Network analysis of transcriptomics data for the prediction and prioritization of membrane-associated biomarkers for idiopathic pulmonary fibrosis (IPF) by bioinformatics approach

Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the CDKN1A, IL6, and MYC genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.

Overexpression of hsa_circ_0001861 inhibits pulmonary fibrosis through targeting miR-296-5p/BCL-2 binding component 3 axis

Pulmonary fibrosis is a progressive lung disorder. Evidence has shown that hsa_circular (circ)RNA_0001861 is dysregulated in pulmonary fibrosis. However, the detailed function of hsa_circRNA_0001861 in pulmonary fibrosis remains unexplored. To investigate the function of hsa_circRNA_0001861 in pulmonary fibrosis, human pulmonary fibroblasts in vitro were used, and cell counting kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) staining were performed to assess cell viability and proliferation, respectively. Western blot analysis and reverse transcription-quantitative PCR (RT-qPCR) were used to evaluate protein and mRNA levels. Meanwhile, the relationship among hsa_circRNA_0001861, miR-296-5p and BCL-2 binding component 3 (BBC3) was investigated by RNA pull-down assays. Furthermore, an in vivo model of lung fibrosis was constructed to assess the function of hsa_circRNA_0001861 in lung fibrosis. The data revealed that TGF‑β1 significantly increased the proliferation of pulmonary fibroblasts, while this phenomenon was markedly abolished by hsa_circRNA_0001861 overexpression. hsa_circRNA_0001861 overexpression markedly inhibited TGF‑β1‑induced fibrosis in pulmonary fibroblasts through the mediation of α-smooth muscle actin, E-cadherin, collagen III and fibronectin 1. Meanwhile, hsa_circRNA_0001861 could bind with miR-296-5p, and BBC3 was identified to be the downstream mRNA of miR-296-5p. In addition, the upregulation of hsa_circRNA_0001861 clearly reversed TGF‑β1‑induced fibrosis and proliferation in pulmonary fibroblasts through the upregulation of BBC3. Furthermore, hsa_circRNA_0001861 upregulation markedly alleviated pulmonary fibrosis in vivo. Hsa_circRNA_0001861 upregulation attenuated pulmonary fibrosis by modulating the miR-296-5p/BBC3 axis. Hence, the present study may provide some insights for the discovery of new methods against pulmonary fibrosis.

Flt1 produced by lung endothelial cells impairs ATII cell transdifferentiation and repair in pulmonary fibrosis

Pulmonary fibrosis is a devastating disease, in which fibrotic tissue progressively replaces lung alveolar structure, resulting in chronic respiratory failure. Alveolar type II cells act as epithelial stem cells, being able to transdifferentiate into alveolar type I cells, which mediate gas exchange, thus contributing to lung homeostasis and repair after damage. Impaired epithelial transdifferentiation is emerging as a major pathogenetic mechanism driving both onset and progression of fibrosis in the lung. Here, we show that lung endothelial cells secrete angiocrine factors that regulate alveolar cell differentiation. Specifically, we build on our previous data on the anti-fibrotic microRNA-200c and identify the Vascular Endothelial Growth Factor receptor 1, also named Flt1, as its main functional target in endothelial cells. Endothelial-specific knockout of Flt1 reproduces the anti-fibrotic effect of microRNA-200c against pulmonary fibrosis and results in the secretion of a pool of soluble factors and matrix components able to promote epithelial transdifferentiation in a paracrine manner. Collectively, these data indicate the existence of a complex endothelial-epithelial paracrine crosstalk in vitro and in vivo and position lung endothelial cells as a relevant therapeutic target in the fight against pulmonary fibrosis.

GP, Kumar SU, El Allali A, Alsamman AM, Zayed H. Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma

Introduction

Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease.

Methods

In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks.

Results

Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc.

