Gene Ontology Semantic Similarity Analysis Using GOSemSim

Integrating UHPLC-Q-TOF-MS/MS, network pharmacology, bioinformatics and experimental validation to uncover the anti-cancer mechanisms of TiaoPi AnChang decoction in colorectal cancer

ETHNOPHARMACOLOGICAL RELEVANCE

The TiaoPi AnChang Decoction (TPACD), a Traditional Chinese Medicine (TCM) prescription based on Xiangsha Liujunzi Decoction, has demonstrated clinical efficacy as an adjuvant therapy for colorectal cancer (CRC) patients. However, its specific ingredients and potential mechanisms of action remain unclear.

AIM OF THE STUDY

To identify the primary active ingredients of TPACD, their molecular targets, and potential mechanisms underlying the efficacy of TPACD in CRC treatment.

MATERIALS AND METHODS

This study investigated the clinically validated TCM formula TPACD. In vitro and in vivo experiments were used to demonstrate TPACD's regulatory effects on various malignant phenotypes of tumors, providing basic research support for its anti-cancer activity. To understand its pharmacodynamic basis, we utilized ultra-high performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry/mass spectrometry (UHPLC-Q-TOF-MS/MS) to analyze TPACD constituents present in the bloodstream. Network pharmacology and bioinformatics analyses were used to identify potential active components and their molecular targets for TPACD's therapeutic effects in CRC. Subsequent experiments further elucidated its pharmacological mechanism.

RESULTS

TPACD inhibits various malignant cellular processes, such as cell proliferation, apoptosis, migration, and invasion, and has shown potential anti-CRC activities both in vitro and in vivo. Following the identification of 109 constituents absorbed into the blood from TPACD, network pharmacology analysis predicted 42 potential anti-CRC targets. Clinical analyses highlighted three genes as prognostic key genes of TPACD's therapeutic action: C-X-C motif chemokine ligand 8 (CXCL8), fatty acid binding protein 4 (FABP4), and matrix metallopeptidase 3 (MMP3). Drug sensitivity analyses, molecular docking simulations and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) identified MMP3 as the most promising target for TPACD's anti-CRC action. Enzyme activity assays confirmed that TPACD inhibits MMP3 enzyme activity. Surface plasmon resonance (SPR) characterized the binding affinity between MMP3 and effective active components of TPACD, including luteolin, quercetin, kaempferol, and liensinine.

CONCLUSIONS

TPACD exhibits anti-CRC activity in vitro and in vivo, with MMP3 identified as a critical target. The active compounds, including luteolin, quercetin, kaempferol, and liensinine, absorbed into the bloodstream, contribute to TPACD's efficacy by targeting MMP3.

Osteoclast-derived exosomes influence osteoblast differentiation in osteoporosis progression via the lncRNA AW011738/ miR-24-2-5p/ TREM1 axis

AIMS

To investigate the molecular mechanism of osteoclast-derived exosomes in osteoporosis.

MAIN METHODS

RANKL induced osteoclast model was screened for significantly differentially expressed lncRNAs and mRNAs by whole RNA sequencing. Exosomes were characterized using electron microscopy, western blotting and nanosight. Overexpression or knockdown of AW011738 was performed to explore its function. The degree of osteoporosis in an osteoporosis model was assessed by mirco-CT. The osteoclast model, osteoblast differentiation ability and the molecular mechanism of lncRNA AW011738/miR-24-2-5p/TREM1 axis in osteoporosis were assessed by dual luciferase reporter gene assay, Western blotting (WB), immunofluorescence and ALP staining. Bioinformatics was used to predict interactions of key osteoporosis-related genes with miRNAs, transcription factors, and potential drugs after upregulation of AW011738. To predict the protein-protein interaction (PPI) network associated with key genes, GO and KEGG analyses were performed on the key genes. The ssGSVA was used to predict changes in the immune microenvironment.

KEY FINDINGS

Osteoclast-derived exosomes containing lncRNA AW011738 decreased the osteogenesis-related markers and accelerated bone loss in OVX mice. Osteoclast (si-AW011738)-derived exosomes showed a significant increase in biomarkers of osteoblast differentiation in vitro compared to the si-NC group. As analyzed by mirco-CT, tail vein injected si-AW011738 OVX mice were less osteoporotic than the control group. AW011738 inhibited osteoblast differentiation by regulating TREM1 expression through microRNA. Meanwhile, overexpression of miR-24-2-5p inhibited TREM1 expression to promote osteoblast differentiation.

SIGNIFICANCE

Osteoclast-derived exosomes containing lncRNA AW011738 inhibit osteogenesis in MC3T3-E1 cells through the lncRNA AW011738/miR-24-2-5p/TREM1 axis and exacerbate osteoporosis in OVX mice.

Molecular basis of the unique osmoregulatory strategy in the inshore hagfish, Eptatretus burgeri

Hagfishes are characterized by omo- and iono-conforming nature similar to marine invertebrates. Conventionally, hagfishes had been recognized as the most primitive living vertebrate that retains plesiomorphic features. However, some of the "ancestral" features of hagfishes, such as rudimentary eyes and the lack of vertebrae, have been proven to be deceptive. Similarly, by the principle of maximum parsimony, the unique body fluid regulatory strategy of hagfishes seems to be apomorphic, since the lamprey, another cyclostome, adopts osmo- and iono-regulatory mechanisms as in jawed vertebrates. Although hagfishes are unequivocally important in discussing the origin and evolution of the vertebrate osmoregulatory system, the molecular basis for the body fluid homeostasis in hagfishes has been poorly understood. In the present study, we explored this matter in the inshore hagfish, Eptatretus burgeri, by analyzing the transcriptomes obtained from the gill, kidney, and muscle of the animals acclimated to distinct environmental salinities. Together with the measurement of parameters in the muscular fluid compartment, our data indicate that the hagfish possesses an ability to conduct free amino acid (FAA)-based osmoregulation at a cellular level, which is in coordination with the renal and branchial FAA absorption. We also revealed that the hagfish does possess the orthologs of the known osmoregulatory genes and that the transepithelial movement of inorganic ions in the hagfish gill and kidney is more complex than previously thought. These observations pose a challenge to the conventional view that the physiological features of hagfishes have been inherited from the last common ancestor of the extant vertebrates.

Integrative analyses of genes associated with oxidative stress and cellular senescence in triple-negative breast cancer

Background

Oxidative stress and cellular senescence (OSCS) have great impacts on the occurrence and progression of triple-negative breast cancer (TNBC). This study was intended to construct a prognostic model based on oxidative stress and cellular senescence related difference expression genes (OSCSRDEGs) for TNBC.

Methods

The Cancer Genome Atlas (TCGA) databases and two Gene Expression Omnibus (GEO) databases were used to identify OSCSRDEGs. The relationship between OSCSRDEGs and immune infiltration was examined using single-sample gene-set enrichment analysis (ssGSEA), ESTIMATE, and the CIBERSORT algorithm. Least absolute shrinkage and selection operator (LASSO) regression analyses, Cox regression and Kaplan-Meier analysis were employed to construct a prognostic model. Receiver operating characteristic (ROC) curves, nomograms, and decision curve analysis (DCA) were used to evaluate the prognostic efficacy. Gene Set Enrichment Analysis (GSEA) Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the potential functions and mechanism.

Results

A comprehensive analysis identified a total of 27 OSCSRDEGs, out of which 15 genes selected for development of a prognostic model. A high degree of statistical significance was observed for the riskscores derived from this model to accurately predict TNBC Overall survival. The decision curve analysis (DCA) and ROC curve analysis further confirmed the superior accuracy of the OSCSRDEGs prognostic model in predicting efficacy. Notably, the nomogram analysis highlighted that DMD exhibited the highest utility within the model. In comparison between high and low OSCScore groups, the infiltration abundance of immune cells was statistically different in the TCGA-TNBC dataset.

Conclusion

These studies have effectively identified four essential OSCSRDEGs (CFI, DMD, NDRG2, and NRP1) and meticulously developed an OSCS-associated prognostic model for individuals diagnosed with TNBC. These discoveries have the potential to significantly contribute to the comprehension of the involvement of OSCS in TNBC.

