Evidence map›Paper›PMID 40424767›Full record

ArticleComputers in biology and medicine2025

Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer.

Sudhanshu Sharma, Rajesh Singh, Shiva Kant, Manoj K Mishra

Abstract read
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Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sudhanshu SharmaCancer Research Center, Department of Biological Sciences, Alabama State University, Montgomery, AL, 36104, USA.
Rajesh SinghMicrobiology, Biochemistry, and Immunology, Cancer Health Equity Institute, Morehouse School of Medicine, Atlanta, GA, USA.
Shiva KantDepartment of Biology and Environmental Sciences, College of Sciences, Auburn University of Montgomery, Montgomery, USA.
Manoj K MishraCancer Research Center, Department of Biological Sciences, Alabama State University, Montgomery, AL, 36104, USA. Electronic address: mmishra@alasu.edu.

Funding

Alabama State University-Auburn University Partnership to Promote Diversity in Aging ResearchR25AG070244 · NIA · ALABAMA STATE UNIVERSITY · PI MISHRA, MANOJ K., SUPPIRAMANIAM, VISHNU D · 2022 to 2024
$876k
NIA NIH HHS R25 AG070244
6 · The paper itself

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival of under 10 % despite current therapies. Aggressive tumor biology, a desmoplastic stroma that limits drug delivery and immune cell infiltration, and profound resistance to apoptosis make it more complex to treat. Here, we describe a multi-layered system biology and drug discovery pipeline that integrates bulk genomics, single-cell spatial transcriptomics, proteomics, competing endogenous RNA (ceRNA) network analysis, and deep learning-driven quantitative structure-activity relationship (QSAR) modeling. By implementing this pipeline, we predicted that TNFRSF10A encodes for the TRAILR1 death receptor as a potential therapeutic target in PDAC. Mutational and expressional analysis also confirmed TNFRSF10A as a putative target in PDAC. Cancer cells within the PDAC microenvironment exhibit aberrantly elevated TNFRSF10A expression. Immune-excluded tumor niches and pro-survival signaling link this elevated expression. Using an advanced transformer-based deep learning approach, SELFormer, combined with QSAR analysis-based virtual screening, we identified previously unexplored FDA-approved drugs and natural compounds, i.e., Temsirolimus, Ergotamine, and capivasertib, with potential TRAILR1 modulatory effects. During molecular dynamics simulations, these repurposed candidates showed the highest binding affinities against TNFRSF10A for 300 ns. These showed favorable binding energies (MM-PBSA), minimal RMSD drift, PCA, and SASA. We propose TNFRSF10A as a therapeutically important PDAC vulnerability nurtured by spatially resolved expression patterns and dynamic molecular modeling. This study has used a novel integration of AI-implemented chemical modeling, high-throughput screening, and a multi-omics approach to unravel and pharmacologically target a cancer compartment-specific weakness in a notoriously drug-resistant cancer.

Indexed as

Carcinoma, Pancreatic DuctalGenomicsPancreatic NeoplasmsReceptors, TNF-Related Apoptosis-Inducing LigandCell Line, TumorDeep LearningHumansMultiomicsProteomicsQuantitative Structure-Activity RelationshipReceptors, TNF-Related Apoptosis-Inducing LigandTNFRSF10A protein, humanArtificial intelligenceCancerMachine learningMD simulationsMulti-omicsTNFRSF10A

Identifiers

PMID40424767
PMCPMC12204372

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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.