Conclusion

To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

First Characterization of the Transcriptome of Lung Fibroblasts of SSc Patients and Healthy Donors of African Ancestry

Systemic sclerosis (SSc) is a connective tissue disorder that results in fibrosis of the skin and visceral organs. SSc-associated pulmonary fibrosis (SSc-PF) is the leading cause of death amongst SSc patients. Racial disparity is noted in SSc as African Americans (AA) have a higher frequency and severity of disease than European Americans (EA). Using RNAseq, we determined differentially expressed genes (DEGs; q < 0.1, log2FC > |0.6|) in primary pulmonary fibroblasts from SSc lungs (SScL) and normal lungs (NL) of AA and EA patients to characterize the unique transcriptomic signatures of AA-NL and AA-SScL fibroblasts using systems-level analysis. We identified 69 DEGs in "AA-NL vs. EA-NL" and 384 DEGs in "AA-SScL vs. EA-SScL" analyses, and a comparison of disease mechanisms revealed that only 7.5% of DEGs were commonly deregulated in AA and EA patients. Surprisingly, we also identified an SSc-like signature in AA-NL fibroblasts. Our data highlight differences in disease mechanisms between AA and EA SScL fibroblasts and suggest that AA-NL fibroblasts are in a "pre-fibrosis" state, poised to respond to potential fibrotic triggers. The DEGs and pathways identified in our study provide a wealth of novel targets to better understand disease mechanisms leading to racial disparity in SSc-PF and develop more effective and personalized therapies.

Investigation of differentially expressed genes and dysregulated pathways involved in multiple sclerosis

Multiple Sclerosis (MS) is a neurodegenerative autoimmune and organ-specific demyelinating disorder, known to affect the central nervous system (CNS). While genetic studies have revealed several critical genes and diagnostic biomarkers associated with MS, the etiology of the disease remains poorly understood. This study is aimed at screening and identifying the key genes and canonical pathways associated with MS. Gene expression profiling of the microarray dataset GSE38010 was used to analyze two control brain samples (control 1; GSM931812, control 2; GSM931813), active inflammation stage samples (CAP1; GSM931815, CAP2; GSM931816) and late subsided stage samples (CP1; GSM931817, CP2; GSM931818) collected from patients ranging between 23 and 54years and both genders. This analysis yielded a list of 58,866 DEGs (29,433 for active-inflammation stage and 29,433 for late-subsided Stage). The interactions between the DEGs were then studied using STRING, Cytoscape software, and MCODE was employed to find the genes that form clusters. Functional enrichment and integrative analysis were performed using ClueGO/CluePedia and MetaCore™. Our data revealed dysregulated key canonical pathways in MS patients. In addition, we identified three hub genes (SCN2A, HTR2A, and HCN1) that may serve as potential biomarkers for the prognosis of MS. Furthermore, the expression patterns of HPCA and PLCB1 provide insights into the progressive stages of MS, indicating that these genes could be used in predicting MS progression. We were able to map potential biomarkers that could be used for the prognosis and diagnosis of MS.

Integrative ontology and pathway-based approach identifies distinct molecular signatures in transcriptomes of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) remains a serious concern globally due to many factors that including late diagnosis, lack of an ideal biomarker for diagnosis and prognosis, and high rate of mortality. In this study, we aimed to identify the essential dysregulated genes and molecular signatures associated with the progression and development of ESCC. The dataset with 15 ESCCs and the 15 adjacent normal tissue samples from the surrounding histopathologically tumor-free mucosa was selected. We applied bioinformatics pipelines including various topological parameters from MCODE, CytoNCA, and cytoHubba to prioritize the most significantly associated DEGs with ESCC. We performed functional enrichment annotation for the identified DEGs using DAVID and MetaCore™ GeneGo platforms. Furthermore, we validated the essential core genes in TCGA and GTEx datasets between the normal mucosa and ESCC for their expression levels. These DEGs were primarily enriched in positive regulation of transferase activity, negative regulation of organelle organization, cell cycle mitosis/S-phase transition, spindle organization/assembly, development, and regulation of angiogenesis. Subsequently, the DEGs were associated with the pathways such as oocyte meiosis, cell cycle, and DNA replication. Our study identified the eight-core genes (AURKA, AURKB, MCM2, CDC20, TPX2, PLK1, FOXM1, and MCM7) that are highly expressed among the ESCC, and TCGA dataset. The multigene comparison and principal component analysis resulted in elevated signals for the AURKA, MCM2, CDC20, TPX2, PLK1, and FOXM1. Overall, our study reported GO profiles and molecular signatures that might help researchers to grasp the pathological mechanisms underlying ESCC development and eventually provide novel therapeutic and diagnostic strategies.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Integrative global co-expression analysis identifies Key MicroRNA-target gene networks as key blood biomarkers for obesity