ELANE is a promising prognostic biomarker that mediates pyroptosis in gastric cancer

Background

Gastric cancer (GC) is a typical malignant tumor and the main cause of cancer-related deaths. Its pathogenesis involves multiple steps, including pyroptosis, although these steps are still uncertain. Pyroptosis, also known as gasdermin-mediated programmed necrosis, participates in various pathological processes in tumors, including GC. ELANE, which encodes neutrophil elastase, is closely associated with GC. Additionally, ELANE has been implicated in GC cell pyroptosis, but this has not been confirmed. Therefore, investigating the link between ELANE and pyroptosis in GC is warranted. This research uses bioinformatics and experiments to examine the relationship between ELANE, pyroptosis, and GC prognosis.

Methods

The GEO and TCGA databases, along with pyroptosis-related genes, were applied to identify pyroptosis-related differentially expressed genes (DEGs). ELANE was selected via primary screening. Using the median expression level of ELANE as the threshold, pyroptosis-related DEGs were divided into low- and high-ELANE groups. Based on the DEGs in these two groups, GO, KEGG and GSEA analyses were conducted to elucidate the mechanisms of ELANE in GC. Furthermore, we plotted ROC and Kaplan-Meier curves to analyze the clinical and pathological features of ELANE expression. The Nomograms tool was applied to calculate the predictive value of ELANE for the clinical outcomes of GC cases. Immunohistochemical analysis was performed to detect the level of ELANE in GC tissues and to validate whether ELANE was involved in pyroptosis in GC cells through cell experiments. Finally, the immune infiltration of ELANE was investigated, and interaction networks (proteins-ELANE, microRNA-ELANE, and small-molecule drug-ELANE) were constructed.

Results

We aimed to investigate the expression of the ELANE gene in GC and study the relationship among ELANE, pyroptosis, and the prognosis of patients with GC. Differential expression analysis of gene-expression datasets from TCGA-STAD and GSE49051 revealed that the expression of the ELANE gene was significantly up-regulated in GC. Using STRING network analysis, we identified multiple proteins involved in the occurrence and development of GC, including interactions between ELANE and GSDMC, a member of the gasdermin protein family. Survival analysis showed that ELANE expression levels significantly affected overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) in patients with GC. Additionally, ROC analysis demonstrated that ELANE was effective in distinguishing GC patients from normal controls (AUC = 0.812). Immunohistochemical analysis showed that ELANE was highly expressed in gastric cancer tissues and was closely related to age, tumor grade, and stage. The cell experiments further confirmed that the high expression of ELANE in gastric cancer cells was associated with pyroptosis. Comprehensive analysis indicated that ELANE could be used as a potential prognostic marker for GC and plays an important role in pyroptosis.

Conclusion

High ELANE expression is related to poor survival and prognosis of patients with GC. It participates in pyroptosis and immune infiltration in GC. Therefore, ELANE is a promising prognostic biomarker for pyroptosis in GC.

Abnormal genes and pathways that drive muscle contracture from brachial plexus injuries: Towards machine learning approach

In order to clarify the pathways closely linked to denervated muscle contracture, this work uses IoMT-enabled healthcare stratergies to examine changes in gene expression patterns inside atrophic muscles following brachial plexus damage. The gene expression Omnibus (GEO) database searching was used to locate the dataset GSE137606, which is connected to brachial plexus injuries. Strict criteria (|logFC|≥2 & adj.p < 0.05) were used to extract differentially expressed genes (DEGs). To identify dysregulated activities and pathways in denervated muscles, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used. Hub genes were found using Cytoscape software's algorithms, which took into account parameters like as proximity, degree, and MNC. Their expression, enriched pathways, and correlations were then examined. The results showed that 316 DEGs were predominantly concentrated in muscle-related processes such as tissue formation and contraction pathways. Of these, 297 DEGs were highly expressed in denervated muscles, whereas 19 DEGs were weakly expressed. GSEA showed improvements in the contraction of striated and skeletal muscles. In addition, it was shown that in denervated muscles, Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1 were elevated hub genes with enriched pathways such adrenergic signaling and tight junction. These results point to possible therapeutic targets for denervated muscular contracture, including Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1. This highlights treatment options for this ailment which enhances the mental state of patient.

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

Metabolic trade-offs in Neonatal sepsis triggered by TLR4 and TLR1/2 ligands result in unique dysfunctions in neural breathing circuits

Neonatal sepsis remains one of the leading causes of mortality in newborns. Several brainstem-regulated physiological processes undergo disruption during neonatal sepsis. Mechanistic knowledge gaps exist at the interplay between metabolism and immune activation to brainstem neural circuits and pertinent physiological functions in neonates. To delineate this association, we induced systemic inflammation either by TLR4 (LPS) or TLR1/2 (PAM3CSK4) ligand administration in postnatal day 5 mice (PD5). Our findings show that LPS and PAM3CSK4 evoke substantial changes in respiration and metabolism. Physiological trade-offs led to hypometabolic-hypothermic responses due to LPS, but not PAM3CSK4, whereas to both TLR ligands blunted respiratory chemoreflexes. Neuroinflammatory pathways modulation in brainstem showed more robust effects in LPS than PAM3CSK4. Brainstem neurons, microglia, and astrocyte gene expression analyses showed unique responses to TLR ligands. PAM3CSK4 did not significantly modulate gene expression changes in GLAST-1 positive brainstem astrocytes. PD5 pups receiving PAM3CSK4 failed to maintain a prolonged metabolic state repression, which correlated to enhanced gasping latency and impaired autoresuscitation during anoxic chemoreflex challenges. In contrast, LPS administered pups showed no significant changes in anoxic chemoreflex. Electrophysiological studies from brainstem slices prepared from pups exposed to either TLR4 or PAM3CSK4 showed compromised transmission between preBötzinger complex and Hypoglossal as an exclusive response to the TLR1/2 ligand. Spatial gene expression analysis demonstrated a region-specific modulation of PAM3CSK4 within the raphe nucleus relative to other anatomical sites evaluated. Our findings suggest that metabolic changes due to inflammation might be a crucial tolerance mechanism for neonatal sepsis preserving neural control of breathing.

Integrating multi-omics data of childhood asthma using a deep association model

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases.

Identification of mitophagy-related hub genes during the progression of spinal cord injury by integrated multinomial bioinformatics analysis

Spinal cord injury (SCI) is a disturbance of peripheral and central nerve conduction that causes disability in sensory and motor function. Currently, there is no effective treatment for SCI. Mitophagy plays a vital role in mitochondrial quality control during various physiological and pathological processes. The study aimed to elucidate the role of mitophagy and identify potential mitophagy-related hub genes in SCI pathophysiology. Two datasets (GSE15878 and GSE138637) were analyzed. Firstly, the differentially expressed genes (DEGs) were identified and mitophagy-related genes were obtained from GeneCards, then the intersection between SCI and mitophagy-related genes was determined. Next, we performed gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), protein-protein interaction network (PPI network), least absolute shrinkage and selection operator (LASSO), and cluster analysis to identify and define the hub genes in SCI. Finally, the link between hub genes and infiltrating immune cells was investigated and the potential transcriptional regulation/small molecular compounds to target hub genes were predicted. In total, SKP1 and BAP1 were identified as hub genes of mitophagy-related DEGs during SCI development and regulatory T cells (Tregs)/resting NK cells/activated mast cells may play an essential role in the progression of SCI. LINC00324 and SNHG16 may regulate SKP1 and BAP1, respectively, through miRNAs. Eleven and eight transcriptional factors (TFs) regulate SKP1 and BAP1, respectively, and six small molecular compounds target BAP1. Then, the mRNA expression levels of BAP1 and SKP1 were detected in the injured sites of spinal cord of SD rats at 6 h and 72 h after injury using RT-qPCR, and found that the level were decreased. Therefore, the pathways of mitophagy are downregulated during the pathophysiology of SCI, and SKP1 and BAP1 could be accessible targets for diagnosing and treating SCI.

