SCN Hg Type 1.2.5.7 and Type 0. SCN bioassays indicated that RIL-72 was susceptible to Type 1.2.5.7 but resistant to Type 0, whereas RIL-137 was resistant to both types. Comparative analysis of gene expression changes induced by Type 1.2.5.7 in the resistant and susceptible lines revealed distinct transcriptome regulation with a number of similarly and oppositely regulated genes. The expression levels of similarly regulated genes in the susceptible line appeared to be insufficient to mount an effective defense against SCN. The functional importance of oppositely regulated genes was confirmed using virus-induced gene silencing (VIGS) and overexpression approaches. Further transcriptome comparisons revealed shared as well as Hg type- and genotype-specific transcriptome reprogramming. Shared transcriptome responses were mediated through common SCN-responsive genes and conserved immune signaling, whereas genotype-specific responses were derived from genetic variability, metabolic and hormonal differences, and varied regulation of protein phosphorylation and ubiquitination. The conserved defense mechanisms together with genotype-specific responses would enable plants to trigger effective and tailored immune responses to various Hg types and adapt the defense response to their genetic backgrounds. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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.

The diagnostic value investigation of programmed cell death genes in heart failure

BACKGROUND

We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF).

METHODS

Three HF gene expression data were extracted from the GEO database, including GSE57345 (training data), GSE141910 and GSE76701 (validation data), followed by differentially PCD related genes (DPCDs) was shown between HF and control samples. Enrichment and protein-protein interaction (PPI) network analyses were performed based on the DPCDs. Subsequently, a diagnostic model was constructed and validated after exploring the diagnostic markers using machine learning. A nomogram was used to determine the clinical diagnostic value. Diagnostic marker-based immune, transcription network, and gene set enrichment (GSE) analyses were performed. Finally, the drug-target network was investigated.

RESULTS

Twenty DPCDs were revealed between the two groups. These genes, such as Serpin Family E Member 1 (SERPINE1), are mainly enriched in pathways such as the regulation of the inflammatory response. A PPI network was constructed using 14 DPCDs. Eight diagnostic markers, such as SERPINE1, CD38 molecule (CD38), and S100 calcium-binding protein A9 (S100A9), were explored using machine learning algorithms, followed by diagnostic model construction. A nomogram and immune-associated analysis was used to validate the diagnostic value of these genes and the model. Moreover, the transcription regulation network and drug-target interactions were further investigated. Finally, qRT-PCR confirmed that the expression levels of eight signature genes (CD14, CD38, CTSK, LAPTM5, S100A9, SERPINE1, SLC11A1, and STAT3) were significantly elevated in the observation group, consistent with the results of bioinformatics analysis.

CONCLUSIONS

This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers.

Benign Evolution of Complex Regional Pain Syndrome (CRPS) Type 1 in Patients Treated with Intravenous Neridronate: A Single-Center Real-Life Experience

OBJECTIVE

To investigate the long-term effects of intravenous neridronate treatment in patients with complex regional pain syndrome type 1 (CRPS) in a real-life setting.

METHODS

We conducted a retrospective study on consecutive CRPS patients treated at our hospital from February 2018 to July 2023. All were treated within three months of the onset of CRPS symptoms. The Patient-Reported Outcomes Measurement Information System 29-Item Health Profile (PROMIS-29) version 2.1 was administered. The main outcome of interest was the evolution of the PROMIS-29 scores from baseline to the last follow-up visit. Patients were categorized as "complete responders" or "non-complete responders". The association of clinical and demographic variables with a complete response was analyzed using chi-square tests and univariate logistic regression.

RESULTS

Thirty-six patients were included, with a median follow-up time of 4.8 years. A significant improvement was noted in the mean numerical pain rating scale (from 6.4 ± 1.9 to 3.1 ± 2.4, p < 0.001), as well as across all PROMIS-29 domains. Physical function improved from 34.2 ± 4.9 to 49.2 ± 9.9, p < 0.001; anxiety from 58.0 ± 6.7 to 49.6 ± 6.9, p < 0.001; depression from 55.3 ± 6.3 to 47.7 ± 6.6, p < 0.001; fatigue from 55.7 ± 7.7 to 50.9 ± 8.7, p < 0.001; sleep disturbance from 53.8 ± 6.8 to 51.3 ± 6.6, p = 0.034; social roles and activities from 41.8 ± 5.2 to 51.8 ± 8.9, p < 0.001; and pain interference from 64.1 ± 5.9 to 52.4 ± 9.9, p < 0.001. The likelihood of achieving a complete response was associated with the male sex, foot or ankle injuries (compared to hand and wrist injuries), and a younger age. No association was found with the type of inciting event or with the body mass index.

CONCLUSIONS

Our real-life data indicate that early treatment with neridronate leads to substantial benefits in patients affected by CRPS type 1. The strongest responses are seen in young patients, males, and those with lower limb involvement.

Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications

Background

Cancer cells induce neutrophil extracellular traps (NETs) to promote tumor progression and metastasis. However, only a few studies have focused on the role of NETs in Neuroblastoma (NB).

