Venn diagrams in bioinformatics

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Investigating the anti-cancer compounds from Calliandra harrisii for precision medicine in pancreatic cancer via in-silico drug design and GC-MS analysis

Pancreatic cancer is a fatal illness caused by mutations in multiple genes. Pancreatic cancer damages the organ that helps in digestion, resulting in symptoms including fatigue, bloating, and nausea. The use of medicinal plants has been crucial in the treatment of numerous disorders. The medicinal plant Calliandra Harrisi has been widely exploited for its possibilities in biology and medicine. The current study aimed to assess the biopotential of biologically active substances against pancreatic cancer. The GC-MS data of these phytochemicals from Calliandra Harrisi were further subjected to computational approaches with pancreatic cancer genes to evaluate their potential as therapeutic candidates. Molecular docking analysis revealed that N-[Carboxymethyl] maleamic acid is the leading molecule responsible for protein denaturation inhibition, having the highest binding affinity of 6.8 kJ/mol among all other compounds with KRAS inflammatory proteins. Furthermore, ADMET analysis and Lipinski's rule validation were also performed revealing its higher absorption in the gastrointestinal tract. The results of the hepatotoxicity test demonstrated that phytochemicals are non-toxic, safe to use, and do not cause necrosis, fibrosis, or vacuolar degeneration even at excessive levels. Calliandra Harrisi has phytoconstituents that have a variety of pharmacological uses in consideration.

Network pharmacology and experimental evidence: ERK/CREB/BDNF signaling pathway is involved in the antidepressive roles of Kaiyu Zhishen decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Major Depressive Disorder (MDD) emerges as a complex psychosomatic condition, notable for its considerable suicidality and mortality rates. Increasing evidence suggests the efficacy of Chinese herbal medicine in mitigating depression symptoms and offsetting the adverse effects associated with conventional Western therapeutics. Notably, clinical trials have revealed the adjunctive antidepressant potential of Kaiyu Zhishen Decoction (KZD) alongside Western medication. However, the standalone antidepressant efficacy of KZD and its underlying mechanisms merit in-depth investigation.

AIM OF THE STUDY

This research aims to elucidate the impact of KZD on MDD and delineate its mechanistic pathways through integrated network pharmacological assessments and empirical in vitro and in vivo analyses.

MATERIALS AND METHODS

To ascertain the optimal antidepressant dosage and mechanism of KZD, a Chronic Unpredictable Mild Stress (CUMS)-induced depression model in mice was established to evaluate depressive behaviors. High-Performance Liquid Chromatography (HPLC) and network pharmacological approaches were employed to predict KZD's antidepressant mechanisms. Subsequently, hippocampal samples were subjected to 4D-DIA proteomic sequencing and validated through Western blot, immunofluorescence, Nissl staining, and pathway antagonist applications. Additionally, cortisol-stimulated PC12 cells were utilized to simulate neuronal damage, analyzing protein and mRNA levels of MAPK-related signals and cell proliferation markers.

RESULTS

The integration of network pharmacology and HPLC identified kaempferol and quercetin as KZD's principal active compounds for MDD treatment. Proteomic and network pharmacological KEGG pathway analyses indicated the MAPK signaling pathway as a critical regulatory mechanism for KZD's therapeutic effect on MDD. KZD was observed to mitigate CUMS-induced upregulation of p-ERK/ERK, CREB, and BDNF protein expressions in hippocampal cells by attenuating oxidative stress, thereby ameliorating neuronal damage and exerting antidepressant effects. The administration of PD98059 counteracted KZD's improvements in depression-like behaviors and downregulated p-ERK/ERK and BDNF protein expressions in the hippocampus.

CONCLUSIONS

This investigation corroborates KZD's pivotal, dose-dependent role in antidepressant activity. Both in vivo and in vitro experiments demonstrate KZD's capacity to modulate the ERK-CREB-BDNF signaling pathway by diminishing ROS expression induced by oxidative stress, enhancing neuronal repair, and thus, manifesting antidepressant properties. Accordingly, KZD represents a promising herbal candidate for further antidepressant research.

What are the different biomolecules involved in the selective recovery of REEs from mining wastewater using FeNPs synthesized from two plant extracts?

Extracting rare earth elements (REEs) from wastewater is crucial for saving the environment, sustainable use of natural resources and economic growth. Reported here is a simple, low cost and one-step synthesis of Fe nanoparticles (FeNPs) based on two plant extracts having the ability to recover REEs. The synthesis of FeNPs using Excoecaria cochinchinensis leaves extract (Ec-FeNPs) exhibited high selectivity for heavy rare earth due to unique biomolecules, achieving separation coefficients (Kd) of 3.16 × 103-4.04 × 106 mL/g and recovery efficiencies ranging from 71.7 to 100 %. Conversely, the synthesis of FeNPs using Pinus massoniana lamb extract (PML-FeNPs) revealed poorer REE recovery efficiencies of 7.2-86.7 %. To understand the differences between Ec-FeNPs and PML-FeNPs in terms of selectivity and efficiency, LC-QTOF-MS served to analyze the biomolecules differences of two plant extracts. In addition, various types of characterization were carried out to identify the different functional groups encapsulated on the surface of FeNPs. These results reveal the source of the difference in the selectivity of Ec-FeNPs and PML-FeNPs for REEs. Furthermore, during DFT calculations, it was found that biomolecules with varying affinities for the surface of FeNPs interact with each other, leading to the formation of structures that exhibit high reactivity towards REEs. Finally, incorporating Spearman correlation analysis demonstrates that the selective removal efficiency of REEs was closely linked to surface complexation, ion exchange, and electrostatic adsorption. Consequently, this work strongly highlights the potential for the practical application of novel adsorbents in this field.

Identification of common genetic factors and immune-related pathways associating more than two autoimmune disorders: implications on risk, diagnosis, and treatment

Autoimmune disorders (ADs) are chronic conditions resulting from failure or breakdown of immunological tolerance, resulting in the host immune system attacking its cells or tissues. Recent studies report shared effects, mechanisms, and evolutionary origins among ADs; however, the possible factors connecting them are unknown. This study attempts to identify gene signatures commonly shared between different autoimmune disorders and elucidate their molecular pathways linking the pathogenesis of these ADs using an integrated gene expression approach. We employed differential gene expression analysis across 19 datasets of whole blood/peripheral blood cell samples with five different autoimmune disorders (rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Crohn's disease, and type 1 diabetes) to get nine key genes-EGR1, RUNX3, SMAD7, NAMPT, S100A9, S100A8, CYBB, GATA2, and MCEMP1 that were primarily involved in cell and leukocyte activation, leukocyte mediated immunity, IL-17, AGE-RAGE signaling in diabetic complications, prion disease, and NOD-like receptor signaling confirming its role in immune-related pathways. Combined with biological interpretations such as gene ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network, our current study sheds light on the in-depth research on early detection, diagnosis, and prognosis of different ADs.

Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways

Despite the fact that introns mean an energy and time burden for eukaryotic cells, they play an irreplaceable role in the diversification and regulation of protein production. As a common feature of eukaryotic genomes, it has been reported that in protein-coding genes, the longest intron is usually one of the first introns. The goal of our work was to find a possible difference in the biological function of genes that fulfill this common feature compared to genes that do not. Data on the lengths of all introns in genes were extracted from the genomes of six vertebrates (human, mouse, koala, chicken, zebrafish and fugu) and two other model organisms (nematode worm and arabidopsis). We showed that more than 40% of protein-coding genes have the relative position of the longest intron located in the second or third tertile of all introns. Genes divided according to the relative position of the longest intron were found to be significantly increased in different KEGG pathways. Genes with the longest intron in the first tertile predominate in a range of pathways for amino acid and lipid metabolism, various signaling, cell junctions or ABC transporters. Genes with the longest intron in the second or third tertile show increased representation in pathways associated with the formation and function of the spliceosome and ribosomes. In the two groups of genes defined in this way, we further demonstrated the difference in the length of the longest introns and the distribution of their absolute positions. We also pointed out other characteristics, namely the positive correlation between the length of the longest intron and the sum of the lengths of all other introns in the gene and the preservation of the exact same absolute and relative position of the longest intron between orthologous genes.

Shared biomarkers and mechanisms in idiopathic pulmonary fibrosis and non-small cell lung cancer

BACKGROUND

Epidemiological evidence has indicated the occurrence of idiopathic pulmonary fibrosis (IPF) with coexisting lung cancer is not a coincidence. The pathogenic mechanisms shared between IPF and non-small cell lung cancer (NSCLC) at the transcriptional level remain elusive and need to be further elucidated.

METHODS

IPF and NSCLC datasets of expression profiles were obtained from the GEO database. Firstly, to detect the shared dysregulated genes positively correlated with both IPF and NSCLC, differentially expressed analysis and WGCNA analysis were carried out. Functional enrichment and the construction of protein-protein network were employed to reveal pathogenic mechanisms related to two diseases mediated by the shared dysregulated genes. Then, the LASSO regression was adopted for screening critical candidate biomarkers for two disorders. Moreover, ROC curves were applied to evaluate the diagnostic value of the candidate biomarkers in both IPF and NSCLC.