Identification of novel endoplasmic reticulum-related genes and their association with immune cell infiltration in major depressive disorder

BACKGROUND

Several lines of evidence point to an interaction between genetic predisposition and environmental factors in the onset of major depressive disorder (MDD). This study is aimed to investigate the pathogenesis of MDD by identifying key biomarkers, associated immune infiltration using bioinformatic analysis and human postmortem sample.

METHODS

The Gene Expression Omnibus (GEO) database of GSE98793 was adopted to identify hub genes linked to endoplasmic reticulum (ER) stress-related genes (ERGs) in MDD. Another GEO database of GSE76826 was employed to validate the novel target associated with ERGs and immune infiltration in MDD. Moreover, human postmortem sample from MDD patients was utilized to confirm the differential expression analysis of hub genes.

RESULTS

We discovered 12 ER stress-related differentially expressed genes (ERDEGs). A LASSO Cox regression analysis helped construct a diagnostic model for these ERDEGs, incorporating immune infiltration analysis revealed that three hub genes (ERLIN1, SEC61B, and USP13) show the significant and consistent expression differences between the two groups. Western blot analysis of postmortem brain samples indicated notably higher expression levels of ERLIN1 and SEC61B in the MDD group, with USP13 also tending to increase compared to control group.

LIMITATIONS

The utilization of the MDD gene chip in this analysis was sourced from the GEO database, which possesses a restricted number of pertinent gene chip samples.

CONCLUSIONS

These findings indicate that ERDEGs especially including ERLIN1, SEC61B, and USP13 associated the infiltration of immune cells may be potential diagnostic indicators for MDD.

Identification of colon adenocarcinoma necroptosis subtypes and tumor antigens for the development of mRNA vaccines

Background

Colon adenocarcinoma (COAD) is a serious public health issue due to high incidence and mortality rate. This study aimed to identify possible tumor antigens and necroptosis subtypes of COAD for the development of mRNA vaccines and the selection of appropriate patients for precision therapy.

Methods

Gene expression profiles and clinical information for COAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. We comprehensively studied the alterations in necroptosis-related genes (NRGs) using cBioPortal, and screened the hub NRGs associated with the prognosis of patients with COAD using Gene Expression Profiling Interactive Analysis 2. Consensuses clustering analysis was performed to identify necroptosis subtypes. Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules of the NRGs. The necroptosis landscape of COAD was assessed using graph learning-based dimensionality reduction. Finally, a drug sensitivity analysis of the two necroptosis subtypes was performed.

Findings

Two tumor antigens, BLC-2-associated X protein (BAX) and interleukin 1 beta (IL1B) were identified based on their associations with prognosis of patients and antigen presenting cell infiltration. Two necroptosis subtypes (N1 and N2) were distinguished in patients with COAD, and they were characterized by their differential survival status and molecular expression levels of immune checkpoint proteins and immunogenetic cell death modulators. Furthermore, the necroptosis landscape of COAD indicated that individual patients had obvious heterogeneity. Co-expression modules were identified using WGCNA, and the hub NRGs were found to be involved in various immune processes. Drug sensitivity analysis indicated that there were significant differences in drug sensitivity between the N1 and N2 subtypes. Cell experiments suggested that both overexpression of BAX and IL1B promoted necroptosis of COAD cells and enhanced the cytotoxicity of CD8+ T cells.

Interpretation

BAX and IL1B are potential antigens for the development of anti-COAD mRNA vaccines, specifically for patients with the N2 subtype. Consequently, this study will guide the development of more effective immunotherapeutic approaches and the identification of appropriate patients.

Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder

Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD.

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/macrophage (Mo/MΦ)-associated gene signatures to elucidate their correlation with the pathogenesis and immune microenvironment of IAs, thereby offering potential avenues for targeted therapy against IAs. Single-cell RNA-sequencing (scRNA-seq) data of IAs were acquired from the Gene Expression Synthesis (GEO) database. The significant infiltration of monocyte subsets in the parietal tissue of IAs was identified using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis (hdWGCNA). The integration of six machine learning algorithms identified four crucial genes linked to these Mo/MΦ. Subsequently, we developed a multilayer perceptron (MLP) neural model for the diagnosis of IAs (independent external test AUC=1.0, sensitivity =100%, specificity =100%). Furthermore, we employed the CIBERSORT method and MCP counter to establish the correlation between monocyte characteristics and immune cell infiltration as well as patient heterogeneity. Our findings offer valuable insights into the molecular characterization of monocyte infiltration in IAs, which plays a pivotal role in shaping the immune microenvironment of IAs. Recognizing this characterization is crucial for comprehending the limitations associated with targeted therapies for IAs. Ultimately, the results were verified by real-time fluorescence quantitative PCR and Immunohistochemistry.

A novel mitochondrial metabolism-related gene signature for predicting the prognosis of oesophageal squamous cell carcinoma

Oesophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers worldwide. Due to the important role of mitochondrial metabolism in cancer progression, a clinical prognostic model based on mitochondrial metabolism and clinical features was constructed in this study to predict the prognosis of ESCC. Firstly, the mitochondrial metabolism scores (MMs) were calculated based on 152 mitochondrial metabolism-related genes (MMRGs) by single sample gene set enrichment analysis (ssGSEA). Subsequently, univariate Cox regression and LASSO algorithm were used to identify prognosis-associated MMRG and risk-stratify patients. Functional enrichment, interaction network and immune-related analyses were performed to explore the features differences in patients at different risks. Finally, a prognostic nomogram incorporating clinical factors was constructed to assess the prognosis of ESCC. Our results found there were differences in clinical features between the MMs-high group and the MMs-low group in the TCGA-ESCC dataset (P<0.05). Afterwards, we identified 6 MMRGs (COX10, ACADVL, IDH3B, AKR1A1, LIAS, and NDUFB8) signature that could accurately distinguish high-risk and low-risk ESCC patients. A predictive nomogram that combined the 6 MMRGs with sex and N stage to predict the prognosis of ESCC was constructed, and the areas under the receiver operating characteristic (ROC) curve at 1, 2 and 3 years were 0.948, 0.927 and 0.848, respectively. Finally, we found that COX10, one of 6 MMRGs, could inhibit the malignant progression of ESCC in vitro. In summary, we constructed a clinical prognosis model based on 6 MMRGs and clinical features which can accurately predict the prognosis of ESCC patients.

Novel early-onset Alzheimer-associated genes influence risk through dysregulation of glutamate, immune activation, and intracell signaling pathways

Alzheimer Disease (AD) is a highly polygenic disease that presents with relatively earlier onset (≤70yo; EOAD) in about 5% of cases. Around 90% of these EOAD cases remain unexplained by pathogenic mutations. Using data from EOAD cases and controls, we performed a genome-wide association study (GWAS) and trans-ancestry meta-analysis on non-Hispanic Whites (NHW, NCase=6,282, NControl=13,386), African Americans (AA NCase=782, NControl=3,663) and East Asians (NCase=375, NControl=838 CO). We identified eight novel significant loci: six in the ancestry-specific analyses and two in the trans-ancestry analysis. By integrating gene-based analysis, eQTL, pQTL and functional annotations, we nominate four novel genes that are involved in microglia activation, glutamate production, and signaling pathways. These results indicate that EOAD, although sharing many genes with LOAD, harbors unique genes and pathways that could be used to create better prediction models or target identification for this type of AD.

The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.

Integrative experimental validation of concomitant miRNAs and transcription factors with differentially expressed genes in acute myocardial infarction

Identification of concomitant miRNAs and transcription factors (TFs) with differential expression (DEGs) in MI is crucial for understanding holistic gene regulation, identifying key regulators, and precision in biomarker and therapeutic target discovery. We performed a comprehensive analysis using Affymetrix microarray data, advanced bioinformatic tools, and experimental validation to explore potential biomarkers associated with human pathology. The search strategy includes the identification of the GSE83500 dataset, comprising gene expression profiles from aortic wall punch biopsies of MI and non-MI patients, which were used in the present study. The analysis identified nine distinct genes exhibiting DEGs within the realm of MI. miRNA-gene/TF and TF-gene/miRNA regulatory relations were mapped to retrieve interacting hub genes to acquire an MI miRNA-TF co-regulatory network. Furthermore, an animal model of I/R-induced MI confirmed the involved gene based on quantitative RT-PCR and Western blot analysis. The consequences of the bioinformatic tool substantiate the inference regarding the presence of three key hub genes (UBE2N, TMEM106B, and CXADR), a central miRNA (hsa-miR-124-3p), and sixteen TFs. Animal studies support the involvement of predicted genes in the I/R-induced myocardial infarction assessed by RT-PCR and Western blotting. Thus, the final consequences suggest the involvement of promising molecular pathways regulated by TF (p53/NF-κB1), miRNA (hsa-miR-124-3p), and hub gene (UBE2N), which may play a key role in the pathogenesis of MI.