Obesity is associated with the quantitative changes in miRNAs and their target genes. However, the molecular basis of their dysregulation and expression status correlations is incompletely understood. Therefore, this study aims to examine the shared differentially expressed miRNAs and their target genes between blood and adipose tissues of obese individuals to identify potential blood-based biomarkers. In this study, 3 gene expression datasets (two mRNA and one miRNA), generated from blood and adipose tissues of 68 obese and 39 lean individuals, were analyzed by a series of robust computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks. The comparison of blood versus tissue datasets has revealed the shared differential expression of 210 genes (59.5% upregulated) involved in lipid metabolism and inflammatory reactions. The blood miRNA (GSE25470) analysis has identified 79 differentially expressed miRNAs (71% downregulated). The miRNA-target gene scan identified regulation of 30 shared genes by 22miRNAs. The gene network analysis has identified the inverse expression correlation between 8 target genes (TP53, DYSF, GAB2, GFRA2, NACC2, FAM53C, JNK and GAB2) and 3 key miRNAs (hsa-mir-940, hsa-mir-765, hsa-mir-612), which are further regulated by 24 key transcription factors. This study identifies potential obesity related blood biomarkers from largescale gene expression data by computational miRNA-target gene interactome and transcription factor network construction methods.

Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in late 2019 in Wuhan, China, and has proven to be highly pathogenic, making it a global public health threat. The immediate need to understand the mechanisms and impact of the virus made omics techniques stand out, as they can offer a holistic and comprehensive view of thousands of molecules in a single experiment. Mastering bioinformatics tools to process, analyze, integrate, and interpret omics data is a powerful knowledge to enrich results. We present a robust and open access computational pipeline for extracting information from quantitative proteomics and transcriptomics public data. We present the entire pipeline from raw data to differentially expressed genes. We explore processes and pathways related to mapped transcripts and proteins. A pipeline is presented to integrate and compare proteomics and transcriptomics data using also packages available in the Bioconductor and providing the codes used. Cholesterol metabolism, immune system activity, ECM, and proteasomal degradation pathways increased in infected patients. Leukocyte activation profile was overrepresented in both proteomics and transcriptomics data. Finally, we found a panel of proteins and transcripts regulated in the same direction in the lung transcriptome and plasma proteome that distinguish healthy and infected individuals. This panel of markers was confirmed in another cohort of patients, thus validating the robustness and functionality of the tools presented.

PDGF Promotes Dermal Fibroblast Activation via a Novel Mechanism Mediated by Signaling Through MCHR1

Systemic sclerosis (SSc) is an autoimmune disease characterized by vasculopathy and excessive fibrosis of the skin and internal organs. To this day, no effective treatments to prevent the progression of fibrosis exist, and SSc patients have disabilities and reduced life expectancy. The need to better understand pathways that drive SSc and to find therapeutic targets is urgent. RNA sequencing data from SSc dermal fibroblasts suggested that melanin-concentrating hormone receptor 1 (MCHR1), one of the G protein-coupled receptors regulating emotion and energy metabolism, is abnormally deregulated in SSc. Platelet-derived growth factor (PDGF)-BB stimulation upregulated MCHR1 mRNA and protein levels in normal human dermal fibroblasts (NHDF), and MCHR1 silencing prevented the PDGF-BB-induced expression of the profibrotic factors transforming growth factor beta 1 (TGFβ1) and connective tissue growth factor (CTGF). PDGF-BB bound MCHR1 in membrane fractions of NHDF, and the binding was confirmed using surface plasmon resonance (SPR). MCHR1 inhibition blocked PDGF-BB modulation of intracellular cyclic adenosine monophosphate (cAMP). MCHR1 silencing in NHDF reduced PDGF-BB signaling. In summary, MCHR1 promoted the fibrotic response in NHDF through modulation of TGFβ1 and CTGF production, intracellular cAMP levels, and PDGF-BB-induced signaling pathways, suggesting that MCHR1 plays an important role in mediating the response to PDGF-BB and in the pathogenesis of SSc. Inhibition of MCHR1 should be considered as a novel therapeutic strategy in SSc-associated fibrosis.