RESULTS

The 20 shared dysregulated genes positively correlated with both IPF and NSCLC were identified after intersecting differentially expressed analysis and WGCNA analysis. Functional enrichment revealed the 20 shared genes mostly enriched in extracellular region, which is critical in the organization of extracellular matrix. The protein-protein networks unrevealed the interaction of the 11 shared genes involving in collagen deposition and the connection between PYCR1 with PSAT1. PSAT1, PYCR1, COL10A1 and KIAA1683 were screened by the LASSO regression. ROC curves comprising area under the curve (AUC) verified the potential diagnostic value of PSAT1 and COL10A1 in both IPF and NSCLC.

CONCLUSIONS

We revealed dysregulated extracellular matrix through aberrant expression of the relevant genes, which provided further understanding for the common molecular mechanisms predisposing the occurrence of both IPF and NSCLC.

Community coalescence under variable hydrochemical conditions of the Chesapeake Bay shaped bacterial diversity and functional traits

Community coalescence related to bacterial mixing events regulates community characteristics and affects the health of estuary ecosystems. At present, bacterial coalescence and its driving factors are still unclear. The present study used a dataset from the Chesapeake Bay (2017) to address how bacterial community coalescence in response to variable hydrochemistry in estuarine ecosystems. We determined that variable hydrochemistry promoted the deterioration of water quality. Temperature, orthophosphate, dissolved oxygen, chlorophyll a, Secchi disk depth, and dissolved organic phosphorus were the key environmental factors driving community coalescence. Bacteria with high tolerance to environmental change were the primary taxa accumulated in community coalescence, and the significance of deterministic processes to communities was revealed. Community coalescence was significantly correlated with the pathways of metabolism and organismal systems, and promoted the co-occurrence of antibiotic resistance and virulence factor genes. Briefly, community coalescence under variable hydrochemical conditions shaped bacterial diversity and functional traits, to optimise strategies for energy acquisition and lay the foundation for alleviating environmental pressures. However, potential pathogenic bacteria in community coalescence may be harmful to human health and environmental safety. The present study provides a scientific reference for ecological management of estuaries.

Comprehensive Transcriptome and Proteome Analyses Reveal the Drought Responsive Gene Network in Potato Roots

The root system plays a decisive role in the growth and development of plants. The water requirement of a root system depends strongly on the plant species. Potatoes are an important food and vegetable crop grown worldwide, especially under irrigation in arid and semi-arid regions. However, the expected impact of global warming on potato yields calls for an investigation of genes related to root development and drought resistance signaling pathways in potatoes. In this study, we investigated the molecular mechanisms of different drought-tolerant potato root systems in response to drought stress under controlled water conditions, using potato as a model. We analyzed the transcriptome and proteome of the drought-sensitive potato cultivar Atlantic (Atl) and the drought-tolerant cultivar Qingshu 9 (Q9) under normal irrigation (CK) and weekly drought stress (D). The results showed that a total of 14,113 differentially expressed genes (DEGs) and 5596 differentially expressed proteins (DEPs) were identified in the cultivars. A heat map analysis of DEGs and DEPs showed that the same genes and proteins in Atl and Q9 exhibited different expression patterns under drought stress. Weighted gene correlation network analysis (WGCNA) showed that in Atl, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG)-enriched pathways were related to pyruvate metabolism and glycolysis, as well as cellular signaling and ion transmembrane transporter protein activity. However, GO terms and KEGG-enriched pathways related to phytohormone signaling and the tricarboxylic acid cycle were predominantly enriched in Q9. The present study provides a unique genetic resource to effectively explore the functional genes and uncover the molecular regulatory mechanism of the potato root system in response to drought stress.

Hippuric acid alleviates dextran sulfate sodium-induced colitis via suppressing inflammatory activity and modulating gut microbiota

Inflammatory bowel disease (IBD) is a chronic inflammatory disease associated with metabolic disorder and gut dysbiosis. Decreased abundance of hippuric acid (HA) was found in patients with IBD. HA, metabolized directly from benzoic acid in the intestine and indirectly from polyphenols, serves as a marker of polyphenol catabolism. While polyphenols and benzoic acid have been shown to alleviate intestinal inflammation, the role of HA in this context remains unknown. Herein, we investigated the effects and mechanism of HA on DSS-induced colitis mice. The results revealed that HA alleviated clinical activity and intestinal barrier damage, decreased pro-inflammatory cytokine production. Metagenomic sequencing suggested that HA treatment restored the gut microbiota, including an increase in beneficial gut bacteria such as Adlercreutzia, Eubacterium, Schaedlerella and Bifidobacterium_pseudolongum. Furthermore, we identified 113 candidate genes associated with IBD that are potentially under HA regulation through network pharmacological analyses. 10 hub genes including ALB, IL-6, HSP90AA1, and others were identified using PPI analysis and validated using molecular docking and mRNA expression analysis. Additionally, KEGG analysis suggested that the renin-angiotensin system (RAS), NF-κB signaling and Rap1 signaling pathways were important pathways in the response of HA to colitis. Thus, HA may provide novel biotherapy options for IBD.

A novel mechanism for regulating lung immune homeostasis: Zukamu granules alleviated acute lung injury in mice by inhibiting NLRP3 inflammasome activation and regulating Th17/Treg cytokine balance

ETHNOPHARMACOLOGICAL RELEVANCE

Acute lung injury (ALI) is a severely acute lung inflammation with high morbidity and mortality. Zukamu granules (ZKMG) is one of the Uygur patent drugs commonly used in clinic, which is included in the National Essential Drugs List (2018 edition). Clinical studies have shown that ZKMG has a significant effect on acute upper respiratory tract infection, and has better anti-inflammatory and antipyretic effects. However, the immunomodulatory mechanism of ZKMG on ALI is still not clear.

AIM OF THE STUDY

The aim of this study is to investigate the lung protective effect and immunomodulatory mechanism of ZKMG on lipopolysaccharide (LPS) -induced ALI mice, and to provide an important basis for the treatment strategy and theoretical basis of ALI.

MATERIALS AND METHODS

First, network pharmacology was used to predict the potential signaling pathways and biological processes of ZKMG related to immunology. Molecular docking technique was used to predict the possibility between the core components of ZKMG acting on NLRP3 protein. In addition, protein levels of F4/80 in lung tissues were assessed by Immunohistochemistry (IHC). The contents of IL-1β, IL-18, IL-17A and IL-10 in the lung tissue and serum, MPO in the lung tissue were detected by enzyme-linked immunosorbent assay (ELISA). Real-time quantitative PCR analysis (RT-qPCR) was used to detect NLRP3 mRNA in lung tissue. Protein levels of NLRP3, Caspase-1, Cleaved caspase-1 p20, ASC, and GSDMD were detected by Western blot (WB).

RESULTS

The results of network pharmacology showed that the immune pathways of ZKMG were mainly Th17 signaling pathway, IL-17 signaling pathway, NOD-like receptor signaling pathway, etc. Molecular docking results showed that the core components of ZKMG had good binding ability to NLRP3 protein. The verification experiments showed that ZKMG can reduce the degree of lung injury, and reduce the level of inflammatory infiltration of neutrophils and macrophages by reducing the content of MPO and F4/80. In addition, ZKMG can reduce NLRP3 mRNA, inhibit the expression of NLRP3/Caspase-1/GSDMD and other related pathway proteins, and reduce inflammatory factors such as IL-1β and IL-18. It can also reduce the content of pro-inflammatory cytokine IL-17A, increase the content of anti-inflammatory cytokine IL-10 in lung tissue.

CONCLUSION

ZKMG can reduce the degree of lung tissue injury in ALI by inhibiting NLRP3/Caspase-1/GSDMD signaling pathway and restoring the IL-17A/IL-10 cytokine balance, and its protective mechanism may be related to the regulation of lung immune homeostasis. It will provide a new strategy for studying the regulation of lung immune homeostasis.

Flowering genes identification, network analysis, and database construction for 837 plants

Flowering is one of the most important biological phenomena in the plant kingdom, which not only has important ecological significance, but also has substantial horticultural ornamental value. In this study, we undertook an exhaustive review of the advancements in our understanding of plant flowering genes. We delved into the identification and conducted comparative analyses of flowering genes across virtually all sequenced angiosperm plant genomes. Furthermore, we established an extensive angiosperm flowering atlas, encompassing a staggering 183 720 genes across eight pathways, along with 10 155 ABCDE mode genes, which play a pivotal role in plant flowering regulation. Through the examination of expression patterns, we unveiled the specificities of these flowering genes. An interaction network between flowering genes of the ABCDE model and their corresponding upstream genes offered a blueprint for comprehending their regulatory mechanisms. Moreover, we predicted the miRNA and target genes linked to the flowering processes of each species. To culminate our efforts, we have built a user-friendly web interface, named the Plant Flowering-time Gene Database (PFGD), accessible at http://pfgd.bio2db.com/. We firmly believe that this database will serve as a cornerstone in the global research community, facilitating the in-depth exploration of flowering genes in the plant kingdom. In summation, this pioneering endeavor represents the first comprehensive collection and comparative analysis of flowering genes in plants, offering valuable resources for the study of plant flowering genetics.

