Bioinformatics analysis reveals 6 key biomarkers associated with non-small-cell lung cancer

Dai, Bai; Ren, Li-qing; Han, Xiaoyu; Liu, Dongjun

doi:10.1177/0300060519887637

Cited by 6 publications

(8 citation statements)

References 37 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…14 Furthermore, Dai detected multiple genes that are abnormally expressed in patients with NSCLC and used bioinformatic analysis to identify hub genes as potential early diagnosis and treatment biomarkers. 15…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis of the circRNA–miRNA–mRNA network for non-small cell lung cancer

et al. 2020

View full text Add to dashboard Cite

Objective Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancers, but its pathogenesis has not been fully elucidated. Therefore, it is valuable to explore the pathogenesis of NSCLC to improve diagnosis and identify novel treatment biomarkers. Methods Circular (circ)RNA, micro (mi)RNA, and gene expression datasets of NSCLC were analyzed to identify those that were differentially expressed between tumor and healthy tissues. Common genes were found and pathway enrichment analyses were performed. Survival analysis was used to identify hub genes, and their level of methylation and association with immune cell infiltration were analyzed. Finally, an NSCLC circRNA–miRNA–mRNA network was constructed. Results Eight miRNAs and 211 common genes were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that cell projection morphogenesis, blood vessel morphogenesis, muscle cell proliferation, and synapse organization were enriched. Ten hub genes were found, of which the expression of DTL and RRM2 was significantly related to NSCLC patient prognosis. Significant methylation changes and immune cell infiltration correlations with DTL and RRM2 were also detected. Conclusions hsa_circ_0001947/hsa-miR-637/RRM2 and hsa_circ_0072305/hsa-miR-127-5p/DTL networks were constructed, and identified molecules may be involved in the occurrence and development of NSCLC.

show abstract

Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis of the circRNA–miRNA–mRNA network for non-small cell lung cancer

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In 2020, Maharjan et al, using bioinformatics analysis, identified 16 biomarkers for lung cancer including Cyclin-B2 (CCNB2), Cell Division Cycle 20 (CDC20), F-Box And Leucine Rich Repeat Protein 3 (FBXL3), and Forkhead Box A2 (FOXA2) [ 20 ]. Dai et al identified CDC20, ECT2, MKI67, TPX2, and TYMS as biomarkers using microarray analysis, where Cell Division Cycle 20 (CDC20), Epithelial Cell Transforming 2 (ECT2), Marker of Proliferation Ki-67 (MKI67), TPX2 Microtubule Nucleation Factor (TPX2), and Thymidylate Synthetase (TYMS) showed worse survival outcome [ 21 ]. Few studies reveal that Cyclin A2 (CCNA2) and Neuromedin U (NMU) were involved with diagnosis and prognosis of NSCLC [ 22 , 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…Bioinformatics analysis has rapidly increased in the last few years for discovering new therapeutic targets and biomarkers for several cancers [19]. showed worse survival outcome [21]. Few studies reveal that Cyclin A2 (CCNA2) and Neuromedin U (NMU) were involved with diagnosis and prognosis of NSCLC [22,23].…”

Section: Discussionmentioning

confidence: 99%

Identification of molecular biomarkers and pathways of NSCLC: insights from a systems biomedicine perspective

Islam

Ahmed

Paul

et al. 2021

Journal of Genetic Engineering and Biotechnology

View full text Add to dashboard Cite

Background Worldwide, more than 80% of identified lung cancer cases are associated to the non-small cell lung cancer (NSCLC). We used microarray gene expression dataset GSE10245 to identify key biomarkers and associated pathways in NSCLC. Results To collect Differentially Expressed Genes (DEGs) from the dataset GSE10245, we applied the R statistical language. Functional analysis was completed using the Database for Annotation Visualization and Integrated Discovery (DAVID) online repository. The DifferentialNet database was used to construct Protein–protein interaction (PPI) network and visualized it with the Cytoscape software. Using the Molecular Complex Detection (MCODE) method, we identify clusters from the constructed PPI network. Finally, survival analysis was performed to acquire the overall survival (OS) values of the key genes. One thousand eighty two DEGs were unveiled after applying statistical criterion. Functional analysis showed that overexpressed DEGs were greatly involved with epidermis development and keratinocyte differentiation; the under-expressed DEGs were principally associated with the positive regulation of nitric oxide biosynthetic process and signal transduction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway investigation explored that the overexpressed DEGs were highly involved with the cell cycle; the under-expressed DEGs were involved with cell adhesion molecules. The PPI network was constructed with 474 nodes and 2233 connections. Conclusions Using the connectivity method, 12 genes were considered as hub genes. Survival analysis showed worse OS value for SFN, DSP, and PHGDH. Outcomes indicate that Stratifin may play a crucial role in the development of NSCLC.

show abstract

“…In adenocarcinomas, for instance, the selected CellDiv features in terms of nuclear solidity and mean inside boundary showed higher expression of genes related to the biological pathway of DNA replication 32 and nucleus development. 33 With the CellDiv features essentially capturing the degree of heterogeneity and diversity in shape, size, and texture of cancer nuclei, this seems to suggest that higher expression of those developmental pathways leads to more disordered or chaotic nuclei. Meanwhile, in LUSC, the family of bone morphogenetic protein and transforming growth factor β receptors, which have been already shown to be implicated in lung cancer carcinogenesis, 34 were found to be associated with CellDiv features that measuring nuclear shape and intensity, clearly suggesting that the diversity features are being driven by the cellular differentiating and adhesion pathways.…”

Section: Discussionmentioning

confidence: 99%

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Bera

Wang

et al. 2020

The Lancet Digital Health

View full text Add to dashboard Cite

Background Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of nonsmall cell lung carcinomas (NSCLCs). MethodsIn this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis.Findings For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1•48 [95% CI 1•06-2•08]; p=0•022 for The Cancer Genome Atlas [TCGA] LUSC group, 2•24 [1•04-4•80]; p=0•039 for the University of Bern LUSC group, and 1•62 [1•15-2•30]; p=0•0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways. Interpretation CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy.

show abstract

Bioinformatics analysis reveals 6 key biomarkers associated with non-small-cell lung cancer

Cited by 6 publications

References 37 publications

Bioinformatics analysis of the circRNA–miRNA–mRNA network for non-small cell lung cancer

Bioinformatics analysis of the circRNA–miRNA–mRNA network for non-small cell lung cancer

Identification of molecular biomarkers and pathways of NSCLC: insights from a systems biomedicine perspective

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Contact Info

Product

Resources

About