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

TOP2A modulates signaling via the AKT/mTOR pathway to promote ovarian cancer cell proliferation

Ovarian cancer (OC) is a form of gynecological malignancy that is associated with worse patient outcomes than any other cancer of the female reproductive tract. Topoisomerase II α (TOP2A) is commonly regarded as an oncogene that is associated with malignant disease progression in a variety of cancers, its mechanistic functions in OC have yet to be firmly established. We explored the role of TOP2A in OC through online databases, clinical samples, in vitro and in vivo experiments. And initial analyses of public databases revealed high OC-related TOP2A expression in patient samples that was related to poorer prognosis. This was confirmed by clinical samples in which TOP2A expression was elevated in OC relative to healthy tissue. Kaplan-Meier analyses further suggested that higher TOP2A expression levels were correlated with worse prognosis in OC patients. In vitro, TOP2A knockdown resulted in the inhibition of OC cell proliferation, with cells entering G1 phase arrest and undergoing consequent apoptotic death. In rescue assays, TOP2A was confirmed to regulate cell proliferation and cell cycle through AKT/mTOR pathway activity. Mouse model experiments further affirmed the key role that TOP2A plays as a driver of OC cell proliferation. These data provide strong evidence supporting TOP2A as an oncogenic mediator and prognostic biomarker related to OC progression and poor outcomes. At the mechanistic level, TOP2A can control tumor cell growth via AKT/mTOR pathway modulation. These preliminary results provide a foundation for future research seeking to explore the utility of TOP2A inhibitor-based combination treatment regimens in platinum-resistant recurrent OC patients.

Overexpression of Basonuclin Zinc Finger Protein 2 in stromal cell is related to mesenchymal phenotype and immunosuppression of mucinous colorectal adenocarcinoma

BACKGROUND

Mucinous carcinoma (MC) is a distinct histologic subtype of colorectal cancer (CRC) that is less studied and associated with poor prognosis. This study aimed to identify MC-specific therapeutic targets and biomarkers to improve the prognosis of this aggressive disease.

METHODS

CRC samples from The Cancer Genome Atlas (TCGA) were categorized into MC and non-MC (NMC) groups based on histologic type. A multi-scale embedded gene co-expression network analysis (MEGENA) was constructed to identify gene modules associated with the MC group. The potential functions of Basonuclin Zinc Finger Protein 2 (BNC2) were further analyzed using the Biomarker Exploration for Solid Tumors (BEST) database. In vivo and in vitro experiments were conducted to validate the predicted results.

RESULTS

We identified the stromal component-related gene, BNC2, in the MC population. This gene is associated with a shorter progression-free interval (PFI) in CRC patients. BNC2 promotes FAP (encoding Fibroblast Activation Protein Alpha) transcription in cancer-associated fibroblasts (CAFs) and is involved in angiogenesis through two pathways. Additionally, BNC2 enhances tumor cell invasiveness in a CAF-dependent manner. Patients with high BNC2 expression benefited less from immunotherapy compared to those with low BNC2 expression.

CONCLUSIONS

Our study highlights the clinical importance of BNC2 in MC, and targeting BNC2 on stromal cells (fibroblasts and endothelial cells) may be an effective strategy for treating MC.

ExomiRHub: A comprehensive database for hosting and analyzing human disease-related extracellular microRNA transcriptomics data

Extracellular microRNA (miRNA) expression data generated by different laboratories exhibit heterogeneity, which poses challenges for biologists without bioinformatics expertise. To address this, we introduce ExomiRHub (http://www.biomedical-web.com/exomirhub/), a user-friendly database designed for biologists. This database incorporates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. ExomiRHub also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. All the data in ExomiRHub were further standardized and curated with annotations. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. Moreover, ExomiRHub provides a web service that allows biologists to analyze their uploaded miRNA expression data. Four additional tools were developed to evaluate the functions and targets of miRNAs and miRNA variations. Through ExomiRHub, we identified extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression, demonstrating its potential to significantly accelerate the discovery of extracellular miRNA biomarkers.

Analysis of the implication of steroid 5 alpha-reductase 3 on prognosis and immune microenvironment in Liver Hepatocellular Carcinoma

INTRODUCTION

This study combined the bioinformatics and in vitro experiment-related technologies to analyze the impact of steroid 5 alpha-reductase 3 (SRD5A3) on the prognosis and immune microenvironment of Liver Hepatocellular Carcinoma (LIHC).

METHOD

Gene expression and clinical data were obtained from public databases. The prognosis was evaluated using survival, multifactor Cox, enrichment, and mutation analyses. This was then verified through in vitro experiments.

RESULTS

The expression level of SRD5A3 in LIHC tissues was significantly higher than that in the adjacent tissues. Kaplan-Meier survival analysis showed that high SRD5A3 expression was associated with poor overall survival (OS) and short progression-free survival in patients with LIHC. Multivariate Cox regression analysis revealed that positive SRD5A3 expression was an independent risk factor for OS in patients with LIHC. Expression of SRD5A3 was negatively correlated with immune cell infiltration of CD4+ T, CD8+ T, and B cells. GO and KEGG enrichment analyses showed that SRD5A3 was significantly enriched in signaling- and tumor metastasis-related pathways. Nomogram and calibration curve showed that the predicted performance of the model was consistent with the actual results. In vitro results confirmed that SRD5A3 knockdown inhibited the migration, invasion, and proliferation of LIHC cells.

CONCLUSIONS

SRD5A3 is actively expressed in LIHC, and positive expression of SRD5A3 is an independent risk factor for different prognoses in patients with LIHC. SRD5A3 can promote the proliferation, migration, and invasion of liver cancer cells and is related to short immune infiltration in patients with LIHC.

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

Identification the role of necroptosis in rheumatoid arthritis by WGCNA network

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. These 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. The close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease.

GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

Comprehensive pan-cancer analysis unveils the significant prognostic value and potential role in immune microenvironment modulation of TRIB3

TRIB3, a pseudokinase, was previously studied within only some specific cancer types, leaving its comprehensive functions in pan-cancer contexts largely unexplored. Here, we performed an integrated analysis of TRIB3 expression, prognosis, genetic alterations, functional enrichment and tumor immune-related characteristics in 33 cancer types. Our results showed that TRIB3 exhibits high expression levels across 24 different cancer types and correlates closely with unfavorable prognoses. Meanwhile, TRIB3 shows mutations in a wide spectrum of 22 distinct cancer types, with the predominant mutation types being missense mutations and gene amplifications, and significant changes in DNA methylation levels in 14 types of cancer. We further discovered that TRIB3 expression is significantly associated with cancer immune-related genome mutations, such as tumor mutational burden (TMB), microsatellite instability (MSI) and DNA mismatch repair (MMR), and infiltration of immunosuppressive cells, such as CD4+ Th2 cells and myeloid-derived suppressor cells (MDSCs), into the tumor microenvironment. These results indicated that the expression of TRIB3 might reshape the tumor immune microenvironment (TIME) and lead to immunosuppressive "cold" tumors. In addition, our results confirmed that the loss of function of TRIB3 inhibits cell proliferation, promotes apoptosis, and leads to significant enrichment of "hot" tumor-related immune pathways, at least in breast cancer cells, which further supports the important role of TRIB3 in cancer prognosis and TIME regulation. Together, this pan-cancer investigation provided a comprehensive understanding of the critical role of TRIB3 in human cancers, and suggested that TRIB3 might be a promising prognostic biomarker and a potential target for cancer immunotherapy.

