M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

Shen, Lianwei; Yue, Shouwei

doi:10.1038/s41598-024-57507-y

Cited by 3 publications

References 40 publications

(43 reference statements)

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Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning

Ding,

Gao,

Huang

et al. 2024

Sci Rep

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Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules, diverse signaling pathways, and multifaceted immune processes. N6-methyladenosine (m6A), a critical epigenetic modification, regulates various immune-metabolic and pathological pathways, though its precise role in TB pathogenesis remains largely unexplored. This study aims to identify m6A-associated genes implicated in TB, elucidate their mechanistic contributions, and evaluate their potential as diagnostic biomarkers and tools for molecular subtyping. Using TB-related datasets from the GEO database, this study identified differentially expressed genes associated with m6A modification. We applied four machine learning algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Generalized Linear Model—to construct diagnostic models focusing on m6A regulatory genes. The Random Forest algorithm was selected as the optimal model based on performance metrics (area under the curve [AUC] = 1.0, p < 0.01), and a clinical predictive model was developed based on these critical genes. Patients were stratified into distinct subtypes according to m6A gene expression profiles, followed by immune infiltration analysis across subtypes. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated the biological functions and pathways associated with the identified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expression of key m6A regulatory genes. Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes—YTHDF1, HNRNPC, LRPPRC, and ELAVL1—identified as critical m6A regulators in TB through the Random Forest model. The diagnostic significance of these genes was further supported by a nomogram, achieving a high predictive accuracy (95% confidence interval [CI]: 0.87–0.94). Consensus clustering classified patients into two m6A subtypes with distinct immune profiles, as principal component analysis (PCA) showed significantly higher m6A scores in Group A than in Group B (p < 0.05). Immune infiltration analysis highlighted significant correlations between key m6A genes and specific immune cell infiltration patterns across subtypes. This study highlights the potential of key m6A regulatory genes as diagnostic biomarkers and immunotherapy targets for TB, supporting their role in TB pathogenesis. Future research should aim to further validate these findings across diverse cohorts to enhance their clinical applicability.

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Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning

Ding,

Gao,

Huang

et al. 2024

Sci Rep

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Multifaceted bioinformatic analysis of m6A‐related ferroptosis and its link with gene signatures and tumour‐infiltrating immune cells in gliomas

Yang,

Hao,

Guiyang

et al. 2024

J Cellular Molecular Medi

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Whether N6‐Methyladenosine (m6A)‐ and ferroptosis‐related genes act on immune responses to regulate glioma progression remains unanswered. Data of glioma and corresponding normal brain tissues were fetched from the TCGA database and GTEx. Differentially expressed genes (DEGs) were identified for GO and KEGG enrichment analyses. The FerrDb database was based to yield ferroptosis‐related DEGs. Hub genes were then screened out using the cytoHubba database and validated in clinical samples. Immune cells infiltrating into the glioma tissues were analysed using the CIBERSORT R script. The association of gene signature underlying the m6A‐related ferroptosis with tumour‐infiltrating immune cells and immune checkpoints in low‐grade gliomas was analysed. Of 6298 DEGs enriched in mRNA modifications, 144 were ferroptosis‐related; NFE2L2 and METTL16 showed the strongest positive correlation. METTL16 knockdown inhibited the migrative and invasive abilities of glioma cells and induced ferroptosis in vitro. NFE2L2 was enriched in the anti‐m6A antibody. Moreover, METTL16 knockdown reduced the mRNA stability and level of NFE2L2 (both p < 0.05). Proportions of CD8+ T lymphocytes, activated mast cells and M2 macrophages differed between low‐grade gliomas and normal tissues. METTL16 expression was negatively correlated with CD8+ T lymphocytes, while that of NFE2L2 was positively correlated with M2 macrophages and immune checkpoints in low‐grade gliomas. Gene signatures involved in the m6A‐related ferroptosis in gliomas were identified via bioinformatic analyses. NFE2L2 interacted with METTL16 to regulate the immune response in low‐grade gliomas, and both molecules may be novel therapeutic targets for gliomas.

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The effect of telomeres in cervical cancer

Xu,

Cao

et al. 2024

Preprint

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Globally, cervical cancer ranks as a prevalent cancer among women and stands as the fourth leading cause of mortality in gynecological cancers. Yet, it's still uncertain how telomeres impact cervical cancer. This research involved acquiring telomere associated genes (TRGs) from TelNet. Clinical data and TRGs expression levels of cervical cancer patients were acquired from the Cancer Genome Atlas (TCGA) database. Within the TCGA-CESC data collection, 327 TRGs were identified between cancerous and healthy tissues, with these genes, which differ in telomeres and are closely linked to cervical cancer, playing a role in various functional processes, predominantly in the cell cycle, DNA replication, and DNA replication. Key genes such as cellular aging, repair of double-strand breaks, and the Fanconi anemia pathway, among others, play a significant role in the cell's life cycle. Dysfunction in these genes could lead to irregularities in the body's cell synthesis and apoptosis processes, potentially hastening cervical cancer's advancement. Subsequently, the data was sequentially analyzed using single-factor cox regression, lasso regression, and multi-factor cox regression techniques, culminating in the creation of the TRGs risk model. Within the discovered TCGA group (p < 0.001), patients with cervical cancer in the group at high risk of TRGs experienced worse results. Furthermore, the TRGs risk score emerged as a standalone risk element for renal cancer. Furthermore, populations vulnerable to TRGs could gain advantages from the administration of specific therapeutic medications. To sum up, our team developed a genetic risk model linked to telomeres to forecast cervical cancer patients' outcomes, potentially aiding in choosing treatment medications for these patients.

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M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

Cited by 3 publications

References 40 publications

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning

Multifaceted bioinformatic analysis of m6A‐related ferroptosis and its link with gene signatures and tumour‐infiltrating immune cells in gliomas

The effect of telomeres in cervical cancer

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