Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

Song, Yexun; Tang, Wen-Fang; Li, Hui

doi:10.1042/bsr20203973

Cited by 16 publications

(10 citation statements)

References 48 publications

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“…identified several hub genes including DLGAP5, TOP2 A, AURKA, AURKB, and CCNA2 from lung adenocarcinoma cell. In clinical samples, qRT-PCR confirmed the presence of these hub genes which could serve as a therapeutic target for molecular cancer therapy [83] .…”

Section: Discussionmentioning

confidence: 88%

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Hasan

Rahman

Islam

et al. 2022

Informatics in Medicine Unlocked

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Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

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Section: Discussionmentioning

confidence: 88%

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Hasan

Rahman

Islam

et al. 2022

Informatics in Medicine Unlocked

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“…Additionally, KIF18B participates in the Wnt/beta-catenin signaling pathway that induces cell proliferation, migration, and invasion in cervical and breast cancers [ 59 , 60 ]. In cases of lung cancer, KIF4A was reported to promote cell proliferation and migration and inhibit apoptosis in LUAD cell lines, whereas KIF18B was suggested to promote LUAD cell proliferation, migration, and invasion via the Rac1/Akt/mammalian target of rapamycin (mTOR) signaling pathway [ 61 , 62 ]. Roles of KIF4A and KIF18B have been substantially reported in LUAD [ 61 , 62 , 63 ], but the present study provides new evidence suggesting their potential roles in LUSC.…”

Section: Discussionmentioning

confidence: 99%

“…In cases of lung cancer, KIF4A was reported to promote cell proliferation and migration and inhibit apoptosis in LUAD cell lines, whereas KIF18B was suggested to promote LUAD cell proliferation, migration, and invasion via the Rac1/Akt/mammalian target of rapamycin (mTOR) signaling pathway [ 61 , 62 ]. Roles of KIF4A and KIF18B have been substantially reported in LUAD [ 61 , 62 , 63 ], but the present study provides new evidence suggesting their potential roles in LUSC. The overexpression and clinical relevance of KIF4A and KIF18B have also been previously established through cross-validation between the public databases and clinical samples [ 61 , 62 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Expression and Prognostic Value of Genes Encoding Microtubule-Associated Proteins in Lung Cancer

Singharajkomron

Yodsurang

Seephan

et al. 2022

IJMS

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Microtubule-associated proteins (MAPs) play essential roles in cancer development. This study aimed to identify transcriptomic biomarkers among MAP genes for the diagnosis and prognosis of lung cancer by analyzing differential gene expressions and correlations with tumor progression. Gene expression data of patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) from the Cancer Genome Atlas (TCGA) database were used to identify differentially expressed MAP genes (DEMGs). Their prognostic value was evaluated by Kaplan–Meier and Cox regression analysis. Moreover, the relationships between alterations in lung cancer hallmark genes and the expression levels of DEMGs were investigated. The candidate biomarker genes were validated using three independent datasets from the Gene Expression Omnibus (GEO) database and by quantitative reverse transcription polymerase chain reaction (qRT-PCR) on clinical samples. A total of 88 DEMGs were identified from TCGA data. The 20 that showed the highest differential expression were subjected to association analysis with hallmark genes. Genetic alterations in TP53, EGFR, PTEN, NTRK1, and PIK3CA correlated with the expression of most of these DEMGs. Of these, six candidates—NUF2, KIF4A, KIF18B, DLGAP5, NEK2, and LRRK2—were significantly differentially expressed and correlated with the overall survival (OS) of the patients. The mRNA expression profiles of these candidates were consistently verified using three GEO datasets and qRT-PCR on patient lung tissues. The expression levels of NUF2, KIF4A, KIF18B, DLGAP5, NEK2, and LRRK2 can serve as diagnostic biomarkers for LUAD and LUSC. Moreover, the first five can serve as prognostic biomarkers for LUAD, while LRRK2 can be a prognostic biomarker for LUSC. Our research describes the novel role and potential application of MAP-encoding genes in clinical practice.

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“…Possibly driven by the community and environmental factors, the observed differences in disease incidence suggest the importance of residential location in risk assessment of lung cancer ( Zhu et al, 2020 ). At present, most of the GEO datasets used for lung cancer research are from different countries ( Song, Tang & Li, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Liu

Liao

et al. 2022

PeerJ

Self Cite

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Background Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis. Methods In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues. Results A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues. Conclusions The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

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Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

Cited by 16 publications

References 48 publications

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Evaluating the Expression and Prognostic Value of Genes Encoding Microtubule-Associated Proteins in Lung Cancer

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

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