Evidence map›Paper›PMID 41303367›Full record

ArticleInternational journal of molecular sciences2025

IGF Pathway Components as Potential Biomarkers in Gastric Cancer.

Betul Ceylaner, Furkan Sahin, Anil Yildiz, Didem Tastekin, Ali Fuat Kaan Gok, Ahmet Tarik Baykal

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

0 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

6 authors.

Betul CeylanerDepartment of Biochemistry, Faculty of Pharmacy, Acibadem Mehmet Ali Aydinlar University, Istanbul 34752, Türkiye.ORCID 0000-0001-8768-6452
Furkan SahinDepartment of Biochemistry and Molecular Biology, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul 34752, Türkiye.ORCID 0000-0002-1296-9711
Anil YildizDepartment of Clinical Oncology, Institute of Oncology, Istanbul University, Istanbul 34116, Türkiye.
Didem TastekinDepartment of Clinical Oncology, Institute of Oncology, Istanbul University, Istanbul 34116, Türkiye.
Ali Fuat Kaan GokDepartment of General Surgery, Surgical Sciences, Istanbul Faculty of Medicine, Istanbul University, Istanbul 34116, Türkiye.
Ahmet Tarik BaykalDepartment of Biochemistry and Molecular Biology, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul 34752, Türkiye.ORCID 0000-0002-8814-7351

Funding

Acibadem Mehmet Ali Aydinlar University THD-2024-2213
6 · The paper itself

Abstract

Gastric cancer is one of the leading causes of cancer-related deaths worldwide, and due to its late-stage diagnosis, the prognosis is poor. Therefore, researching biomarkers that can be used in early diagnosis and prognostic processes is crucial. Insulin-like growth factor (IGF) signaling pathways play critical roles in cellular proliferation, apoptosis, differentiation, and tumor progression. This study investigated the biomarker potential of IGF-1, IGFBP-4, IGFBP-5, and PAPP-A in gastric cancer. Forty gastric cancer patients and forty healthy individuals were included, and protein levels in serum samples were measured using the ELISA method. The findings showed that IGF-1 levels were significantly decreased in the gastric cancer group, while IGFBP-4 levels were significantly increased. IGFBP-5 levels were also lower in gastric cancer patients. In contrast, PAPP-A levels did not differ significantly between the two groups. ROC analyses revealed that IGF-1, IGFBP-4, and IGFBP-5 have good discriminatory properties in the diagnosis of gastric cancer, while PAPP-A offers low diagnostic value. In conclusion, this study suggests that IGF pathway components, particularly IGF-1, IGFBP-4, and IGFBP-5, might be promising biomarker candidates for gastric cancer.

Indexed as

Biomarkers, TumorInsulin-Like Growth Factor Binding Protein 4Insulin-Like Growth Factor Binding Protein 5Insulin-Like Growth Factor IStomach NeoplasmsAdultAgedCase-Control StudiesFemaleHumansMaleMiddle AgedPregnancy-Associated Plasma Protein-AROC CurveSignal TransductionBiomarkers, TumorIGF1 protein, humanIGFBP5 protein, humanInsulin-Like Growth Factor Binding Protein 4Insulin-Like Growth Factor Binding Protein 5Insulin-Like Growth Factor IPAPPA protein, humanPregnancy-Associated Plasma Protein-Abiomarkergastric cancerIGF signaling

Identifiers

PMID41303367
PMCPMC12652497

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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.