WGCNA and machine learning analysis identifi ed SAMD9 and IFIT3 as primary Sjögren's Syndrome key genes

Background

Current treatments for primary Sjögren's Syndrome (pSS) are with limited effect, partially due to the heterogeneity and uncleared mechanism.

Methods

We got GSE40568 (Japan) and GSE40611 (USA), and analyzed them with WGCNA to find key Differentially expressed genes (DEGs) between pSS and healthy salivary glands (SG). Key pSS genes (KPGs) were further selected through 3 machine-learning methods. The expression of KPGs was validated via two other GEO datasets (GSE127952 and GSE154926). Infiltrated immune cells, ceRNA network, and potential compounds were explored.

Results

Our study identified 376 DEGs from the pSS patients, with 186 genes located in the "plum2" module, showing the strongest correlation with clinical characteristics. SAMD9 and IFIT3 emerged as KPGs with excellent diagnostic potential. SAMD9 demonstrated close association with immune cell infiltration. We constructed a lncRNA-miRNA-mRNA network comprising 2 KPGs, 12 miRNAs, 124 lncRNAs, and potential therapeutic targets.

Conclusion

In the investigation of pSS public datasets, our study revealed two potential critical mediators in the pathological process of pSS salivary glands, namely SAMD9 and IFIT3. Furthermore, we put forth a hypothesis regarding the ceRNA network and made predictions regarding potential therapeutic drugs targeting these two genes.

Identification of Biomarkers, Pathways, Immune Properties of Mitophagy Genes, and Prediction Models for Intervertebral Disc Degeneration

Background

Intervertebral disc degeneration (IDD) is the leading cause of low back pain (LBP). The mechanism of IDD development and progression is not fully understood. Peripheral biomarkers are increasingly vital non-radioactive methods in early detection and diagnosis for IDD. Nevertheless, less attention has been paid to the role of mitophagy genes in the progress of IDD. This study aimed to identify the mitophagy disease-causing genes in the process of IDD and mitophagy diagnostic biomarkers for IDD.

Methods

Mitophagy-related differentially expressed genes (MRDEGs) related to IDD were investigated by analyzing the microarray datasets of IDD cases from GEO, PathCards and Molecular Signatures Databases. We used R software, WGCNA, PPI, mRNA-miRNA, mRNA-TF, GO, KEGG, GSEA, GSVA and Cytoscape to analyze and visualize the data. We further used ssGSEA for immunoinfiltration analysis to obtain different immune cell infiltration. LASSO model was developed to screen for genes that met the diagnostic gene model requirements. Finally, qRT-PCR, Western blotting and HE were used to verify hub genes and their expression from clinical IDD samples.

Results

We identified 14 MRDEGs and 12 hub genes. GO, KEGG, GSEA and GSVA analyses demonstrated that hub genes were critical for the development of IDD. LASSO diagnostic model consisted of six hub genes, among which SQSTM1, ATG7 and OPTN were significantly different between the two IDD disease subtypes. At the same time, SQSTM1 also had a high correlation with immune characteristic subtypes. The results of qRT-PCR and Western blotting also indicated that these genes were significantly differentially expressed in nucleus pulposus cells (NPCs) of the IDD group.

Conclusion

We explored an association between MRDEGs-associated signature in IDD and validated that hub genes like SQSTM1 might serve as biomarkers for diagnostic and therapeutic targets for IDD. Meanwhile, this study can provide new insights into the functional characteristics and mechanism of mitophagy in the development of IDD.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

EPHB2 as a key mediator of glioma progression: Insights from microenvironmental receptor ligand-related prognostic gene signature

Malignant gliomas, characterized by pronounced heterogeneity, a complex microenvironment, and a propensity for relapse and drug resistaniguree, pose significant challenges in oncology. This study aimed to investigate the prognostic value of Ligand and Receptor related genes (LRRGs) within the glioma microenvironment. An intersection of 71 ligand-related genes (LRGs) and 2628 receptor-related genes (RRGs) yielded a total of 69 LRRGs. Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic RiskScore model comprising 28 LRRGs was constructed. The model demonstrated robust prognostic value, further validated in the TCGA-GBMLGG dataset. Subsequent analyses included differential gene expression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment (GSEA), and gene set variation (GSVA) within RiskScore groups. Additionally, evaluations of PPI, mRNA-RBP, mRNA-TF, and mRNA-drug interaction networks were conducted. Four hub genes were identified through differential expression analysis of the 28 LRRGs across various GSE datasets. A multivariate Cox prognostic model was constructed for nomogram analysis, gene mutation analysis, and related expression distribution. This study underscores the role of LRRGs in intercellular communication within the glioma microenvironment and identifies four hub genes crucial for prognostic assessment in clinical glioma patients. These findings offer a potential evaluation framework for glioma patients, enhancing our understanding of the disease and informing future therapeutic strategies.

Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer

Background

Exosomes are promising tools for the development of new diagnostic and therapeutic approaches. Exosomes possess the ability to activate signaling pathways that contribute to the remodeling of the tumor microenvironment, angiogenesis, and the regulation of immune responses. We aimed to develop a prognostic score based on exosomes derived from breast cancer.

Materials and methods

Training was conducted on the TCGA-BRCA dataset, while validation was conducted on GSE20685, GSE5764, GSE7904, and GSE29431. A total of 121 genes related to exosomes were retrieved from the ExoBCD database. The Cox proportional hazards model is used to develop risk score model. The GSVA package was utilized to analyze single-sample gene sets and identify exosome signatures, while the WGCNA package was utilized to identify gene modules associated with clinical outcomes. The clusterProfiler and GSVA R packages facilitated gene set enrichment and variation analyses. Furthermore, CIBERSORT quantified immune infiltration, and a correlation between gene expression and drug sensitivity was assessed using the TIDE algorithm.

Results

An exosome-related prognostic score was established using the following selected genes: ABCC9, PIGR, CXCL13, DOK7, CD24, and IVL. Various immune cells that promote cancer immune evasion were associated with a high-risk prognostic score, which was an independent predictor of outcome. High-risk and low-risk groups exhibited significantly different infiltration abundances (p < 0.05). By conducting a sensitivity comparison, we found that patients with high-risk scores exhibited more favorable responses to immunotherapy than those with low-risk scores.

Conclusion

The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.

Unveiling the mitophagy puzzle in non-alcoholic fatty liver disease (NAFLD): Six hub genes for early diagnosis and immune modulatory roles

Background

Non-alcoholic fatty liver disease (NAFLD) stands as a predominant chronic liver ailment globally, yet its pathogenesis remains elusive. This study aims to identify Hub mitophagy-related genes (MRGs), and explore the underlying pathological mechanisms through which these hub genes regulate NAFLD.

Methods

A total of 3 datasets were acquired from the GEO database and integrated to identify differentially expressed genes (DEGs) in NAFLD and perform Gene Set Enrichment Analysis (GSEA). By intersecting DEGs with MRGs, mitophagy-related differentially expressed genes (MRDEGs) were obtained. Then, hub MRGs with diagnostic biomarker capability for NAFLD were screened and a diagnostic prediction model was constructed and assessed using Nomogram, Decision Curve Analysis (DCA), and ROC curves. Functional enrichment analysis was conducted on the identified hub genes to explore their biological significance. Additionally, regulatory networks were constructed using databases. NAFLD was stratified into high and low-risk groups based on the Riskscore from the diagnostic prediction model. Furthermore, single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were employed to analyze immune cell infiltration patterns and the relationship between Hub MRGs and immune cells.