Exosome Heterogeneity Affects the Distal "Barrier-Crossing" Trafficking of Exosome Encapsulated Quantum Dots

The biological activities of nanoparticles (NPs), which include endocytosis by macrophages and subsequent intracellular degradation and/or release, transfer to other cells, or translocation across tissue barriers, highly depend on their fate in living organisms. Yet, translocation across barriers, especially the distal "barrier-crossing" trafficking of NPs, is still unclear. The exosome (Exo) plays a crucial role in intercellular communication and biological barrier trafficking. Here, we report that ZnCdSe@ZnS quantum dots (QDs), as a representation of NPs in biomedical applications, could cross the blood-brain barrier and approach the mouse brain via active Exo encapsulation. By employing multiple techniques, we demonstrated that QDs were internalized by macrophages (J774A.1) and tumor cells (HeLa) and then released to the extracellular environment along with Exo. Exo encapsulation facilitates the distal barrier-crossing trafficking of QDs in vivo, while Exo biogenesis inhibitor GW4869 suppressed the QDs enriched in the brains of mice with a 4T1-Luc breast cancer xenograft. Interestingly, Exo heterogeneity affects the distal trafficking of enveloped QDs. Exo derived from tumorous HeLa cells, not macrophages, that were enriched in functional proteins with cell adhesion, cell migration, axon guidance, and cell motility, showed a better capacity for the remote trafficking of QDs. This study proposes Exo as a vehicle to deliver exogenous NPs to translocate across the distal barrier and provides further information for biomedical application and the risk assessment of NPs.

Bidirectional regulation effect of rhubarb as laxative and astringent by metabolomics studies

ETHNOPHARMACOLOGICAL RELEVANCE

Rhubarb, a prominent traditional Chinese medicine, has been employed as a potent laxative for centuries and garnered particular popularity among the youth owing to its notable efficacy in weight management. Historical records indicated that rhubarb initially exhibited robust laxative properties, but extended and consistent usage may lead to an astringent response in the later stage of long-term use. In contrast, steamed pieces of rhubarb (SR), preparing through the process of steaming with wine, have demonstrated a gentle laxative effect with no reported adverse effects.

AIM OF THE STUDY

Our study was designed to explore the intricate mechanisms underlying laxative and astringent properties of rhubarb through metabolomics research.

MATERIALS AND METHODS

In this investigation, we employed a serum metabolomics approach utilizing the UPLC-Q-Extractive-Orbitrap-MS method to delve into the contrasting laxative and astringent effects of rhubarb, as well as to unravel the mechanisms of underpinning its bidirectional regulatory influence. To commence, we assessed alterations in Evacuation Index (EI) values, intestinal hormone levels, and colon histopathology in mice to gauge rhubarb's laxative and astringent effects. Subsequently, metabolomics methodology was employed for cluster analysis through Principal Component Analysis (PCA) and biomarker identification via Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). Then, we delved into the distinctions in characteristic biomarkers, metabolic pathways, their association with pathological changes, and correlation heatmap for biomarkers between raw pieces of rhubarb (RR) and SR to gain insights into the potential mechanisms behind rhubarb's bidirectional regulatory effects.

RESULTS

Our findings revealed that RR exhibited a laxative effect in the early stage and transitioned to an astringent effect in the later stage, as indicated by the EI values. In contrast, SR consistently demonstrated a mild laxative effect. Biochemical indexes and histopathological assessments unveiled that RR triggered its astringent effect by inhibiting secretion of motilin (MTL), promoting secretion of vasoactive intestinal peptide (VIP) and epinephrine (EPI), and inducing onset of inflammation. Furthermore, serum metabolomics analysis identified 59 discriminative biomarkers modulated by RR and SR. Through comprehensive analysis, we elucidated the in vivo transformation relationships among multiple endogenous metabolites. Notably, our results underscored the down-regulation of certain phosphatidylcholines (PCs), amino acids, acylcarnitines, and up-regulation of lysophosphatidylcholines (LysoPCs) played pivotal roles in the onset of gut dysfunction, intestinal inflammation, gut barrier damage, and gastrointestinal motility disorder upon prolonging RR administration, ultimately contributing to its astringent effect. Additionally, our correlation analysis elucidated that anthraquinones, stilbenes, and phenylbutanones were the pharmacodynamic material basis responsible for inducing the astringent effect of RR.

CONCLUSION

This study provides valuable insights into the bidirectional regulatory effects of rhubarb and sheds light on its underlying mechanisms through a comprehensive metabolomics approach.

TFRC, associated with hypoxia and immune, is a prognostic factor and potential therapeutic target for bladder cancer

BACKGROUND

Bladder cancer is a common malignancy of the urinary system, and the survival rate and recurrence rate of patients with muscular aggressive (MIBC) bladder cancer are not ideal. Hypoxia is a pathological process in which cells acquire special characteristics to adapt to anoxic environment, which can directly affect the proliferation, invasion and immune response of bladder cancer cells. Understanding the exact effects of hypoxia and immune-related genes in BLCA is helpful for early assessment of the prognosis of BLCA. However, the prognostic model of BLCA based on hypoxia and immune-related genes has not been reported.

PURPOSE

Hypoxia and immune cell have important role in the prognosis of bladder cancer (BLCA). The aim of this study was to investigate whether hypoxia and immune related genes could be a novel tools to predict the overall survival and immunotherapy of BLCA patients.

METHODS

First, we downloaded transcriptomic data and clinical information of BLCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A combined hypoxia and immune signature was then constructed on the basis of the training cohort via least absolute shrinkage and selection operator (LASSO) analysis and validated in test cohort. Afterwards, Kaplan-Meier curves, univariate and multivariate Cox and subgroup analysis were employed to assess the accuracy of our signature. Immune cell infiltration, checkpoint and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were used to investigate the immune environment and immunotherapy of BLCA patients. Furthermore, we confirmed the role of TFRC in bladder cancer cell lines T24 and UMUC-3 through cell experiments.

RESULTS

A combined hypoxia and immune signature containing 8 genes were successfully established. High-risk group in both training and test cohorts had significantly poorer OS than low-risk group. Univariate and multivariate Cox analysis indicated our signature could be regarded as an independent prognostic factor. Different checkpoint was differently expressed between two groups, including CTLA4, HAVCR2, LAG3, PD-L1 and PDCD1. TIDE analysis indicated high-risk patients had poor response to immunotherapy and easier to have immune escape. The drug sensitivity analysis showed that high-risk group patients were more potentially sensitive to many drugs. Meanwhile, TFRC could inhibit the proliferation and invasion ability of T24 and UMUC-3 cells.

CONCLUSION

A combined hypoxia and immune-related gene could be a novel predictive model for OS and immunotherapy estimation of BLCA patients and TFRC could be used as a potential therapeutic target in the future.

Identification and validation of key miRNAs and a microRNA-mRNA regulatory network associated with liver cancer

MiRNAs play crucial regulatory roles in the growth and development of tumor cells by serving as carriers of post-transcriptional regulatory information derived from genes. Investigating the potential function and clinical significance of miRNA-mediated mRNA regulatory networks in liver cancer can offer novel insights and therapeutic strategies for the treatment of this disease. We identified 300 differentially expressed miRNAs, and five miRNAs were identified to be correlated with overall survival and could be used as an independent prognostic. GO enrichment analysis mainly included carboxylic acid biosynthesis, organic acid biosynthesis, peroxisomal membrane, microsomal membrane, DNA binding, C-acyltransferase activity, etc. KEGG enrichment analysis showed that the pathways of target genes related to liver cancer were mainly focused on butyric acid metabolism and partial amino acid metabolism. Eight of the top 10 HUB genes were associated with prognosis, and the expression of four genes was positively correlated with prognosis, of which ABAT, BHMT, and SHMT1 were target genes of hsa-miR-5003-3p. MiR-5003-3p inhibits ABAT/BHMT/SHMT1 expression, thereby promoting liver cancer development. Overall, our study provides new ideas for the treatment of liver cancer, and these five miRNAs may be independent prognostic biomarkers and therapeutic targets for liver cancer patients. And miR-5003-3p may be a critical factor in the mechanism of liver cancer development.

Integrated analysis of miRNA-mRNA expression of newly emerging swine H3N2 influenza virus cross-species infection with tree shrews

BACKGROUND

Cross-species transmission of zoonotic IAVs to humans is potentially widespread and lethal, posing a great threat to human health, and their cross-species transmission mechanism has attracted much attention. miRNAs have been shown to be involved in the regulation of IAVs infection and immunity, however, few studies have focused on the molecular mechanisms underlying miRNAs and mRNAs expression after IAVs cross-species infection.

METHODS

We used tree shrews, a close relative of primates, as a model and used RNA-Seq and bioinformatics tools to analyze the expression profiles of DEMs and DEGs in the nasal turbinate tissue at different time points after the newly emerged swine influenza A virus SW2783 cross-species infection with tree shrews, and miRNA-mRNA interaction maps were constructed and verified by RT-qPCR, miRNA transfection and luciferase reporter assay.

RESULTS

14 DEMs were screened based on functional analysis and interaction map, miR-760-3p, miR-449b-2, miR-30e-3p, and miR-429 were involved in the signal transduction process of replication and proliferation after infection, miR-324-3p, miR-1301-1, miR-103-1, miR-134-5p, miR-29a, miR-31, miR-16b, miR-34a, and miR-125b participate in negative feedback regulation of genes related to the immune function of the body to activate the antiviral immune response, and miR-106b-3p may be related to the cross-species infection potential of SW2783, and the expression level of these miRNAs varies in different days after infection.

