scholarly journals A2M is a potential core gene in intrahepatic cholangiocarcinoma

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Guanran Zhang ◽  
Xuyue Liu ◽  
Zhengyang Sun ◽  
Xiaoning Feng ◽  
Haiyan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Protein Interaction ◽  
Intrahepatic Cholangiocarcinoma ◽  
Therapeutic Targets ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
Potential Core ◽  
Protein Protein Interaction ◽  
Microarray Datasets

Abstract Background Intrahepatic cholangiocarcinoma (ICC) is a type of malignant tumor ranking the second in the incidence of primary liver cancer following hepatocellular carcinoma. Both the morbidity and mortality have been increasing in recent years. Small duct type of ICC has potential therapeutic targets. But overall, the prognosis of patients with ICC is usually very poor. Methods To search latent therapeutic targets for ICC, we programmatically selected the five most suitable microarray datasets. Then, we made an analysis of these microarray datasets (GSE26566, GSE31370, GSE32958, GSE45001 and GSE76311) collected from the Gene Expression Omnibus (GEO) database. The GEO2R tool was effective to find out differentially expressed genes (DEGs) between ICC and normal tissue. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were executed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v 6.8. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to analyze protein–protein interaction of these DEGs and protein–protein interaction of these DEGs was modified by Cytoscape3.8.2. Survival analysis was performed using Gene Expression Profiling Interactive Analysis (GEPIA) online analysis tool. Results A total of 28 upregulated DEGs and 118 downregulated DEGs were screened out. Then twenty hub genes were selected according to the connectivity degree. The survival analysis results showed that A2M was closely related to the pathogenesis and prognosis of ICC and was a potential therapeutic target for ICC. Conclusions According to our study, low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients. The value of A2M in the treatment of ICC needs to be further studied.

Identification of Key Biomarkers Associated with Survival and Prognosis in Colon Adenocarcinoma

2021 ◽  
Author(s):  
Yanjie Zhou ◽  
Lu Jiang ◽  
Jiang Lin ◽  
Wendong Tang ◽  
Wenqian Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Protein Interaction ◽  
Colon Adenocarcinoma ◽  
High Rate ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Core Genes

Abstract Background: Colorectal cancer (CRC) has a high rate of relapse and recurrence that result in poor prognosis and unsatisfactory outcomes. Colon adenocarcinoma (COAD) is the most prevalent type of CRC. It is crucial to identify novel molecular biomarkers for the early diagnosis, prognosis evaluation and disease monitoring of COAD.Methods: Three gene expression profile data were downloaded from the Gene Expression Omnibus(GEO), and the differential expression genes(DEGs) were identified by GEO2R. Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted by WebGestalt online tool. String database and Cytoscape software were used for protein–protein interaction (PPI) network construction and module analysis. The top 20 Hub Genes were screened from the PPI network using MCC algorithm on CytoHubba app of Cytoscape software, and were verified by ONCOMINE database then. The core genes affecting CRC prognosis were screened by GEPIA2 survival analysis web tool. Finally, the expression level and clinical indicators including core genes was analyzed by TCGA-COAD dataset.Results: In total, 413 differentially expressed genes (DEGs) were identified, and the GO and KEGG enrichment analyses of DEGs were processed. After, the protein–protein interaction (PPI) network was constructed and 20 hub genes were identified. Furthermore, three core genes were selected via survival analysis . Finally, the diagnostic and prognostic value of these core genes was verified by clinical analysis of TCGA-COAD dataset.Conclusion: SPP1, GRP and GNGT1 were all over-expressed in COAD, and may be regarded as novel diagnostic and prognostic biomarkers for COAD.

