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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 2, 2024

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

Язык: Английский

The effect of telomeres in cervical cancer

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 22, 2024

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

Язык: Английский

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

Journal of Cellular and Molecular Medicine, Год журнала: 2024, Номер 28(17)

Опубликована: Сен. 1, 2024

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

Язык: Английский