CONCLUSIONS

The miRNA regulatory networks were constructed and 14 DEMs were identified, some of them can affect the replication and proliferation of viruses by regulating signal transduction, while others can play an antiviral role by regulating the immune response. It indicates that abnormal expression of miRNAs plays a crucial role in the regulation of cross-species IAVs infection, which lays a solid foundation for further exploration of the molecular regulatory mechanism of miRNAs in IAVs cross-species infection and anti-influenza virus targets.

Qingfei Formula Protects against Human Respiratory Syn cytial Virus-induced Lung Inflammatory Injury by Regulating the M APK Signaling Pathway

OBJECTIVE

Qingfei formula (QF) is an empirical formula that shows good clinical efficacy in treating human respiratory syncytial virus pneumonia (RSVP). However, the underlying mechanism remains unclear. This study explores the possible pharmacological actions of QF in RSVP treatment.

METHODS

We used a network pharmacology approach to identify the active ingredients of QF, forecast possible therapeutic targets, and analyze biological processes and pathways. Molecular docking simulation was used to evaluate the binding capability of active ingredients and therapeutic targets. Finally, in vivo experiments confirmed the reliability of network pharmacology-based prediction of underlying mechanisms.

RESULTS

The study identified 92 potential therapeutic targets and corresponding 131 active ingredients. Enrichment analysis showed that QF downregulated the MAPK signaling pathway and suppressed the inflammatory injury to the lungs induced by the RSV virus. Molecular docking simulations demonstrated that the core active ingredients of QF could stably bind to genes associated with the MAPK signaling pathway. QF had a protective effect against pneumonia in RSV-infected mice. The QF group exhibited a significant reduction in the levels of inflammatory mediators, interleukin- 6 (IL-6), interleukin-8 (CXCL8, IL-8), and P-STAT3, compared to the RSV-induced group. The QF group showed remarkably inhibited MAPK1+3(P-ERK1+2) and MAPK8(P-JNK) protein expression.

CONCLUSION

The current study showed that QF downregulated the MAPK signaling pathway, which inhibited pulmonary inflammation triggered by RSV infection. This study recommends the appropriate use of QF in the clinical management of RSVP.

Development of an ubiquitin-proteasome system signature for predicting prognosis and providing therapeutic guidance for patients with triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a pathological subtype with a high mortality, and the development of inhibitors in the ubiquitin-proteasome system (UPS) component could be a novel therapeutic tool.

METHODS

Triple-negative breast cancer data were obtained from The Cancer Genome Atlas (TCGA), and subtype analysis was performed by consistent clustering analysis to identify molecular subtypes of TNBC according to UPS characteristics. Differential analysis, COX and least absolute shrinkage and selection operator (LASSO) COX regression analyses were performed to select genes associated with overall survival in TNBC. The final prognostic model (UPS score) was determined using the LASSO COX model. The model performance was assessed using receiver operating characteristic (ROC) curves and survival curves. In addition, the results of the UPS score on analyzing the abundance of immune cell infiltration and immunotherapy were explored. Finally, we developed a nomogram for TNBC survival prediction.

RESULTS

Two UPS subtypes (UPSMS1 and UPSMS2) showing significant survival differences were classified. COX regression analysis on differentially expressed genes in UPSMS1 and UPSMS2 filtered five genes that affected overall survival. Based on the regression coefficients and expression data of the five genes, we built a prognostic assessment system (UPS score). The UPS score showed consistent prognostic and therapeutic guidance values. Finally, the ROC curve of the nomogram and UPS score showed the highest predictive efficacy compared with traditional clinical prognostic indicators.

CONCLUSION

The UPS score represented a promising prognostic tool to predict overall survival and immune status and guide personalized treatment selection in TNBC patients, and this study may provide a more practical alternative for clinical monitoring and management of TNBC.

Probable targets and mechanism of ginsenoside Rg1 for non-alcoholic fatty liver disease: a study integrating network pharmacology, molecular docking, and molecular dynamics simulation

Ginsenoside Rg1 (GRg1), a key bioactive component of medicinal herbs, has shown beneficial effects on non-alcoholic fatty liver disease (NAFLD) and numerous other conditions. Nevertheless, the specific targets that are actively involved and the potential mechanisms underlying NAFLD treatment remain unclear. This study aimed to elucidate the therapeutic effects and mechanism of GRg1 in alleviating NAFLD using a combined approach of network pharmacology and molecular biology validation. The analysis yielded 294 targets for GRg1 and 1293 associated with NAFLD, resulting in 89 overlapping targets. Through protein-protein interactions (PPI) network topology analysis, 10 key targets were identified. Upon evaluating the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis, GRg1 may exert therapeutic effects on NAFLD by negatively regulating the apoptotic process, insulin and endocrine resistance, the AGE-RAGE signaling pathway in diabetic complications, and the Estrogen, PI3K/Akt, and MAPK pathways. The three differential gene targets for Akt1, EGFR, and IGF1 were identified through the compound-target network in conjunction with the aforementioned methods. The molecular docking and molecular dynamics (MD) simulations showed that AKT1 and EGFR had a strong binding affinity with GRg1. Overall, our findings point to a novel therapeutic strategy involving NAFLD, with further in vivo and in vitro studies promising to deepen our comprehension and validate its potential advantages.Communicated by Ramaswamy H. Sarma.

Metabolomic, Proteomic, and Single-Cell Proteomic Analysis of Cancer Cells Treated with the KRASG12D Inhibitor MRTX1133

Mutations in KRAS are common drivers of human cancers and are often those with the poorest overall prognosis for patients. A recently developed compound, MRTX1133, has shown promise in inhibiting the activity of KRASG12D mutant proteins, which is one of the main drivers of pancreatic cancer. To better understand the mechanism of action of this compound, I performed both proteomics and metabolomics on four KRASG12D mutant pancreatic cancer cell lines. To obtain increased granularity in the proteomic observations, single-cell proteomics was successfully performed on two of these lines. Following quality filtering, a total of 1498 single cells were analyzed. From these cells, 3140 total proteins were identified with approximately 953 proteins quantified per cell. At 48 h of treatment, two distinct populations of cells can be observed based on the level of effectiveness of the drug in decreasing the total abundance of the KRAS protein in each respective cell, with results that are effectively masked in the bulk cell analysis. All mass spectrometry data and processed results are publicly available at www.massive.ucsd.edu at accessions PXD039597, PXD039601, and PXD039600.

Identification of a valuable gene network for the diagnosis and treatment of non-obstructive azoospermia: in-silico analyses - experimental research

Introduction

Non-obstructive azoospermia (NOA) is an etiology of infertility in men. NOA may have various classifications; however, hypogonadotropic hypogonadism can be regarded as a class of NOA associated with genetic factors. Former studies have shown that noncoding RNA (ncRNA) plays an essential role in NOA incidence, but few studies have been performed on the NOA-related ncRNA interaction network. In the current study, genes, NOA-related microRNA (miRNA), and circular RNA (circRNA) were found by bioinformatics methods to offer a new perspective on NOA treatment.

Methods

The gonadotropin-releasing hormone receptor (GnRHR)-related protein-protein interaction (PPI) network was extracted by searching in 'string-database'. GO, KEGG, and Enrichr databases were used to identify pathways, molecular function, and biological processing. Four databases, including TargetScan, mirDIP, miRmap, and miRWalk, were used to extract miRNAs. At last, the circ2GO, circBase, and literature were used to identify circRNAs and their genes.

Results

The current study identified the four proteins associated with the GnRHR signaling; eight shared miRNAs that affect the expression of found proteins and 25 circRNAs and their origin genes that regulate the miRNAs' function.

Conclusion

The two miRNAs, hsa-miR-134-3p and hsa-miR-513C-3p, the three genes, VCAN, NFATC3, and PRDM5, and their associated circRNAs can perform as a valuable gene network in the diagnosis and treatment of NOA pathogenesis.

Alleviation of Photoreceptor Degeneration Based on Fullerenols in rd1 Mice by Reversing Mitochondrial Dysfunction via Modulation of Mitochondrial DNA Transcription and Leakage

Poor therapeutic outcomes of antioxidants in ophthalmologic clinical applications, including glutathione during photoreceptor degeneration in retinitis pigmentosa (RP), are caused by limited anti-oxidative capacity. In this study, fullerenols are synthesized and proven to be highly efficient in vitro radical scavengers. Fullerenol-based intravitreal injections significantly improve the flash electroretinogram and light/dark transition tests performed for 28 days on rd1 mice, reduce the thinning of retinal outer nuclear layers, and preserve the Rhodopsin, Gnat-1, and Arrestin expressions of photoreceptors. RNA-sequencing, RT-qPCR, and Western blotting validate that mitochondrial DNA (mt-DNA)-encoded genes of the electron transport chain (ETC), such as mt-Nd4l, mt-Co1, mt-Cytb, and mt-Atp6, are drastically downregulated in the retinas of rd1 mice, whereas nuclear DNA (n-DNA)-encoded genes, such as Ndufa1 and Atp5g3, are abnormally upregulated. Fullerenols thoroughly reverse the abnormal mt-DNA and n-DNA expression patterns of the ETC and restore mitochondrial function in degenerating photoreceptors. Additionally, fullerenols simultaneously repress Flap endonuclease 1 (FEN1)-mediated mt-DNA cleavage and mt-DNA leakage via voltage-dependent anion channel (VDAC) pores by downregulating the transcription of Fen1 and Vdac1, thereby inactivating the downstream pro-inflammatory cGAS-STING pathway. These findings demonstrate that fullerenols can effectively alleviate photoreceptor degeneration in rd1 mice and serve as a viable treatment for RP.

