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

Liu, Ping; Li, Hui; Liao, Chunfeng; Tang, Yuling; Li, Mengzhen; Wang, Zhouyu; Wu, Qi; Zhou, Yun

doi:10.7717/peerj.12731

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…In addition, five algorithms in the CytoHubba ( Chin et al, 2014 ) plug-in were applied: maximum neighborhood component, degree, maximal clique centrality, closeness, and edge percolated component ( Yang et al, 2019 ; Liu et al, 2022 ). In the PPI network, the scores of the MRDEGs were initially calculated, and then the MRDEGs were arranged in the order of their scores.…”

Section: Methodsmentioning

confidence: 99%

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

Zhao,

Wu,

Zhao

et al. 2023

Front. Neurosci.

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BackgroundAlzheimer’s disease (AD), a neurodegenerative disorder with progressive symptoms, seriously endangers human health worldwide. AD diagnosis and treatment are challenging, but molecular biomarkers show diagnostic potential. This study aimed to investigate AD biomarkers in the peripheral blood.MethodUtilizing three microarray datasets, we systematically analyzed the differences in expression and predictive value of mitophagy-related hub genes (MRHGs) in the peripheral blood mononuclear cells of patients with AD to identify potential diagnostic biomarkers. Subsequently, a protein–protein interaction network was constructed to identify hub genes, and functional enrichment analyses were performed. Using consistent clustering analysis, AD subtypes with significant differences were determined. Finally, infiltration patterns of immune cells in AD subtypes and the relationship between MRHGs and immune cells were investigated by two algorithms, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).ResultsOur study identified 53 AD- and mitophagy-related differentially expressed genes and six MRHGs, which may be potential biomarkers for diagnosing AD. Functional analysis revealed that six MRHGs significantly affected biologically relevant functions and signaling pathways such as IL-4 Signaling Pathway, RUNX3 Regulates Notch Signaling Pathway, IL-1 and Megakaryocytes in Obesity Pathway, and Overview of Leukocyteintrinsic Hippo Pathway. Furthermore, CIBERSORT and ssGSEA algorithms were used for all AD samples to analyze the abundance of infiltrating immune cells in the two disease subtypes. The results showed that these subtypes were significantly related to immune cell types such as activated mast cells, regulatory T cells, M0 macrophages, and neutrophils. Moreover, specific MRHGs were significantly correlated with immune cell levels.ConclusionOur findings suggest that MRHGs may contribute to the development and prognosis of AD. The six identified MRHGs could be used as valuable diagnostic biomarkers for further research on AD. This study may provide new promising diagnostic and therapeutic targets in the peripheral blood of patients with AD.

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Section: Methodsmentioning

confidence: 99%

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

Zhao,

Wu,

Zhao

et al. 2023

Front. Neurosci.

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“…12 In LUAD, SPAG5 has been found to promote tumor proliferation and motility and can be used as a prognostic marker. [19][20][21][22] Despite existing studies that suggest SPAG5 is an oncogene in LUAD, a more comprehensive assessment of its role in LUAD and its relation to immune cell infiltration is needed.…”

Section: Introductionmentioning

confidence: 99%

SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas

Xiao,

Xu,

Chen

et al. 2023

Clin Med Insights Oncol

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Background: Sperm-associated antigen 5 (SPAG5) has been identified as a novel driver oncogene involved in multiple cancers; however, its role in lung adenocarcinoma (LUAD) needs further investigation. Our study aims to elucidate the potential significance of SPAG5 in LUAD prognosis and its implications for the efficacy of immunotherapy. Methods: In this study, we used bioinformatics analysis and tissue microarray (TMA) staining to examine the potential role of SPAG5 in LUAD survival and response to immunotherapy. We used the Oncomine, TIMER2.0, Gene Expression Profiling Interactive Analysis (GEPIA), Sangerbox, PredicScan, and Kaplan-Meier Plotter databases to examine the expression and prognostic role of SPAG5 in the LUAD of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other databases. We also used Cancer Single-cell State Atlas (CancerSEA) and Tumor Immune Estimation Resource (TIMER2.0) to analyze the association of SPAG5 with malignant phenotype and tumor immune microenvironment. Furthermore, Immune Cell Abundance Identifier (ImmuCellAI) analysis of TCGA sequencing data was used to predict the role of SPAG5 in determining the response to immune checkpoint blockade (ICB) treatment in LUAD. Co-expression analysis of programmed death-ligand 1 (PD-L1) and SPAG5 was performed using LUAD TMA immunohistochemistry (IHC) analysis. Results: Our findings indicate that SPAG5 is overexpressed in LUAD and is positively correlated with advanced clinical stage, poor overall survival, relapse-free survival, and progression-free survival outcomes. SPAG5 may be involved in regulating the cell cycle, proliferation, invasion, DNA damage and repair, and tumor immunosuppression. Furthermore, TMA IHC analysis showed a positive correlation between PD-L1 expression in LUAD and SPAG5 which suggests that SPAG5 may serve as a potential predictor of response to ICB therapy in LUAD. Conclusions: Our results highlight the role of SAPG5 in promoting a tumor malignancy phenotype and immunosuppression in LUAD and suggest that SPAG5 may serve as a potential response marker for ICB therapy.

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Diagnosis and Prognosis of Non-small Cell Lung Cancer based on Machine Learning Algorithms

Zhou

Dong

Sun

et al. 2023

CCHTS

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Background: Non-small cell lung cancer (NSCLC) has been the subject of intense scholarly debate. We aimed to identify the potential biomarkers via bioinformatics analysis. Methods: from gene expression omnibus database (GEO). R software was applied to screen differentially expressed genes (DEGs)and analyze immune cell infiltrates. Gene set enrichment analysis (GSEA) showed significant function and pathway in two groups. The diagnostic markers were further investigated by multiple machine learning algorithms [least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)]. Various online analytic platforms were utilized to explore the expression and prognostic value of differential genes. Furthermore, western blotting was performed to test the effects of genes on cell proliferation in vitro. Results: We identified 181 DEGs shared by two datasets and selected nine diagnostic markers. Those genes were also significantly overexpressed in the third dataset. Topoisomerase II alpha (TOP2A) is overexpressed in lung cancer and associated with a poor prognosis, which was confirmed using immunohistochemistry (IHC) and western blotting. Additionally, TOP2A showed a negative correlation with immune cells, such as CD8+ T cells, eosinophils and natural killer (NK) cell. Conclusion: Collectively, for the first time, we applied multiple machine learning algorithms, online databases and experiments in vitro to show that TOP2A is a potential biomarker for lung adenocarcinoma and could facilitate the development of new treatment strategies.

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

Cited by 3 publications

References 43 publications

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas

Diagnosis and Prognosis of Non-small Cell Lung Cancer based on Machine Learning Algorithms

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