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

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.

scholarly journals Transcriptional Regulation Based on Network of Autophagy Identifies Key Genes and Potential Mechanisms inHuman Osteoarthritis

Cartilage ◽  
2020 ◽  
pp. 194760352095163
Author(s):  
Jiamei Liu ◽  
Qin Fu ◽  
Shengye Liu
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Polymerase Chain Reaction Analysis ◽  
Analysis Tool ◽  
Protein Protein Interaction ◽  
Synovial Tissues ◽  
Protein Protein Interaction Network

Objective Osteoarthritis (OA) is a chronic arthropathy that frequently occurs in the middle-aged and elderly population. The aim of this study was to investigate the molecular mechanism of OA based on autophagy theory. Design We downloaded the gene expression profile from the Gene Expression Omnibus repository. Differentially expressed genes (DEGs) related to the keyword “autophagy” were identified using the scanGEO online analysis tool. DEGs representing the same expression trend were screened using the MATCH function. Clinical synovial specimens were collected for identification, pathological diagnosis, hematoxylin and eosin staining, and real-time polymerase chain reaction analysis. Differential expression of mRNAs in the synovial membrane tissues and chondrocyte monolayer samples from OA patients was used to identify potential OA biomarkers. Protein-protein interactions were established by the STRING website and visualized with Cytoscape. Functional and pathway enrichment analyses were performed using the Metascape database. Results GABARAPL1, GABARAPL2, and ATG13 were obtained as co-expressed autogenes in the 3 data sets. They were all downregulated among OA synovial tissues compared with non-OA synovial tissues ( P < 0.01). A protein-protein interaction network was constructed based on these 3 genes and included 63 genes. A functional analysis revealed that these genes were associated with autophagy-related functions. The top hub genes in the protein-protein interaction network were presented. Furthermore, 3 key modules were extracted to be core control modules. Conclusions These results offer an important molecular understanding of the key transcriptional regulatory genes and modules based on the network of potential autophagy mechanisms in human OA.

scholarly journals Identification and Prognostic Analysis of Hub Genes in Bladder Cancer

2020 ◽  
Author(s):  
Haoran Yi ◽  
Lingzi Liu ◽  
Huajie Song ◽  
Hengcheng Zhu
Keyword(s):  
Bladder Cancer ◽  
Protein Interaction ◽  
Function Analysis ◽  
Therapeutic Targets ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Prognostic Analysis ◽  
Incidence And Mortality

Abstract Background:Bladder cancer(BC) is one of the most common tumors worldwide. Its incidence and mortality rate rank first in urological malignancies. Due to the lack of credible predictors, most patients are not timely diagnosed and treated. Moreover, in the past 30 years, the clinical treatment of BC had seen little progress, and the 5-year survival rates of patients were flat.Therefore,identifying novel potential markers or therapeutic targets are urgently required for the diagnosis and prognosis of BC.Methods: The BC gene expression chip data （GSE121711）were downloaded from the GEO database and the BLCA RNA-seq data were downloaded from the TCGA database. The differentially expressed genes (DEGs) were identified by R software using limma package and the edgeR package, and obtained the overlapped DEGs from two databases. Then, the Gene Ontology(GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapped DEGs were performed through DAVID database, and the protein–protein interaction(PPI) network was constructed to screen Hub genes for regulatory protein expression in BC. Expression and prognostic analysis of the hub genes were performed by UALCAN and Kaplan-Meier plotter.Results: A total of 372 overlap DEGs were obtained, of which 93 were up-regulated and 279 were down-regulated. These genes were mainly associated with the function and pathway enrichment such as glycosaminoglycan binding, vasculature development, Cell cycle, Proteoglycans in cancer. The protein-protein interaction network analysis obtained 12 hub genes. Among these hub genes，HMMR，NCAPG2，SMC4, TROAP were closely related to the survival rate of bladder cancer patients revealed that these genes might be the key genes play an important role in the occurrence and progression.Conclusion:Therefore, our current studies demonstrated thatHMMR, NCAPG2, SMC4, TROAP are potential prognostic biomarkers for BC.In the future, these may also become clinical therapeutic targets.