Anticancer Effect of Active Component of Astragalus Membranaceus Combined with Olaparib on Ovarian Cancer Predicted by Network-Based Pharmacology

In China, a traditional Chinese medicine formulation called astragalus membranaceus (AM) has been utilised for more than 20 years to treat tumors with extraordinary effectiveness. The fundamental mechanisms, nevertheless, are still not well understood. The aim of this study is identifying its possible therapeutic targets and to evaluate the effects of AM in combination with a PARP inhibitor (olaparib) in the treatment of BRCA wild-type ovarian cancer. Significant genes were collected from Therapeutic Target Database and Database of Gene-Disease Associations. The components of AM were analyzed using the Traditional Chinese Medicine System Pharmacology (TCMSP) database to screen the active ingredients of AM based on their oral bioavailability and drug similarity index. In order to find intersection targets, Venn diagrams and STRING website diagrams were employed. STRING was also used to create a protein-protein interaction network. In order to create the ingredient-target network, Cytoscape 3.8.0 was used. DAVID database was utilized to carry out enrichment and pathway analyses. The binding ability of the active compounds of AM to the core targets of AM-OC was verified with molecular docking using AutoDock software. Experimental validations, including cell scratch, cell transwell, cloning experiment, were conducted to verify the effects of AM on OC cells. A total of 14 active ingredients of AM and 28 AM-OC-related targets were screened by network pharmacology analysis. The ten most significant Gene Ontology (GO) biological function analyses, as well as the 20 foremost Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathways were selected. Moreover, molecular docking results showed that bioactive compound (quercetin) demonstrated a good binding ability with tumor protein p53 (TP53), MYC, vascular endothelial growth factor A (VEGFA), phosphatase and tensin homolog (PTEN), AKT serine/threonine kinase 1 (AKT1) and cyclin D1 (CCND1) oncogenes. According to experimental methods, in vitro OC cell proliferation and migration appeared to be inhibited by quercetin, which also increased apoptosis. In addition, the combination with olaparib further enhanced the effect of quercetin on OC. Based on network pharmacology, molecular docking, and experimental validation, the combination of PARP inhibitor and quercetin enhanced the anti-proliferative activity in BRCA wild-type ovarian cancer cells, which supplies the theoretical groundwork for additional pharmacological investigation.

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression

BACKGROUND

HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination.

METHODS

This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated.

RESULTS

Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases.

CONCLUSION

To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CB1R and CB2R, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inflammatory bowel disease. Given the high similarity of CB1R and CB2R, generating subtype-selective ligands is still an open challenge. In this work, the Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) compound prediction platform has been generated based on explainable machine learning to support the design of selective CB1R and CB2R ligands. Multilayer classifiers were combined with Shapley value analysis to facilitate explainable predictions. In test calculations, CIRCE predictions reached ∼80% accuracy and structural features determining ligand predictions were rationalized. CIRCE was designed as a web-based prediction platform that is made freely available as a part of our study.

Parkinson's disease patients combined with constipation tend to have higher serum expression of microRNA 29c, prominent neuropsychiatric disorders, possible RBD conversion, and a substandard quality of life

INTRODUCTION

The symptom of constipation has been confirmed as an early diagnose criteria for Parkinson's disease (PD). Furthermore, evidences suggest that pathogenesis of PD initiates in gut, rather than brain. If so, identifying biomarkers for constipation in PD might have potentials to assist early diagnosis and initial treatment.

METHOD

We first identified that microRNA 29c (miR-29c) was dysregulated both in PD and constipation patients through bioinformatics analysis. Then, serological analysis of the expression of miR-29c in 67 PD patients with constipation (PD-C), 51 PD patients without constipation (PD-NC), and 50 healthy controls (HC) was carried out by qPCR. Demographic and clinical features were also compared. Patients in PD-C group were further classified into two groups: those with prodromal stage constipation (PD-C-Pro) (n = 36) and those with clinical stage constipation (PD-C-Clinic) (n = 31), to explore their different characteristics.

RESULTS

The levels of miR-29c in PD-C group were higher than that in PD-NC group, both higher than HC group. PD-C-Pro group's miR-29c levels were statistically higher compared with PD-C-Clinic group's. What is more, PD-C group had higher scores of MDS-UPDRS-I, NMSS, NMSS3, NMSS4, NMSS6, NMSS9, SCOPA-AUT, HAMD, HAMA, RBDSQ, CSS, and PACQOL compared with PD-NC party. Relative to the PD-C-Clinic, patients in PD-C-Pro group had higher MDS-UPDRS-I, NMSS, NMSS3, HAMD, and HAMA scores, and were more likely to have RBD.

CONCLUSION

Our results indicated that miR-29c seems to be an underlying cause for developing constipation in patients with PD and PD-C identifies a group of patients with more severe non-motor impairment, prominent neuropsychiatric disorders, and possible RBD conversion as well as a substandard quality of life. We further confirmed that there is a close relationship between symptoms representing the same pathological origin, especially constipation and RBD.

Exploring the potential relationship between frozen shoulder and Dupuytren's disease through bioinformatics analysis and machine learning

Background

Frozen shoulder (FS) and Dupuytren's disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions.

Methods

GSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein-protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS.

Results

We identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration.

Conclusion

We identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.

Immune-related risk prognostic model for clear cell renal cell carcinoma: Implications for immunotherapy

Clear cell renal cell carcinoma (ccRCC) is associated with complex immune interactions. We conducted a comprehensive analysis of immune-related differentially expressed genes in patients with ccRCC using data from The Cancer Genome Atlas and ImmPort databases. The immune-related differentially expressed genes underwent functional and pathway enrichment analysis, followed by COX regression combined with LASSO regression to construct an immune-related risk prognostic model. The model comprised 4 IRGs: CLDN4, SEMA3G, CAT, and UCN. Patients were stratified into high-risk and low-risk groups based on the median risk score, and the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, confirming the reliability of the model from various perspectives. Further comparison of immune infiltration, tumor mutation load, and immunophenoscore (IPS) comparison between the 2 groups indicates that the high-risk group could potentially demonstrate a heightened sensitivity towards immunotherapy checkpoints PD-1, CTLA-4, IL-6, and LAG3 in ccRCC patients. The proposed model not only applies to ccRCC but also shows potential in developing into a prognostic model for renal cancer, thus introducing a novel approach for personalized immunotherapy in ccRCC.

Unraveling the mechanism of tetrandrine combined with Buyang Huanwu Decoction against silicosis using network pharmacology and molecular docking analyses

Silicosis is an incurable chronic disease characterized by lung fibrosis and inflammation. The combination of tetrandrine and Buyang Huanwu Decoction (BYHWD) has a curative effect on silicosis. However, the mechanism of action and the key active constituent in BYHWD are still unclear. The present study employed network pharmacology and molecular docking to determine the mechanism of action and the key active components of BYHWD of Tetrandrine in combination with BYHWD for silicosis. The primary elements and targets of BYHWD were obtained from the Traditional Chinese Medicine Systems Pharmacology and analysis platform. The targets associated with tetrandrine and silicosis were identified and extracted from the Comparative Toxicogenomics Database and GeneCards database. The potential targets for the treatment of silicosis using a combination of Tetrandrine and BYHWD were identified by considering the overlapping targets between compound drugs and silicosis. These targets were then utilized to construct protein-protein interaction networks, compound drug-ingredient-target networks, and perform enrichment analyses. The top 5 active ingredients present in the compound drug-ingredient-target network are tetrandrine, quercetin, luteolin, kaempferol, and beta-carotene. Similarly, the top 6 hub genes in the protein-protein interaction network are FGF2, MMP-9, MMP-1, IL-10, IL-17A, and IL-6. The molecular docking suggested that the active components may easily access the active pocket of the hub gene. The in-silico investigation suggested that quercetin might be the active component in BYHWD responsible for therapeutic effectiveness against silicosis. This study identified the active compound and potential molecular mechanism underlying the therapeutic effects of BYHWD in combination with tetrandrine for treating silicosis. Notably, we found that quercetin may serve as the key compound in BYHWD for the treatment of silicosis.

A pan-cancer analysis for the oncogenic role of cyclin-dependent kinase inhibitor 1B in human cancers

BACKGROUND

Human health and life are threatened by cancer with high morbidity and mortality worldwide. In many experiments, CDKN1B level is associated with cancer risk, Nevertheless, no pan-cancer analysis has been conducted on CDKN1B in human cancers.

METHODS

With the help of bioinformatics, a pan-cancer analysis was conducted on the expression levels of CDKN1B in cancer tissues and adjacent tissues from the TCGA, CPTAC and GEO databases. The CDKN1B expression levels in tumor patients was further validated using immunohistochemistry (IHC) and quantitative real-time PCR.

RESULTS

In the study, we first investigated the cancer-related roles of CDKN1B's in 40 tumors with malignancy. The CDKN1B gene encodes the p27^Kip1 protein, which can block the production cyclin-dependent kinase (CDK), which is obviously related to the function and survival of cancer cells and alters the prognosis of cancer patients. Furthermore, CDKN1B function requires both protein processing and RNA metabolism. Additionally, the elevated expression of the CDKN1B gene and protein was validated in several cancer tissues from the patients.