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis

AIM

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

METHODS

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

RESULTS

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

CONCLUSIONS

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

Identification of Impacted Pathways and Transcriptomic Markers as Potential Mediators of Pulmonary Fibrosis in Transgenic Mice Expressing Human IGFBP5

Pulmonary fibrosis is a serious disease characterized by extracellular matrix (ECM) component overproduction and remodeling. Insulin-like growth factor-binding protein 5 (IGFBP5) is a conserved member of the IGFBP family of proteins that is overexpressed in fibrotic tissues and promotes fibrosis. We used RNA sequencing (RNAseq) to identify differentially expressed genes (DEGs) between primary lung fibroblasts (pFBs) of homozygous (HOMO) transgenic mice expressing human IGFBP5 (hIGFBP5) and wild type mice (WT). The results of the differential expression analysis showed 2819 DEGs in hIGFBP5 pFBs. Functional enrichment analysis confirmed the pro-fibrotic character of IGFBP5 and revealed its impact on fundamental signaling pathways, including cytokine–cytokine receptor interaction, focal adhesion, AGE-RAGE signaling, calcium signaling, and neuroactive ligand-receptor interactions, to name a few. Noticeably, 7% of the DEGs in hIGFBP5-expressing pFBs are receptors and integrins. Furthermore, hub gene analysis revealed 12 hub genes including Fpr1, Bdkrb2, Mchr1, Nmur1, Cnr2, P2ry14, and Ptger3. Validation assays were performed to complement the RNAseq data. They confirmed significant differences in the levels of the corresponding proteins in cultured pFBs. Our study provides new insights into the molecular mechanism(s) of IGFBP5-associated pulmonary fibrosis through possible receptor interactions that drive fibrosis and tissue remodeling.

Identification of key molecular markers of acute coronary syndrome using peripheral blood transcriptome sequencing analysis and mRNA-lncRNA co-expression network construction

Acute coronary syndrome (ACS) is a term used to describe major cardiovascular diseases, and treatment of in-stent restenosis in patients with ACS remains a major clinical challenge. Further investigation into molecular markers of ACS may aid early diagnosis, and the treatment of ACS and post-treatment recurrence. In the present study, total RNA was extracted from the peripheral blood samples of 3 patients with ACS, 3 patients with percutaneous coronary intervention (PCI)_non-restenosis, 3 patients with PCI_restenosis and 3 healthy controls. Subsequently, RNA library construction and high-throughput sequencing were performed. DESeq2 package in R was used to screen genes that were differentially expressed between the different samples. Moreover, the intersection of the differentially expressed mRNAs (DEmRNAs) and differentially expressed long noncoding RNAs (DElncRNAs) obtained. GeneCodis4.0 was used to perform function enrichment for DEmRNAs, and lncRNA-mRNA co-expression network was constructed. The GSE60993 dataset was utilized for diagnostic analysis, and the aforementioned investigations were verified using in vitro studies. Results of the present study revealed a large number of DEmRNAs and DElncRNAs in the different groups. We selected genes in the top 10 of differential expression and also involved in the co-expression of lncRNA-mRNA for diagnostic analysis in the GSE60993 dataset. The area under curve (AUC) of PDZK1IP1 (0.747), PROK2 (0.769) and LAMP3 (0.725) were all >0.7. These results indicated that the identified mRNAs and lncRNAs may act as potential clinical biomarkers, and more specifically, PDZK1IP1, PROK2 and LAMP3 may act as potential biomarkers for the diagnosis of ACS.

Comparison of critical biomarkers in 2 erectile dysfunction models based on GEO and NOS-cGMP-PDE5 pathway

BACKGROUND

Erectile dysfunction is a disease commonly caused by diabetes mellitus (DMED) and cavernous nerve injury (CNIED). Bioinformatics analyses including differentially expressed genes (DEGs), enriched functions and pathways (EFPs), and protein-protein interaction (PPI) networks were carried out in DMED and CNIED rats in this study. The critical biomarkers that may intervene in nitric oxide synthase (NOS, predominantly nNOS, ancillary eNOS, and iNOS)-cyclic guanosine monophosphate (cGMP)-phosphodiesterase 5 enzyme (PDE5) pathway, an important mechanism in erectile dysfunction treatment, were then explored for potential clinical applications.

METHODS

GSE2457 and GSE31247 were downloaded. Their DEGs with a |logFC (fold change)| > 0 were screened out. Database for Annotation, Visualization and Integrated Discovery (DAVID) online database was used to analyze the EFPs in Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes networks based on down-regulated and up-regulated DEGs respectively. PPI analysis of 2 datasets was performed in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Interactions with an average score greater than 0.9 were chosen as the cutoff for statistical significance.