Two hub genes of bipolar disorder, a bioinformatics study based on the GEO database

Bipolar disorder is a mood illness that affects many people. It has a high recurrence frequency and will cause significant damage to the patient's social function. At present, the pathogenesis of BD is not clear. The National Center for Biotechnology Information (NCBI) established and maintained the Gene Expression Omnibus (GEO) database, a gene expression database. For bioinformatics analysis, researchers can obtain expression data from the internet. At present, the samples of the dataset used in the research of BD are mostly from brain tissue, and the data containing blood samples are rarely used. GEO databases (GSE46416, GSE5388, and GSE5389) were used to retrieve public data, and utilizing the online tool GEO2R, differentially expressed genes (DEGs) were retrieved. The common DEGs between the samples of patients with BD and the samples of the normal population were screened by Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to perform functional annotation and pathway enrichment analysis of DEGs. A protein-protein interaction network (PPI) was built to investigate hub genes on this basis. There were 117 up-regulated DEGs and 38 down-regulated DEGs discovered, with two hub genes [SRC, CDKN1A] among the up-regulated DEGs. These two hub genes were also highly enriched in the oxytocin signaling pathway, proteoglycans in cancer and bladder cancer, according to KEGG analysis. The results of the receiver operating characteristic curve (ROC) of SRC and CDKN1A in the three datasets strongly suggested that SRC and CDKN1A were potential diagnostic markers of BD. The results strongly suggest that SRC and CDKN1A are related to the pathogenesis of BD.

scBlood: A comprehensive single-cell accessible chromatin database of blood cells

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

Reduced transcriptome analysis in zebrafish uncovers disruptors of spliceosome and ribosome biosynthesis

Abnormal biosynthesis of spliceosomes and ribosomes can lead to their dysfunction, which in turn disrupts protein synthesis and results in various diseases. While genetic factors have been extensively studied, our understanding of how environmental compounds interfere with spliceosome and ribosome biosynthesis remains limited. In the present study, we employed a Reduced Transcriptome Analysis (RTA) approach, integrating large-scale transcriptome data sets of zebrafish and compiling a specific zebrafish gene panel focusing on the spliceosome and ribosome, to elucidate the potential disruptors targeting their biosynthesis. Transcriptomic data sets for 118 environmental substances and 1400 related gene expression profiles were integrated resulting in 513 exposure signatures. Among these substances, several categories including PCB126, transition metals Lanthanum (La) and praseodymium (Pr), heavy metals Cd2+ and AgNO3 and atrazine were highlighted for inducing the significant transcriptional alterations. Furthermore, we found that the transcriptional patterns were distinct between categories, yet overlapping patterns were generally observed within each group. For instance, over 82 % differentially expressed ribosomal genes were shared between La and Pr within the equivalent concentration range. Additionally, transcriptional complexities were also evident across various organs and developmental stages of zebrafish, with notable differences in the inhibition of the transcription of various spliceosome subunits. Overall, our results provide novel insights into the understanding of the adverse effects of environmental compounds, thereby contributing to their environmental risk assessments.

In-silico modeling of the interplay between APOE4, NLRP3, and ACE2-SPIKE complex in neurodegeneration between Alzheimer and SARS-CoV: implications for understanding pathogenesis and developing therapeutic strategies

The multifaceted interplay between neurodegenerative pathologies, including Alzheimer's disease (AD), and the highly virulent severe acute respiratory syndrome coronavirus (SARS-CoV), is implicated in various conditions. AD and SARS-CoV pathogenesis involve the APOE4 allele, NLRP3 inflammasome, and ACE2-SPIKE complex. APOE4, a genetic polymorphism of the APOE gene, is associated with an increased susceptibility to AD. NLRP3, an inflammatory protein of the innate immune system, plays a pivotal role in immune response cascades. In SARS-CoV, the ACE2 receptor serves as the principal portal for cellular entry, while APOE4 intricately interacts with the ACE2-spike protein complex, enhancing viral internalization process. The interaction of NLRP3 with the ACE2-spike protein complex leads to increased inflammatory signaling. The convergence of APOE4/NLRP3 and ACE2-spike protein complex interactions suggests a possible link between SARS and AD. Therefore, the current research centralizes the association between by utilizing SARS-CoV datasets to explore possible mechanisms that account for the pathogenesis of SARS-CoV and AD. The work is further extended to unveil the molecular interactions of APOE4 and NLRP3 with the ACE2-Spike protein complex at the molecular level by employing molecular dynamics simulation techniques. The therapeutic efficacy of Chyawanprash nutraceuticals is evaluated as their inhibitory potential towards APOE4-ACE2-Spike protein and NLRP3-ACE2-Spike protein complexes. Notably, our simulations unequivocally demonstrate the robust and enduring binding capability of the compound Phyllantidine with the target complexes throughout the simulation period. The findings of the studies further corroborate the primary hypothesis of APOE4 and NLRP3 as driver factors in the pathogenesis of both SARS-CoV and AD. Therefore, this research establishes a paradigm for comprehending the complex interaction between AD and SARS-CoV and lays the groundwork for further study in this domain.Communicated by Ramaswamy H. Sarma.

Exploration of Clozapine-Induced Cardiomyopathy and Its Mechanism

In this study, by pooling the clinical data of patients who died with a history of long-term clozapine use and by examining their hearts, it was found that long-term clozapine use can lead to cardiomyopathy and that its presentation resembles arrhythmogenic cardiomyopathy (ACM), i.e., it exhibits a predominantly right ventricular fatty infiltration with mild left ventricular damage. The transcriptomic data of rat cardiomyocytes after clozapine intervention were analyzed by transcriptomic approach to explore the causes of clozapine cardiomyopathy. The cause of clozapine cardiomyopathy was then explored by a transcriptomic approach, which revealed that its clozapine action on cardiomyocytes enriched cardiomyocyte-related differential genes in biological processes such as muscle development and response to hypoxia, as well as pathways such as fatty acid metabolism and cellular autophagy. Transcriptomic analysis showed that Egr1, Egr2, ler2, Jun, Mapk9, Nr1d2, Atf3, Bhlhe40, Crem, Cry1, Cry2, Dbp were hub genes for clozapine injury to the myocardium, and that these genes may play an important role in the myocardial ACM-like changes caused by clozapine. Combined with the results of pathological examination and transcriptomic analysis, it can be concluded that the long-term action of clozapine on cardiomyocytes leads to cellular autophagy and subsequent structural remodeling of the heart, and in the remodeling affects fatty acid metabolism, which eventually leads to ACM-like changes.

MiR-27a inhibits the growth and metastasis of multiple myeloma through regulating Th17/Treg balance

BACKGROUND

The imbalance between T helper 17 (Th17) and T regulatory (Treg) cells plays a key role in the progression of multiple myeloma (MM).

METHODS

The gene expression profiles of MM were acquired and examined from the Gene Expression Omnibus (GEO) database (GSE72213). Our research involved experimental investigations conducted using the MOPC-MM mouse model. Dysregulation of Treg and Th17 cells was evaluated through flow cytometry, while the levels of inflammatory factors were measured using the enzyme-linked immunosorbent assay. Cell proliferation was gauged using the Cell Counting Kit-8 assay, and cell apoptosis was quantified via flow cytometry. Cell metastasis capabilities were determined by conducting transwell assays. To confirm the relationship between miR-27a and PI3K, a dual-luciferase reporter assay was employed. Finally, proteins associated with the PI3K/AKT/mTOR signaling pathway were assessed using western blotting.

RESULTS

MiR-27a exhibited reduced expression levels in MM. Moreover, it exerted control over the equilibrium of Th17 and Treg cells while reducing the expression of inflammatory mediators such as TGF-β1 and IL-10 in an in vivo setting. Elevated miR-27a levels led to the inhibition of cell viability, colony formation capacity, migratory and invasive traits in an in vitro context. The PI3K/AKT/mTOR signaling pathway was identified as a direct target of miR-27a and could reverse the effects induced by miR-27a in MM cells. Notably, PI3K was directly targeted by miR-27a.

CONCLUSIONS

Our study revealed that miR-27a inhibited MM evolution by regulating the Th17/Treg balance. Inhibition of the PI3K/AKT/mTOR signaling pathway by miR-27a may play a potential mechanistic role.