CONCLUSIONS

These results showed that the levels of CDKN1B were considerably different in a number of cancer tissues, offering a potential future target for cancer therapy.

Bioinformatics for wet-lab scientists: practical application in sequencing analysis

BACKGROUND

Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. MAIN: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments.

CONCLUSION

Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.

Exploring the pharmacological mechanisms of Tripterygium wilfordii against diabetic kidney disease using network pharmacology and molecular docking

Background

Tripterygium wilfordii (TW), when formulated in traditional Chinese medicine (TCM), can effectively treat diabetic kidney disease (DKD). However, the pharmacological mechanism associated with its success has not yet been elucidated. The current work adopted network pharmacology and molecular docking for exploring TW-related mechanisms in treating DKD. Methods: In the present work, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was employed to obtain the effective components and candidate targets of TW. Additionally, this work utilized the UniProt protein database for screening and standardizing human-derived targets for effective components. The Cytoscape software was utilized to construct an effective component-target network for TW. Targets for DKD were acquired in the GEO, DisGeNET, GeneCards, and OMIM databases. Additionally, a Venn diagram was also plotted to select the possible targets of TW for treating DKD. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted to explore the TW-related mechanism underlying DKD treatment. This work also built a protein-protein interaction (PPI) network based on the Cytoscape and String platform. Then, molecular docking was conducted in order to assess the affinity of key proteins for related compounds. Results: In total, 29 active components and 134 targets of TW were acquired, including 63 shared targets, which were identified as candidate therapeutic targets. Some key targets and important pathways were included in the effect of TW in treating DKD. Genes with higher degrees, including TNF and AKT1, were identified as hub genes of TW against DKD. Molecular docking showed that TNF and AKT1 bind well to the main components in TW (kaempferol, beta-sitosterol, triptolide, nobiletin, and stigmasterol).

Conclusions

TW primarily treats DKD by acting on two targets (AKT1 and TNF) via the five active ingredients kaempferol, beta-sitosterol, triptolide, nobiletin, and stigmasterol.

The elucidation of the anti-inflammatory mechanism of EMO in rheumatoid arthritis through an integrative approach combining bioinformatics and experimental verification

Introduction: Emodin (EMO), a natural derivative of the anthraquinone family mainly extracted from rhubarb (Rheum palmatum), has previously been demonstrated to possess superior anti-inflammatory properties from a single target or pathway. In order to explore the underlying mechanism of action of EMO against rheumatoid arthritis (RA), a network pharmacology approach was employed. Methods: A gene expression profile from GSE55457 available from the Gene Expression Omnibus (GEO) database was used to identify the targets of EMO action. Further, single cell RNA sequencing data from GEO database of RA patients (GSE159117) were downloaded and analysed. To further investigate the anti-RA effect of EMO on MH7A cells, the expression of IL-6 and IL-1β were monitored. Finally, RNA-seq analyses were conducted on synovial fibroblasts from EMO-treated. Result: We screened the key targets of EMO against RA using network pharmacology methods, including HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB and FN1, and their reliability was verified using ROC curve. Single-cell RNA sequencing data analysis showed that these core target proteins mainly played a role by modulating monocytes. The anti-RA effect of EMO was further verified with MH7A cells, which showed that EMO could block cell differentiation and reduce the expression of IL-6 and IL-1β. WB experiments confirmed that EMO could affect the expression of COX2, HMBG1 and the phosphorylation of p38. Finally, sequencing of synovial fibroblasts from rats treated with EMO showed consistent results with those predicted and verified, further proving the anti-inflammatory effect of EMO. Conclusion: Our research shows that EMO inhibits inflammatory response of rheumatoid arthritis (RA) by targeting HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB, FN1 and Monocytes/macrophages.

Integrating metabolomics and network pharmacology to assess the effects of quercetin on lung inflammatory injury induced by human respiratory syncytial virus

Quercetin (QR) has significant anti-respiratory syncytial virus (RSV) effects. However, its therapeutic mechanism has not been thoroughly explored. In this study, a lung inflammatory injury model caused by RSV was established in mice. Untargeted lung tissue metabolomics was used to identify differential metabolites and metabolic pathways. Network pharmacology was used to predict potential therapeutic targets of QR and analyze biological functions and pathways modulated by QR. By overlapping the results of the metabolomics and the network pharmacology analyses, the common targets of QR that were likely to be involved in the amelioration of RSV-induced lung inflammatory injury by QR were identified. Metabolomics analysis identified 52 differential metabolites and 244 corresponding targets, while network pharmacology analysis identified 126 potential targets of QR. By intersecting these 244 targets with the 126 targets, hypoxanthine-guanine phosphoribosyltransferase (HPRT1), thymidine phosphorylase (TYMP), lactoperoxidase (LPO), myeloperoxidase (MPO), and cytochrome P450 19A1 (CYP19A1) were identified as the common targets. The key targets, HPRT1, TYMP, LPO, and MPO, were components of purine metabolic pathways. The present study demonstrated that QR effectively ameliorated RSV-induced lung inflammatory injury in the established mouse model. Combining metabolomics and network pharmacology showed that the anti-RSV effect of QR was closely associated with purine metabolism pathways.

Glutathione peroxidase 3 is a novel clinical diagnostic biomarker and potential therapeutic target for neutrophils in rheumatoid arthritis

BACKGROUND

Neutrophils have a critical role in the pathogenesis of rheumatoid arthritis (RA) with immune system dysfunction. However, the molecular mechanisms of this process mediated by neutrophils still remain elusive. The purpose of the present study is to identify hub genes in neutrophils for diagnosis and treatment of RA utilizing publicly available datasets.

METHODS

Gene expression profiles were downloaded from the Gene Expression Omnibus, and batch-corrected and normalized expression data were obtained using the ComBat package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were used to conduct significantly functional analysis and crucial pathways. The resulting co-expression genes modules and hub genes were generated based on the weighted gene co-expression network analysis and visualization by Cytoscape. Flow cytometry was conducted to detect reactive oxygen species (ROS) levels in neutrophils.

RESULTS

Neutrophils underwent transcriptional changes in synovial fluid (SF) of RA patients, different from peripheral blood of healthy controls or patients with RA. Especially, glycolysis, HIF-1 signaling, NADH metabolism, and oxidative stress were affected. These hub genes were strongly linked with classical glycolysis-related genes (ENO1, GAPDH, and PKM) responsible for ROS production. The antioxidant enzyme glutathione peroxidase 3 (GPX3), a ROS scavenger, was first identified as a hub gene in RA neutrophils. Neutrophils from patients with autoinflammatory and autoimmune diseases had markedly enhanced ROS levels, most notably in RA SF.

CONCLUSION

This research recognized hub genes and explored the characteristics of neutrophils in RA. Our findings suggest that the novel hub gene GPX3 is involved in the neutrophil-driven oxidative stress-mediated pathogenesis of RA. It has the potency to be a target for neutrophil-directed RA therapy.

Identification of the Genetic Influence of SARS-CoV-2 Infections on IgA Nephropathy Based on Bioinformatics Method

INTRODUCTION

Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way.

METHODS

We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs.

RESULTS

We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks.

CONCLUSION

We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment.

Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination

Severe acute mountain sickness (sAMS) can be life-threatening, but little is known about its genetic basis. The study was aimed to explore the genetic susceptibility of sAMS for the purpose of prediction, using microarray data from 112 peripheral blood mononuclear cell (PBMC) samples of 21 subjects, who were exposed to very high altitude (5260 m), low barometric pressure (406 mmHg), and hypobaric hypoxia (VLH) at various timepoints. We found that exposure to VLH activated gene expression in leukocytes, resulting in an inverted CD4/CD8 ratio that interacted with other phenotypic risk factors at the genetic level. A total of 2286 underlying risk genes were input into the support vector machine recursive feature elimination (SVM-RFE) system for machine learning, and a model with satisfactory predictive accuracy and clinical applicability was established for sAMS screening using ten featured genes with significant predictive power. Five featured genes (EPHB3, DIP2B, RHEBL1, GALNT13, and SLC8A2) were identified upstream of hypoxia- and/or inflammation-related pathways mediated by microRNAs as potential biomarkers for sAMS. The established prediction model of sAMS holds promise for clinical application as a genetic screening tool for sAMS.

Label-Free Mass Spectrometry Proteomics Reveals Different Pathways Modulated in THP-1 Cells Infected with Therapeutic Failure and Drug Resistance Leishmania infantum Clinical Isolates

As the world is facing increasing difficulties to treat leishmaniasis with current therapies, deeper investigation into the molecular mechanisms responsible for both drug resistance and treatment failure (TF) is essential in drug discovery and development. So far, few available drugs cause severe side effects and have developed several resistance mechanisms. Drug resistance and TF parasite strains from clinical isolates may have acquired altered expression of proteins that characterize specific mechanisms leading to therapy inefficacy. This work aims to identify the biochemical pathways of THP-1 human monocytes infected by different Leishmania infantum clinical isolates from patients with either resistance or with TF outcome, using whole cell differential Mass Spectrometry proteomics. We have adopted network enrichment analysis to integrate the transcriptomics and the proteomic results of infected cells studies. Transferrin receptor C (TFRC) and nucleoside diphosphate kinase 3 (NDK3) were discovered as overexpressed proteins in THP-1 cells infected with paromomycin, antimony, and miltefosine resistant L. infantum lines. The overall achievements represent founding concepts to confirm new targets involved in the parasitic drug resistance and TF mechanisms, and to consider in perspective the importance of a dual host-guest pharmacological approach to treat the acute stage of the disease.

