Evidence map›Paper›PMID 39995112›Full record

ArticleClinical and translational medicine2025

Comprehensive transcriptome, miRNA and kinome profiling identifies new treatment options for personalized lung cancer therapy.

Shen Zhong, Yvonne Börgeling, Patrick Zardo, Danny Jonigk, Jürgen Borlak

Abstract read
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Article in Clinical and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

5 authors.

Shen ZhongCentre for Pharmacology and Toxicology, Hannover Medical School, Hannover, Germany.
Yvonne BörgelingInstitute of Virology, University of Münster, Münster, Germany.
Patrick ZardoClinic for Cardiothoracic and Transplantation Surgery, Hannover Medical School, Hannover, Germany.
Danny JonigkInstitute for Pathology, Hannover Medical School, Hannover, Germany.
Jürgen BorlakCentre for Pharmacology and Toxicology, Hannover Medical School, Hannover, Germany.ORCID 0000-0003-1558-9519

Funding

Chinese Scholarship Council 202008080022Stiftung Gerdes, Wolfsburg, Germany
6 · The paper itself

Abstract

backgroundBasic research identified oncogenic driver mutations in lung cancer (LC). However, <10% of patients carry driver mutations. Thus, most patients are not recommended for first-line kinase inhibitor (KI)-based therapies. Through enabling technologies and bioinformatics, we gained deep insight into patient-specific signalling networks which permitted novel KI-based treatment options in LC.

methodsWe performed molecular pathology, transcriptomics and miRNA profiling across 95 well-characterized LC patients. We confirmed results based on cross-linked immunoprecipitation-sequencing data, and used N = 524 adeno- and 497 squamous cell carcinomas as validation sets. We employed the PamGene platform to identify aberrant kinases, validated the results by evaluating independent siRNA and CRISPR-mediated mRNA knockdown studies in human LC cell lines.

resultsTranscriptomics revealed 439, 1240, 383 and 320 significantly upregulated genes, respectively, for adeno-, squamous, neuroendocrine and metastatic cases, and there are 1092, 1477, 609 and 1267 downregulated DEGs. Based on gene enrichment analysis and experimentally validated miRNA-gene interactions, we constructed regulatory networks specific for adeno-, squamous, neuroendocrine and metastatic LC. Molecular profiling discovered 137 significantly upregulated kinases (range 2-26-fold) of which 65 and 72, respectively, are tyrosine and serine-threonine kinases while 6 kinases carry driver mutations. Meanwhile, there are 21 kinases commonly upregulated irrespective of the histological type of LC. Bioinformatics decoded networks in which kinases function as master regulators. Typically, the networks consisted of 14, 9, 16 and 19 highly regulated kinases in adeno-, squamous, neuroendocrine and metastatic LC. Inhibition of kinases which function as master regulators disrupted the signalling networks, and their gene knock-down studies confirmed inhibition of cell proliferation in a panel of human LC cell lines. Additionally, the proposed molecular profiling enables KI-based therapies in patients with acquired drug resistance.

conclusionsOur study broadens the perspective of KI-based therapies in LC, and we propose a framework to overcome acquired drug resistance.

Indexed as

Lung NeoplasmsMicroRNAsPrecision MedicineTranscriptomeFemaleGene Expression ProfilingHumansMaleMicroRNAskinase inhibitorlung cancerneoadjuvant therapyprecision medicine

Identifiers

PMID39995112
PMCPMC11850761

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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.