HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence.

RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier (HydRA), which leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNA-binding capacity with unparalleled specificity and sensitivity using support vector machines (SVMs), convolutional neural networks (CNNs), and Transformer-based protein language models. Occlusion mapping by HydRA robustly detects known RBDs and predicts hundreds of uncharacterized RNA-binding associated domains. Enhanced CLIP (eCLIP) for HydRA-predicted RBP candidates reveals transcriptome-wide RNA targets and confirms RNA-binding activity for HydRA-predicted RNA-binding associated domains. HydRA accelerates construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains.

SMG: self-supervised masked graph learning for cancer gene identification.

Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein-protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.

A protein network refinement method based on module discovery and biological information.

BACKGROUND: The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs. METHODS: Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules. RESULTS: To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.

BACKGROUND: In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species. RESULTS: We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset. CONCLUSIONS: Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

Sex-dependent differences in the ability of nicotine to modulate discrimination learning and cognitive flexibility in mice.

Nicotine, an addictive compound found in tobacco, functions as an agonist of nicotinic acetylcholine receptors (nAChRs) in the brain. Interestingly, nicotine has been reported to act as a cognitive enhancer in both human subjects and experimental animals. However, its effects in animal studies have not always been consistent, and sex differences have been identified in the effects of nicotine on several behaviors. Specifically, the role that sex plays in modulating the effects of nicotine on discrimination learning and cognitive flexibility in rodents is still unclear. Here, we evaluated sex-dependent differences in the effect of daily nicotine intraperitoneal (i.p.) administration at various doses (0.125, 0.25, and 0.5 mg/kg) on visual discrimination (VD) learning and reversal (VDR) learning in mice. In male mice, 0.5 mg/kg nicotine significantly improved performance in the VDR, but not the VD, task, while 0.5 mg/kg nicotine significantly worsened performance in the VD, but not VDR task in female mice. Furthermore, 0.25 mg/kg nicotine significantly worsened performance in the VD and VDR task only in female mice. Next, to investigate the cellular mechanisms that underlie the sex difference in the effects of nicotine on cognition, transcriptomic analyses were performed focusing on the medial prefrontal cortex tissue samples from male and female mice that had received continuous administration of nicotine for 3 or 18 days. As a result of pathway enrichment analysis and protein-protein interaction analysis using gene sets of differentially expressed genes, decreased expression of postsynaptic-related genes in males and increased expression of innate immunity-related genes in females were identified as possible molecular mechanisms related to sex differences in the effects of nicotine on cognition in discrimination learning and cognitive flexibility. Our result suggests that nicotine modulates cognitive function in a sex-dependent manner by alternating the expression of specific gene sets in the medial prefrontal cortex.

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method.

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein-protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision-recall curve of 0.63 and a Cohen's Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network's information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer.

Despite progress made with immune checkpoint inhibitors and targeted therapies, skin cancer remains a significant public health concern in the United States. The intricacies of the disease, encompassing genetics, immune responses, and external factors, call for a comprehensive approach. Techniques in systems genetics, including transcriptional correlation analysis, functional pathway enrichment analysis, and protein-protein interaction network analysis, prove valuable in deciphering intricate molecular mechanisms and identifying potential diagnostic and therapeutic targets for skin cancer. Recent studies demonstrate the efficacy of these techniques in uncovering molecular processes and pinpointing diagnostic markers for various skin cancer types, highlighting the potential of systems genetics in advancing innovative therapies. While certain limitations exist, such as generalizability and contextualization of external factors, the ongoing progress in AI technologies provides hope in overcoming these challenges. By providing protocols and a practical example involving Braf, we aim to inspire early-career experimental dermatologists to adopt these tools and seamlessly integrate these techniques into their skin cancer research, positioning them at the forefront of innovative approaches in combating this devastating disease.

Identification of function modules in the co-expression protein-protein interaction network of Bombyx mori in response to Beauveria bassiana infection.