Bioinformatics Identification of Therapeutic Gene Targets for Gastric Cancer

INTRODUCTION

The global prevalence of gastric cancer (GC) is increasing, and novel chemotherapeutic targets are needed.

METHODS

We searched for potential biomarkers for GC in three microarray data sets within the Gene Expression Omnibus (GEO) database. FunRich (v3.1.3) was used to perform Gene Ontology (GO) analyses and STRUN and Cytoscape (v3.6.0) were employed to construct a protein-protein interaction (PPI) network. To explore hub gene expression and survival, we used Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier (KM) plotter. Drugs that were closely related to key genes were screened by the Gene Set Cancer Analysis (GSCA), and relevant correlations were verified experimentally. We validated that the sensitivity of a GC cell line to these drugs was correlated with fibrillin 1 (FBN1) mRNA expression levels.

RESULTS

We identified 83 upregulated and 133 downregulated differentially expressed genes (DEGs) and these were enriched with regards to their cellular component (extracellular and exosomes), molecular function (extracellular matrix structural constituent and catalytic activity), and biological process (cell growth and/or maintenance and metabolism). The biological pathways most prominently involved were epithelial-to-mesenchymal transition (EMT) and β3 integrin cell surface interactions. For the PPI network, we selected 10 hub genes, and 70% of these were significantly connected to poor overall survival (OS) in patients with GC. We found a significant link between the expression of FBN1 and two small molecule drugs (PAC-1 and PHA-793887).

CONCLUSIONS

Overall, we suggest that these hub genes can be used as biomarkers and novel targets for GC. FBN1 may be associated with drug resistance in gastric cancer.

Pan-Cancer analysis of TERT and Validation in Osteosarcoma Cell Lines

PURPOSE

To investigate the possible functions of TERT in pan-cancer and OS.

METHODS

First, differential TERT gene expression analysis was conducted using multi-omics data integrative analyses, including differential expression, prognosis, the correlation between infiltrating inflammatory immune cells, and mutation in pan-cancer. Furthermore, differential TERT gene expression analysis was conducted using mRNA expression profiles related to OS based on the GEO Datasets. Various differentially expressed genes were chosen based on a fitness threshold for further investigations. Finally, the function of the TERT gene was assessed in OS cells, including cellular proliferation, migration, and metastasis.

RESULTS

Pan-cancer research demonstrated that variable expression of TERT was not only associated with numerous types of human cancer but was also intimately linked to DNA methylation. Bioinformatic investigation revealed a link between the differential expression of TERT with immune cell infiltration in the tumor microenvironment (TME). In vitro studies indicated that inhibition of TERT decreased OS cell proliferation, motility, and metastasis.

CONCLUSION

TERT may serve as a useful genomic biomarker for the diagnosis and prediction of pan-cancer and as a prospective therapeutic target for the treatment of OS.

Xanthine oxidase, a therapeutic target of realgar for non-small cell lung cancer

Background

The effects of realgar against non-small cell lung cancer (NSCLC) have been massively studied, but the direct therapeutic targets of realgar remain unclear. This study aimed to identify the molecular targets of realgar against NSCLC and explore their therapeutic mechanisms based on a network pharmacology approach and experimental validations.

Methods

The BATMAN-TCM and Digsee databases were used to predict realgar targets and NSCLC-related genes, respectively. A protein-protein interaction network was constructed for each gene set, and the overlapping genes were identified as potential targets of realgar against NSCLC. The correlation between potential targets and NSCLC was analyzed using The Cancer Genome Atlas and International Cancer Genome Consortium databases, and the key target was validated by in-silico and in-vitro experiments.

Results

Twenty-three overlapping genes, including xanthine oxidase (XO), were identified as potential targets of realgar against NSCLC. XO was selected as the key target for validation, as it was found to be upregulated in NSCLC tumor tissue, which correlated with poor overall survival. A possible interaction between realgar and XO was revealed by molecular docking which was further validated experimentally. Realgar treatment suppressed the activity of XO in NSCLC cells, as demonstrated by the unchanged XO protein levels. Finally, the mechanism of action of XO as a target against NSCLC through the cell-cell junction organization pathway was investigated.

Conclusions

Overall, this study proposes a potential molecular mechanism illustrating that XO is a target of realgar against NSCLC and highlights the usefulness of XO as a therapeutic target for NSCLC.

Comparative efficacy and pharmacological mechanism of Chinese patent medicines against anthracycline-induced cardiotoxicity: An integrated study of network meta-analysis and network pharmacology approach

Background

This study aimed to evaluate the efficacy of Chinese patent medicines (CPMs) combined with dexrazoxane (DEX) against anthracycline-induced cardiotoxicity (AIC) and further explore their pharmacological mechanism by integrating the network meta-analysis (NMA) and network pharmacology approach.

Methods

We searched for clinical trials on the efficacy of DEX + CPMs for AIC until March 10, 2023 (Database: PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure, China Science and Technology Journal and China Online Journals). The evaluating outcomes were cardiac troponin I (cTnI) level, creatine kinase MB (CK-MB) level, left ventricular ejection fraction (LVEF) value, and electrocardiogram (ECG) abnormal rate. Subsequently, the results of NMA were further analyzed in combination with network pharmacology.

Results

We included 14 randomized controlled trials (RCTs) and 1 retrospective cohort study (n = 1,214), containing six CPMs: Wenxinkeli (WXKL), Cinobufotalin injection (CI), Shenqifuzheng injection (SQFZ), Shenmai injection (SM), Astragalus injection (AI) and AI + CI. The NMA was implemented in Stata (16.0) using the mvmeta package. Compared with using DEX only, DEX + SM displayed the best effective for lowering cTnI level (MD = -0.44, 95%CI [-0.56, -0.33], SUCRA 93.4%) and improving LVEF value (MD = 14.64, 95%CI [9.36, 19.91], SUCRA 98.4%). DEX + SQFZ showed the most effectiveness for lowering CK-MB level (MD = -11.57, 95%CI [-15.79, -7.35], SUCRA 97.3%). And DEX + AI + CI has the highest effectiveness for alleviating ECG abnormalities (MD = -2.51, 95%CI [-4.06, -0.96], SUCRA 96.8%). So that we recommended SM + DEX, SQFZ + DEX, and DEX + AI + CI as the top three effective interventions against AIC. Then, we explored their pharmacological mechanism respectively. The CPMs' active components and AIC-related targets were screened to construct the component-target network. The potential pathways related to CPMs against AIC were determined by KEGG. For SM, we identified 118 co-targeted genes of active components and AIC, which were significantly enriched in pathways of cancer pathways, EGFR tyrosine kinase inhibitor resistance and AGE-RAGE signaling pathway in diabetic complications. For SQFZ, 41 co-targeted genes involving pathways of microRNAs in cancer, Rap1 signaling pathway, MAPK signaling pathway, and lipid and atherosclerosis. As for AI + CI, 224 co-targeted genes were obtained, and KEGG analysis showed that the calcium signaling pathway plays an important role except for the consistent pathways of SM and SQFZ in anti-AIC.

Conclusions

DEX + CPMs might be positive efficacious interventions from which patients with AIC will derive benefits. DEX + SM, DEX + SQFZ, and DEX + AI + CI might be the preferred intervention for improving LVEF value, CK-MB level, and ECG abnormalities, respectively. And these CPMs play different advantages in alleviating AIC by targeting multiple biological processes.

An integrative pan-cancer analysis reveals the carcinogenic effects of NCAPH in human cancer

BACKGROUND

Non-chromosomal structure maintenance protein condensin complex I subunit H (NCAPH) has been reported to play a regulatory role in a variety of cancers and is associated with tumor poor prognosis. This study aims to explore the potential role of NCAPH with a view to providing insights on pathologic mechanisms.

METHODS

The expression of NCAPH in different tumors was explored by The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx). The prognostic value of NCAPH was retrieved through GEPIA and Kaplan-Meier Plotter databases. Tumor Immunity Estimation Resource (TIMER) and Single-Sample Gene Set Enrichment Analysis (GSEA) to search for the association of NCAPH with tumor immune infiltration. The cBioPortal and PhosphoSite Plus databases showed NCAPH phosphorylation status in tumors. Gene set enrichment analysis (GSEA) was performed using bioinformatics.

RESULTS

Our findings revealed that NCAPH showed high expression levels in a wide range of tumor types, and was strongly correlated with the prognosis of patients. Moreover, a higher phosphorylation level at S59, S67, S76, S190, S222 and T38 site was discovered in head and neck squamous cell carcinoma (HNSC). NCAPH overexpression was positively correlated with the infiltration level of CD8+T cells and myeloid dendritic infiltration in breast cancer and thymoma.

CONCLUSIONS

The up-regulation of NCAPH was significantly correlated with the poor prognosis and immune infiltration in pan-cancer, and NCAPH could be served as a potential immunotherapeutic target for cancers.

