Transcriptome profiling of lncRNA and co-expression network in the vaginal epithelial tissue of women with lubrication disorders

Background Vaginal lubrication is a crucial physiological response that occurs at the beginning of sexual arousal. However, research on lubrication disorders (LD) is still in its infancy, and the role of long non-coding RNAs (lncRNAs) in LD remains unclear. This study aimed to explore the function of lncRNAs in the pathogenesis of vaginal LD. Methods The expression profiles of LD and normal control (NC) lncRNAs were examined using next-generation sequencing (NGS), and eight selected differentially expressed lncRNAs were verified by quantitative real-time PCR. We conducted GO annotation and KEGG pathway enrichment analyses to determine the principal functions of significantly deregulated genes. LncRNA-mRNA co-expression and protein-protein interaction (PPI) networks were constructed and the lncRNA transcription factors (TFs) were predicted. Results From the results, we identified 181,631 lncRNAs and 145,224 mRNAs in vaginal epithelial tissue. Subsequently, our preliminary judgment revealed a total of 499 up-regulated and 337 down-regulated lncRNAs in LD. The top three enriched GO items of the dysregulated lncRNAs included the following significant terms: “contractile fiber part,” “actin filament-based process,” and “contractile fiber”. The most enriched pathways were “cell-extracellular matrix interactions,” “muscle contraction,” “cell-cell communication,” and “cGMP-PKG signaling pathway”. Our results also showed that the lncRNA-mRNA co-expression network was a powerful platform for predicting lncRNA functions. We determined the three hub genes, ADCY5, CXCL12, and NMU, using PPI network construction and analysis. A total of 231 TFs were predicted with RHOXF1, SNAI2, ZNF354C and TBX15 were suspected to be involved in the mechanism of LD. Conclusion In this study, we constructed the lncRNA-mRNA co-expression network, predicted the lncRNA TFs, and comprehensively analyzed lncRNA expression profiles in LD, providing a basis for future studies on LD clinical biomarkers and therapeutic targets. Further research is also needed to fully determine lncRNA’s role in LD development.

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Gene Co-Expression Network Analysis Confirms Four Independent Predictors of Survival In Ovarian Cancer Patients Under Optimal Debulking Surgery

10.21203/rs.3.rs-743172/v1 ◽

2021 ◽

Author(s):

Cong Zhang ◽

Tao Zhu ◽

Ting Hu ◽

Qian Sun

Keyword(s):

Ovarian Cancer ◽

Network Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Clusters ◽

Debulking Surgery ◽

Screening Criteria ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Optimal Debulking

Abstract Background: Serious ovarian cancer (OvCa) is the most common histological type of epithelial OvCa with poor prognosis. Despite received optimal cytoreduction and standard chemotherapy, a large proportion of patients are forced to recurrence or death within three years. To identify exact prognostic biomarkers associated with overall survival (OS) is urgent requirements of exploring rapid tumor progression mechanisms and developing novel strategies for immunotherapy.Methods: The gene expression profiles of GSE49997, GSE9891 and TCGA were screened through rigorous criteria using R software and Bioconductor package. Weighted gene co-expression network analysis (WGCNA) was constructed to figure out gene clusters associated with OS. Protein-protein interaction (PPI) networks were built through STRING website. Prognostic values of potential biomarkers were validated using forest map and Kaplan-Meier analysis.Results: According to screening criteria, 788 samples and 10402 genes were reserved as the modeling dataset. We detected five modules related to OS and intersected 108 genes through WGCNA after random sampling. PPI network analysis, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed potential mechanisms of above biomarkers. Conclusions: Four exact biomarkers (CANT1, P4HB, DUS1L and SIRT7) were confirmed as independent predictors of survival in OvCa patients with success of debulking surgery, which might provide promising biomarkers for prognostic judgement in ovarian cancer.

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Molecular Pathways Associated with Kallikrein 6 Overexpression in Colorectal Cancer

Genes ◽

10.3390/genes12050749 ◽

2021 ◽

Vol 12 (5) ◽

pp. 749

Author(s):

Ritu Pandey ◽

Muhan Zhou ◽

Yuliang Chen ◽

Dalila Darmoul ◽

Conner C. Kisiel ◽

...