Beauveria bassiana (B. bassiana) is a common fungal disease in sericulture. Previous research has primarily focused on investigating genes involved in innate immunity. However, the response of Bombyx mori (B. mori) to B. bassiana requires the coordination of other biological processes in addition to the immune system. We measured protein expression profile of B. mori after inoculating B. bassiana using iTRAQ technology in previous. Here we constructed a co-expression protein-protein interaction network of B. mori in response to B. bassiana infection. Subnetworks and modules were analyzed, and the functions of these modules were annotated. The results revealed the identification of numerous proteins associated with cellular immunity, including those involved in phagosomes, lysosomes, mTOR signaling, sugar metabolism, and the ubiquitin-proteasome pathway. Meanwhile, we observed that the pathways involved in protein synthesis were activated, including pyruvate and purine metabolism, RNA transport, ribosome, protein processing in endoplasmic reticulum, and protein export pathways, during B. bassiana infection. Based on this analysis, we selected six candidate genes (shock protein, ribosome, translocon, actin muscle-type A2, peptidoglycan recognition protein, and collagenase) that were found to be related to the response to B. bassiana. Further verification experiments demonstrated significant changes in their expression levels after inoculation with B. bassiana. These research findings provide new insights into the molecular mechanism of insect immune response to fungal infection.

Identification of key genes and long non­coding RNA expression profiles in osteoporosis with rheumatoid arthritis based on bioinformatics analysis.

BACKGROUND: Although rheumatoid arthritis (RA) is a chronic systemic tissue disease often accompanied by osteoporosis (OP), the molecular mechanisms underlying this association remain unclear. This study aimed to elucidate the pathogenesis of RA and OP by identifying differentially expressed mRNAs (DEmRNAs) and long non-coding RNAs (lncRNAs) using a bioinformatics approach. METHODS: Expression profiles of individuals diagnosed with OP and RA were retrieved from the Gene Expression Omnibus database. Differential expression analysis was conducted. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway enrichment analyses were performed to gain insights into the functional categories and molecular/biochemical pathways associated with DEmRNAs. We identified the intersection of common DEmRNAs and lncRNAs and constructed a protein-protein interaction (PPI) network. Correlation analysis between the common DEmRNAs and lncRNAs facilitated the construction of a coding-non-coding network. Lastly, serum peripheral blood mononuclear cells (PBMCs) from patients with RA and OP, as well as healthy controls, were obtained for TRAP staining and qRT-PCR to validate the findings obtained from the online dataset assessments. RESULTS: A total of 28 DEmRNAs and 2 DElncRNAs were identified in individuals with both RA and OP. Chromosomal distribution analysis of the consensus DEmRNAs revealed that chromosome 1 had the highest number of differential expression genes. GO and KEGG analyses indicated that these DEmRNAs were primarily associated with " platelets (PLTs) degranulation", "platelet alpha granules", "platelet activation", "tight junctions" and "leukocyte transendothelial migration", with many genes functionally related to PLTs. In the PPI network, MT-ATP6 and PTGS1 emerged as potential hub genes, with MT-ATP6 originating from mitochondrial DNA. Co-expression analysis identified two key lncRNA-mRNA pairs: RP11 - 815J21.2 with MT - ATP6 and RP11 - 815J21.2 with PTGS1. Experimental validation confirmed significant differential expression of RP11-815J21.2, MT-ATP6 and PTGS1 between the healthy controls and the RA + OP groups. Notably, knockdown of RP11-815J21.2 attenuated TNF + IL-6-induced osteoclastogenesis. CONCLUSIONS: This study successfully identified shared dysregulated genes and potential therapeutic targets in individuals with RA and OP, highlighting their molecular similarities. These findings provide new insights into the pathogenesis of RA and OP and suggest potential avenues for further research and targeted therapies.

Unlocking the potential of edible Ulva sp. seaweeds: Metabolomic profiling, neuroprotective mechanisms, and implications for Parkinson's disease management.

Green seaweed (Ulva sp.) is frequently used as a food component and nutraceutical agent because of its high polysaccharide and natural fiber content in Asian countries. This study investigates both metabolomic profiling of Ulva sp. and the neuroprotective efficacy of its ethanol extract and its underlying mechanisms in a rotenone-induced rat model of neurodegeneration, mimicking Parkinson's disease (PD) in humans. Metabolomic profiling of Ulva sp. extract was done using liquid chromatography high resolution electrospray ionization mass spectrometry and led to the identification of 22 compounds belonging to different chemical classes.Catenin Beta Additionally, this study demonstrated the neuroprotective properties against rotenone-induced PD, which was achieved through the suppression of elevated levels of tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and IL-6 together with the inhibition of reactive oxygen species (ROS) generation, apoptosis, inflammatory mediators, and the phosphoinositide 3-kinases/serine/threonine protein kinase (PI3K/AKT) pathway. Using a protein-protein interaction network, AKT1, GAPDH, TNF-α, IL-6, caspase 3, signal transducer and activator of transcription 3, Catenin Beta 1, epidermal growth factor receptor, B-cell lymphoma -2, and HSP90AA1 were identified as the top 10 most significant genes. Finally, molecular docking results showed that compounds 1, 3, and 7 might possess a promising anti-parkinsonism effect by binding to active sites of selected hub genes. Therefore, it is hypothesized that the Ulva sp. extract has the potential to be further developed as a potential therapeutic agent for the treatment of PD.