Results

The integrated dataset comprised 122 NAFLD samples and 31 control samples. After screening, 18 MRDEGs were identified. Subsequently, six hub MRGs (NR4A1, PPP2R2A, P4HA1, TUBB6, DUSP1, NAMPT) with diagnostic potential were selected through WGCNA, logistic regression, SVM, RF, and LASSO models, all significantly downregulated in NAFLD samples compared to the control group. A diagnostic prediction model based on these six genes demonstrated robust predictive performance. Functional enrichment analysis of the six hub genes revealed involvement in processes such as protein phosphorylation or dephosphorylation. Correlation analysis demonstrated a significant association between hub MRGs and infiltrating immune cells.

Conclusion

We identified six hub MRGs in NAFLD and constructed a diagnostic prediction model based on these six genes, applicable for early NAFLD diagnosis. These genes may participate in regulating NAFLD progression through the modulation of mitophagy and immune activation. Our findings may contribute to subsequent clinical and basic research on NAFLD.

Necroptosis pathway emerged as potential diagnosis markers in spinal cord injury

The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

FGCD: a database of fungal gene clusters related to secondary metabolism

Fungal secondary metabolites are not necessary for growth, but they are important for fungal metabolism and ecology because they provide selective advantages for competition, survival and interactions with the environment. These various metabolites are widely used as medicinal precursors and insecticides. Secondary metabolism genes are commonly arranged in clusters along chromosomes, which allow for the coordinate control of complete pathways. In this study, we created the Fungal Gene Cluster Database to store, retrieve, and visualize secondary metabolite gene cluster information across fungal species. The database was created by merging data from RNA sequencing, Basic Local Alignment Search Tool, genome browser, enrichment analysis and the R Shiny web framework to visualize and query putative gene clusters. This database facilitated the rapid and thorough examination of significant gene clusters across fungal species by detecting, defining and graphically displaying the architecture, organization and expression patterns of secondary metabolite gene clusters. In general, this genomic resource makes use of the tremendous chemical variety of the products of these ecologically and biotechnologically significant gene clusters to our further understanding of fungal secondary metabolism. Database URL: https://www.hebaubioinformatics.cn/FungalGeneCluster/.

A deep learning adversarial autoencoder with dynamic batching displays high performance in denoising and ordering scRNA-seq data

By providing high-resolution of cell-to-cell variation in gene expression, single-cell RNA sequencing (scRNA-seq) offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we propose a deep neural generative framework, the dynamic batching adversarial autoencoder (DB-AAE), which excels at denoising scRNA-seq datasets. DB-AAE directly captures optimal features from input data and enhances feature preservation, including cell type-specific gene expression patterns. Comprehensive evaluation on simulated and real datasets demonstrates that DB-AAE outperforms other methods in denoising accuracy and biological signal preservation. It also improves the accuracy of other algorithms in establishing pseudo-time inference. This study highlights DB-AAE's effectiveness and potential as a valuable tool for enhancing the quality and reliability of downstream analyses in scRNA-seq research.

m6A/m1A/m5C-Associated Methylation Alterations and Immune Profile in MDD

Major depressive disorder (MDD) is a prevalent psychiatric condition often accompanied by severe impairments in cognitive and functional capacities. This research was conducted to identify RNA modification-related gene signatures and associated functional pathways in MDD. Differentially expressed RNA modification-related genes in MDD were first identified. And a random forest model was developed and distinct RNA modification patterns were discerned based on signature genes. Then, comprehensive analyses of RNA modification-associated genes in MDD were performed, including functional analyses and immune cell infiltration. The study identified 29 differentially expressed RNA modification-related genes in MDD and two distinct RNA modification patterns. TRMT112, MBD3, NUDT21, and IGF2BP1 of the risk signature were detected. Functional analyses confirmed the involvement of RNA modification in pathways like phosphatidylinositol 3-kinase signaling and nucleotide oligomerization domain (NOD)-like receptor signaling in MDD. NUDT21 displayed a strong positive correlation with type 2 T helper cells, while IGF2BP1 negatively correlated with activated CD8 T cells, central memory CD4 T cells, and natural killer T cells. In summary, further research into the roles of NUDT21 and IGF2BP1 would be valuable for understanding MDD prognosis. The identified RNA modification-related gene signatures and pathways provide insights into MDD molecular etiology and potential diagnostic biomarkers.

A partial epithelial-mesenchymal transition signature for highly aggressive colorectal cancer cells that survive under nutrient restriction

Partial epithelial-mesenchymal transition (p-EMT) has recently been identified as a hybrid state consisting of cells with both epithelial and mesenchymal characteristics and is associated with the migration, metastasis, and chemoresistance of cancer cells. Here, we describe the induction of p-EMT in starved colorectal cancer (CRC) cells and identify a p-EMT gene signature that can predict prognosis. Functional characterisation of starvation-induced p-EMT in HCT116, DLD1, and HT29 cells showed changes in proliferation, morphology, and drug sensitivity, supported by in vivo studies using the chorioallantoic membrane model. An EMT-specific quantitative polymerase chain reaction (qPCR) array was used to screen for deregulated genes, leading to the establishment of an in silico gene signature that was correlated with poor disease-free survival in CRC patients along with the CRC consensus molecular subtype CMS4. Among the significantly deregulated p-EMT genes, a triple-gene signature consisting of SERPINE1, SOX10, and epidermal growth factor receptor (EGFR) was identified. Starvation-induced p-EMT was characterised by increased migratory potential and chemoresistance, as well as E-cadherin processing and internalisation. Both gene signature and E-cadherin alterations could be reversed by the proteasomal inhibitor MG132. Spatially resolving EGFR expression with high-resolution immunofluorescence imaging identified a proliferation stop in starved CRC cells caused by EGFR internalisation. In conclusion, we have gained insight into a previously undiscovered EMT mechanism that may become relevant when tumour cells are under nutrient stress, as seen in early stages of metastasis. Targeting this process of tumour cell dissemination might help to prevent EMT and overcome drug resistance. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Long noncoding RNA LINC00675 drives malignancy in acute myeloid leukemia via the miR-6809 -CDK6 axis

Hematological malignancies such as acute myeloid leukemia (AML) have a low cure rate and a high recurrence rate. Long noncoding RNAs (LNCs) are essential regulators of tumorigenesis and progression. The role of lncRNA LINC00675 in AML has rarely been reported. This study revealed elevated LINC00675 expression in AML that promotes proliferation and inhibits apoptosis. Mechanistically, LINC00675 combines with miR-6809 to promote the expression of CDK6 in vitro and in vivo. Immune-checkpoint genes were expressed more highly in LINC00675-high patients. A high level of LINC00675 expression may make patients more susceptible to palbociclib treatments. In conclusion, our study demonstrated that LINC00675 is an oncogenic lncRNA that enhances the malignancy of AML by upregulating CDK6 expression through miR-6809 sponging, providing a new perspective and feasible target for the diagnosis and treatment of AML.

Machine learning and experimental validation identified autophagy signature in hepatic fibrosis

Background

The molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.

Methods

Gene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.

Results

A total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.

Conclusion

ATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.

Predicting Diagnostic Biomarkers Associated with Pyroptosis in Neuropathic Pain Based on Machine Learning and Experimental Validation

Purpose

Previous studies have shown that pyroptosis plays a vital role in the progress of neuropathic pain (NP), but the molecular mechanisms have not been fully elucidated. The aim of this study was to identify crucial pyroptosis-related genes (PRGs) in NP.

Methods

We identified pyroptosis-related differentially expressed genes (PRDEGs) in NP by machine learning analysis of the GSE24982 and GSE60670 datasets. Furthermore, these PRDEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and Friends analysis, respectively. Meanwhile, receiver operator characteristic (ROC) analysis was performed to assess the diagnostic value of PRDEGs in NP. Finally, we performed immune infiltration analysis of key PRDEGs using CIBERSORTR R package.

