UNASSIGNED: In-depth understanding of osteonecrosis of femoral head (ONFH) has revealed that degeneration of the hip cartilage plays a crucial role in ONFH progression. However, the underlying molecular mechanisms and susceptibility to environmental factors in hip cartilage that contribute to ONFH progression remain elusive.
UNASSIGNED: We conducted a multiomics study and chemical-gene interaction analysis of hip cartilage in ONFH. The differentially expressed genes (DEGs) involved in ONFH progression were identified in paired hip cartilage samples from 36 patients by combining genome-wide DNA methylation profiling, gene expression profiling, and quantitative proteomics. Gene functional enrichment and pathway analyses were performed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Functional links between proteins were discovered through protein-protein interaction (PPI) networks. The ONFH-associated chemicals were identified by integrating the DEGs with the chemical-gene interaction sets in the Comparative Toxicogenomics Database (CTD). Finally, the DEGs, including MMP13 and CHI3L1, were validated via quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC).
UNASSIGNED: Twenty-two DEGs were identified across all three omics levels in ONFH cartilage, 16 of which were upregulated and six of which were downregulated. The collagen-containing extracellular matrix (ECM), ECM structural constituents, response to amino acids, the relaxin signaling pathway, and protein digestion and absorption were found to be primarily involved in cartilage degeneration in ONFH. Moreover, ten major ONFH-associated chemicals were identified, including, benzo(a)pyrene, valproic acid, and bisphenol A.
UNASSIGNED: Overall, our study identified several candidate genes, pathways, and chemicals associated with cartilage degeneration in ONFH, providing novel clues into the etiology and biological processes of ONFH progression.

The timely and precise diagnosis of appendicitis was deemed essential. This study sought to examine the diagnostic significance of hub genes linked to appendicitis and to delve deeper into the pathophysiology of the condition. Differential gene expression analysis revealed distinct genes in the appendicitis group compared to other abdominal pain group, while weighted gene co-expression network analysis identified appendicitis-associated modules. Further analysis of common genes was conducted using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis. The diagnostic efficiency of hub genes was explored through the use of nomograms and receiver operator characteristic curves. Additionally, immunoinfiltration analysis was performed to investigate the immune cell infiltration in both groups. The causal relationship between hub genes and appendicitis, as well as gut microbiota and appendicitis, was ultimately examined through Mendelian randomization. By conducting differential expression analysis and weighted gene co-expression network analysis, a total of 757 common genes were identified. Subsequent Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses revealed that these common genes were primarily associated with positive regulation of cell adhesion, focal adhesion, protein serine kinase activity, and amyotrophic lateral sclerosis. Utilizing Cytoscape software, the top 10 genes with the highest degree of interaction were identified as RPS3A, RPSA, RPL5, RPL37A, RPS27L, FLT3LG, ARL6IP1, RPL32, MRPL3, and GSPT1. Evaluation using nomograms and receiver operator characteristic curves demonstrated the diagnostic value of these hub genes. Ultimately, a causal relationship between hub genes and appendicitis was not identified in our study. Nevertheless, our findings indicate that appendicitis is correlated with 9 gut microbiota. This study identified 5 hub genes, specifically HSP90AA1, RPL5, MYC, CD44, and RPS3A, which exhibit diagnostic significance of appendicitis. Furthermore, the elucidation of these hub genes aids in enhancing our comprehension of the molecular pathways implicated in the development of appendicitis.