White matter hyperintensity genetic risk factor TRIM47 regulates autophagy in brain endothelial cells

White matter hyperintensity (WMH) is strongly correlated with age-related dementia and hypertension, but its pathogenesis remains obscure. Genome-wide association studies identified TRIM47 at the 17q25 locus as a top genetic risk factor for WMH formation. TRIM family is a class of E3 ubiquitin ligase with pivotal functions in autophagy, which is critical for brain endothelial cell (ECs) remodeling during hypertension. We hypothesize that TRIM47 regulates autophagy and its loss-of-function disturbs cerebrovasculature. Based on transcriptomics and immunohistochemistry, TRIM47 is found highly expressed by brain ECs in human and mouse, and its transcription is upregulated by artificially induced autophagy while downregulated in hypertension-like conditions. Using in silico simulation, immunocytochemistry and super-resolution microscopy, we predicted a highly conserved binding site between TRIM47 and the LIR (LC3-interacting region) motif of LC3B. Importantly, pharmacological autophagy induction increased Trim47 expression on mouse ECs (b.End3) culture, while silencing Trim47 significantly increased autophagy with ULK1 phosphorylation induction, transcription, and vacuole formation. Together, we demonstrate that TRIM47 is an endogenous inhibitor of autophagy in brain ECs, and such TRIM47-mediated regulation connects genetic and physiological risk factors for WMH formation but warrants further investigation.

Identification of senescence-related genes for potential therapeutic biomarkers of atrial fibrillation by bioinformatics, human histological validation, and molecular docking

Background

Cellular senescence is pivotal in the occurrence and progression of atrial fibrillation (AF). This study aimed to identify senescence-related genes that could be potential therapeutic biomarkers for AF.

Methods

AF-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus dataset. Weighted gene co-expression network analysis (WGCNA) was used to analyze important modules and potential hub genes. Integrating senescence-related genes, potential biomarkers were identified. Their differential expression levels were then validated in human atrial tissue, HL-1 cells, and Angiotensin II-infused mice. Finally, molecular docking analysis was conducted to predict potential interactions between potential biomarkers and the senolytic drug Navitoclax.

Results

We identified seven genes common to AF-related DEGs and senescence-related genes. Three significant modules were selected from WGCNA analysis. Taken together, three senescence-related genes (ETS1, SP1, and WT1) were found to be significantly associated with AF. Protein-protein interaction network analysis revealed biological connections among the predicted target genes of ETS1, SP1, and WT1. Notably, ETS1, SP1, and WT1 exhibited significant differential expression in clinical samples as well as in vitro and in vivo models. Molecular docking revealed favorable binding affinity between senolytic Navitoclax and these potential biomarkers.

Conclusions

This study highlights ETS1, SP1, and WT1 as crucial senescence-related genes associated with AF, offering potential therapeutic targets, with supportive evidence of binding affinity with senolytic Navitoclax. These findings provide novel insights into AF pathogenesis from a senescence perspective.

Bioinformatics analysis of immune infiltration in human diabetic retinopathy and identification of immune-related hub genes and their ceRNA networks

Diabetic retinopathy (DR) is the most common microvascular complication in diabetic patients, and recent studies have shown that immune regulatory mechanisms are closely associated with retinal damage in DR. Therefore, this study focused on exploring immune cells and immune-related genes (IRGs) in DR and gaining insight into the ceRNA mechanisms by which IRGs regulate DR progression. Four datasets from human DR model retinal tissues were obtained from the Gene Expression Omnibus (GEO) database. R software was first used to identify differentially expressed mRNAs (DE-mRNAs) in the dataset GSE160306-mRNAs, then the distribution of immune cells in the gene matrix was analyzed by xCell and ImmuCellAI, ImmPort and InnateDB database were used to obtain immune-related hub genes (IRHGs) in the DR, and finally the STRING online tool and Cytoscape to construct the immune-related ceRNA network. The datasets GSE102485, GSE160308 and GSE160306-lncRNAs were used to validate the results of the ceRNA network further. The results of immune cell infiltration analysis showed that macrophages are important immune cells in DR; immune-related gene screening showed that FCGR2B is an IRHG in DR, and 2 immune-related ceRNA networks of IRHG were obtained: DDN-AS1/miR-10a-5p/FCGR2B and LINC01515/miR-10a-5p/FCGR2B. Our study suggests that infiltration of immune cells, especially the immune role of macrophages, is an important component of DR progression; the immune-related hub gene FCGR2B and its ceRNA network may be a key regulatory network for DR progression. The discovery of key immune cells, IRHG and ceRNA networks in this study may provide new prospects for early intervention and targeted treatment of DR.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Multi-omic analysis identifies the molecular mechanism of hepatocellular carcinoma with cirrhosis

Hepatocellular carcinoma with cirrhosis promotes the advancement of malignancy and the development of fibrosis in normal liver tissues. Understanding the pathological mechanisms underlying the development of HCC with cirrhosis is important for developing effective therapeutic strategies. Herein, the RNA-sequencing (RNA-seq) data and corresponding clinical features of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) database using the University of California Santa Cruz (UCSC) Xena platform. The enrichment degree of hallmarkers for each TCGA-LIHC cohort was quantified by ssGSEA algorithm. Weighted gene co-expression network analysis (WGCNA) revealed two gene module eigengenes (MEs) associated with cirrhosis, namely, MEbrown and MEgreen. Analysis of these modules using AUCell showed that MEbrown had higher enrichment scores in all immune cells, whereas MEgreen had higher enrichment scores in malignant cells. The CellChat package revealed that both immune and malignant cells contributed to the fibrotic activity of myofibroblasts through diverse signaling pathways. Additionally, spatial transcriptomic data showed that hepatocytes, proliferating hepatocytes, macrophages, and myofibroblasts were located in closer proximity in HCC tissues. These cells may potentially participate in the process of stimulating myofibroblast fibrotic activity, which may be related to the development of liver fibrosis. In summary, we made full use of multi-omics data to explore gene networks and cell types that may be involved in the development and progression of cirrhosis in HCC.

Exploring MPC1 as a potential ferroptosis-linked biomarker in the cervical cancer tumor microenvironment: a comprehensive analysis

BACKGROUND

The increasing problems of drug and radiotherapy resistance in cervical cancer underscores the need for novel methods for its management. Reports indicate that the expression of MPC1 may be associated with the tumor microenvironment and the occurrence of ferroptosis in cervical cancer. The objective of this study was to visually illustrate the prognostic significance and immunological characterization of MPC1 in cervical cancer.

METHODS

The expression profile and prognostic significance of MPC1 were analyzed using various databases, including UALCAN, TIMER2, GEPIA2, and Kaplan-Meier Plotter. TISIDB, TIMER2, and immunohistochemical analysis were used to investigate the correlation between MPC1 expression and immune infiltration. GO enrichment analysis, KEGG analysis, Reactome analysis, ConsensusPathDB, and GeneMANIA were used to visualize the functional enrichment of MPC1 and signaling pathways related to MPC1. The correlation analysis was carried out to examine the relationship between MPC1 and Ferroptosis gene in TIMER 2.0, ncFO, GEPIA Database and Kaplan-Meier Plotter.

RESULTS

We demonstrated that the expression levels of MPC1 in cervical cancer tissues were lower than those in normal cervical tissues. Kaplan-Meier survival curves showed shorter overall survival in cervical cancer patients with low levels of MPC1 expression. The expression of MPC1 was related to the infiltrating levels of tumor-infiltrating immune cells in cervical cancer. Moreover, MPC1 expression was associated with the iron-mediated cell death pathway, and several important ferroptosis genes were upregulated in cervical cancer cells. Furthermore, after knocking down MPC1 in HeLa cells, the expression of these genes decreased.

CONCLUSION

These findings indicate that MPC1 functions as a prognostic indicator and plays a role in the regulation of the ferroptosis pathway in cervical cancer.

Regulation of microglia inflammation and oligodendrocyte demyelination by Engeletin via the TLR4/RRP9/NF-κB pathway after spinal cord injury

Microglia polarization is crucial for neuroinflammatory response after spinal cord injury (SCI). Small molecule compounds and hub genes play an important role in regulating microglia polarization, reducing neuroinflammatory response and oligodendrocyte demyelination after SCI. In this study, suitable data sets were used to screen hub genes, and Western blot and Immunofluorescence (IF) experiments were used to confirm the expressions of proteins related to SDAD1, RRP9 and NF-κB pathways under LPS/SCI conditions. Engeletin (ENG) reduced microglia polarization and inflammation in vivo and in vitro via the SDAD1, RRP9 or NF-κB signaling pathways. In addition, ENG binds to the membrane receptor Toll-like receptor 4 (TLR4) through small molecule-protein docking. COIP experiment and protein-protein docking revealed protein-protein interaction (PPI) between RRP9 and SDAD1. By gene knock-down (KD) / overexpression (OE) and Western blot experiments, RRP9 and SDAD1 can regulate inflammatory response through NF-κB signaling and ribosome biogenesis pathway. Western blot analysis showed that CU increased the expression of SDAD1, RRP9 and NF-κB pathway related proteins through TLR1/2, while C34 decreased the expression of SDAD1 and RRP9 proteins through TLR4. These results suggest that ENG can reduce inflammation through TLR4/RRP9(SDAD1)/NF-κB signaling pathway. In addition, we demonstrated that oligodendrocyte apoptosis and demyelination could be influenced by the regulation of microglia and tissue inflammation. In conclusion, this study found the gene Rrp9/Sdad1 and the small molecule compound ENG, which control the inflammatory response of microglia, and further explored the related mechanism of oligodendrocyte demyelination, which has important theoretical significance.