Identification of essential proteins based on edge features and the fusion of multiple-source biological information.

BACKGROUND: A major current focus in the analysis of protein-protein interaction (PPI) data is how to identify essential proteins. As massive PPI data are available, this warrants the design of efficient computing methods for identifying essential proteins. Previous studies have achieved considerable performance. However, as a consequence of the features of high noise and structural complexity in PPIs, it is still a challenge to further upgrade the performance of the identification methods. METHODS: This paper proposes an identification method, named CTF, which identifies essential proteins based on edge features including h-quasi-cliques and uv-triangle graphs and the fusion of multiple-source information. We first design an edge-weight function, named EWCT, for computing the topological scores of proteins based on quasi-cliques and triangle graphs. Then, we generate an edge-weighted PPI network using EWCT and dynamic PPI data. Finally, we compute the essentiality of proteins by the fusion of topological scores and three scores of biological information. RESULTS: We evaluated the performance of the CTF method by comparison with 16 other methods, such as MON, PeC, TEGS, and LBCC, the experiment results on three datasets of Saccharomyces cerevisiae show that CTF outperforms the state-of-the-art methods. Moreover, our method indicates that the fusion of other biological information is beneficial to improve the accuracy of identification.

Preliminary study on toxicological mechanism of golden cuttlefish (Sepia esculenta) larvae exposed to cd.

BACKGROUND: Cadmium (Cd) flows into the ocean with industrial and agricultural pollution and significantly affects the growth and development of economic cephalopods such as Sepia esculenta, Amphioctopus fangsiao, and Loligo japonica. As of now, the reasons why Cd affects the growth and development of S. esculenta are not yet clear. RESULTS: In this study, transcriptome and four oxidation and toxicity indicators are used to analyze the toxicological mechanism of Cd-exposed S. esculenta larvae. Indicator results indicate that Cd induces oxidative stress and metal toxicity. Functional enrichment analysis results suggest that larval ion transport, cell adhesion, and some digestion and absorption processes are inhibited, and the cell function is damaged. Comprehensive analysis of protein-protein interaction network and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to explore S. esculenta larval toxicological mechanisms, and we find that among the 20 identified key genes, 14 genes are associated with neurotoxicity. Most of them are down-regulated and enriched to the neuroactive ligand-receptor interaction signaling pathway, suggesting that larval nervous system might be destroyed, and the growth, development, and movement process are significantly affected after Cd exposure. CONCLUSIONS: S. esculenta larvae suffered severe oxidative damage after Cd exposure, which may inhibit digestion and absorption functions, and disrupt the stability of the nervous system. Our results lay a function for understanding larval toxicological mechanisms exposed to heavy metals, promoting the development of invertebrate environmental toxicology, and providing theoretical support for S. esculenta artificial culture.

Identification of hub genes and potential inhibitory compounds in the process of liver transplantation through transcriptome sequencing.

Liver transplantation (LT) is the best choice for patients with end-stage liver diseases. In order to better understand pathophysiological alterations in LT, we aimed to identify potential hub genes and inhibitory compounds involved in the LT process. Four pairs of peripheral blood mononuclear cell (PBMC) samples of the LT recipients before and after surgery were collected and taken for transcriptome sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for the screened differentially expressed genes (DEGs) between pre- and post-operation groups. Common DEGs were obtained from GO and KEGG enriched pathways, followed by protein-protein interaction (PPI) network construction, hub gene identification, module analysis, and structure-based virtual screening process (SBVS). Compared to the pre-operation stage, 4745 genes were down-regulated and 798 up-regulated after LT. GO analysis showed that the DEGs were enriched in ribosome-related translation regulation, and KEGG analysis indicated that infection and immune-related pathways and diseases were largely enriched. A large number of down-regulated DEGs were not only associated with ribosome-related pathways but also with the alterations of epigenetic modifications, in particular ubiquitination. Moreover, through the PPI network of 29 common genes from GO and KEGG-enriched pathways, 7 hub genes were identified, including PTEN, MYC, EIF2S1, EIF4EBP1, HSP90AB1, TP53, and HSPA8, which were mainly involved in the PI3K-AKT signaling pathway. SBVS of the seed molecule PTEN (PDB code: 1D5R) predicted top hits compounds that may serve as potential inhibitors of PTEN, of which the compound ZINC4235331 had the lowest binding affinity of -10 kcal/mol. The significance of screened hub genes and potential inhibitors involved in the process of LT provides novel therapeutic strategies for improving the outcomes of LT recipients during surgery.

