scholarly journals Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7313 ◽  
Author(s):  
Tingting Guo ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Prion Diseases ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes

Background Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood. Methods Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database. Results Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.

scholarly journals Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huan Deng ◽  
Qingqing Hang ◽  
Dijian Shen ◽  
Yibi Zhang ◽  
Ming Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Transforming Growth Factor ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Prognostic Indicators ◽  
Hub Genes ◽  
Coexpressed Genes

Abstract Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

scholarly journals Underlying Genes and Molecular Mechanism of Keloids Investigated by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
chuying LI ◽  
Mei-Tong Jin ◽  
Yin-Li Luo ◽  
Zhe-Hu Jin ◽  
Long-Quan PI
Keyword(s):  
Expression Profiles ◽  
Normal Skin ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Underlying Mechanism ◽  
Cholinergic Receptor ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: We aimed to identify the overlapping differentially expressed genes (DEGs) of keloids distinguished from normal scar and normal skin and relevant underlying mechanism using integrated bioinformatics methods.Methods: The expression profiles of 18 keloid samples, 7 normal skin and 5 normal scar, were obtained from the GSE7890, GSE44270, GSE92566, and GSE3189 datasets in the Gene Expression Omnibus database. DEGs were identified using the LIMMA package in R. Gene ontology (GO) functional enrichment analysis was performed using the R software. A DEG-associated protein–protein interaction (PPI) network was constructed using STRING and MCODE was used for module analysis of the PPI network. Moreover, the hub genes were verified by qRT-PCR. The predicted DEGs, their regulatory miRNA and TF regulation network was analyzed using miRnet. Results: A total of 978 common DEGs were identified in the keloid samples. Genes with more than 45 interaction degrees, including neuropeptide Y (NPY), opioid receptor mu 1 (OPRM1), cholinergic receptor muscarinic 2 (CHRM2), and proopiomelanocortin (POMC), were found in the PPI network. Hsa-miR-335 and Sp1 as upstream-regulators regulated CHRM2, NPY, and POMC. Functional enrichment analysis revealed that hub genes were commonly enriched in the “G protein-coupled receptor signaling pathway” GO_BP termConclusion: Taken together, CHRM2, NPY, POMC, and OPRM1 potentially have crucial roles in keloid disease. Furthermore, miR-335 and Sp1 are potential targets for preventing keloid formation.

scholarly journals Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

2020 ◽  
Author(s):  
Yiyuan Zhang ◽  
Rongguo Yu ◽  
Jiayu Zhang ◽  
Eryou Feng ◽  
Haiyang Wang ◽  
...  
Keyword(s):  
Subchondral Bone ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Common Chronic Disease ◽  
Drug Candidates ◽  
Therapeutic Modalities ◽  
Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

scholarly journals Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

2021 ◽  
Author(s):  
Nana Yang ◽  
Qianghua Wang ◽  
Biao Ding ◽  
Yinging Gong ◽  
Yue Wu ◽  
...  
Keyword(s):  
Iron Homeostasis ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

scholarly journals Xiaoyu Xiezhuo Drink Protects against Ischemia-Reperfusion Acute Kidney Injury in Aged Mice through Inhibiting the TGF-β1/Smad3 and HIF1 Signaling Pathways

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Qingqing Ye ◽  
Hongbo Chen ◽  
Hongzhen Ma ◽  
Xiaojun Xiang ◽  
Shouci Hu ◽  
...  
Keyword(s):  
Drug Targets ◽  
Ischemia Reperfusion ◽  
Kidney Injury ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Aged Mice ◽  
Tgf Β1

Acute kidney injury (AKI) is responsible for significant mortality among hospitalized patients that is especially troubling aged people. An effective self-made Chinese medicine formula, Xiaoyu Xiezhuo Drink (XXD), displayed therapeutic effects on AKI. However, the compositions and underlying mechanisms of XXD remain to be elucidated. In this study, we used the ultra-high-performance liquid chromatography method coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) to investigate the chemical components in XXD. Then, the absorbable components of XXD were identified based on the five principles and inputted into the SwissTargetPrediction and STITCH databases to identify the drug targets. AKI-related targets were collected from the GenCLiP 3, GeneCards, and DisGeNET databases. The crossover genes of XXD and AKI were identified for functional enrichment analysis. The protein-protein interaction (PPI) network of crossover genes was constructed, followed by the identification of hub genes. Subsequently, the effects and potential mechanisms of XXD on AKI predicted by the network pharmacology and bioinformatics analyses were experimentally validated in ischemia-reperfusion (I/R) injury-induced AKI aged mouse models. A total of 122 components in XXD were obtained; among them, 58 components were found that could be absorbed in the blood. There were 800 potential drug targets predicted from the 58 absorbable components in AKI which shared 36 crossover genes with AKI-related targets. The results of functional enrichment analysis indicated that crossover genes mostly associated with the response to oxidative stress and the HIF1 signaling pathway. In the PPI network analysis, 12 hub genes were identified, including ALB, IL-6, TNF, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, and HMOX1. In AKI aged mice, XXD prominently alleviated I/R injury-induced renal dysfunction, abnormal renal pathological changes, and cellular senescence, inflammation, and oxidative damage with a reduction in the expression level of the inflammatory mediator, α-SMA, collagen-1, F4/80, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, ICAM-1, TGF-β1, Smad3, and p-Smad3 and an increase of nephridial tissue p-H3, Ki67, HMOX1, MMP-9, and Smad7 levels. In summary, our findings suggest that XXD has renoprotective effects against AKI in aged mice via inhibiting the TGF-β1/Smad3 and HIF1 signaling pathways.