Primary aldosteronism (PA) and obstructive sleep apnea (OSA) are both considered independent risk factors for hypertension, which can lead to an increase in cardiovascular disease incidence and mortality. Clinical studies have found a bidirectional relationship between OSA and PA. However, the underlying mechanism between them is not yet clear. This study aims to investigate the shared genetic characteristics and potential molecular mechanisms of PA and OSA. We obtained microarray datasets of aldosterone-producing adenoma (APA) and OSA from the gene expression omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to select co-expression modules associated with APA and OSA, and common genes of the two diseases were obtained by intersection. Subsequently, hub genes for APA and OSA were identified through functional enrichment analysis, protein-protein interaction (PPI), datasets, and public database. Finally, we predicted the transcription factors (TFs) and mirRNAs of the hub genes. In total, 52 common genes were obtained by WGCNA. The Gene Ontology (GO) of common genes includes interleukin-1 response, cytokine activity, and chemokine receptor binding. Functional enrichment analysis highlighted the TNF, IL-17 signaling, and cytokine-cytokine receptor interactions related to APA and OSA. Through PPI, datasets, and public databases verification, we identified 5 hub genes between APA and OSA (IL6, ATF3, PTGS2, CCL2, and CXCL2). Our study identified shared 5 hub genes between APA and OSA (IL6, ATF3, PTGS2, CCL2, and CXCL2). Through bioinformatics analysis, we found that the 2 disorders showed relative similarity in terms of inflammation, stress, and impaired immune function. The identification of hub genes may offer potential biomarkers for the diagnosis and prognosis of PA and OSA.

Liver cancer is the fourth leading cause of cancer-related deaths with a steadily increasing rate worldwide, as a well-known hallmark of liver cancer, metabolic alterations are related to liposomal changes, a common characteristic of primary liver cancers based on recent lipidomics studies. Peroxisome proliferator-activated receptor α (PPARα) is a ligand-activated transcription factor with important lipid homeostasis function, therefore we aimed to understand the molecular mechanisms and pathways that activate PPARα after using PPAR-α agonist WY-14643 and identify candidate biomarkers related to PPARα activity and evaluate their effects in liver cancer. The data from differently expressed genes (DEGs) between liver cancer tissue from obese subjects alone and liver tissue after treatment were evaluated by DESeq2 and module genes were analyzed using weighted gene co-expression network analysis (WGCNA). Final candidate genes were identified by intersecting genes among highly ranked DEGs and the brown module, which demonstrated a significant negative correlation with drug treatments. We conducted a protein-protein interaction network, and KEGG enrichment analysis, and core hub genes (CD40, CXCL9, CXCL10, TNFSF14, GBP2, GBP3, APOL3, CLDN1) were identified using the cyto-hubba plugin, among them we focused on GBP2 that plays key roles in oncogenesis and evaluate its expressional with clinical outcomes. In conclusion, the WGCNA-based co-expression network identified GBP2 as one of the hub genes with a negative relation with PPARα agonist treatments. higher expression of GBP2 was closely associated with HCC progression. Therefore, GBP2 might be a potential candidate for the study of PPARα activity in HCC.

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.

Naïve pluripotency is sustained by a self-reinforcing gene regulatory network (GRN) comprising core and naïve pluripotency-specific transcription factors (TFs). Upon exiting naïve pluripotency, embryonic stem cells (ESCs) transition through a formative post-implantation-like pluripotent state, where they acquire competence for lineage choice. However, the mechanisms underlying disengagement from the naïve GRN and initiation of the formative GRN are unclear. Here, we demonstrate that phosphorylated AKT acts as a gatekeeper that prevents nuclear localisation of FoxO TFs in naïve ESCs. PTEN-mediated reduction of AKT activity upon exit from naïve pluripotency allows nuclear entry of FoxO TFs, enforcing a cell fate transition by binding and activating formative pluripotency-specific enhancers. Indeed, FoxO TFs are necessary and sufficient for the activation of the formative pluripotency-specific GRN. Our work uncovers a pivotal role for FoxO TFs in establishing formative post-implantation pluripotency, a critical early embryonic cell fate transition.

