Abstract
Background:
Immunotherapy is a pivotal approach in combating acute myeloid leukemia (AML), with the identification of immunomarkers being imperative. This investigation aimed to delineate biomarkers linked with immune-related genes (IRGs) in AML, thereby providing a theoretical framework for AML therapeutics.
Methods:
This research utilized AML-specific datasets [GSE9476 and The Cancer Genome Atlas (TCGA)-AML] alongside 1,793 IRGs. Initially, weighted gene co-expression network analysis (WGCNA) was employed to identify module genes using an integrative and systematic methodology. Differential gene expression analyses were conducted on GSE9476 and aggregated AML data from the University of California Santa Cruz (UCSC) Xena platform, alongside the Genotype-Tissue Expression (GTEx) database, to identify differentially expressed genes (DEGs). These DEGs were then intersected with WGCNA module genes and IRGs to isolate potential candidate genes. Kaplan-Meier (K-M) survival curves were subsequently utilized to identify pivotal genes with significant survival disparities. The prognostic significance of these genes was further assessed through both univariate and multivariate Cox regression analyses to pinpoint biomarkers. Finally, analyses focusing on functional enrichment associated with the identified biomarkers.
Results:
Using WGCNA, a cohort of 3,611 modular genes was identified. Intersection analysis involving WGCNA, DEGs, and IRGs led to the identification of eight promising candidate genes. Subsequent K-M survival assessments distilled these to six paramount genes, all of which underwent rigorous independent prognostic evaluation. Notably, GNAI1 emerged as a potential biomarker, demonstrating marginal significance with a P value of 0.056. Enrichment analyses elucidated that GNAI1 predominantly participates in key signaling pathways, notably oxidative phosphorylation and ubiquitin-mediated proteolysis. Comprehensive immunological profiling revealed a significant association of GNAI1 with the 10 distinct immune cell types. Specifically, CD56dim natural killer (NK) cells and type T helper 17 (Th17) cells exhibited a pronounced negative correlation with GNAI1. Conversely, an array of eight other immune cell types, including type T helper 2 (Th2) cells and activated B cells, demonstrated a robust positive correlation with GNAI1.
Conclusions:
GNAI1, associated with IRGs in AML, was identified as a biomarker, providing a basis for understanding AML pathogenesis and offering new avenues for therapeutic strategies.