RESULTS

From a total of 1710 DEGs in GSE2457, 772 were down-regulated and 938 were up-regulated, in contrast to the 836 DEGs in GSE31247, from which 508 were down-regulated and 328 were up-regulated. The 25 common EFPs such as aging and response to hormone were identified in both models. PPI results showed that the first 10 hub genes in DMED were all different from those in CNIED.

CONCLUSIONS

The intervention of iNOS with the hub gene complement component 3 in DMED and the aging process in both DMED and CNIED deserves attention.

Biomarkers of Blood from Patients with Atherosclerosis Based on Bioinformatics Analysis

Atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that attach to arteries and cause cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, computerized tomography, and nuclear scans, assess cardiovascular disease risk and treatment targets. However, there is currently no simple blood biochemical index or biological target for the diagnosis of atherosclerosis. Therefore, it is of interest to find a biochemical blood marker for atherosclerosis. Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using the Robustrankaggreg algorithm. The genes considered more critical by the Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification. Twenty-one possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY, and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cells. In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the analysis of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.

Bio-informatics and in Vitro Experiments Reveal the Mechanism of Schisandrin A Against MDA-MB-231 cells

Schisandrin A (SchA) has been reported to have good anti-cancer effects. However, its anti-cancer mechanism in breast cancer remains unknown. This study aimed to explore the mechanism of SchA in breast cancer treatment using bio-informatics analysis and in vitro experiments. The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Cards, and PharmMapper databases were used to screen the candidate targets of SchA against MDA-MB-231 cells selected as the tested cell line through MTT analysis. The functions and pathways of the targets were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and further analyzed using DAVID 6.8.1 database. Network pharmacology analysis revealed 77 candidate targets, 31 signal pathways, and 208 GO entries (P < 0.05). The targets regulated serine-type endopeptidase and protein tyrosine kinase activities, thereby promoting the migration and inhibiting the apoptosis of MDA-MB-231 cells. Comprehensive analysis of the ‘Protein–Protein Interaction’ (PPI) and ‘Component-Targets-Pathways’ (C-T-P) networks constructed using Cytoscape 3.7.1 software revealed four core targets: EGFR, PIK3R1, MMP9 and Caspase 3. Their docking scores with SchA were subsequently investigated through molecular docking. The wound healing, Hoechst 33342/PI, and western blot assays confirmed that SchA significantly down-regulated EGFR, PIK3R1, and MMP9, but up-regulated cleaved-caspase 3, thus inhibiting the migration and promoting the apoptosis of MDA-MB-231 cells. Reckoning the findings of the study, SchA is a potential adjuvant treatment for breast cancer.

Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis

Idiopathic pulmonary fibrosis (IPF) is a progressive disease whose etiology remains unknown. The purpose of this study was to explore hub genes and pathways related to IPF development and prognosis. Multiple gene expression datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis (WGCNA) was performed and differentially expressed genes (DEGs) identified to investigate Hub modules and genes correlated with IPF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were performed on selected key genes. In the PPI network and cytoHubba plugin, 11 hub genes were identified, including ASPN, CDH2, COL1A1, COL1A2, COL3A1, COL14A1, CTSK, MMP1, MMP7, POSTN, and SPP1. Correlation between hub genes was displayed and validated. Expression levels of hub genes were verified using quantitative real-time PCR (qRT-PCR). Dysregulated expression of these genes and their crosstalk might impact the development of IPF through modulating IPF-related biological processes and signaling pathways. Among these genes, expression levels of COL1A1, COL3A1, CTSK, MMP1, MMP7, POSTN, and SPP1 were positively correlated with IPF prognosis. The present study provides further insights into individualized treatment and prognosis for IPF.

Meta-analysis identifying epithelial-derived transcriptomes predicts poor clinical outcome and immune infiltrations in ovarian cancer

BACKGROUND

Previous studies revealed that the epithelial component is associated with the modulation of the ovarian tumor microenvironment (TME). However, the identification of key transcriptional signatures of laser capture microdissected human ovarian cancer epithelia remains lacking.