Common gene signatures and molecular mechanisms of diabetic nephropathy and metabolic syndrome

Background

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. Multiple metabolic toxicities, redox stress, and endothelial dysfunction contribute to the development of diabetic glomerulosclerosis and DN. Metabolic syndrome (MetS) is a pathological state in which the body's ability to process carbohydrates, fats, and proteins is compromised because of metabolic disorders, resulting in redox stress and renal remodeling. However, a causal relationship between MetS and DN has not been proven. This study aimed to provide valuable information for the clinical diagnosis and treatment of MetS with DN.

Methods

Here, transcriptome data of DN and MetS patients were obtained from the Gene Expression Omnibus database, and seven potential biomarkers were screened using bioinformatics analysis. In addition, the relationship between these marker genes and metabolism and immune infiltration was explored. Among the identified marker genes, the relationship between PLEKHA1 and the cellular process, oxidative phosphorylation (OXPHOS), in DN was further investigated through single-cell analysis.

Results

We found that PLEKHA1 may represent an important biomarker that perhaps initiates DN by activating B cells, proximal tubular cells, distal tubular cells, macrophages, and endothelial cells, thereby inducing OXPHOS in renal monocytes.

Conclusion

Overall, our findings can aid in further investigation of the effects of drug treatment on single cells of patients with diabetes to validate PLEKHA1 as a therapeutic target and to inform the development of targeted therapies.

Systems biology and in silico-based analysis of PCOS revealed the risk of metabolic disorders

Background

Polycystic ovarian syndrome (PCOS) is a common condition of hyperandrogenism, chronic ovulation, and polycystic ovaries in females during the reproduction and maturation of the ovum. Although PCOS has been associated with metabolic disorders, including type 2 diabetes (T2D), obesity (OBE), and cardiovascular disease (CVD), Causal connection and molecular features are still unknown.

Purpose

Therefore, we investigated the shared common differentially expressed genes (DEGs), pathways, and networks of associated proteins in PCOS and metabolic diseases with therapeutic intervention.

Methods

We have used a bioinformatics pipeline to analyze transcriptome data for the polycystic ovarian syndrome (PCOS), type 2 diabetes (T2D), obesity (OBE), and cardiovascular diseases (CVD) in female patients. Then we employed gene-disease association network, gene ontology (GO) and signaling pathway analysis, selection of hub genes from protein-protein interaction (PPI) network, molecular docking, and gold benchmarking approach to screen potential hub proteins.

Result

We discovered 2225 DEGs in PCOS patients relative to healthy controls and 34, 91, and 205 significant DEGs with T2D, Obesity, and CVD, respectively. Gene Ontology analysis revealed several significant shared and metabolic pathways from signaling pathway analysis. Furthermore, we identified ten potential hub proteins from PPI analysis that may serve as a therapeutic intervention in the future. Finally, we targeted one significant hub protein, IGF2R (PDB ID: 2V5O), out of ten hub proteins based on the Maximal clique centrality (MCC) algorithm and literature review for molecular docking study. Enzastaurin (-12.5), Kaempferol (-9.1), Quercetin (-9.0), and Coumestrol (-8.9) kcal/mol showed higher binding affinity in the molecular docking approach than 19 drug compounds. We have also found that the selected four compounds displayed favorable ADMET properties compared to the native ligand.

Conclusion

Our in-silico research findings identified a shared molecular etiology between PCOS and metabolic diseases that may suggest new therapeutic targets and warrants future experimental validation of the key targets.

Hsa_circ_0015278 Regulates FLT3-ITD AML Progression via Ferroptosis-Related Genes

AML with the FLT3-ITD mutation seriously threatens human health. The mechanism by which circRNAs regulate the pathogenesis of FLT3-ITD mutant-type AML through ferroptosis-related genes (FerRGs) remains unclear. Differentially expressed circRNAs and mRNAs were identified from multiple integrated data sources. The target miRNAs and mRNAs of the circRNAs were predicted using various databases. The PPI network, ceRNA regulatory network, GO, and KEGG enrichment analyses were performed. The "survival" and the "pROC" R packages were used for K-M and ROC analysis, respectively. GSEA, immune infiltration analysis, and clinical subgroup analysis were performed. Finally, circRNAs were validated by Sanger sequencing and qRT-PCR. In our study, 77 DECircs-1 and 690 DECircs-2 were identified. Subsequently, 11 co-up-regulated DECircs were obtained by intersecting DECircs-1 and DECircs-2. The target miRNAs of the circRNAs were screened by CircInteractome, circbank, and circAtlas. Utilizing TargetScan, ENCORI, and miRWalk, the target mRNAs of the miRNAs were uncovered. Ultimately, 73 FerRGs were obtained, and the ceRNA regulatory network was constructed. Furthermore, MAPK3 and CD44 were significantly associated with prognosis. qRT-PCR results confirmed that has_circ_0015278 was significantly overexpressed in FLT3-ITD mutant-type AML. In summary, we constructed the hsa_circ_0015278/miRNAs/FerRGs signaling axis, which provides new insight into the pathogenesis and therapeutic targets of AML with FLT3-ITD mutation.

Bariatric Surgery Associates with Nonalcoholic Steatohepatitis/Hepatocellular Carcinoma Amelioration via SPP1 Suppression

Nonalcoholic steatohepatitis (NASH) is one of the most common chronic liver diseases worldwide and no effective drugs or treatments have been approved for disease management. Recently, bariatric surgery (BS) is considered to be a novel disease-modifying therapy for NASH and liver metabolic diseases, according to clinical follow-up studies. Despite the revealment of physiopathological alterations, underlying mechanisms and key factors remain indeterminate. This study included multiple bulk RNA-sequencing datasets to investigate transcriptome variation in one-year follow-up BS and diet management (Diet) NASH patients' liver biopsies. Liver functions, fibrosis, and carcinogenesis were predicted in liver samples via hallmark-based function enrichment analysis. Key factors generated from multi-dataset comparison were further assessed with hepatocellular carcinoma (HCC) progression and prognosis. BS leads to active gene expression alterations in NASH liver in comparison to diet management (Diet). Both approaches reduce cell stress and immune response, whereas BS contributes to higher metabolic levels and lower apoptosis levels. The macrophage infiltration, adipose accumulation, and fibroblast activation were revealed to be lower in post-BS NASH livers, further demonstrating positive correlations mutually. Seven key genes (MNDA, ALOX5AP, PECAM1, SPP1, CD86, FGF21, CSTA) were screened out as potential macrophage-associated and carcinogenetic factors suppressed by BS. SPP1 was identified as a crucial factor participating in BS intervened NASH-HCC progression. This study determined that BS exerts potentially superior protective functions in NASH livers compared to diet management. SPP1 may serve as a novel factor to study the functionalities of BS on NASH patients.

Identification of ferroptosis‑associated genes in chronic kidney disease

Ferroptosis serves a pivotal role in developing chronic kidney disease (CKD). The present study aimed to detect and confirm the relevance of potential ferroptosis-related genes in CKD using bioinformatics and experimentation strategies. The original GSE15072 mRNA expression dataset was retrieved from the Gene Expression Omnibus database. Subsequently, the potential differentially expressed genes associated with ferroptosis of CKD were screened using R software. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses, correlation analysis and protein-protein interactions (PPI) were performed for differentially expressed ferroptosis-associated genes (DFGs). Lastly, the expression levels of the top nine DFGs were measured in the kidney tissue of Adriamycin-induced CKD rats and healthy controls via reverse transcription-quantitative (RT-q)PCR analysis. Overall, 49 DFGs among 21 patients with CKD and nine healthy controls were identified. GO and KEGG enrichment analyses demonstrated that these DFGs were primarily involved in 'ferroptosis' and 'mitophagy'. PPI findings indicated that these ferroptosis-associated genes interacted with one another. RT-qPCR of CKD tissue from the rat model revealed that STAT3, MAPK14, heat shock protein (HSP)A5, MTOR and solute carrier family 2 member 1 (SLC2A1) mRNA levels in CKD were upregulated. Overall, 49 potential ferroptosis-associated genes of CKD were identified via bioinformatics analyses. STAT3, MAPK14, HSPA5, MTOR and SLC2A1 may influence CKD onset by regulating ferroptosis. The present results add to the existing body of knowledge about CKD and may be useful in the treatment of CKD.

STXBP3 and GOT2 predict immunological activity in acute allograft rejection

Background

Acute allograft rejection (AR) following renal transplantation contributes to chronic rejection and allograft dysfunction. The current diagnosis of AR remains dependent on renal allograft biopsy which cannot immediately detect renal allograft injury in the presence of AR. In this study, sensitive biomarkers for AR diagnosis were investigated and developed to protect renal function.

Methods

We analyzed pre- and postoperative data from five databases combined with our own data to identify the key differently expressed genes (DEGs). Furthermore, we performed a bioinformatics analysis to determine the immune characteristics of DEGs. The expression of key DEGs was further confirmed using the real-time quantitative PCR (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and immunohistochemical (IHC) staining in patients with AR. ROC curves analysis was used to estimate the performance of key DEGs in the early diagnosis of AR.

Results

We identified glutamic-oxaloacetic transaminase 2 (GOT2) and syntaxin binding protein 3 (STXBP3) as key DEGs. The higher expression of STXBP3 and GOT2 in patients with AR was confirmed using RT-qPCR, ELISA, and IHC staining. ROC curve analysis also showed favorable values of STXBP3 and GOT2 for the diagnosis of early stage AR.

Conclusions

STXBP3 and GOT2 could reflect the immunological status of patients with AR and have strong potential for the diagnosis of early-stage AR.