Evidence map›Paper›PMID 42631856›Full record

ArticleMolecular biology reports2026

SMAD4 mRNA expression as a diagnostic biomarker in non-small cell lung cancer.

Tamara Babic, Jelena Karanovic, Tijana Stankovic, Miodrag Dragoj, Aleksandra Nikolic

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Article in Molecular biology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Tamara BabicGene Regulation in Cancer Group, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444A, Belgrade, 11042, Serbia. tamara.babic@imgge.bg.ac.rs.ORCID http://orcid.org/0000-0001-7117-7944
Jelena KaranovicGene Regulation in Cancer Group, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444A, Belgrade, 11042, Serbia.
Tijana StankovicDepartment of Neurobiology, Institute for Biological Research Sinisa Stankovic National Institute of the Republic of Serbia University of Belgrade, Bulevar Despota Stefana 142, Belgrade, 11108, Serbia.
Miodrag DragojDepartment of Neurobiology, Institute for Biological Research Sinisa Stankovic National Institute of the Republic of Serbia University of Belgrade, Bulevar Despota Stefana 142, Belgrade, 11108, Serbia.
Aleksandra NikolicGene Regulation in Cancer Group, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444A, Belgrade, 11042, Serbia.

Funding

Science Fund of the Republic of Serbia 6052315
6 · The paper itself

Abstract

backgroundNon-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related mortality worldwide, largely due to late-stage diagnosis. Although histopathology is the gold standard, there is a need for reliable molecular biomarkers to improve early detection and support less invasive diagnostic strategies. SMAD4, a known tumor suppressor, has been implicated in cancer progression, but its diagnostic relevance in NSCLC requires further investigation. This study aimed to evaluate SMAD4 mRNA expression in NSCLC and assess its association with clinicopathological features and its potential as a tissue-based diagnostic biomarker. METHODS AND

resultsSMAD4 mRNA expression was quantified using qPCR in human lung cell lines and tissue samples from 49 NSCLC patients. Findings were validated using transcriptomic datasets from TCGA and GTEx. Associations with clinicopathological parameters were analyzed, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Proteomic validation was performed using CPTAC data. SMAD4 mRNA expression was significantly downregulated in NSCLC tissues compared to adjacent non-tumor tissues, consistent across clinical samples, cell lines, and public datasets. Expression levels varied significantly across histological subtypes and tumor grades. ROC analysis demonstrated good discriminatory ability in the patient cohort (AUC = 0.7995; 95% CI: 0.7056-0.8933), which was confirmed in the GSE32863 validation cohort (AUC = 0.8047; 95% CI: 0.7236-0.8858). Analysis of publicly available CPTAC proteomic data confirmed reduced SMAD4 protein levels, supporting concordant transcriptomic and proteomic downregulation.

conclusionsSMAD4 mRNA shows promise as a diagnostic biomarker for NSCLC. Further validation in larger cohorts and exploration of its application in minimally invasive diagnostics, such as liquid biopsy, are warranted.

Indexed as

Biomarkers, TumorCarcinoma, Non-Small-Cell LungLung NeoplasmsSmad4 ProteinAgedCell Line, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedProteomicsRNA, MessengerROC CurveBiomarkers, TumorRNA, MessengerSmad4 ProteinSMAD4 protein, humanCancer prognosisDiagnostic biomarkerGene expressionLung cancerSMAD4

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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.