METHODS

We identified the differentially expressed transcriptional signatures of human ovarian cancer epithelia by meta-analysis of GSE14407, GSE2765, GSE38666, GSE40595, and GSE54388. Then we investigated the enrichment of KEGG pathways that are associated with epithelia-derived transcriptomes. Finally, we investigated the correlation of key epithelia-hub genes with the survival prognosis and immune infiltrations. Finally, we investigated the genetic alterations of key prognostic hub genes and their diagnostic efficacy in ovarian cancer epithelia.

RESULTS

We identified 1339 differentially expressed genes (DEGs) in ovarian cancer epithelia including 541upregulated and 798 downregulated genes. We identified 21 (such as E2F4, FOXM1, TFDP1, E2F1, and SIN3A) and 11 (such as JUN, DDX4, FOSL1, NOC2L, and HMGA1) master transcriptional regulators (MTRs) that are interacted with upregulated and the downregulated genes in ovarian tumor epithelium, respectively. The STRING-based analysis identified hub genes (such as CDK1, CCNB1, AURKA, CDC20, and CCNA2) in ovarian cancer epithelia. The significant clusters of identified hub genes are associated with the enrichment of KEGG pathways including cell cycle, DNA replication, cytokine-cytokine receptor interaction, pathways in cancer, and focal adhesion. The upregulation of SCNN1A and CDCA3 and the downregulation of SOX6 are correlated with a shorter survival prognosis in ovarian cancer (OV). The expression level of SOX6 is negatively correlated with immune score and positively correlated with tumor purity in OV. Moreover, SOX6 is negatively correlated with the infiltration of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages in OV. Also, SOX6 is negatively correlated with various immune markers including CD8A, PRF1, GZMA, GZMB, NKG7, CCL3, and CCL4, indicating the immune regulatory efficiency of SOX6 in the TME of OV. Furthermore, SCNN1A, CDCA3, and SOX6 genes are genetically altered in OV and the expression levels of SCNN1A and SOX6 genes showed diagnostic efficacy in ovarian cancer epithelia.

CONCLUSIONS

The identified ovarian cancer epithelial-derived key transcriptional signatures are significantly correlated with survival prognosis and immune infiltrations, and may provide new insight into the diagnosis and treatment of epithelial ovarian cancer.

Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy

Background

Astragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics.

Materials and Methods

The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.

Results

Twenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.

Conclusions

AR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.

Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

Identification of the correlations between interleukin-27 (IL-27) and immune-inflammatory imbalance in preterm birth

Preterm birth (PTB) is an immune-inflammatory disease that needs to be resolved. This study aimed to identify the role of interleukin-27 (IL-27), an immunomodulatory factor, in PTB and its associated mechanisms. Here, we analyzed the high-throughput of samples data from the maternal-fetal interface to the peripheral circulation obtained from public databases and reported that the elevated IL-27 was involved with the onset of PTB. Further bioinformatics analyses (e.g. GeneMANIA and GSEA) revealed that IL-27 overexpression in the peripheral circulation as well as maternal-fetal interface is related to the activation of the immune-inflammatory process represented by IFN-γ signaling, etc. In addition, IL-27 and immune infiltration correlation analysis demonstrated that IL-27 mediates this immune-inflammatory imbalance, plausibly mainly through monocyte-macrophage and neutrophils. This finding was further validated by analyzing additional datasets. Overall, this is the first study to elaborate on the role of IL-27-mediated immuno-inflammation in PTB from the perspective of bioinformatics, which may provide a novel strategy for the prevention and treatment of PTB.

A Comprehensive Analysis of the Downregulation of miRNA-1827 and Its Prognostic Significance by Targeting SPTBN2 and BCL2L1 in Ovarian Cancer

Background: Previous studies demonstrated that miRNA-1827 could repress various cancers on proliferation, angiogenesis, and metastasis. However, little attention has been paid to its role in ovarian cancer as a novel biomarker or intervention target, especially its clinical significance and underlying regulatory network.

Methods: A meta-analysis of six microarrays was adopted here to determine the expression trend of miRNA-1827, and was further validated by gene expression profile data and cellular experiments. We explored the functional annotations through enrichment analysis for the differentially expressed genes targeted by miRNA-1827. Subsequently, we identified two hub genes, SPTBN2 and BCL2L1, based on interaction analysis using two online archive tools, miRWALK (it consolidates the resources of 12 miRNA-focused servers) and Gene Expression Profiling Interactive Analysis (GEPIA). Finally, we validated their characteristics and clinical significance in ovarian cancer.