scholarly journals Identification of novel potential biomarkers in hepatocarcinoma cancer; a transcriptome analysis

2021 ◽  
Author(s):  
Mohib kakar ◽  
Muhammad Mehboob ◽  
Muhammad Akram ◽  
Imran Iqbal ◽  
Hafza Ijaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Predictive Biomarkers ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Molecular Therapy ◽  
Hub Genes ◽  
Healthy Control

Abstract Objective The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Methods GEO contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC samples and 10 healthy samples as control. GSE62232 was analyzed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting genes interacting. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analyzed using GEPIA to estimate the effect of their differential expression on cancer progression. Results We identified the top 10 hub genes through Cytohubba plugin. These genes include Cell Cycle Regulatory Cyclins and Cyclin-dependent proteins CCNA2, CCNB1 and CDK1. The pathogenesis and prognosis of HCC may be directly linked with the aforementioned genes. Conclusion In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

scholarly journals Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Lixian Chen ◽  
Zhonglu Ren ◽  
Yongming Cai
Keyword(s):  
Prognostic Factors ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

scholarly journals Identification of Novel Potential Biomarkers in Hepatocarcinoma Cancer; A Transcriptome Analysis

2021 ◽  
Author(s):  
Mohib kakar ◽  
Muhammad Mehboob ◽  
Muhammad Akram ◽  
Imran Iqbal ◽  
Hafza Ijaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Predictive Biomarker ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Molecular Therapy ◽  
Hub Genes ◽  
Healthy Control

Abstract The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Gene Expression Omnibus (GEO) contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server GEO. A total of 91 samples were collected, including 81 HCC samples and 10 healthy samples as control. GSE62232 was analyzed through GEO2R, and functional enrichment analysis was performed to extract rational information from a set of DEGs. The protein-protein relationship networking search method was used for extracting interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analyzed using GEPIA to estimate the effect of their differential expression on cancer progression. We identified the top 10 hub genes through Cytohubba plugin. These genes include cell cycle regulatory cyclins and cyclin-dependent proteins CCNA2, CCNB1 and CDK1. The pathogenesis and prognosis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarker studies that could promote selective molecular therapy for HCC.

Identification Of Putative Gene Signatures Associated With Diagnosis And Prognosis Of Breast Cancer

2021 ◽  
Vol 44 (3) ◽  
pp. E45-54
Author(s):  
Chao Tan ◽  
Fang Zuo ◽  
Mingqian Lu ◽  
Sai Chen ◽  
Zhenzhen Tian ◽  
...  
Keyword(s):  
Risk Model ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Support Vector ◽  
Ppi Network ◽  
Hub Genes ◽  
Putative Gene ◽  
Network Analyses ◽  
Cluster 2

Purpose: This study aimed to identify potential diagnostic and therapeutic biomakers for the development ofbreast cancer (BC). Methods: GSE86374 dataset containing 159 samples was acquired from the Gene Expression Omnibus (GEO) database followed by differentially expressed genes (DEGs) identification and cluster analysis. Corresponding functional enrichment and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Prognostic evaluation using clinical information obtained from TCGA database and hub genes was conducted to screen for crucial indicators for BC progression. The risk model was established and validated. Results: In total, 186 DEGs were identified and grouped into four clusters: 96 in cluster 1; 69 in cluster 2; 16 in  cluster 3; and 5 in cluster 4. Functional enrichment analysis showed that DEGs, including ADH1B in cluster 1,  were dramatically enriched in the tyrosine and drug metabolism pathways, while genes in cluster 2, including  SPP1 and RRM2, played crucial roles in PI3K-Akt and p53 signalling pathway. SPP1 and RRM2 served as hub  genes in the PPI network, resulting in an support vector machine classifier with good accuracy and specificity.Ad ditionally, the results of prognostic analysis suggest that age, metastasis stage, SPP1 and ADH1B were correlated with risk of BC, which was validated by using the established risk model analysis. Conclusion: SPP1, RRM2 and ADH1B appear to play vital roles in the development of BC. Age and TNM stage  were also preferentially associated with risk of developing BC. Evaluation of the risk model based on larger sample size and further experimental validation are required.

scholarly journals Screening a prognosis-related target gene in patients with HER-2 positive breast cancer by bioinformatics analysis

2021 ◽  
Author(s):  
Song Wang ◽  
Yi Quan
Keyword(s):  
Breast Cancer ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Study Objective ◽  
Her 2 ◽  
Positive Breast Cancer

Objective: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is related to a poor prognosis. Thus, the study objective was to determine a target gene and explore the associated molecular mechanisms in HER-2 positive breast cancer. Methods: Three RNA expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and were used to identify differentially expressed genes (DEGs) using R software. A Protein-Protein Interaction (PPI) network was constructed and hub genes were determined. Subsequently, the relationship between clinical parameters and hub genes was examined to screen target gene. Next, DNA methylation and genomic alterations of the target gene were evaluated. To further explore potential molecular mechanisms, genes co-expressed with the target gene were performed functional enrichment analysis Results: The differential expression analysis revealed 217 DEGs in HER-2 positive breast cancer tissues compared to normal breast tissues. RRM2 was the only hub gene closely associated with lymphatic metastasis and prognosis in HER-2 positive breast cancer. Additionally, RRM2 was frequently often amplified and negatively associated with the methylation level. Functional enrichment analysis showed that the co-expression genes were mainly involved in cell cycle. Conclusions: The present study identified RRM2 as a target gene associated with the initiation, progression and prognosis of HER-2 positive breast cancer, which may contribute to provide a new biomarker and therapeutic target.

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