Exploring the significance and potential mechanisms of hippo pathway-associated genes in prognosis of glioma patients

PURPOSE

Despite the efforts of countless researchers to develop glioma treatment strategies, the current therapeutic effect of glioma is still not ideal, and it is necessary to further explore the mechanism to guide treatment. Thus, this study aims to introduce a novel approach for predicting patient prognosis and guiding further treatment interventions.

METHODS

Initially, we conducted a differential gene expression analysis to identify Hippo pathway-associated genes overexpressed in tumors and determined genes correlated with prognosis. Subsequently, employing cluster analysis, we categorized samples into two groups and performed further analyses including prediction, immune cell infiltration abundance, and drug response rates. We utilized weighted gene co-expression analysis to reveal gene sets with high co-variation, delineate inter-sample gene correlation patterns, and conduct enrichment analysis. Prognostic models were built using ten machine learning algorithms combined in 101 different combinations, followed by evaluation and validation. Immune infiltration analysis, differential expression analysis of depleted T cell-related markers, drug sensitivity analysis, and exploration of pathway dysregulation were performed for different risk groups. Quality control and batch integration were performed, and single-cell data were analyzed using dimensionality reduction clustering algorithms and annotation tools to evaluate the activity of the prognostic model in malignant cells.

RESULTS

We conducted data filtering to identify genes overexpressed in tumors, intersecting these genes with Hippo pathway-related genes, identifying 62 genes correlated with prognosis, and performing cluster analysis to divide tumor tissues into two groups. Cluster 2 exhibited a poorer prognosis and demonstrated differences in immune cell infiltration. Utilizing weighted gene co-expression analysis on Cluster 2, we identified gene modules, conducted functional enrichment analysis, and delineated pathways. Employing a combined model based on ten machine learning algorithm combinations, we selected the optimal prognostic model system and validated the model's predictive ability within the dataset. Through immune-related analysis and drug sensitivity analysis, we uncovered differences in immune infiltration and varying sensitivities to chemotherapy drugs. Additionally, the enrichment analysis of gene set revealed discrepancies in upregulation within relevant pathways between the high and low-risk groups. Finally, annotation and evaluation of malignant cells via single-cell analysis showed increased activity of the prognostic model and variations in distribution across different prognostic levels in malignant cells.

CONCLUSION

This study introduces a novel approach utilizing the Hippo pathway and associated genes for glioma prognosis research, demonstrating the potential and significance of this method in evaluating the outcome for patients with glioma. These findings hold substantial clinical significance in guiding therapy and predicting outcomes for individuals diagnosed with glioma, offering significant clinical utility.

Astrocytes originated from neural stem cells drive the regenerative remodeling of pathologic CSPGs in spinal cord injury

Neural degeneration is a hallmark of spinal cord injury (SCI). Multipotent neural precursor cells (NPCs) have the potential to reconstruct the damaged neuron-glia network due to their tri-lineage capacity to generate neurons, astrocytes, and oligodendrocytes. However, astrogenesis is the predominant fate of resident or transplanted NPCs in the SCI milieu adding to the abundant number of resident astrocytes in the lesion. How NPC-derived astrocytes respond to the inflammatory milieu of SCI and the mechanisms by which they contribute to the post-injury recovery processes remain largely unknown. Here, we uncover that activated NPC-derived astrocytes exhibit distinct molecular signature that is immune modulatory and foster neurogenesis, neuronal maturity, and synaptogenesis. Mechanistically, NPC-derived astrocytes perform regenerative matrix remodeling by clearing inhibitory chondroitin sulfate proteoglycans (CSPGs) from the injury milieu through LAR and PTP-σ receptor-mediated endocytosis and the production of ADAMTS1 and ADAMTS9, while most resident astrocytes are pro-inflammatory and contribute to the pathologic deposition of CSPGs. These novel findings unravel critical mechanisms of NPC-mediated astrogenesis in SCI repair.

Applicability of epigenetic age models to next-generation methylation arrays

BACKGROUND

Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.

METHODS

We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.

RESULTS

The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.

CONCLUSION

Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.

Crispr-mediated genome editing reveals a preponderance of non-oncogene addictions as targetable vulnerabilities in pleural mesothelioma

Pleural mesothelioma (PM) is an aggressive cancer with limited treatment options. In particular, the frequent loss of tumor suppressors, a key oncogenic driver of the disease that is therapeutically intractable, has hampered the development of targeted cancer therapies. Here, we interrogate the PM genome using CRISPR-mediated gene editing to systematically uncover PM cell susceptibilities and provide an evidence-based rationale for targeted cancer drug discovery. This analysis has allowed us to identify with high confidence numerous known and novel gene dependencies that are surprisingly highly enriched for non-oncogenic pathways involved in response to various stress stimuli, in particular DNA damage and transcriptional dysregulation. By integrating genomic analysis with a series of in vitro and in vivo functional studies, we validate and prioritize several non-oncogene addictions conferred by CDK7, CHK1, HDAC3, RAD51, TPX2, and UBA1 as targetable vulnerabilities, revealing previously unappreciated aspects of PM biology. Our findings support the growing consensus that stress-responsive non-oncogenic signaling plays a key role in the initiation and progression of PM and provide a functional blueprint for the development of unprecedented targeted therapies to combat this formidable disease.

Integrated bioinformatics reveals genetic links between visceral obesity and uterine tumors

Visceral obesity (VO), characterized by excess fat around internal organs, is a recognized risk factor for gynecological tumors, including benign uterine leiomyoma (ULM) and malignant uterine leiomyosarcoma (ULS). Despite this association, the shared molecular mechanisms remain underexplored. This study utilizes an integrated bioinformatics approach to elucidate common molecular pathways and identify potential therapeutic targets linking VO, ULM, and ULS. We analyzed gene expression datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) in each condition. We found 101, 145, and 18 DEGs in VO, ULM, and ULS, respectively, with 37 genes overlapping across all three conditions. Functional enrichment analysis revealed that these overlapping DEGs were significantly enriched in pathways related to cell proliferation, immune response, and transcriptional regulation, suggesting shared biological processes. Protein-protein interaction network analysis identified 14 hub genes, of which TOP2A, APOE, and TYMS showed significant differential expression across all three conditions. Drug-gene interaction analysis identified 26 FDA-approved drugs targeting these hub genes, highlighting potential therapeutic opportunities. In conclusion, this study uncovers shared molecular pathways and actionable drug targets across VO, ULM, and ULS. These findings deepen our understanding of disease etiology and offer promising avenues for drug repurposing. Experimental validation is needed to translate these insights into clinical applications and innovative treatments.

Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer

Copper (Cu) is used as a cofactor in all organisms, and yet it can be toxic at high intracellular concentrations, causing cell death. Diethyldithiocarbamate (DDC) is a Cu ionophore that can transport Cu effectively into the cell. Copper-diethyldithiocarbamate (Cu-DDC) can treat prostate cancer (PCa) and may correlate with the cell death process. However, the specific Cu-DDC-related cell death genes in PCa are still unknown. Information about the Cu-DDC-related cell death genes was obtained from a previous study. Concurrently, the RNA expression profiles and clinical data were downloaded from public databases such as GEO, TCGA, and CPGEA. Using data from TCGA database, the logistic and lasso regression models were generated using R software. The influence of these genes in affecting PCa progression and prognosis was analyzed. Finally, the expression of these genes was verified in clinical samples. We found five Cu-DDC-related cell death genes associated with the occurrence of PCa from GSE35988, a gene dataset, namely, CDKN2A, PRC1, CDK1, SOX2, and ZNF365. CDKN2A, PRC1, and CDK1 are known to influence PCa patients' disease-free survival (DFS) status and were overexpressed, whereas SOX2 and ZNF365 were under-expressed in PCa in the different databases. Some of these genes can affect PCa progression. Consistent with the database results, the mRNA and protein expression of CDKN2A, PRC1, and CDK1 was also higher in clinical samples. In conclusion, we identified five hub genes which are important for Cu-DDC-related cell death process that can predict the development of PCa.