Transcriptome analysis preliminary reveals the immune response mechanism of golden cuttlefish (Sepia esculenta) larvae exposed to Cd.

As a well-known marine metal element, Cd can significantly affect bivalve mollusk life processes such as growth and development. However, the effects of Cd on the molecular mechanisms of the economically important cephalopod species Sepia esculenta remain unclear. In this study, S. esculenta larval immunity exposed to Cd is explored based on RNA-Seq. The analyses of GO, KEGG, and protein-protein interaction (PPI) network of 1,471 differentially expressed genes (DEGs) reveal that multiple immune processes are affected by exposure such as inflammatory reaction and cell adhesion. Comprehensive analyses of KEGG signaling pathways and the PPI network are first used to explore Cd-exposed S. esculenta larval immunity, revealing the presence of 16 immune-related key and hub genes involved in exposure response. Results of gene and pathway functional analyses increase our understanding of Cd-exposed S. esculenta larval immunity and improve our overall understanding of mollusk immune functions.

Gene network analyses of larvae under different egg-protecting behaviors provide novel insights into immune response mechanisms of Amphioctopus fangsiao.

Amphioctopus fangsiao was a representative economic species in cephalopods, which was vulnerable to marine bacteria. Vibrio anguillarum was a highly infectious pathogen that have recently been found to infect A. fangsiao and inhibit its growth and development. There were significant differences in the immune response mechanisms between egg-protected and egg-unprotected larvae. To explore larval immunity under different egg-protecting behaviors, we infected A. fangsiao larvae with V. anguillarum for 24 h and analyzed the transcriptome data about egg-protected and egg-unprotected larvae infected with 0, 4, 12, and 24 h using weighted gene co-expression networks (WGCNA) and protein-protein interaction (PPI) networks. Network analyses revealed a series of immune response processes after infection, and identified six key modules and multiple immune-related hub genes. Meanwhile, we found that ZNF family, such as ZNF32, ZNF160, ZNF271, ZNF479, and ZNF493 might play significant roles in A. fangsiao immune response processes. We first creatively combined WGCNA and PPI network analysis to deeply explore the immune response mechanisms of A. fangsiao larvae with different egg-protecting behaviors. Our results provided further insights into the immunity of V. anguillarum infected invertebrates, and laid the foundation for exploring the immune differences among cephalopods with different egg protecting behaviors.

Dopamine receptors gene overexpression in the microenvironment of invasive gastric cancer and its potential implications.

BACKGROUND: Gastric cancer (GC) is the fifth most common cancer worldwide and the most commonly diagnosed cancer in Iran. The nervous system provides proximity to tumor cells by releasing neurotransmitters such as dopamine and presenting them to the corresponding receptor-bearing tumors. While nerve fibers infiltrate the tumor microenvironment, little is known about the expression levels of dopamine (DA), dopamine receptors (DRs), and catechol-O-methyltransferase (COMT) in GC patients. METHODS: DRs and COMT expression were analyzed in 45 peripheral blood mononuclear cells (PBMCs) and 20 paired tumor and adjacent tissue of GC patients by quantitative polymerase chain reaction. DA was measured in plasma specimens using enzyme-linked immunosorbent assay. Protein-protein interaction analysis was carried out to identify GC-related hub genes. RESULTS: Increased expression of DRD1-DRD3 was found in tumor specimens compared with adjacent non-cancerous specimens (P < 0.05). A positive correlation was found between DRD1 and DRD3 expression (P = 0.009); DRD2 and DRD3 expression (P = 0.04). Plasma levels of dopamine were significantly lower in patients (1298 pg/ml) than in controls (4651 pg/ml). DRD1-DRD4 and COMT were up-regulated in PBMCs of patients compared with controls (P < 0.0001). Bioinformatic analyses showed 30 hub genes associated with Protein kinase A and extracellular signal-regulated kinase signaling pathways. CONCLUSIONS: The findings indicated dysregulation of DRs and COMT mRNA expression in GC and suggest that the brain- gastrointestinal axis may mediate gastric cancer development. Network analysis revealed that combination treatments could be considered for optimizing and improving the precision treatment of GC.