Keyword(s):

Colorectal Cancer ◽

Extracellular Matrix ◽

Expression Profiles ◽

Cell Communication ◽

Regulatory Pathways ◽

Clinical Biomarkers ◽

Associated Proteins ◽

Cancer Genome Atlas ◽

Protein Kinase Signaling ◽

Kallikrein 6

Colorectal cancer (CRC) remains one of the leading causes of cancer-related death worldwide. The high mortality of CRC is related to its ability to metastasize to distant organs. The kallikrein-related peptidase Kallikrein 6 (KLK6) is overexpressed in CRC and contributes to cancer cell invasion and metastasis. The goal of this study was to identify KLK6-associated markers for the CRC prognosis and treatment. Tumor Samples from the CRC patients with significantly elevated KLK6 transcript levels were identified in the RNA-Seq data from Cancer Genome Atlas (TCGA) and their expression profiles were evaluated using Gene Ontology (GO), Phenotype and Reactome enrichment, and protein interaction methods. KLK6-high cases had a distinct spectrum of mutations in titin (TTN), APC, K-RAS, and MUC16 genes. Differentially expressed genes (DEGs) found in the KLK6-overexpressing CRCs were associated with cell signaling, extracellular matrix organization, and cell communication regulatory pathways. The top KLK6-interaction partners were found to be the members of kallikrein family (KLK7, KLK8, KLK10), extracellular matrix associated proteins (keratins, integrins, small proline rich repeat, S100A families) and TGF-β, FOS, and Ser/Thr protein kinase signaling pathways. Expression of selected KLK6-associated genes was validated in a subset of paired normal and tumor CRC patient-derived organoid cultures. The performed analyses identified KLK6 itself and a set of genes, which are co-expressed with KLK6, as potential clinical biomarkers for the management of the CRC disease.

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Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis

BMC Medical Genomics ◽

10.1186/s12920-019-0632-2 ◽

2019 ◽

Vol 12 (1) ◽

Author(s):

Elham Karimizadeh ◽

Ali Sharifi-Zarchi ◽

Hassan Nikaein ◽

Seyedehsaba Salehi ◽

Bahar Salamatian ◽

...

Keyword(s):

Gene Expression ◽

Systemic Sclerosis ◽

Protein Interaction ◽

Expression Profiles ◽

Functional Enrichment ◽

Analysis Tool ◽

Protein Protein Interaction ◽

Ppi Networks ◽

The Common ◽

Different Tissues

Abstract Background Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients. Methods An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein–protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters. Results A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues. Conclusions Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes.

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Comprehensive analysis of an lncRNA-miRNA-mRNA competing endogenous RNA network in pulpitis

PeerJ ◽

10.7717/peerj.7135 ◽

2019 ◽

Vol 7 ◽

pp. e7135 ◽

Cited By ~ 5

Author(s):

Fangcao Lei ◽

Han Zhang ◽

Xiaoli Xie

Keyword(s):

Differentially Expressed Genes ◽

Protein Interaction ◽

Dental Pulp ◽

Expression Profiles ◽

Differentially Expressed ◽

Hub Genes ◽

Regulatory Relationship ◽

Protein Protein Interaction ◽

Qrt Pcr ◽

Ppi Networks

Background Pulpitis is a common inflammatory disease that affects dental pulp. It is important to understand the molecular signals of inflammation and repair associated with this process. Increasing evidence has revealed that long noncoding RNAs (lncRNAs), via competitively sponging microRNAs (miRNAs), can act as competing endogenous RNAs (ceRNAs) to regulate inflammation and reparative responses. The aim of this study was to elucidate the potential roles of lncRNA, miRNA and messenger RNA (mRNA) ceRNA networks in pulpitis tissues compared to normal control tissues. Methods The oligo and limma packages were used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively) based on expression profiles in two datasets, GSE92681 and GSE77459, from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were further analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) networks and modules were established to screen hub genes using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the Molecular Complex Detection (MCODE) plugin for Cytoscape, respectively. Furthermore, an lncRNA-miRNA-mRNA-hub genes regulatory network was constructed to investigate mechanisms related to the progression and prognosis of pulpitis. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was applied to verify critical lncRNAs that may significantly affect the pathogenesis in inflamed and normal human dental pulp. Results A total of 644 upregulated and 264 downregulated differentially expressed genes (DEGs) in pulpitis samples were identified from the GSE77459 dataset, while 8 up- and 19 downregulated probes associated with lncRNA were identified from the GSE92681 dataset. Protein–protein interaction (PPI) based on STRING analysis revealed a network of DEGs containing 4,929 edges and 623 nodes. Upon combined analysis of the constructed PPI network and the MCODE results, 10 hub genes, including IL6, IL8, PTPRC, IL1B, TLR2, ITGAM, CCL2, PIK3CG, ICAM1, and PIK3CD, were detected in the network. Next, a ceRNA regulatory relationship consisting of one lncRNA (PVT1), one miRNA (hsa-miR-455-5p) and two mRNAs (SOCS3 and PLXNC1) was established. Then, we constructed the network in which the regulatory relationship between ceRNA and hub genes was summarized. Finally, our qRT-PCR results confirmed significantly higher levels of PVT1 transcript in inflamed pulp than in normal pulp tissues (p = 0.03). Conclusion Our study identified a novel lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of pulpitis.

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Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

BioMed Research International ◽

10.1155/2014/375262 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 15

Author(s):

Min Li ◽

Weijie Chen ◽

Jianxin Wang ◽

Fang-Xiang Wu ◽

Yi Pan

Keyword(s):

Gene Expression ◽

Computational Methods ◽

Protein Complex ◽

Protein Complexes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Protein Protein Interaction ◽

Cellular Life ◽

Ppi Networks ◽

Core Proteins

Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data ofSaccharomyces cerevisiaeand the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.