Evaluation of Gremlin-1 as a therapeutic target in metabolic dysfunction-associated steatohepatitis

Gremlin-1 has been implicated in liver fibrosis in metabolic dysfunction-associated steatohepatitis (MASH) via inhibition of bone morphogenetic protein (BMP) signalling and has thereby been identified as a potential therapeutic target. Using rat in vivo and human in vitro and ex vivo model systems of MASH fibrosis, we show that neutralisation of Gremlin-1 activity with monoclonal therapeutic antibodies does not reduce liver inflammation or liver fibrosis. Still, Gremlin-1 was upregulated in human and rat MASH fibrosis, but expression was restricted to a small subpopulation of COL3A1/THY1+ myofibroblasts. Lentiviral overexpression of Gremlin-1 in LX-2 cells and primary hepatic stellate cells led to changes in BMP-related gene expression, which did not translate to increased fibrogenesis. Furthermore, we show that Gremlin-1 binds to heparin with high affinity, which prevents Gremlin-1 from entering systemic circulation, prohibiting Gremlin-1-mediated organ crosstalk. Overall, our findings suggest a redundant role for Gremlin-1 in the pathogenesis of liver fibrosis, which is unamenable to therapeutic targeting.

A small TAT-TrkB peptide prevents BDNF receptor cleavage and restores synaptic physiology in Alzheimer's disease

In Alzheimer's disease (AD), amyloid β (Aβ)-triggered cleavage of TrkB-FL impairs brain-derived neurotrophic factor (BDNF) signaling, thereby compromising neuronal survival, differentiation, and synaptic transmission and plasticity. Using cerebrospinal fluid and postmortem human brain samples, we show that TrkB-FL cleavage occurs from the early stages of the disease and increases as a function of pathology severity. To explore the therapeutic potential of this disease mechanism, we designed small TAT-fused peptides and screened their ability to prevent TrkB-FL receptor cleavage. Among these, a TAT-TrkB peptide with a lysine-lysine linker prevented TrkB-FL cleavage both in vitro and in vivo and rescued synaptic deficits induced by oligomeric Aβ in hippocampal slices. Furthermore, this TAT-TrkB peptide improved the cognitive performance, ameliorated synaptic plasticity deficits and prevented Tau pathology progression in vivo in the 5XFAD mouse model of AD. No evidence of liver or kidney toxicity was found. We provide proof-of-concept evidence for the efficacy and safety of this therapeutic strategy and anticipate that this TAT-TrkB peptide has the potential to be a disease-modifying drug that can prevent and/or reverse cognitive deficits in patients with AD.

Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls

Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop chemoresistance, leading to poor outcomes due to the disease's high heterogeneity. In this study, we used publicly available single-cell RNA sequencing data and machine learning to classify AML patients and healthy, monocytes, dendritic and progenitor cells population. We found that gene expression profiles of AML patients and healthy controls can be classified at the individual level with high accuracy (>70 %) when using progenitor cells, suggesting the existence of subject-specific single cell transcriptomics profiles. The analysis also revealed molecular determinants of patient heterogeneity (e.g. TPSD1, CT45A1, and GABRA4) which could support new strategies for patient stratification and personalized treatment in leukaemia.

PerturbAtlas: a comprehensive atlas of public genetic perturbation bulk RNA-seq datasets

Manipulating gene expression is crucial for understanding gene function, with high-throughput sequencing techniques such as RNA-seq elucidating the downstream mechanisms involved. However, the lack of a standardized metadata format for small-scale perturbation expression datasets in public repositories hinders their reuse. To address this issue, we developed PerturbAtlas, an add-value resource that re-analyzes publicly archived RNA-seq libraries to provide quantitative data on gene expression, transcript profiles, and alternative splicing events following genetic perturbation. PerturbAtlas assists users in identifying trends at the gene and isoform levels in perturbation assays by re-analyzing a curated set of 122 801 RNA-seq libraries across 13 species. This resource is freely available at https://perturbatlas.kratoss.site as both raw data tables and an interactive browser, allowing searches by species, tissue or genomic features. The results provide detailed information on alterations following perturbations, accessible through both forward and reverse approaches, thereby enabling the exploration of perturbation consequences and the identification of potential causal perturbations.

Comprehensive analysis of prognostic and immunological role of basement membrane-related genes in soft tissue sarcoma

BACKGROUND

Soft tissue sarcoma (STS) represents highly multifarious malignant tumors that often occur in adolescents and have a poor prognosis. The basement membrane, as an ancient cellular matrix, was recently proven to play a vital role in developing abundant tumors. The relationship between basement membrane-related genes and STS remains unknown.

METHODS

Consensus clustering was employed to identify subgroups related to differentially expressed basement membrane-related genes. Cox and least absolute shrinkage and selection operator regression analyses were utilized to construct this novel signature. Then, we established a nomogram and calibration curve, including the risk score and available clinical characteristics. Finally, we carried out functional enrichment analysis and immune microenvironment analysis to investigate enriched pathways and the tumor immune microenvironment related to the novel signature.

RESULTS

A prognostic predictive signature consisting of eight basement membrane-related genes was established. Kaplan-Meier survival curves demonstrated that the patients in the high-risk group had a poor prognosis. Independent analysis illustrated that this risk model could be an independent prognostic predictor. We validated the accuracy of our signature in the validation data set. In addition, gene set enrichment analysis and immune microenvironment analysis showed that patients with low-risk scores were enriched in some pathways associated with immunity. Finally, in vitro experiments showed significantly differential expression levels of these signature genes in STS cells and PSAT1 could promote the malignant behavior of STS.

CONCLUSIONS

The novel signature is a promising prognostic predictor for STS. The present study may improve the prognosis and enhance individualized treatment for STS in the future.

Single-cell RNA sequencing analysis provides novel insights into the role of apoptosis-related genes in muscle aging

OBJECTIVE

This study employed a comprehensive single-cell analysis approach to explore the role of cell apoptosis-related genes in muscle aging.

METHODS

The single-cell RNA sequencing data from the GSE143704 dataset were used to identify distinct cell clusters and assess gene expression patterns related to apoptosis activation. The "limma" package was used to identify hub genes, after which we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to identify relevant pathways. Additionally, Gene Set Enrichment Analysis(GSEA) and Gene Set Variation Analysis (GSVA) were used to uncover relevant biological pathways. The Receiver Operating Characteristic Curve (ROC) was used to evaluate the diagnostic value of the hub genes. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the immune cell infiltration levels.

RESULTS

Single-cell sequencing data from muscle aging patients allowed the identification of various cell types, including epithelial cells, adipocytes, and tissue-resident macrophages. By conducting a differential expression analysis that intersected active and nonactive apoptosis, as well as comparing elderly and young samples, a total of 22 hub genes were identified (p < 0.05). The 22 hub genes have discriminative ability as potential biomarkers for diagnosing muscle aging. The enrichment analysis indicated that these genes were closely associated with diverse pathways, including "response to UV-B" and "extracellular matrix organization" (p < 0.05). Furthermore, GSEA and GSVA indicated that multiple pathways emerged-for example, the "complement and coagulation cascades", "proteasome", "insulin signaling pathway", and "MAPK signaling pathway". Additionally, the analysis of immune cell infiltration revealed positive correlations between most of the hub genes and immune cells.

CONCLUSION

Our study identified 22 apoptosis-related genes involved in muscle aging and indicated their potential diagnostic value. These findings offer a novel perspective on the pathogenesis of muscle aging and present potential targets for therapeutic interventions.

