Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods

Li, Gao; Li, Guosheng; Li, Jian‐Di; He, Juan; Zhou, Hua-Fu; Kong, Jinliang

doi:10.1016/j.csbj.2021.11.026

Cited by 14 publications

(12 citation statements)

References 45 publications

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“…Then, we selected the overlapping genes of validated targets and differentially expressed genes (FC ≥ 2 or ≤ 0.5 and p < 0.05) with downregulated m6A levels (FC ≤ 0.5 and p < 0.05) in the microarray results, and we selected the top 20 genes with decreased m6A levels and differential expression. We further screened 5 immunity-related genes according to the literature: downregulated EBNA1-binding protein 2 (EBNA1BP2) ( 19 ), diacylglycerol O-acyltransferase-1 (DGAT1) ( 20 ), sex determining region Y-box 9 (SOX9) ( 21 ), SP4 ( 22 ) and upregulated myeloid differentiation factor 88 (MYD88) ( 23 ). The qRT–PCR results showed that the differences in METTL3, YTHDC1, IGF2BP2, HNRNPA2B1, EBNA1BP2, DGAT1, SOX9 and MYD88 expression were statistically significant, in accordance with the microarray analysis, while differences in HNRNPC and SP4 expression were not significant ( Figures 7A-H ).…”

Section: Resultsmentioning

confidence: 99%

Section: Validation Of Key Regulators and Immunity-related Genes By Q...mentioning

confidence: 99%

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The m6A methylation profiles of immune cells in type 1 diabetes mellitus

Wang

Luo

et al. 2022

Front. Immunol.

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BackgroundType 1 diabetes mellitus (T1DM) is caused by immune cell-mediated β-cell dysfunction. In recent decades, N6-methyladenosine (m6A) has attracted widespread attention in the scientific research field because it plays vital roles in the pathogenesis of immunity-related diseases, including autoimmune diseases. However, neither the m6A modification profile nor the potential role it plays in T1DM pathogenesis has been investigated to date.Materials and MethodsAn m6A mRNA epitranscriptomic microarray analysis was performed to analyze m6A regulator expression patterns and m6A methylation patterns in immune cells of T1DM patients (n=6) and healthy individuals (n=6). A bioinformatics analysis was subsequently performed to explore the potential biological functions and signaling pathways underlying T1DM pathogenesis. Furthermore, mRNA expression and m6A methylation levels were subsequently verified by qRT–PCR and methylated RNA immunoprecipitation–qPCR (MeRIP–qPCR), respectively, in the T1DM and healthy groups (n=6 per group).ResultsAmong the multiple m6A regulators, METTL3 and IGF2BP2 had significantly downregulated expression, and YTHDC1 and HNRNPA2B1 had significantly upregulated expression in the T1DM group relative to the healthy group. The microarray analysis revealed 4247 differentially methylated transcripts, including 932 hypermethylated and 3315 hypomethylated transcripts, and 4264 differentially expressed transcripts, including 1818 upregulated transcripts and 2446 downregulated transcripts in the T1DM group relative to the healthy group. An association analysis between methylation and gene expression demonstrated that the expression of 590 hypermethylated transcripts was upregulated, and that of 1890 hypomethylated transcripts was downregulated. Pearson correlation analysis showed significant correlations between the expression levels of differentially expressed m6A regulators and the methylation levels of differentially methylated transcripts and significant correlations between the expression levels of differentially expressed m6A regulators and that of differentially expressed transcripts. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses demonstrated that differentially methylated transcripts were involved in pathways related to immunity, including some closely associated with T1DM.ConclusionsOur study presents m6A regulator expression patterns and m6A methylation patterns of immune cells in T1DM, showing that the m6A mark and m6A regulators are promising targets for T1DM diagnosis and treatment.

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Section: Resultsmentioning

confidence: 99%

Section: Validation Of Key Regulators and Immunity-related Genes By Q...mentioning

confidence: 99%

The m6A methylation profiles of immune cells in type 1 diabetes mellitus

Wang

Luo

et al. 2022

Front. Immunol.

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“…Furthermore, True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN) were obtained to summarize the results of diagnostic analysis and calculate the SEN, SPEC, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR). Finally, the summary receiver operating characteristic (SROC) curve was drawn to obtain the area under the curve (AUC) of SROC according to previous reports ( Gao et al, 2021 ; He et al, 2021 ; Huang W. Y. et al, 2021 ; Liang et al, 2021a , b ).…”

Section: Methodsmentioning

confidence: 99%

“…Finally, the summary receiver operating characteristic (SROC) curve was drawn to obtain the area under the curve (AUC) of SROC according to previous reports (Gao et al, 2021;He et al, 2021;Huang W. Y. et al, 2021;Liang et al, 2021a,b).…”

Section: Differential Expression Of Cyclin B2 Between Cerebral Ischem...mentioning

confidence: 99%

Upregulation of CCNB2 and Its Perspective Mechanisms in Cerebral Ischemic Stroke and All Subtypes of Lung Cancer: A Comprehensive Study

Yan

Chen

et al. 2022

Front. Integr. Neurosci.

