Evidence map›Paper›PMID 39005266›Full record

ArticlebioRxiv : the preprint server for biology2024

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma.

Enakshi Saha, Marouen Ben Guebila, Viola Fanfani, Katherine H Shutta, Dawn L DeMeo, John Quackenbush, Camila M Lopes-Ramos

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Enakshi SahaDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.ORCID 0000-0003-2938-539X
Marouen Ben GuebilaDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Viola FanfaniDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Katherine H ShuttaDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Dawn L DeMeoChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115.
John QuackenbushDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.ORCID 0000-0002-2702-5879
Camila M Lopes-RamosDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.

Funding

Tissue and Pathology ResourcesP50CA127003 · NCI · DANA-FARBER CANCER INST · PI SHIVDASANI, RAMESH A · 2007 to 2023
$33.2M
Respiratory Computational Discovery CoreP01HL114501 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI SILVERMAN, EDWIN K · 2013 to 2025
$24.9M
SYSTEMS APPROACHES TO THE EPIDEMIOLOGY, GENETICS AND GENOMICS OF LUNG DISEASEST32HL007427 · NHLBI · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI DAWN L DEMEO, Edwin K Silverman · 1985 to 2026
$13.6M
Unraveling the Complexities of Risk and Mechanism in CancerR35CA220523 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI QUACKENBUSH, JOHN · 2018 to 2024
$6.0M
WebMeV: A Robust Platform for Intuitive Genomic Data AnalysisU24CA231846 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI QUACKENBUSH, JOHN · 2019 to 2023
$3.2M
Networks Tools to Understand Sex- and Gender-Specific Drivers of DiseaseR01HG011393 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI DEMEO, DAWN L, QUACKENBUSH, JOHN · 2021 to 2024
$2.1M
Mentoring in Patient Oriented Research in Lung Disease through the Lens of Sex as a Biological VariableK24HL171900 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI DAWN L DEMEO · 2024 to 2026
$386k
Sex chromosome gene regulatory networks and COPDK01HL166376 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI LOPES-RAMOS, CAMILA · 2023 to 2024
$324k
NCI NIH HHS P50 CA127003NCI NIH HHS R35 CA220523NCI NIH HHS U24 CA231846NHGRI NIH HHS R01 HG011393NHLBI NIH HHS K01 HL166376NHLBI NIH HHS K24 HL171900NHLBI NIH HHS P01 HL114501NHLBI NIH HHS T32 HL007427
6 · The paper itself

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.

Identifiers

PMID39005266
PMCPMC11244978

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.