Methods

First, based on the expression of NET-related genes, consensus clustering analysis was conducted to cluster NB samples into different subtypes. Differential analysis was performed to identify DEGs between subtypes. Functional items and related pathways of DEGs were identified using enrichment analysis. Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. Furthermore, independent prognostic analysis was performed. Immune infiltration analysis was performed to identify differential immune cells. Finally, the verification of prognostic model genes were taken by the immunohistochemical staining and quantitative real-time PCR.

Results

Consensus clustering analysis demonstrated that NB samples were clustered into two subtypes. There were 125 DEGs between the two subtypes of NB. Moreover, the enrichment analysis results showed that the DEGs were mainly associated with 'external side of plasma membrane,' 'immune receptor activity' 'regulation of leukocyte migration' GO items. There were also several GO items related to neutrophils, such as regulation of neutrophil migration and differentiation. KEGG pathways revealed that the DEGs were correlated with in immunity-related activities, including 'Complement and coagulation cascades,' 'Neutrophil extracellular trap formation, 'T cell receptor signaling pathway,' 'PD-L1 expression and PD-1 checkpoint pathway in cancer' and so on. A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. Furthermore, a nomogram for forecasting the survival rates of patients with NB was established based on the risk score, age at diagnosis, and MYCN status. Eight differential immune cells (CD8 + T cells, resting mast cells, etc.) were acquired between the two risk subgroups. The expression levels of five prognostic model genes at the protein and mRNA were verified and all results were consistent with the results of our bioinformatics analysis.

Conclusion

We initially found that five NET-related genes were significantly differentially expressed in NETs-associated molecular isoforms and two Netrg molecular isoforms were found to be associated with poorer prognosis. This stratification might provide insight into the prediction of prognosis and ideal immunotherapy strategies for patients with NB. However, we also noted that the formation of NETs is a complex biological process involving the regulation of multiple cytokines and cellular interactions. Therefore, the exact roles of these genes and their specific mechanisms in the formation of NETs and the development of NB still need to be further investigated.

Dissecting L-glutamine metabolism in acute myeloid leukemia: single-cell insights and therapeutic implications

BACKGROUND

Acute myeloid leukemia (AML) is a rapidly progressing blood cancer. The prognosis of AML can be challenging, emphasizing the need for ongoing research and innovative approaches to improve outcomes in individuals affected by this formidable hematologic malignancy.

METHODS

In this study, we used single-cell RNA sequencing (scRNA-seq) from AML patients to investigate the impact of L-glutamine metabolism-related genes on disease progression.

RESULTS

Our analysis revealed increased glutamine-related activity in CD34 + pre-B cells, suggesting a potential regulatory role in tumorigenesis and AML progression. Furthermore, intercellular communication analysis revealed a significant signaling pathway involving macrophage migration inhibitory factor signaling through CD74 + CD44 within CD34 + pre-B cells, which transmit signals to pre-dendritic cells and monocytes. Ligands for this pathway were predominantly expressed in stromal cells, naïve T cells, and CD34 + pre-B cells. CD74, the pertinent receptor, was predominantly detected in a variety of cellular components, including stromal cells, pre-dendritic cells, plasmacytoid dendritic cells, and hematopoietic progenitors. The study's results provide insights into the possible interplay among these cell types and their collective contribution to the pathogenesis of AML. Moreover, we identified 10 genes associated with AML prognosis, including CCL5, CD52, CFD, FABP5, LGALS1, NUCB2, PSAP, S100A4, SPINK2, and VCAN. Among these, CCL5 and CD52 have been implicated in AML progression and are potential therapeutic targets.

CONCLUSIONS

This thorough examination of AML biology significantly deepens our grasp of the disease and presents pivotal information that could guide the creation of innovative treatment strategies for AML patients.

Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning

Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over the past few decades. However, the intricate interplay between genetic factors and endocrine disruptors (EDCs) in the pathogenesis of OP remains inadequately elucidated. The objective of this research is to examine the environmental pollutants and their regulatory mechanisms that could potentially influence the pathogenesis of OP, in order to establish a theoretical foundation for the targeted prevention and medical management of individuals with OP. Utilizing CTD and GEO datasets, network toxicology and bioinformatics analyses were conducted to identify target genes from a pool of 98 co-associated genes. Subsequently, a novel prediction model was developed employing a multiple machine learning algorithm. The efficacy of the model was validated based on the area under the receiver operating characteristic curve. Finally, real-time quantitative polymerase chain reaction (qRT-PCR) was used to confirm the expression levels of key genes in clinical samples. We have identified significant genes (FOXO3 and LUM) associated with OP and conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, immune infiltration analysis, and molecular docking analysis. Through the analysis of these key genes, we have identified 13 EDCs that have the potential to impact OP. Several endocrine disruptors, such as Dexamethasone, Perfluorononanoic acid, genistein, cadmium, and bisphenol A, have been identified as notable environmental pollutants that impact the OP. Molecular docking analysis revealed significant binding affinity of major EDCs to the post-translational protein structures of key genes. This study demonstrates that EDCs, including dexamethasone, perfluorononanoic acid, genistein, cadmium, and bisphenol A, can be identified as important environmental pollutants affecting OP, and that FOXO3 and LUM have the potential to be diagnostic markers for OP. These results elucidate a novel association between EDCs regulated by key genes and the onset of OP.