The Development of Inhibitors Targeting the Mixed Lineage Leukemia 1 (MLL1)-WD Repeat Domain 5 Protein (WDR5) Protein- Protein Interaction

2020 ◽  
Vol 27 (33) ◽  
pp. 5530-5542
Author(s):  
Xiaoqing Ye ◽  
Gang Chen ◽  
Jia Jin ◽  
Binzhong Zhang ◽  
Yinda Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Fusion Proteins ◽  
Recent Progress ◽  
Mixed Lineage Leukemia ◽  
Core Complex ◽  
Histone Methyltransferases ◽  
Protein Protein Interaction ◽  
Histone 3 ◽  
Repeat Domain

Mixed Lineage Leukemia 1 (MLL1), an important member of Histone Methyltransferases (HMT) family, is capable of catalyzing mono-, di-, and trimethylation of Histone 3 lysine 4 (H3K4). The optimal catalytic activity of MLL1 requires the formation of a core complex consisting of MLL1, WDR5, RbBP5, and ASH2L. The Protein-Protein Interaction (PPI) between WDR5 and MLL1 plays an important role in abnormal gene expression during tumorigenesis, and disturbing this interaction may have a potential for the treatment of leukemia harboring MLL1 fusion proteins. In this review, we will summarize recent progress in the development of inhibitors targeting MLL1- WDR5 interaction.

scholarly journals Key Genes and Prognostic Analysis in HER2+ Breast Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382098329
Author(s):  
Yujie Weng ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Zhongxian Li ◽  
Rong Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Differentially Expressed ◽  
Protein Kinase Activity ◽  
Protein Protein Interaction ◽  
Risk Of Death ◽  
Her2 Breast Cancer ◽  
Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

Identification of CCNB2 expression in triple-negative breast cancer based on bioinformatics results

2021 ◽  
Author(s):  
jintao cao ◽  
SHUAI SUN ◽  
RAN LI ◽  
RUI MIN ◽  
XINGYU FAN ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Protein Complex ◽  
Triple Negative ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
The Core ◽  
Core Genes

Abstract Background The current epidemiology shows that the incidence of breast cancer is increasing year by year and tends to be younger. Triple-negative breast cancer is the most malignant of breast cancer subtypes. The application of bioinformatics in tumor research is becoming more and more extensive. This study provided research ideas and basis for exploring the potential targets of gene therapy for triple-negative breast cancer (TNBC). Methods We analyzed three gene expression profiles (GSE64790、GSE62931、GSE38959) selected from the Gene Expression Omnibus (GEO) database. The GEO2R online analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify the pathways and functional annotation of DEGs. Protein–protein interaction network of these DEGs were visualized by the Metascape gene-list analysis tool so that we could find the protein complex containing the core genes. Subsequently, we investigated the transcriptional data of the core genes in patients with breast cancer from the Oncomine database. Moreover, the online Kaplan–Meier plotter survival analysis tool was used to evaluate the prognostic value of core genes expression in TNBC patients. Finally, immunohistochemistry (IHC) was used to evaluated the expression level and subcellular localization of CCNB2 on TNBC tissues. Results A total of 66 DEGs were identified, including 33 up-regulated genes and 33 down-regulated genes. Among them, a potential protein complex containing five core genes was screened out. The high expression of these core genes was correlated to the poor prognosis of patients suffering breast cancer, especially the overexpression of CCNB2. CCNB2 protein positively expressed in the cytoplasm, and its expression in triple-negative breast cancer tissues was significantly higher than that in adjacent tissues. Conclusions CCNB2 may play a crucial role in the development of TNBC and has the potential as a prognostic biomarker of TNBC.

scholarly journals Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Yun Tang ◽  
Xiaobo Yang ◽  
Huaqing Shu ◽  
Yuan Yu ◽  
Shangwen Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Acute Kidney Injury ◽  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Kidney Injury ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Blood Samples ◽  
Potential Biomarkers

Abstract Background Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI). Methods Two gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI. Results We identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future. Conclusions Septic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI.

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