Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

Selvaraj, Gurudeeban; Kaliamurthi, Satyavani; Kaushik, Aman Chandra; Khan, Abbas; Wei, Yijun; Cho, William C.S.; Gu, Kai; Wei, Dong‐Qing

doi:10.1016/j.jbi.2018.09.004

Cited by 52 publications

(32 citation statements)

References 73 publications

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“…Thus, we performed integrated biological network analysis to identify the potential target genes (PTGs) of the shared cmiRNA signatures BSO and ASO of LUAD samples. Microarray analysis of gene expression profiles is a standard and well-known method to identify key hub genes and pathways [19,20]. Initially, we collected the cmiRNA and cmRNA datasets from the Gene Expression Omnibus (GEO) database.…”

Section: Introductionmentioning

confidence: 99%

Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

Huang

Wei

Kaliamurthi

et al. 2020

JPM

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Analysis of circulating miRNAs (cmiRNAs) before surgical operation (BSO) and after the surgical operation (ASO) has been informative for lung adenocarcinoma (LUAD) diagnosis, progression, and outcomes of treatment. Thus, we performed a biological network analysis to identify the potential target genes (PTGs) of the overexpressed cmiRNA signatures from LUAD samples that had undergone surgical therapy. Differential expression (DE) analysis of microarray datasets, including cmiRNAs (GSE137140) and cmRNAs (GSE69732), was conducted using the Limma package. cmiR-1246 was predicted as a significantly upregulated cmiRNA of LUAD samples BSO and ASO. Then, 9802 miR-1246 target genes (TGs) were predicted using 12 TG prediction platforms (MiRWalk, miRDB, and TargetScan). Briefly, 425 highly expressed overlapping miRNA-1246 TGs were observed between the prediction platform and the cmiRNA dataset. ClueGO predicted cell projection morphogenesis, chemosensory behavior, and glycosaminoglycan binding, and the PI3K–Akt signaling pathways were enriched metabolic interactions regulating miRNA-1245 overlapping TGs in LUAD. Using 425 overlapping miR-1246 TGs, a protein–protein interaction network was constructed. Then, 12 PTGs of three different Walktrap modules were identified; among them, ubiquitin-conjugating enzyme E2C (UBE2C), troponin T1(TNNT1), T-cell receptor alpha locus interacting protein (TRAIP), and ubiquitin c-terminal hydrolase L1(UCHL1) were positively correlated with miR-1246, and the high expression of these genes was associated with better overall survival of LUAD. We conclude that PTGs of cmiRNA-1246 and key pathways, namely, ubiquitin-mediated proteolysis, glycosaminoglycan binding, the DNA metabolic process, and the PI3K–Akt–mTOR signaling pathway, the neurotrophin and cardiomyopathy signaling pathway, and the MAPK signaling pathway provide new insights on a noninvasive prognostic biomarker for LUAD.

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Section: Introductionmentioning

confidence: 99%

Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

Huang

Wei

Kaliamurthi

et al. 2020

JPM

Self Cite

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“…However, different expression profile with various research background provided diverse results. [ 5 – 8 ] For example, Gurudeeban et al presented that EGR-1 was associated with NSCLC through network analysis. [ 5 ] Bai, et al showed that KIF2C in association with progression in lung adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

“…[ 5 – 8 ] For example, Gurudeeban et al presented that EGR-1 was associated with NSCLC through network analysis. [ 5 ] Bai, et al showed that KIF2C in association with progression in lung adenocarcinoma. [ 6 ] Ying, et al pointed that AURKB might a hub gene in NSCLC.…”