Identification of key genes in diabetic nephropathy based on lipid metabolism

Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes with a high incidence rate. Notably, the disturbance of lipid metabolism is associated with DN progression. The present study aimed to identify lipid metabolism-related hub genes associated with DN for improved diagnosis of DN. The gene expression profile data of DN and healthy samples (GSE142153) were obtained from the Gene Expression Omnibus database, and the lipid metabolism-related genes were obtained from the Molecular Signatures Database. Differentially expressed genes (DEGs) between DN and healthy samples were analyzed. The weighted gene co-expression network analysis (WGCNA) was performed to examine the relationship between genes and clinical traits to identify the key module genes associated with DN. Next, the Venn Diagram R package was used to identify the lipid metabolism-related genes associated with DN and their protein-protein interaction (PPI) network was constructed. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The hub genes were identified using machine-learning algorithms. The Gene Set Enrichment Analysis (GSEA) was used to analyze the functions of the hub genes. The present study also investigated the immune infiltration discrepancies between DN and healthy samples, and assessed the correlation between the immune cells and hub genes. Finally, the expression levels of key genes were verified by reverse transcription-quantitative (RT-q)PCR. The present study determined 1,445 DEGs in DN samples. In addition, 694 DN-related genes in MEyellow and MEturquoise modules were identified by WGCNA. Next, the Venn Diagram R package was used to identify 17 lipid metabolism-related genes and to construct a PPI network. GO analysis revealed that these 17 genes were markedly associated with 'phospholipid biosynthetic process' and 'cholesterol biosynthetic process', while the KEGG analysis showed that they were enriched in 'glycerophospholipid metabolism' and 'fatty acid degradation'. In addition, SAMD8 and CYP51A1 were identified through the intersections of two machine-learning algorithms. The results of GSEA revealed that the 'mitochondrial matrix' and 'GTPase activity' were the markedly enriched GO terms in both SAMD8 and CYP51A1. Their KEGG pathways were mainly concentrated in the 'pathways of neurodegeneration-multiple diseases'. Immune infiltration analysis showed that nine types of immune cells had different expression levels in DN (diseased) and healthy samples. Notably, SAMD8 and CYP51A1 were both markedly associated with activated B cells and effector memory CD8 T cells. Finally, RT-qPCR confirmed the high expression of SAMD8 and CYP51A1 in DN. In conclusion, lipid metabolism-related genes SAMD8 and CYP51A1 may play key roles in DN. The present study provides fundamental information on lipid metabolism that may aid the diagnosis and treatment of DN.

Exogenous PD-L1 binds to PD-1 to alleviate and prevent autism-like behaviors in maternal immune activation-induced male offspring mice

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder caused by the interaction of multiple pathogenic factors. Epidemiological studies and animal experiments indicate that maternal immune activation (MIA) is closely related to the development of ASD in offspring. A large number of pro-inflammatory cytokines are transferred from the placenta to the fetal brain during MIA, which impedes fetal neurodevelopment and is accompanied by activation of immune cells and microglia. Programmed cell death protein 1 (PD-1) can be highly expressed on the surface of various activated immune cells, when combined with programmed cell death-ligand 1 (PD-L1), it can activate the PD-1/PD-L1 pathway and exert powerful immunosuppressive effects, suggesting that this immune checkpoint may have the potential to treat MIA-induced ASD. This study combined bioinformatics analysis and experimental validation to explore the efficacy of Fc-fused PD-L1 (PD-L1-Fc) in treating MIA-induced ASD. Bioinformatics analysis results showed that in human placental inflammation, IL-6 was upregulated, T cells proliferated significantly, and the PD-1/PD-L1 pathway was significantly enriched. The experimental results showed that intraperitoneal injection of poly(I:C) induced MIA in pregnant mice resulted in significant expression of IL-6 in their serum, placenta, and fetal brain. At the same time, the expression of PD-1 and PD-L1 in the placenta and fetal brain increased, CD4+ T cells in the spleen were significantly activated, and PD-1 expression increased. Their offspring mice exhibited typical ASD-like behaviors. In vitro experiments on primary microglia of offspring mice have confirmed that the expression of IL-6, PD-1, and PD-L1 is significantly increased, and PD-L1-Fc effectively reduced their expression levels. In the prefrontal cortex of MIA offspring mice, there was an increase in the expression of IL-6, PD-1, and PD-L1; activation of microglial cells, and colocalization with PD-1. Then we administered brain stereotaxic injections of PD-L1-Fc to MIA offspring mice and intraperitoneal injections to MIA pregnant mice. The results indicated that PD-L1-Fc effectively suppressed neuroinflammation in the frontal cortex of offspring mice and partially ameliorated ASD-like behaviors; MIA in pregnant mice was significantly alleviated, and the offspring mice they produced did not exhibit neuroinflammation or ASD-like behaviors. In summary, we have demonstrated the therapeutic ability of PD-L1-Fc for MIA-induced ASD, aiming to provide new strategies and insights for the treatment of ASD.