Results: The comprehensive meta-analysis revealed that miRNA-1827 was markedly downregulated in clinical and cellular specimens. Transfection of the miRNA-1827 mimic could significantly inhibit cellular proliferation. Concerning its target genes, they were involved in diverse biological processes related to tumorigenesis, such as cell proliferation, migration, and the apoptosis signaling pathway. Moreover, interaction analysis proved that two hub genes, SPTBN2 and BCL2L1, were highly associated with poor prognosis in ovarian cancer.

Conclusion: These integrated bioinformatic analyses indicated that miRNA-1827 was dramatically downregulated in ovarian cancer as a tumor suppressor. The upregulation of its downstream modulators, SPTBN2 and BCL2L1, was associated with an unfavorable prognosis. Thus, the present study has identified miRNA-1827 as a potential intervention target for ovarian cancer based on our bioinformatic analysis processes.

Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis

Background

Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive.

Objective

This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion.

Methods

Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the “LIMMA” package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA.

Results

We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion (P < 0.05). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA.

Conclusions

We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis

Background

Breast cancer is one of the most commonly diagnosed cancers all over the world, and it is now the leading cause of cancer death among females. The aim of this study was to find DEGs (differentially expressed genes) which can predict poor prognosis in breast cancer and be effective targets for breast cancer patients via bioinformatical analysis.

Methods

GSE86374, GSE5364, and GSE70947 were chosen from the GEO database. DEGs between breast cancer tissues and normal breast tissues were picked out by GEO2R and Venn diagram software. Then, DAVID (Database for Annotation, Visualization, and Integrated Discovery) was used to analyze these DEGs in gene ontology (GO) including molecular function (MF), cellular component (CC), and biological process (BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. Next, STRING (Search Tool for the Retrieval of Interacting Genes) was used to investigate potential protein-protein interaction (PPI) relationships among DEGs and these DEGs were analyzed by Molecular Complex Detection (MCODE) in Cytoscape. After that, UALCAN, GEPIA (gene expression profiling interactive analysis), and KM (Kaplan-Meier plotter) were used for the prognostic information and core genes were qualified.

Results

There were 96 upregulated genes and 98 downregulated genes in this study. 55 upregulated genes were selected as hub genes in the PPI network. For validation in UALCAN, GEPIA, and KM, 5 core genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) were found to highly expressed in breast cancer tissues with poor prognosis. They differentially expressed between different subclasses of breast cancer.

Conclusion

These five genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) could be potential targets for therapy in breast cancer and prediction of prognosis on the basis of bioinformatical analysis.

An integrative analysis to distinguish between emphysema (EML) and alpha-1 antitrypsin deficiency-related emphysema (ADL)-A systems biology approach

Lung Emphysema is an abnormal enlargement of the air sacs followed by the destruction of alveolar walls without any prominent fibrosis. This study primarily identifies the differentially expressed genes (DEGs), interactions between them, and their significant involvement in the activated signaling cascades. The dataset with ID GSE1122 (five normal lung tissue samples, five of usual emphysema, and five of alpha-1 antitrypsin deficiency-related emphysema) from the gene expression omnibus (GEO) was analyzed using the GEO2R tool. The physical association between the DEGs were mapped using the STRING tool and was visualized in the Cytoscape software. The enriched functional processes were identified with the ClueGO plugin's help from Cytoscape. Further integrative functional annotation was performed by implying the GeneGo Metacore™ to distinguish the enriched pathway maps, process networks, and GO processes. The results from this analysis revealed the critical signaling cascades that have been either activated or inhibited due to identified DEGs. We found the activated pathways such as immune response IL-1 signaling pathway, positive regulation of smooth muscle migration, BMP signaling pathway, positive regulation of leukocyte migration, NIK/NF-kappB signaling, and cytochrome-c oxidase activity. Finally, we mapped four crucial genes (CCL5, ALK, TAC1, CD74, and HLA-DOA) by comparing the functional annotations that could be significantly influential in emphysema molecular pathogenesis. Our study provides insights into the pathogenesis of emphysema and helps in developing potential drug targets against emphysema.

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

OBJECTIVES

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients.

METHODS

GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN.

RESULTS

There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN.

CONCLUSIONS

We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.