Targeting FOS attenuates malignant phenotypes of breast cancer: Evidence from in silico and in vitro studies.

Data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases can reveal important information behind molecular biomarkers and their associated oncogenesis. Therefore, this study was based on in silico predictions and in vitro experiments to explore regulatory network associated with breast carcinogenesis. The breast cancer (BC)-related data sets were retrieved from GEO database, followed by differential analysis and protein-protein interaction (PPI) analysis. Then, Fos proto-oncogene, AP-1 transcription factor subunit (FOS)-associated gene network was constructed, and the key gene-related genes in BC were screened by LinkedOmics. Finally, FOS expression was determined in BC tissues and cells, and gain-of-function assays were performed to define the role of FOS in BC cells. It was noted that seven differentially expressed genes (EGR1, RASSF9, FOSB, CDC20, KLF4, PTGS2, and FOS) were obtained from BC microarray data sets. FOS was the gene with the most nodes in PPI analysis. Poor FOS mRNA expression was identified in BC patients. Furthermore, FOS was mainly located in the extracellular matrix and was involved in cell processes. FOS was downregulated in BC tissues and cells, and FOS overexpression restrained the malignant phenotypes of BC cells. Collectively, ectopic expression of FOS curtails the development of BC.

Integrated analysis of transcriptomic and protein-protein interaction data reveals cadmium stress response in Geobacter sulfurreducens.

Bacteria have evolved several mechanisms to resist Cd toxicity, which are crucial for Cd detoxication and have the potential to be used for bioremediation of Cd. Geobacter species are widely found in anaerobic environments and play important roles in natural biogeochemical cycles. However, the transcriptomic response of Geobacter sulfurreducens under Cd stress have not been fully elucidated. Through integrated analysis of transcriptomic and protein-protein interaction (PPI) data, we uncovered a global view of mRNA changes in Cd-induced cellular processes in this study. We identified 182 differentially expressed genes (|log2(fold change)| > 1, adjusted P < 0.05) in G. sulfurreducens exposed to 0.1 mM CdCl2 using RNA sequencing (RNA-seq). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that CdCl2 significantly affected sulfur compound metabolic processes. In addition, through PPI network analysis, hub genes related to molecular chaperones were identified to play important role in Cd stress response. We also identified a Cd-responsive transcriptional regulator ArsR2 (coded by GSU2149) and verified the function of ArsR2-ParsR2 regulatory circuit in Escherichia coli. This study provides new insight into Cd stress response in G. sulfurreducens, and identified a potential sensor element for Cd detection.

Novel biomarkers identified by weighted gene co-expression network analysis for atherosclerosis.

BACKGROUND: This study aimed to screen out the potential diagnostic biomarkers for atherosclerosis (AS). METHODS: We downloaded the gene expression profiles GSE66360, GSE28829, GSE41571, GSE71226, and GSE100927 from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the "limma" package in R. Weighted gene co-expression network analysis (WGCNA) was applied to reveal the correlation between genes in different samples. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The interaction pairs of proteins were retained by the STRING database, and the protein-protein interaction (PPI) network was visualized with the hub genes. Finally, the R packages "ggpubr" and "preprocessCore" were used to analyze immune cell infiltration. RESULTS: In total, 40 overlapping genes both in GSE66360 and GSE28829 were found to be related to the occurrence of AS. Further, the top 10 network hub genes including TYROBP, CSF1R, TLR2, CD14, CCL4, FCER1G, CD163, TREM1, PLEK, and C5AR1 were identified as significant key genes. Moreover, four genes (TYROBP, CSF1R, FCGR1B, and CD14) were verified that could efficiently diagnose AS. Finally, the gene TYROBP was found to have a strong correlation with immune-infiltrating cells. CONCLUSION: Our study identified four genes (TYROBP, CSF1R, FCGR1B, and CD14) that may be effective biomarkers for AS, with the potential to guide the clinical diagnosis of AS.