Results

We found that 5 PRDEGs by least absolute shrinkage and selection operator (LASSO) regression and random forest and verified by RT-qPCR. GO, KEGG and GSEA revealed that these PRDEGs were mainly enriched in regulation of neuron death, IL-4 signaling, IL-23 pathway, and NF-κB pathway. ROC analysis revealed that most of the PRDEGs performed well in diagnosing NP. We also revealed transcription factors, miRNA regulatory networks and drug interaction networks of PRDEGs. For immune infiltration analysis, PRDEGs were mainly correlated with dendritic cells, monocytes and follicular T helper cells, suggested that it might be involved in the regulation of neuroimmune-related signaling.

Conclusion

A total of five PRDEGs were can be employed as NP biomarkers, particularly Tlr4, Il1b and Casp8, and provide additional evidence for a vital role of pyroptosis in NP.

Identification of hub genes significantly linked to temporal lobe epilepsy and apoptosis via bioinformatics analysis

Background

Epilepsy stands as an intricate disorder of the central nervous system, subject to the influence of diverse risk factors and a significant genetic predisposition. Within the pathogenesis of temporal lobe epilepsy (TLE), the apoptosis of neurons and glial cells in the brain assumes pivotal importance. The identification of differentially expressed apoptosis-related genes (DEARGs) emerges as a critical imperative, providing essential guidance for informed treatment decisions.

Methods

We obtained datasets related to epilepsy, specifically GSE168375 and GSE186334. Utilizing differential expression analysis, we identified a set of 249 genes exhibiting significant variations. Subsequently, through an intersection with apoptosis-related genes, we pinpointed 16 genes designated as differentially expressed apoptosis-related genes (DEARGs). These DEARGs underwent a comprehensive array of analyses, including enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, prediction of miRNA and transcription factors, and molecular docking analysis.

Results

In the epilepsy datasets examined, we successfully identified 16 differentially expressed apoptosis-related genes (DEARGs). Subsequent validation in the external dataset GSE140393 revealed the diagnostic potential of five biomarkers (CD38, FAIM2, IL1B, PAWR, S100A8) with remarkable accuracy, exhibiting an impressive area under curve (AUC) (The overall AUC of the model constructed by the five key genes was 0.916, and the validation set was 0.722). Furthermore, a statistically significant variance (p < 0.05) was observed in T cell CD4 naive and eosinophil cells across different diagnostic groups. Exploring interaction networks uncovered intricate connections, including gene-miRNA interactions (164 interactions involving 148 miRNAs), gene-transcription factor (TF) interactions (22 interactions with 20 TFs), and gene-drug small molecule interactions (15 interactions involving 15 drugs). Notably, IL1B and S100A8 demonstrated interactions with specific drugs.

Conclusion

In the realm of TLE, we have successfully pinpointed noteworthy differentially expressed apoptosis-related genes (DEARGs), including CD38, FAIM2, IL1B, PAWR, and S100A8. A comprehensive understanding of the implications associated with these identified genes not only opens avenues for advancing our comprehension of the underlying pathophysiology but also bears considerable potential in guiding the development of innovative diagnostic methodologies and therapeutic interventions for the effective management of epilepsy in the future.

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

ProFun: A web server for functional enrichment analysis of parasitic protozoan genes

BACKGROUND

The initial step to interpreting putative biological functions from comparative multi-omics studies usually starts from a differential expressed gene list followed by functional enrichment analysis (FEA). However, most FEA packages are designed exclusively for humans and model organisms. Although parasitic protozoan is the most important pathogen in the tropics, no FEA package is available for protozoan functional (ProFun) enrichment analysis. To speed up comparative multi-omics research on parasitic protozoans, we constructed ProFun, a web-based, user-friendly platform for the research community.

METHODS

ProFun utilizes the Docker container, ShinyProxy, and R Shiny to construct a scalable web service with load-balancing infrastructure. We have integrated a series of visual analytic functions, in-house scripts, and custom-made annotation packages to create three analytical modules for 40 protozoan species: (1) Gene Overlaps; (2) Over-representation Analysis (ORA); (3) Gene Set Enrichment Analysis (GSEA).

RESULTS

We have established ProFun, a web server for functional enrichment analysis of differentially expressed genes. FEA becomes as simple as pasting a list of gene IDs into the textbox of our website. Users can customize enrichment parameters and results with just one click. The intuitive web interface and publication-ready charts enable users to reveal meaningful biological events and pinpoint potential targets for further studies.

CONCLUSION

ProFun is the first web application that enables gene functional enrichment analysis of parasitic protozoans. In addition to supporting FEA analysis, ProFun also allows the comparison of FEA results across complicated experimental designs. ProFun is freely available at http://dalek.cgu.edu.tw:8080/app/profun.

Compact CRISPR genetic screens enabled by improved guide RNA library cloning

CRISPR genome editing approaches theoretically enable researchers to define the function of each human gene in specific cell types, but challenges remain to efficiently perform genetic perturbations in relevant models. In this work, we develop a library cloning protocol that increases sgRNA uniformity and greatly reduces bias in existing genome-wide libraries. We demonstrate that our libraries can achieve equivalent or better statistical power compared to previously reported screens using an order of magnitude fewer cells. This improved cloning protocol enables genome-scale CRISPR screens in technically challenging cell models and screen formats.

Molecular insights into sarcopenia: ferroptosis-related genes as diagnostic and therapeutic targets

Ferroptosis, characterized by iron accumulation and lipid peroxidation, leads to cell death. Growing evidence suggests the involvement of ferroptosis in sarcopenia. However, the fundamental ferroptosis-related genes (FRGs) for sarcopenia diagnosis, prognosis, and therapy remain elusive. This study aimed to identify molecular biomarkers of ferroptosis in sarcopenia patients. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal and sarcopenia samples were identified using the 'limma' package in R software. FRGs were extracted from GeneCards and FerrDB databases. Functional enrichment analysis determined the roles of DEGs using the 'clusterProfiler' package. A protein-protein network was constructed using Cytoscape software. Immune infiltration analysis and receiver operating characteristic (ROC) analysis were performed. mRNA-miRNA, mRNA-TF, and mRNA-drug interactions were predicted using ENCORI, hTFtarget, and CHIPBase databases. The network was visualized using Cytoscape. We identified 46 FRGs in sarcopenia. Functional enrichment analysis revealed their involvement in critical biological processes, including responses to steroid hormones and glucocorticoids. KEGG enrichment analysis implicated pathways such as carbon metabolism, ferroptosis, and glyoxylate in sarcopenia. Totally, 11 hub genes were identified, and ROC analysis demonstrated their potential as sensitive and specific markers for sarcopenia in both datasets. Additionally, differences in immune cell infiltration were observed between normal and sarcopenia samples. The hub genes identified in this study are closely associated with ferroptosis in sarcopenia and can effectively differentiate sarcopenia from controls. CDKN1A, CS, DLD, FOXO1, HSPB1, LDHA, MDH2, and YWHAZ show high sensitivity and specificity for sarcopenia diagnosis.Communicated by Ramaswamy H. Sarma.

The sorafenib resistance-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide. Most patients with advanced HCC acquire sorafenib resistance. Drug resistance reflects the heterogeneity of tumors and is the main cause of tumor recurrence and death.We identified and validated sorafenib resistance related-genes (SRGs) as prognostic biomarkers for HCC. We obtained SRGs from the Gene Expression Omnibus and selected four key SRGs using the least absolute shrinkage and selection operator, random forest, and Support Vector Machine-Recursive feature elimination machine learning algorithms. Samples from the The Cancer Genome Atlas (TCGA)-HCC were segregated into two groups by consensus clustering. Following difference analysis, 19 SRGs were obtained through univariate Cox regression analysis, and a sorafenib resistance model was constructed for risk stratification and prognosis prediction. In multivariate Cox regression analysis, the risk score was an independent predictor of overall survival (OS). Patients classified as high-risk were more sensitive to other chemotherapy drugs and showed a higher expression of the common immune checkpoints. Additionally, the expression of drug-resistance genes was verified in the International Cancer Genome Consortium cohort. A nomogram model with a risk score was established, and its prediction performance was verified by calibration chart analysis of the TCGA-HCC cohort. We conclude that there is a significant correlation between sorafenib resistance and the tumor immune microenvironment in HCC. The risk score could be used to identify a reliable prognostic biomarker to optimize the therapeutic benefits of chemotherapy and immunotherapy, which can be helpful in the clinical decision-making for HCC patients.