Several miRNA-based studies on Theileria-transformed bovine cells have been conducted; however, the mechanism by which transformed cells exhibit uncontrolled proliferation is not yet fully understood. Therefore, it is necessary to screen more microRNAs that may play a role in the transformation process of host cells infected with Theileria annulata to better understand the transformation mechanisms of Theileria-infected cells. RNA sequencing was used to analyze miRNAs expression in the host bovine lymphocytes infected with T. annulata at different time points after buparvaquone (BW720) treatment and DMSO treatment (control groups). Differential miRNAs related to cell proliferation and apoptosis were identified through comparison with gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, and a regulatory network of miRNA-mRNA was constructed. In total, 272 differentially expressed miRNAs were found at 36, 60 and 72 h. The miRNAs change of bta-miR-2285t, novel-miR-622, bta-miR-2478, and novel-miR-584 were significant. Analysis of 27 of these co-differential expressed miRNAs revealed that 15 miRNAs were down-regulated and 12 miRNAs were up-regulated. A further analysis of the changes in the expression of each of these 27 miRNAs in the three datasets suggested that bta-miR-2285t, bta-miR-345-5p, bta-miR-34a, bta-miR-150, and the novel-miR-1372 had significantly changed. Predicted target genes for these 27 miRNAs were analyzed by KEGG and the results demonstrated that EZR, RASSF, SOCS1 were mainly enriched in the signaling pathway microRNAs in cancer. MAPKAPK2, RELB, FLT3LG, and GADD45B were mainly enriched in the MAPK signaling pathway, and some genes were enriched in Axon guidance. This study has provided valuable information to further the understanding of the regulatory function of miRNAs in the host microenvironment and host-parasite interaction mechanisms.

UNASSIGNED: Clear cell renal cell carcinoma (ccRCC) is a renal cortical malignancy with a complex pathogenesis. Identifying ideal biomarkers to establish more accurate promising prognostic models is crucial for the survival of kidney cancer patients.
UNASSIGNED: Seurat R package was used for single-cell RNA-sequencing (scRNA-seq) data filtering, dimensionality reduction, clustering, and differentially expressed genes analysis. Gene coexpression network analysis (WGCNA) was performed to identify the cytotoxicity-related module. The independent cytotoxicity-related risk model was established by the survival R package, and Kaplan-Meier (KM) survival analysis and timeROC with area under the curve (AUC) were employed to confirm the prognosis and effectiveness of the risk model. The risk and prognosis in patients suffering from ccRCC were predicted by establishing a nomogram. A comparison of the level of immune infiltration in different risk groups and subtypes using the CIBERSORT, MCP-counter, and TIMER methods, as well as assessment of drug sensitivity to conventional chemotherapeutic agents in risk groups using the pRRophetic package, was made.
UNASSIGNED: Eleven ccRCC subpopulations were identified by single-cell sequencing data from the GSE224630 dataset. The identified cytotoxicity-related T-cell cluster and module genes defined three cytotoxicity-related molecular subtypes. Six key genes (SOWAHB, SLC16A12, IL20RB, SLC12A8, PLG, and HHLA2) affecting prognosis risk genes were selected for developing a risk model. A nomogram containing the RiskScore and stage revealed that the RiskScore contributed the most and exhibited excellent predicted performance for prognosis in the calibration plots and decision curve analysis (DCA). Notably, high-risk patients with ccRCC demonstrate a poorer prognosis with higher immune infiltration characteristics and TIDE scores, whereas low-risk patients are more likely to benefit from immunotherapy.
UNASSIGNED: A ccRCC survival prognostic model was produced based on the cytotoxicity-related signature, which had important clinical significance and may provide guidance for ccRCC treatment.

Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.

Chilean peach growers have achieved worldwide recognition for their high-quality fruit products. Among the main factors influencing peach fruit quality, sweetness is pivotal for maintaining the market\'s competitiveness. Numerous studies have been conducted in different peach-segregating populations to unravel SSC regulation. However, different cultivars may also have distinct genetic conformation, and other factors, such as environmental conditions, can significantly impact SSC. Using a transcriptomic approach with a gene co-expression network analysis, we aimed to identify the regulatory mechanism that controls the sugar accumulation process in an \'O × N\' peach population. This population was previously studied through genomic analysis, associating LG5 with the genetic control of the SSC trait. The results obtained in this study allowed us to identify 91 differentially expressed genes located on chromosome 5 of the peach genome as putative new regulators of sugar accumulation in peach, together with a regulatory network that involves genes directly associated with sugar transport (PpSWEET15), cellulose biosynthesis (PpCSLG2), flavonoid biosynthesis (PpPAL1), pectin modifications (PpPG, PpPL and PpPMEi), expansins (PpEXPA1 and PpEXPA8) and several transcription factors (PpC3H67, PpHB7, PpRVE1 and PpCBF4) involved with the SSC phenotype. These results contribute to a better understanding of the genetic control of the SSC trait for future breeding programs in peaches.