Identification of Key Long Non-Coding RNAs and Gene Networks in Acute Myeloid Leukemia: A Bioinformatics Approach

Acute Myeloid Leukemia (AML) is a malignant disease with complex genetic implantation that is marked by the rapid accumulation of abnormal myeloid cells that appear clear. The prognosis remains unfavorable due to the relapsing nature and drug resistance. In addition to the prognostic determinants, there should be an understanding of the mechanisms governing the disease in order to develop novel therapeutic approaches. Through the application of bioinformatics, each mobile hope: an angry approach would not surface against the domestic liars. The pain struggle seems almost banal: reaching nearby areas of the distribution and examining many regions there. According to what is stated below, the goal is to find useful long non-coding RNAs and gene networks using advanced bioinformatics techniques that help acute myeloid leukemia grow properly. Methods and materials: Publicly available microarray datasets were sourced from the Gene Expression Omnibus (GEO) database. With the DESeq۲ and Limma R packages, we were able to find genes that were expressed differently in AML samples compared to control samples. The adjusted p-value < ۰.۰۵ and |logFC| ≥ ۱ were used as thresholds. To account for batch effects, the Combat technique from the SVA package was employed. Principal component analysis (PCA) ensured data quality. We used Weighted Gene Co-expression Network Analysis (WGCNA) on the ۱۳,۰۰۰ most variable genes to find gene modules linked to AML. We used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to find out more about the genes' biological functions and pathways. Protein-protein interaction (PPI) networks were constructed to further investigate key regulatory genes. AML lncRNAs were identified using the RNAinter database and literature review, with a focus on the interaction between lncRNAs and common transcription factors (TF). Results: From DEG analysis, ۷۴۰ genes were identified as significantly altered in AML, with ۴۵۶ downregulated and ۲۸۴ upregulated. The yellow gene module had the strongest link to AML, according to WGCNA. It had ۱۵۹ key genes connected to DNA repair, cell cycle control, and p۵۳ signaling pathways. Functional enrichment analyses confirmed that these genes are involved in critical cellular processes that are relevant to AML pathogenesis. Twenty important genes, such as CDC۲۰, BUB۱B, and BRCA۱, were found to be central to AML-related networks by PPI analysis. Discussion: Based on bioinformatics predictions and prior research, selected lncRNAs (LINC۰۱۵۷۸, LINC۰۱۰۰۳) were identified as significantly downregulated in AML and associated with these key pathways; these are highly likely to exert an influence on the AML prognostic. This bioinformatics study uncovered important lncRNAs and gene networks involved in AML, with key lncRNAs emerging as potential tumor suppressors. These findings lay the groundwork for future investigations into their mechanistic roles in AML, as well as their usefulness as biomarkers for diagnosis and therapeutic targets.

کلیدواژه ها:

AML ، lncRNAs ، Bioinformatics ، WGCNA