Single-cell Transcriptomics Reveals Activation of Macrophages in All-trans Retinoic Acid (atRA)-induced Cleft Palate

Cleft palate is among the most common birth defects with an impact on swallowing and speaking and is difficult to diagnose with ultrasound during pregnancy. In this study, we systematically capture the cellular composition of all-trans retinoic acid (atRA)-exposed and normal embryonic gestation 16.5 days mouse palate by the single-cell RNA sequencing technique. The authors identified 14 major cell types with the largest proportion of fibroblasts. The proportion of myeloid cells in atRA-exposed palate was markedly higher than those in the normal palate tissue, especially M1-like macrophages and monocytes. The upregulated genes of the different expression genes between atRA-exposed palate and normal palate tissue were linked to the biological processes of leukocyte chemotaxis and migration. Protein TLR2, CXCR4, THBS1, MRC1, transcription factor encoding genes Cebpb, Fos, Jun, Rela, and signaling pathway IL-17 and phagosome were found to be significantly involved in these processes. Subsequently, cellular communication network analysis suggested that myeloid-centered cell interactions SELL, SELPLG, MIF, CXCL, ANNEXIN, THBS, and NECTIN were significantly more activated in atRA-exposed palate. Overall, we delineate the single-cell landscape of atRA-induced cleft palate, revealing the effects of overexposure to atRA during palate tissue development and providing insights for the diagnosis of cleft palate.

Rank aggregation of independent genetic screen results highlights new strategies for adoptive cellular transfer therapy of cancer

Efficient intratumoral infiltration of adoptively transferred cells is a significant barrier to effectively treating solid tumors with adoptive cellular transfer (ACT) therapies. Our recent forward genetic, whole-genome screen identified T cell-intrinsic gene candidates that may improve tumor infiltration of T cells. Here, results are combined with five independent genetic screens using rank aggregation to improve rigor. This resulted in a combined total of 1,523 candidate genes - including 1,464 genes not currently being evaluated as therapeutic targets - that may improve tumor infiltration of T cells. Gene set enrichment analysis of a published human dataset shows that these gene candidates are differentially expressed in tumor infiltrating compared to circulating T cells, supporting translational potential. Importantly, adoptive transfer of T cells overexpressing gain-of-function candidates (AAK1ΔN125, SPRR1B, and EHHADH) into tumor-bearing mice resulted in increased T cell infiltration into tumors. These novel gene candidates may be considered as potential therapeutic candidates that can aid adoptive cellular therapy in improving T cell infiltration into solid tumors.

Integrative analyses and validation of ferroptosis-related genes and mechanisms associated with cerebrovascular and cardiovascular ischemic diseases

BACKGROUND

There has been a gradual increase in the occurrence of cardiovascular and cerebrovascular ischemic diseases, particularly as comorbidities. Yet, the mechanisms underlying these diseases remain unclear. Ferroptosis has emerged as a potential contributor to cardio-cerebral ischemic processes. Therefore, this study investigated the shared biological mechanisms between the two processes, as well as the role of ferroptosis genes in cardio-cerebral ischemic damage, by constructing co-expression modules for myocardial ischemia (MI) and ischemic stroke (IS) and a network of protein-protein interactions, mRNA-miRNA, mRNA-transcription factors (TFs), mRNA-RNA-binding proteins (RBPs), and mRNA-drug interactions.

RESULTS

The study identified seven key genes, specifically ACSL1, TLR4, ADIPOR1, G0S2, PDK4, HP, PTGS2, and subjected them to functional enrichment analysis during ischemia. The predicted miRNAs were found to interact with 35 hub genes, and interactions were observed between 11 hub genes and 30 TF transcription factors. Additionally, 10 RBPs corresponding to 16 hub genes and 163 molecular compounds corresponding to 30 hub genes were identified. This study also clarified the levels of immune infiltration between MI and IS and different subtypes. Finally, we identified four hub genes, including TLR4, by using a diagnostic model constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis; ADIPOR1, G0S2, and HP were shown to have diagnostic value for the co-pathogenesis of MI and cerebral ischemia by both validation test data and RT-qPCR assay.

CONCLUSIONS

To the best our knowledge, this study is the first to utilize multiple algorithms to comprehensively analyze the biological processes of MI and IS from various perspectives. The four hub genes, TLR4, ADIPOR1, G0S2, and HP, have proven valuable in offering insights for the investigation of shared injury pathways in cardio-cerebral injuries. Therefore, these genes may serve as diagnostic markers for cardio-cerebral ischemic diseases.

Ratiometric Inclusion of Fibroblasts Promotes Both Castration-Resistant and Androgen-Dependent Tumorigenic Progression in Engineered Prostate Cancer Tissues

To investigate the ratiometric role of fibroblasts in prostate cancer (PCa) progression, this work establishes a matrix-inclusive, 3D engineered prostate cancer tissue (EPCaT) model that enables direct coculture of neuroendocrine-variant castration-resistant (CPRC-ne) or androgen-dependent (ADPC) PCa cells with tumor-supporting stromal cell types. Results show that the inclusion of fibroblasts within CRPC-ne and ADPC EPCaTs drives PCa aggression through significant matrix remodeling and increased proliferative cell populations. Interestingly, this is observed to a much greater degree in EPCaTs formed with a small number of fibroblasts relative to the number of PCa cells. Fibroblast coculture also results in ADPC behavior more similar to the aggressive CRPC-ne condition, suggesting fibroblasts play a role in elevating PCa disease state and may contribute to the ADPC to CRPC-ne switch. Bulk transcriptomic analyses additionally elucidate fibroblast-driven enrichment of hallmark gene sets associated with tumorigenic progression. Finally, the EPCaT model clinical relevancy is probed through a comparison to the Cancer Genome Atlas (TCGA) PCa patient cohort; notably, similar gene set enrichment is observed between EPCaT models and the patient primary tumor transcriptome. Taken together, study results demonstrate the potential of the EPCaT model to serve as a PCa-mimetic tool in future therapeutic development efforts.

A comprehensive atlas of epigenetic regulators reveals tissue-specific epigenetic regulation patterns

Epigenetic machinery contributes to gene regulation in eukaryotic species. However, the machinery including more than 600 epigenetic regulator (ER) genes responsible for reading, writing, and erasing histone modifications and DNA modifications remains largely uncharacterized across species. We compile a comprehensive list of ERs based on an evolutionary analysis across 23 species, which is the most comprehensive ER list in various species until recently. We further perform comparative transcriptomic analyses across different tissues in humans, mice, as well as other amniote species. We observe a consistent tissue-of-origin expression specificity pattern of duplicated ER genes across species and suggest links between expression specificity and ER gene evolution as well as ER function. Additional analyses further suggest that ER duplication can generate tissue-specific ER genes with the same epigenetic substrates, which may be closely related to their regulatory specificity in tissue development. Our work can serve as a foundation to better comprehend the tissue-specific expression patterns of ER genes from an evolutionary perspective and also the functional implications of ERs in tissue-specific epigenetic regulation.

A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.

Role of arachidonic acid metabolism in intervertebral disc degeneration: identification of potential biomarkers and therapeutic targets via multi-omics analysis and artificial intelligence strategies

BACKGROUND

Intervertebral disc degeneration (IVDD) is widely recognized as the primary etiological factor underlying low back pain, often necessitating surgical intervention as the sole recourse in severe cases. The metabolic pathway of arachidonic acid (AA), a pivotal regulator of inflammatory responses, influences the development and progression of IVDD.