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Cyclin B2 (CCNB2) belongs to type B cell cycle family protein, which is located on chromosome 15q22, and it binds to cyclin-dependent kinases (CDKs) to regulate their activities. In this study, 103 high-throughput datasets related to all subtypes of lung cancer (LC) and cerebral ischemic stroke (CIS) with the data of CCNB2 expression were collected. The analysis of standard mean deviation (SMD) and summary receiver operating characteristic (SROC) reflecting expression status demonstrated significant up-regulation of CCNB2 in LC and CIS (Lung adenocarcinoma: SMD = 1.40, 95%CI [0.98–1.83], SROC = 0.92, 95%CI [0.89–0.94]. Lung squamous cell carcinoma: SMD = 2.56, 95%CI [1.64–3.48]. SROC = 0.97, 95%CI [0.95–0.98]. Lung small cell carcinoma: SMD = 3.01, 95%CI [2.01–4.01]. SROC = 0.98, 95%CI [0.97–0.99]. CIS: SMD = 0.29, 95%CI [0.05–0.53], SROC = 0.68, 95%CI [0.63–0.71]). Simultaneously, protein-protein interaction (PPI) analysis indicated that CCNB2 is the hub molecule of crossed high-expressed genes in CIS and LC. Through Multiscale embedded gene co-expression network analysis (MEGENA), a gene module of CIS including 76 genes was obtained and function enrichment analysis of the CCNB2 module genes implied that CCNB2 may participate in the processes in the formation of CIS and tissue damage caused by CIS, such as “cell cycle,” “protein kinase activity,” and “glycosphingolipid biosynthesis.” Afterward, via single-cell RNA-seq analysis, CCNB2 was found up-regulated on GABAergic neurons in brain organoids as well as T cells expressing proliferative molecules in LUAD. Concurrently, the expression of CCNB2 distributed similarly to TOP2A as a module marker of cell proliferation in cell cluster. These findings can help in the field of the pathogenesis of LC-related CIS and neuron repair after CIS damage.

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“…Thirty SCLC cohorts were classified into 15 new cohorts based on the same platforms. Batch effects in merged cohorts (e.g., GPL6884, consisting of GSE32036 and GSE4127) were eliminated using the limma software package [ 20 – 22 ]. Finally, the mRNA expression levels in the SCLC cohorts were normalized via the limma package.…”

Section: Methodsmentioning

confidence: 99%

Ogt Demonstrated Conspicuous Clinical Significance in Cancers, from Pan-Cancer to Small-Cell Lung Cancer

Tang

et al. 2022

Journal of Oncology

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The clinical progression of small-cell lung cancer (SCLC) remains pessimistic. The aim of the present study was to promote the understanding of the clinical significance and mechanism of O-linked N-acetylglucosamine (GlcNAc) transferase (OGT) in SCLC. Wilcoxon tests, standardized mean difference (SMD), and Kruskal–Wallis tests were utilized to compare OGT level differences among the experimental and control groups. The univariate Cox regression analysis, Kaplan–Meier curves, and receiver operating characteristic curves were applied to determine OGT’s clinical relevance in cancers. The Spearman correlation analysis and enrichment analysis were utilized to explore the underlying mechanisms of OGT in cancers. For the first time in the field, we provide an overview of OGT in 32 cancers using a large number of samples (n = 21,196), determining distinct OGT expression in 25 cancers and its prognosis effects in 12 cancers. Furthermore, using 950 samples from multiple sources, upregulated OGT was found in both mRNA and protein levels in SCLC (SMD = 0.93, 95% CI [0.24, 1.63]). Higher OGT levels represented a more unfavorable disease-free interval for SCLC patients ( p < 0.001 ). The research also identified OGT expression as a potential marker for SCLC prediction (sensitivity = 0.79, specificity = 0.86, and AUC = 0.88). The high expression of OGT in SCLC may result from the positive regulation of two transcription factors—DEK and XRN2. We primarily investigated the underlying mechanisms of OGT in SCLC. Herein, based on the analyses from pan-cancer to SCLC, OGT demonstrated conspicuous clinical significance. OGT may be an underlying biomarker for the treatment and identification of some cancers, including SCLC.

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Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods

Cited by 14 publications

References 45 publications

The m6A methylation profiles of immune cells in type 1 diabetes mellitus

The m6A methylation profiles of immune cells in type 1 diabetes mellitus

Upregulation of CCNB2 and Its Perspective Mechanisms in Cerebral Ischemic Stroke and All Subtypes of Lung Cancer: A Comprehensive Study

Ogt Demonstrated Conspicuous Clinical Significance in Cancers, from Pan-Cancer to Small-Cell Lung Cancer

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