Integrating multiomics and Single-Cell communication analysis to uncover Ankylosing spondylitis mechanisms

BACKGROUND

Ankylosing spondylitis (AS) is a chronic inflammatory joint disorder, necessitating early diagnosis and effective treatment. The specific mechanism of action of Cassia twigs in the treatment of AS is not fully understood.

METHODS

Blood samples and clinical data from 28,458 individuals (6,101 with AS, 22,357 without AS) were collected. To construct a predictive model, we utilized logistic regressions and machine learning techniques to create a dynamic nomogram. Immune cell infiltration was evaluated using the GSE73754 dataset. Subsequently, we obtained vertebral bone marrow blood from AS patients for 10X single-cell sequencing. We also extracted and purified total RNA from hip joint ligament tissue samples from six AS patients and six non-AS patients. The genes related to the expression of AS and Cassia twigs were analyzed comprehensively, and the specific drug targets were identified by molecular docking. The interactions between immune cells through cell communication analysis were elucidated.

RESULTS

We developed a dynamic nomogram incorporating the neutrophil count (NEUT) and other variables. Neutrophil immune responses were confirmed through immune infiltration analysis utilizing GSE73754. We observed the early involvement of neutrophils in the pathology of AS. The CAT-expressing Cassia twigs gene could be used as a drug target for the treatment of AS. Moreover, comprehensive RNA analysis revealed notable CAT expression in neutrophils and various other immune cells.

CONCLUSIONS

Neutrophils play dual roles in AS, regulating inflammation and initiating differentiation signals to other cells. The CAT gene, which is expressed in Cassia twigs, has emerged as a potential therapeutic target for AS treatment.

Tail-anchored membrane protein SLMAP3 is essential for targeting centrosomal proteins to the nuclear envelope in skeletal myogenesis

The positioning and communication between the nucleus and centrosomes are essential in cell division, differentiation and tissue formation. During skeletal myogenesis, the nuclei become evenly spaced with the switch of the microtubule-organizing centre (MTOC) from the centrosome to the nuclear envelope (NE). We report that the tail-anchored sarcolemmal membrane associated protein 3 (SLMAP3), a component of the MTOC and NE, is crucial for myogenesis because its deletion in mice leads to a reduction in the NE-MTOC formation, mislocalization of the nuclei, dysregulation of the myogenic programme and abnormal embryonic myofibres. SLMAP3-/- myoblasts also displayed a similar disorganized distribution of nuclei with an aberrant NE-MTOC and defective myofibre formation and differentiation programming. We identified novel interactors of SLMAP3, including pericentrin, PCM1 (pericentriolar material 1), AKAP9 (A-kinase anchoring protein 9), kinesin-1 members Kif5B (kinesin family member 5B), KCL1 (kinesin light chain 1), KLC2 (kinesin light chain 2) and nuclear lamins, and observed that the distribution of centrosomal proteins at the NE together with Nesprin-1 was significantly altered by the loss of SLMAP3 in differentiating myoblasts. SLMAP3 is believed to negatively regulate Hippo signalling, but its loss was without impact on this pathway in developing muscle. These results reveal that SLMAP3 is essential for skeletal myogenesis through unique mechanisms involving the positioning of nuclei, NE-MTOC dynamics and gene programming.

PPIC-labeled CAFs: Key players in neoadjuvant chemotherapy resistance for gastric cancer

BACKGROUND

Gastric cancer (GC) is the fourth leading cause of cancer deaths, with advanced cases having a median survival of less than one year. Neoadjuvant chemotherapy (NCT) is vital but faces drug resistance issues, partly due to cancer-associated fibroblasts (CAFs). Yet, specific CAF subpopulations contributing to resistance are poorly understood.

METHODS

Differentially expressed genes (DEGs) between chemosensitive and resistant GC patients were identified using GEO2R. Single-cell sequencing (scRNA-seq) identified CAF-related genes. Immunohistochemistry verified key genes in NCT-treated GC samples, analyzing their correlation with tumor regression grade (TRG) and clinicopathological characteristics.

RESULTS

PPIC as a gene highly expressed in CAFs was closely associated with NCT resistance in gastric cancer. Immunohistochemistry results revealed positivity for the expression of cyclophilin C (CypC), encoded by PPIC, in the 5-fluorouracil and cisplatin NCT resistant and -sensitive groups of gastric cancer patients at rates of 69.7 % (76/109) and 43.6 % (24/55), respectively (p < 0.001). The high expression of CypC in CAFs was positively correlated to tumor size (p = 0.025), T stage (p = 0.004), TNM stage (p = 0.004), and vascular invasion (p = 0.027). In cancer cells the expression of CypC was associated with OS (p = 0.026). However, in CAFs, CypC expression was not related to OS (p = 0.671).

CONCLUSIONS

PPIC-labeled CAF subgroups are related to NCT resistance and poor prognosis in GC and they may cause drug resistance through signaling pathways such as glucose metabolism and extracellular matrix remodeling. However, the exact mechanism behind the involvement of PPIC-labeled CAF in drug resistance of GC requires further study.

Small RNA sequencing reveals snoRNAs and piRNA-019825 as novel players in diabetic kidney disease

INTRODUCTION

Micro- and macrovascular complications are common among persons with type 2 diabetes. Recently there has been growing interest to investigate the potential of circulating small non-coding RNAs (sncRNAs) as contributors to the development of diabetic complications. In this study we investigate to what extent circulating sncRNAs levels associate with prevalent diabetic kidney disease (DKD) in persons with type 2 diabetes.

METHODS

Plasma sncRNAs levels were determined using small RNA-seq, allowing detection of miRNAs, snoRNAs, piRNAs, tRNA fragments, and various other sncRNA classes. We tested for differentially expressed sncRNAs in persons with type 2 diabetes, with DKD (n = 69) or without DKD (n = 405). In secondary analyses, we also tested the association with eGFR, albuminuria (UACR), and the plasma proteome.

RESULTS

In total seven sncRNAs were negatively associated with prevalent DKD (all PFDR ≤ 0.05). Including one microRNA (miR-143-5p), five snoRNAs (U8, SNORD118, SNORD24, SNORD107, SNORD87) and a piRNA (piR-019825 | DQ597218). Proteomic analyses showed that the seven sncRNAs, and especially the piRNA piR-019825, were associated with plasma levels of 24 proteins of which several have known associations with kidney function including TNF sR-I (TNFRFS1A), DAN (NBL1) and cystatin C (CST3).

CONCLUSION

We have identified novel small non-coding RNAs, primarily from classes other than microRNAs, that are associated with diabetic kidney disease. Our results show that the involvement of small non-coding RNAs in DKD goes beyond the already known microRNAs and also involves other classes of sncRNA, in particular snoRNAs and the piRNA piR-019825, that have never been studied before in relation to kidney function.

PRRX1 silencing is required for metastatic outgrowth in melanoma and is an independent prognostic of reduced survival in patients

Paired related homeobox 1 (PRRX1) is an inducer of epithelial-to-mesenchymal transition (EMT) in different types of cancer cells. We detected low PRRX1 expression in nevus but increased levels in primary human melanoma and cell lines carrying the BRAFV600E mutation. High expression of PRRX1 correlates with invasiveness and enrichment of genes belonging to the EMT programme. Conversely, we found that loss of PRRX1 in metastatic samples is an independent prognostic predictor of poor survival for melanoma patients. Here, we show that stable depletion of PRRX1 improves the growth of melanoma xenografts and increases the number of distant spontaneous metastases, compared to controls. We provide evidence that loss of PRRX1 counteracts the EMT phenotype, impairing the expression of other EMT-related transcription factors, causing dysregulation of the ERK and signal transducer and activator of transcription 3 (STAT3) signaling pathways, and abrogating the invasive and migratory properties of melanoma cells while triggering the up-regulation of proliferative/melanocytic genes and the expression of the neural-crest-like markers nerve growth factor receptor (NGFR; also known as neurotrophin receptor p75NTR) and neural cell adhesion molecule L1 (L1CAM). Overall, our results indicate that loss of PRRX1 triggers a switch in the invasive programme, and cells de-differentiate towards a neural crest stem cell (NCSC)-like phenotype that accounts for the metastatic aggressiveness.