METHODS

Initially, a comparative analysis was conducted to investigate the relationship between AA expression patterns and different stages of IVDD using single-cell sequencing (scRNA-seq) data. Additionally, three machine learning methods (LASSO, random forest, and support vector machine recursive feature elimination) were employed to identify hub genes associated with IVDD. Subsequently, a novel artificial intelligence prediction model was developed for IVDD based on an artificial neural network algorithm and validated using an independent dataset. The identified hub genes were further subjected to functional enrichment, immune infiltration, and Connectivity Map analysis. Moreover, external validation was performed using flow cytometry and real-time reverse transcription polymerase chain reaction analysis.

RESULTS

Both scRNA-seq and bulk RNA-seq data revealed a positive correlation between the severity of IVDD and the AA metabolic pathway. They also revealed increased AA metabolic activity in macrophages and neutrophils, as well as enhanced intercellular communication with nucleus pulposus cells. Utilizing advanced machine learning algorithms, five hub genes (AKR1C3, ALOX5, CYP2B6, EPHX2, and PLB1) were identified, and an incipient diagnostic model was developed with an AUC of 0.961 in the training cohort and 0.72 in the validation cohort. An in-depth exploration of the functionality of these hub genes revealed their notable association with inflammatory responses and immune cell infiltration. Lastly, AH6809 was found to delay IVDD by inhibiting AKR1C3.

CONCLUSIONS

This study offers comprehensive insights into potential biomarkers and small molecules associated with the early pathogenesis of IVDD. The identified biomarkers and the developed integrated diagnostic model hold great promise in predicting the onset of early IVDD. AH6809 was established as a therapeutic target for AKR1C3 in the treatment of IVDD, as evidenced by computer simulations and biological experiments.

Normobaric hypoxia shows enhanced FOXO1 signaling in obese mouse gastrocnemius muscle linked to metabolism and muscle structure and neuromuscular innervation

Skeletal muscle relies on mitochondria for sustainable ATP production, which may be impacted by reduced oxygen availability (hypoxia). Compared with long-term hypoxia, the mechanistic in vivo response to acute hypoxia remains elusive. Therefore, we aimed to provide an integrated description of the Musculus gastrocnemius response to acute hypoxia. Fasted male C57BL/6JOlaHsd mice, fed a 40en% fat diet for six weeks, were exposed to 12% O2 normobaric hypoxia or normoxia (20.9% O2) for six hours (n = 12 per group). Whole-body energy metabolism and the transcriptome response of the M. gastrocnemius were analyzed and confirmed by acylcarnitine determination and Q-PCR. At the whole-body level, six hours of hypoxia reduced energy expenditure, increased blood glucose and tended to decreased the respiratory exchange ratio (RER). Whole-genome transcriptome analysis revealed upregulation of forkhead box-O (FOXO) signalling, including an increased expression of tribbles pseudokinase 3 (Trib3). Trib3 positively correlated with blood glucose levels. Upregulated carnitine palmitoyltransferase 1A negatively correlated with the RER, but the significantly increased in tissue C14-1, C16-0 and C18-1 acylcarnitines supported that β-oxidation was not regulated. The hypoxia-induced FOXO activation could also be connected to altered gene expression related to fiber-type switching, extracellular matrix remodeling, muscle differentiation and neuromuscular junction denervation. Our results suggest that a six-hour exposure of obese mice to 12% O2 normobaric hypoxia impacts M. gastrocnemius via FOXO1, initiating alterations that may contribute to muscle remodeling of which denervation is novel and warrants further investigation. The findings support an early role of hypoxia in tissue alterations in hypoxia-associated conditions such as aging and obesity.

Identification of PANoptosis-related biomarkers and immune infiltration characteristics in psoriasis

BACKGROUND

PANoptosis may play a vital role in psoriasis. We investigated the relationship between PANoptosis in psoriasis.

METHODS

Genes information was mainly obtained from GeneCards and the gene expression omnibus database. Genefunctions identification was based on gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Gene set enrichment analysis was used to identify enriched signaling pathways in psoriasis. We constructed PPI networks using the search tool for the retrieval of interacting genes database and Cytoscape and explored mRNA-miRNA, mRNA-TF, and mRNA-drug interaction networks. Receiver operating characteristic curves were performed to screen potential biomarkers among these hub genes. Immune cell infiltration was analyzed using the Pearson algorithm, and the correlation between immune-cell abundance and PANoptosis-related differentially expressed gene (PDGs) was investigated.

RESULTS

We identified 10 PDGs, which were mainly involved in pyroptosis, cytokine-mediated signaling pathways, Salmonella infection and NOD-like receptor signaling pathway. The activated pathways were mostly proinflammatory and immunoregulatory pathways between immune cells. BAK1, CASP4, IL18, and IRF1 were identified as hub genes in the mRNA-miRNA network, and BAK1, IRF1, and PYCARD were hub genes in the mRNA-TF network. CASP1 was found to be the most targeted gene by drugs or molecular compounds. We found PDGs were positively associated with proinflammatory immune cell infiltration and negatively associated with anti-inflammatory or regulatory immune cells.

CONCLUSION

We confirmed the role of PANoptosis in psoriasis for the first time and predicted hub genes and immune characteristics, which provides new ideas for further investigation of psoriasis on pathogenic mechanisms and therapeutic strategies.

Use of a ferroptosis-related gene signature to construct diagnostic and prognostic models for assessing immune infiltration in metabolic dysfunction-associated fatty liver disease

Introduction: Metabolic dysfunction-associated fatty liver disease (MAFLD), a serious health problem worldwide, can involve ferroptosis. This study aimed to comprehensively analyze the ferroptosis-related genes associated with MAFLD. Methods: Ferroptosis-related differentially expressed genes (FRDEGs) were identified in patients with MAFLD and healthy individuals. Gene ontology functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used to analyze the relevant action pathways of the FRDEGs. The Encyclopedia of RNA Interactomes, CHIPBase, and comparative toxicogenomics databases were used to build mRNA-miRNA, mRNA-transcription factor (TF), and mRNA-drug interaction networks, respectively. A diagnostic model was constructed and bioinformatics analysis methods, such as least absolute shrinkage and selection operator regression analysis, Cox regression analysis, nomogram-based analysis, consensus clustering analysis, and single-sample GSEA, were used to systematically investigate the prognostic values and immunologic characteristics. Results: A total of 13 FRDEGs were obtained and eight were used to construct a diagnostic model and perform a prognostic analysis. Hub genes were also used to construct mRNA-miRNA and mRNA-TF interaction networks and potential drug or molecular compounds. Two MAFLD subtypes were identified: cluster2, which represents an "immunoactive" type, and cluster1, which represents an "immunosuppressive" type; a significant correlation was observed between the immune cell contents and the expression of three FRDEGs (NR4A1, FADS2, and SCD). Conclusion: A ferroptosis-related gene signature was constructed to diagnose MAFLD-associated steatohepatitis, predict the prognosis of MAFLD patients, and analyze the immunologic characteristics of MAFLD. Our findings may provide insights into developing innovative MAFLD treatment techniques.

Integrated single-cell and bulk RNA sequencing analysis identified pyroptosis-related signature for diagnosis and prognosis in osteoarthritis

Osteoarthritis (OA), a degenerative disease of the joints, has one of the highest disability rates worldwide. This study investigates the role of pyroptosis-related genes in osteoarthritis and their expression in different chondrocyte subtypes at the individual cell level. Using OA-related datasets for single-cell RNA sequencing and RNA-seq, the study identified PRDEGs and DEGs and conducted Cox regression analysis to identify independent prognostic factors for OA. CASP6, NOD1, and PYCARD were found to be prognostic factors. Combined Weighted Gene Correlation Network Analysis with PPI network, a total of 15 hub genes related to pyroptosis were involved in the notch and oxidative phosphorylation pathways, which could serve as biomarkers for the diagnosis and prognosis of OA patients. The study also explored the heterogeneity of chondrocytes between OA and normal samples, identifying 19 single-cell subpopulation marker genes that were significantly different among 7 chondrocyte cell clusters. AGT, CTSD, CYBC, and THYS1 were expressed differentially among different cell subpopulations, which were associated with cartilage development and metabolism. These findings provide valuable insights into the molecular mechanisms underlying OA and could facilitate the development of new therapeutic strategies for this debilitating disease.