Section: Introductionmentioning

confidence: 99%

Integration of gene profile to explore the hub genes of lung adenocarcinoma

et al. 2020

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Background: Lung cancer is a leading cause of morbidity diseases worldwide, but the key mechanisms of lung cancer remain elusive. This study aims to integrate of GSE 118370 and GSE 32863 profile and identify the key genes and pathway involved in human lung adenocarcinoma. Methods: R software (RStudio, Version info: R 3.2.3, Forrester, USA) were utilized to find the differentially expressed genes. All the differentially expressed genes were analyzed by gene ontology, kyoto encyclopedia of genes and genomes. Protein-protein interaction networks were constructed by STRING database and analyzed by Cytohubber and Module. The cancer genome atlas database was used to verification the expression of hub genes. Quantitative reverse transcription-PCR was used to verify the bio-information results. Results: Sixty-four lung adenocarcinoma and 64 adjacent normal tissues were used for integration analysis. Five hundred ninety-nine co-expression genes were locked. Biological processes mainly enriched in angiogenesis. Cellular component focused on extracellular exosome and molecular function aimed on protein disulfide isomerase activity. Cytohubber analysis showed that GNG11, FPR2, P4HB, PIK3R1, CDC20, ADCY4, TIMP1, IL6, CXC chemokine ligand (CXCL)12, and GAS6 acted as the hub genes during lung adenocarcinoma. Module analysis presented Chemokine signaling pathway was a key pathway. Quantitative reverse transcription-PCR showed that the expression level of GNG11, FPR2, PIK3R1, ADCY4, IL6, CXCL12, and GAS6 were significantly decreased and P4HB, CDC20 and TIMP1 were increased in human adenocarcinoma tissues ( P < .05). The cancer genome atlas online analysis showed GNG11 was not associated with survival. Conclusions: This study firstly reported GNG11 acting as a hub gene in adenocarcinoma. GNG11 could be used as a biomarker for human adenocarcinoma. Chemokine signaling pathway might play important roles in lung adenocarcinoma.

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“…Bioinformatics analysis surfaces with the emergence of massive gene data, and is a powerful methodology that can be used in discovering disease-causing genes, biomarkers, and therapeutic targets through global analysis. For example, using bioinformatics analysis methods, Gurudeeban Selvaraj, et al [16] identifi ed that mitogen-activated protein kinase 1 and aurora kinase are the hub nodes and identifi ed candidate drugs in lung adenocarcinoma. However, the application of bioinformatics analysis methods in kidney diseases has just started.…”

Section: Introductionmentioning

confidence: 99%

Novel key gene tenascin C related to extracellular matrix accumulation in diabetic nephropathy kidney tubules: Results of integrative bioinformatics analysis

Zhang¹,

Wang²,

He³

et al. 2020

Glob J Obes Diabetes Metab Syndr

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Background: As the incidence of type 2 diabetes increases year by year, the number of individuals diagnosed with Diabetic Nephropathy (DN) has increased steeply. DN is characterized by glomerular sclerosis, tubulointerstitial fi brosis and atrophy. However, most of the previous studies on the pathogenesis of DN were focused on glomeruli, and now more and more evidences show that tubulointerstitial fi brosis plays an important role in the progress of DN. Bioinformatics analysis can be used in discovering disease-causing genes, biomarkers, and therapeutic targets through global analysis. In this study, we used this method to fi nd and verify novel genes in DN. Materials and methods: Microarray expression levels were downloaded from Gene Expression Omnibus datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted after Differentially Expressed Genes (DEGs) were identifi ed. Hub genes were fi ltered on the basis of the result of GO enrichment, and functional analysis was performed by browsing the GeneCards website and the latest literatures. The expression levels of all the hub genes in HK2 cells stimulated by high glucose were verifi ed, and TeNascin C (TNC), which is important and interesting, was verifi ed through animal experiment. Result: The relative expression levels of 54 samples were obtained, and 382 DEGs were identifi ed. 286 GO terms and 100 KEGG pathways were enriched remarkably. Nine of the key genes of interest: MMP7, TNC, LUM, LTF, IGLC1, LYZ, CXCL6, CYP27B1 and FOS were selected and verifi ed in HK2 cells stimulated by high glucose. quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) indicated that the mRNA levels of most of the hub genes were up-regulated and had an accordance rate of approximately 66.7%. In addition, the expression of TNC in renal tubular tissue of STZ-induced diabetic nephropathy rat gradually increased with time. Conclusion: Several novel key genes were discovered and verifi ed in this study, and TNC plays an important role in the renal fi brosis of DN and is expected to be a new therapeutic target. This study provides a theoretical basis and data resources for further research.

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Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

Abstract: Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.

Cited by 52 publications

References 73 publications

Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

Integration of gene profile to explore the hub genes of lung adenocarcinoma

Novel key gene tenascin C related to extracellular matrix accumulation in diabetic nephropathy kidney tubules: Results of integrative bioinformatics analysis

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