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

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

EBF1-COX4I2 signaling axis promotes a myofibroblast-like phenotype in cancer-associated fibroblasts (CAFs) and is associated with an immunosuppressive microenvironment

Immunotherapy has limited response rates in colorectal cancer (CRC) due to an immunosuppressive tumor microenvironment (TME). Combining transcriptome sequencing, clinical specimens, and functional experiments, we identified a unique group of CAF subpopulations (COX4I2 + ) with inhibited mitochondrial respiration and enhanced glycolysis. Through bioinformatics predictions and luciferase reporter assays, we determined that EBF1 can upstreamly regulate COX4I2 transcription. COX4I2 + CAFs functionally and phenotypically resemble myofibroblasts, are important for the formation of the fibrotic TME, and are capable of activating the M2 phenotype of macrophages. In vitro experiments demonstrated that COX4I2 + CAFs promote immunosuppressive TME by blocking CD8 + T cell infiltration and inducing CD8 + T cell dysfunction. Using multiple independent cohorts, we also found a strong correlation between the immunotherapy response rate of CRC patients and COX4I2 expression in their tumors. Our results identify a CAF subpopulation characterized by activation of the EBF1-COX4I2 axis, and this group of CAFs can be targeted to improve cancer immunotherapy outcomes.

Intestinal flora and inflammatory bowel disease: Causal relationships and predictive models

Background

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is significantly influenced by intestinal flora. Understanding the genetic and microbiotic interplay is crucial for IBD prediction and treatment.

Methods

We used Mendelian randomization (MR), transcriptomic analysis, and machine learning techniques, integrating data from the MiBioGen Consortium and various GWAS datasets. SNPs associated with intestinal flora were mapped to genes, with LASSO regression refining gene selection. Differentially expressed genes (DEGs) and immune infiltration patterns were identified through transcriptomic analysis. Six machine learning models were used for predictive modeling.

Findings

MR analysis identified 25 gut microbiota classifications causally related to IBD. SNP mapping and gene expression analysis highlighted 24 significant genes. Drug target MR and colocalization validated these genes' causal relationships with IBD. Key pathways identified included the PI3K-Akt signaling pathway and epithelial-mesenchymal transition. Immune infiltration analysis revealed distinct patterns between high and low LASSO score groups. Machine learning models demonstrated high predictive value, with soft voting enhancing reliability.

Interpretation

By integrating MR, transcriptomic analysis, and sophisticated machine learning approaches, this study elucidates the causal relationships between intestinal flora and IBD. The application of machine learning not only enhanced predictive modeling but also offered new insights into IBD pathogenesis, highlighted potential therapeutic targets, and established a robust framework for predicting IBD onset.

Impact of essential genes on the success of genome editing experiments generating 3313 new genetically engineered mouse lines

The International Mouse Phenotyping Consortium (IMPC) systematically produces and phenotypes mouse lines with presumptive null mutations to provide insight into gene function. The IMPC now uses the programmable RNA-guided nuclease Cas9 for its increased capacity and flexibility to efficiently generate null alleles in the C57BL/6N strain. In addition to being a valuable novel and accessible research resource, the production of 3313 knockout mouse lines using comparable protocols provides a rich dataset to analyze experimental and biological variables affecting in vivo gene engineering with Cas9. Mouse line production has two critical steps - generation of founders with the desired allele and germline transmission (GLT) of that allele from founders to offspring. A systematic evaluation of the variables impacting success rates identified gene essentiality as the primary factor influencing successful production of null alleles. Collectively, our findings provide best practice recommendations for using Cas9 to generate alleles in mouse essential genes, many of which are orthologs of genes linked to human disease.

Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer. Although there is increasing evidence linking ccRCC to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. Though drug therapies are the best choice after the metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving it for almost 2 years. In this case, multi-targeted different variants of therapeutic drugs are essential for effective treatment against ccRCC. To understand molecular mechanisms of ccRCC development and progression, and explore multi-targeted different variants of therapeutic drugs, it is essential to identify ccRCC-causing key genes (KGs). In order to obtain ccRCC-causing KGs, at first, we detected 133 common differentially expressed genes (cDEGs) between ccRCC and control samples based on nine (9) microarray gene-expression datasets with NCBI accession IDs GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895. Then, we filtered these cDEGs through survival analysis with the independent TCGA and GTEx database and obtained 54 scDEGs having significant prognostic power. Next, we used protein-protein interaction (PPI) network analysis with the reduced set of 54 scDEGs to identify ccRCC-causing top-ranked eight KGs (PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, SERPINA5). The pan-cancer analysis with KGs based on TCGA database showed the significant association with different subtypes of kidney cancers including ccRCC. The gene regulatory network (GRN) analysis revealed some crucial transcriptional and post-transcriptional regulators of KGs. The scDEGs-set enrichment analysis significantly identified some crucial ccRCC-causing molecular functions, biological processes, cellular components, and pathways that are linked to the KGs. The results of DNA methylation study indicated the hypomethylation and hyper-methylation of KGs, which may lead the development of ccRCC. The immune infiltrating cell types (CD8+ T and CD4+ T cell, B cell, neutrophil, dendritic cell and macrophage) analysis with KGs indicated their significant association in ccRCC, where KGs are positively correlated with CD4+ T cells, but negatively correlated with the majority of other immune cells, which is supported by the literature review also. Then we detected 10 repurposable drug molecules (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib.) by molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with the independent receptors, also supported their potent against ccRCC. Therefore, these outputs might be useful inputs/resources to the wet-lab researchers and clinicians for considering an effective treatment strategy against ccRCC.

Exosome-related gene identification and diagnostic model construction in hepatic ischemia-reperfusion injury

Hepatic ischemia-reperfusion injury (HIRI) may cause severe hepatic impairment, acute hepatic insufficiency, and multiorgan system collapse. Exosomes can alleviate HIRI. Therefore, this study explored the role of exosomal-related genes (ERGs) in HIRI using bioinformatics to determine the underlying molecular mechanisms and novel diagnostic markers for HIRI. We merged the GSE12720, GSE14951, and GSE15480 datasets obtained from the Gene Expression Omnibus (GEO) database into a combined gene dataset (CGD). CGD was used to identify differentially expressed genes (DEGs) based on a comparison of the HIRI and healthy control cohorts. The impact of these DEGs on HIRI was assessed through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). ERGs were retrieved from the GeneCards database and prior studies, and overlapped with the identified DEGs to yield the set of exosome-related differentially expressed genes (ERDEGs). Functional annotations and enrichment pathways of these genes were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Diagnostic models for HIRI were developed using least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM) algorithms. Key genes with diagnostic value were identified from the overlap, and single-sample gene-set enrichment analysis (ssGSEA) was conducted to evaluate the immune infiltration characteristics. A molecular regulatory interaction network was established using Cytoscape software to elucidate the intricate regulatory mechanisms of key genes in HIRI. Finally, exosome score (Es) was obtained using ssGSEA and the HIRI group was divided into the Es_High and Es_Low groups based on the median Es. Gene expression was analyzed to understand the impact of all genes in the CGD on HIRI. Finally, the relative expression levels of the five key genes in the hypoxia-reoxygenation (H/R) model were determined using quantitative real-time PCR (qRT-PCR). A total of 3810 DEGs were identified through differential expression analysis of the CGD, and 61 of these ERDEGs were screened. Based on GO and KEGG enrichment analyses, the ERDEGs were mainly enriched in wound healing, MAPK, protein kinase B signaling, and other pathways. GSEA and GSVA revealed that these genes were mainly enriched in the TP53, MAPK, TGF[Formula: see text], JAK-STAT, MAPK, and NFKB pathways. Five key genes (ANXA1, HNRNPA2B1, ICAM1, PTEN, and THBS1) with diagnostic value were screened using the LASSO regression and SVM algorithms and their molecular interaction network was established using Cytoscape software. Based on ssGSEA, substantial variations were found in the expression of 18 immune cell types among the groups (p < 0.05). Finally, the Es of each HIRI patient was calculated. ERDEGs in the Es_High and Es_Low groups were enriched in the IL18, TP53, MAPK, TGF[Formula: see text], and JAK-STAT pathways. The differential expression of these five key genes in the H/R model was verified using qRT-PCR. Herein, five key genes were identified as potential diagnostic markers. Moreover, the potential impact of these genes on pathways and the regulatory mechanisms of their interaction network in HIRI were revealed. Altogether, our findings may serve as a theoretical foundation for enhancing clinical diagnosis and elucidating underlying pathogeneses.