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Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

Current Bioinformatics ◽

10.2174/1574893612666171020103747 ◽

2018 ◽

Vol 14 (1) ◽

pp. 4-10

Author(s):

Fang Jing ◽

Shao-Wu Zhang ◽

Shihua Zhang

Keyword(s):

Gene Ontology ◽

Topological Structure ◽

Metabolic Networks ◽

Biological Network ◽

Genomic Sequence ◽

Network Alignment ◽

Future Directions ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

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Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

Journal of International Medical Research ◽

10.1177/0300060521993980 ◽

2021 ◽

Vol 49 (3) ◽

pp. 030006052199398

Author(s):

Jinwu Peng ◽

Zhili Duan ◽

Yamin Guo ◽

Xiaona Li ◽

Xiaoqin Luo ◽

...

Keyword(s):

Random Forest ◽

Hydatid Disease ◽

Characteristic Curve ◽

Receiver Operator Characteristic Curve ◽

Random Forest Model ◽

Hepatic Hydatid Disease ◽

Forest Model ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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Transcriptome profiling analysis of muscle tissue reveals potential candidate genes affecting water holding capacity in Chinese Simmental beef cattle

Scientific Reports ◽

10.1038/s41598-021-91373-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lili Du ◽

Tianpeng Chang ◽

Bingxing An ◽

Mang Liang ◽

Xinghai Duan ◽

...

Keyword(s):

Beef Cattle ◽

Candidate Genes ◽

Molecular Mechanism ◽

Meat Quality ◽

Relevant Literature ◽

Transcriptome Profiling ◽

Water Holding Capacity ◽

Potential Candidate ◽

Protein Protein Interaction ◽

Water Holding

AbstractWater holding capacity (WHC) is an important sensory attribute that greatly influences meat quality. However, the molecular mechanism that regulates the beef WHC remains to be elucidated. In this study, the longissimus dorsi (LD) muscles of 49 Chinese Simmental beef cattle were measured for meat quality traits and subjected to RNA sequencing. WHC had significant correlation with 35 kg water loss (r = − 0.99, p < 0.01) and IMF content (r = 0.31, p < 0.05), but not with SF (r = − 0.20, p = 0.18) and pH (r = 0.11, p = 0.44). Eight individuals with the highest WHC (H-WHC) and the lowest WHC (L-WHC) were selected for transcriptome analysis. A total of 865 genes were identified as differentially expressed genes (DEGs) between two groups, of which 633 genes were up-regulated and 232 genes were down-regulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed that DEGs were significantly enriched in 15 GO terms and 96 pathways. Additionally, based on protein–protein interaction (PPI) network, animal QTL database (QTLdb), and relevant literature, the study not only confirmed seven genes (HSPA12A, HSPA13, PPARγ, MYL2, MYPN, TPI, and ATP2A1) influenced WHC in accordance with previous studies, but also identified ATP2B4, ACTN1, ITGAV, TGFBR1, THBS1, and TEK as the most promising novel candidate genes affecting the WHC. These findings could offer important insight for exploring the molecular mechanism underlying the WHC trait and facilitate the improvement of beef quality.

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Identification of Key Candidate Genes Involved in the Progression of Idiopathic Pulmonary Fibrosis

Molecules ◽

10.3390/molecules26041123 ◽

2021 ◽

Vol 26 (4) ◽

pp. 1123

Author(s):

Yu Cui ◽

Jie Ji ◽

Jiwei Hou ◽

Yi Tan ◽

Xiaodong Han

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Candidate Genes ◽

Expression Profiles ◽

Biological Processes ◽

Protein Protein Interaction ◽

Novel Treatments ◽

Kegg Pathway Analysis ◽

Cell Components ◽

Upregulated Genes

Idiopathic pulmonary fibrosis (IPF) is a lethal, agnogenic interstitial lung disease with limited therapeutic options. To investigate vital genes involved in the development of IPF, we integrated and compared four expression profiles (GSE110147, GSE53845, GSE24206, and GSE10667), including 87 IPF samples and 40 normal samples. By reanalyzing these datasets, we managed to identify 62 upregulated genes and 20 downregulated genes in IPF samples compared with normal samples. Differentially expressed genes (DEGs) were analyzed by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to illustrate relevant pathways of IPF, biological processes, molecular function, and cell components. The DEGs were then subjected to protein–protein interaction (PPI) for network analysis, serving to find 11 key candidate genes (ANXA3, STX11, THBS2, MMP1, MMP9, MMP7, MMP10, SPP1, COL1A1, ITGB8, IGF1). The result of RT-qPCR and immunohistochemical staining verified our finding as well. In summary, we identified 11 key candidate genes related to the process of IPF, which may contribute to novel treatments of IPF.

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