npj AgingCancer BiologyPeer-reviewed journal article
Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma
Enakshi SahaiD, Marouen Ben GuebilaiD, Viola FanfaniiD, Katherine Hoff ShuttaiD, Dawn Lisa DeMeoiD, John F QuackenbushiD, Camila M. Lopes‐RamosiD
Abstract
Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/601689v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1a8c735org.highwire.dtl.DTLVardef@1ae8833org.highwire.dtl.DTLVardef@16fd65dorg.highwire.dtl.DTLVardef@16287e5_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 9
- FWCI
- 2.32
- Field percentile
- top 10%
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