Determination of key hub genes in Leishmaniasis as potential factors in diagnosis and treatment based on a bioinformatics study

Leishmaniasis is an infectious disease caused by protozoan parasites from different species of leishmania. The disease is transmitted by female sandflies that carry these parasites. In this study, datasets on leishmaniasis published in the GEO database were analyzed and summarized. The analysis in all three datasets (GSE43880, GSE55664, and GSE63931) used in this study has been performed on the skin wounds of patients infected with a clinical form of leishmania (Leishmania braziliensis), and biopsies have been taken from them. To identify differentially expressed genes (DEGs) between leishmaniasis patients and controls, the robust rank aggregation (RRA) procedure was applied. We performed gene functional annotation and protein-protein interaction (PPI) network analysis to demonstrate the putative functionalities of the DEGs. The study utilized Molecular Complex Detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) to detect molecular complexes within the protein-protein interaction (PPI) network and conduct analyses on the identified functional modules. The CytoHubba plugin's results were paired with RRA analysis to determine the hub genes. Finally, the interaction between miRNAs and hub genes was predicted. Based on the RRA integrated analysis, 407 DEGs were identified (263 up-regulated genes and 144 down-regulated genes). The top three modules were listed after creating the PPI network via the MCODE plug. Seven hub genes were found using the CytoHubba app and RRA: CXCL10, GBP1, GNLY, GZMA, GZMB, NKG7, and UBD. According to our enrichment analysis, these functional modules were primarily associated with immune pathways, cytokine activity/signaling pathways, and inflammation pathways. However, a UBD hub gene is interestingly involved in the ubiquitination pathways of pathogenesis. The mirNet database predicted the hub gene's interaction with miRNAs, and results revealed that several miRNAs, including mir-146a-5p, crucial in fighting pathogenesis. The key hub genes discovered in this work may be considered as potential biomarkers in diagnosis, development of agonists/antagonist, novel vaccine design, and will greatly contribute to clinical studies in the future.

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis. Unfortunately, statistical and biological differences between single cell and bulk transcriptomic data make it challenging to use existing gene set collections, which were developed for bulk tissue analysis, on scRNA-seq data. In this paper, we describe a procedure for customizing gene set collections originally created for bulk tissue analysis to reflect the structure of gene activity within specific cell types. Our approach leverages information about mean gene expression in the 81 human cell types profiled via scRNA-seq by the Human Protein Atlas (HPA) Single Cell Type Atlas. This HPA information is used to compute cell type-specific gene and gene set weights that can be used to filter or weight gene set collections. As demonstrated through the analysis of immune cell scRNA-seq data using gene sets from the Molecular Signatures Database (MSigDB), accounting for cell type-specificity can significantly improve gene set testing power and interpretability. An example vignette along with gene and gene set weights for the 81 HPA SCTA cell types and the MSigDB collections are available at https://hrfrost.host.dartmouth.edu/SCGeneSetOpt/ .

Expression assays of selected lncRNAs in non-functioning pituitary adenomas

PURPOSE

Non-functioning pituitary adenomas (NFPAs) are a group of these neoplasms originated from the adenohypophyse and do not show evidence of hormonal oversecretion. However, different genes and lncRNAs have been found to be dysregulated in these samples.

MATERIAL AND METHODS

In this study, in order to identify novel biomarkers, a set of regulatory lncRNAs for the two important hub genes, i.e. STAT3 and EGFR were selected and subjected to experimental investigation. These lncRNAs were EGFR-AS1 for the EGFR gene, and LINC00240, FALEC and SNHG12 for the STAT3 gene.

RESULTS

All studied genes were down-regulated in NFPA samples compared with normal tissues adjacent to the tumors (NTATs), except for FALEC whose expression was not different between these two sets of samples. EGFR was the most significantly down-regulated gene in NFPAs (Expression ratio (95% CI) = 0.009 (0.002-0.04), P value < 0.0001). ROC curve analyses proposed that the expression levels of SNHG12, EGFR, EGFR-AS1 and LINC00240 can be used to distinguish NFPAs from NTATs with AUC values of 0.88, 0.83, 0.7 and 0.66, respectively. Spearman's correlation analyses showed significant correlations between FALEC and EGFR-AS1 in both types of tissues, and between FALEC and EGFR in NFPAs. Moreover, expression of LINC00240 was correlated with EGFR-AS1, FALEC and SNHG12 in NFPAs.

CONCLUSION

Taken together, EGFR and STAT3-related lncRNAs may be involved in the pathogenesis of NFPA.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Identification and experimental verification of senescence-related gene signatures and molecular subtypes in idiopathic pulmonary arterial hypertension

Evidences illustrate that cell senescence contributes to the development of pulmonary arterial hypertension. However, the molecular mechanisms remain unclear. Since there may be different senescence subtypes between PAH patients, consistent senescence-related genes (SRGs) were utilized for consistent clustering by unsupervised clustering methods. Senescence is inextricably linked to the immune system, and the immune cells in each cluster were estimated by ssGSEA. To further screen out more important SRGs, machine learning algorithms were used for identification and their diagnostic value was assessed by ROC curves. The expression of hub genes were verified in vivo and in vitro. Transcriptome analysis was used to assess the effects of silence of hub gene on different pathways. Three senescence molecular subtypes were identified by consensus clustering. Compared with cluster A and B, most immune cells and checkpoint genes were higher in cluster C. Thus, we identified senescence cluster C as the immune subtype. The ROC curves of IGF1, HOXB7, and YWHAZ were remarkable in both datasets. The expression of these genes was increased in vitro. Western blot and immunohistochemical analyses revealed that YWHAZ expression was also increased. Our transcriptome analysis showed autophagy-related genes were significantly elevated after silence of YWHAZ. Our research provided several prospective SRGs and molecular subtypes. Silence of YWHAZ may contribute to the clearance of senescent endothelial cells by activating autophagy.

Shared diagnostic genes and potential mechanisms between polycystic ovary syndrome and recurrent miscarriage revealed by integrated transcriptomics analysis and machine learning

Objective

More and more studies have found that polycystic ovary syndrome (PCOS) is significantly associated with recurrent spontaneous abortion (RSA), but the specific mechanism is not yet clear.

Methods

Based on the GEO database, we downloaded the PCOS (GSE10946, GSE6798 and GSE137684) and RSA (GSE165004, GSE26787 and GSE22490) datasets and performed differential analysis, weighted gene co-expression network (WGCNA), functional enrichment, and machine learning, respectively, on the datasets of the two diseases, Nomogram and integrated bioinformatics analysis such as immune infiltration analysis. Finally, the reliability of the diagnostic gene was verified by external verification and collection of human specimens.

Results

In this study, PCOS and RSA datasets were obtained from Gene Expression Omnibus (GEO) database, and a total of 23 shared genes were obtained by differential analysis and WGCNA analysis. GO results showed that the shared genes were mainly enriched in the functions of lipid catabolism and cell cycle transition (G1/S). DO enrichment revealed that shared genes are mainly involved in ovarian diseases, lipid metabolism disorders and psychological disorders. KEGG analysis showed significant enrichment of Regulation of lipolysis in adipocytes, Prolactin signaling pathway, FoxO signaling pathway, Hippo signaling pathway and other pathways. A diagnostic gene FAM166 B was obtained by machine learning and Nomogram screening, which mainly played an important role in Cellular component. GSEA analysis revealed that FAM166B may be involved in the development of PCOS and RSA by regulating the cell cycle, amino acid metabolism, lipid metabolism, and carbohydrate metabolism. CIBERSORT analysis showed that the high expression of FAM166 B was closely related to the imbalance of multiple immune cells. Further verification by qPCR suggested that FAM166 B could be used as a common marker of PCOS and RSA.

Conclusions

In summary, this study identified FAM166B as a common biomarker for PCOS and RSA, and conducted in-depth research and analysis of this gene, providing new data for basic experimental research and early prognosis, diagnosis and treatment of clinical diseases.