Evidence map›Paper›PMID 40723362›Full record

ArticleBiology2025

Identification of Novel Molecular Panel as Potential Biomarkers of PAN-Gastrointestinal Cancer Screening: Bioinformatics and Experimental Analysis.

Fatemeh Hajibabaie, Parisa Mohamadynejad, Laleh Shariati, Kamran Safavi, Navid Abedpoor

Abstract read
In one paragraph

Article in Biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Advances inOncology letters · 2026
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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

5 authors.

Fatemeh HajibabaieDepartment of Biology, ShK.C., Islamic Azad University, ShahreKord 88137-33395, Iran.ORCID 0000-0001-6153-9921
Parisa MohamadynejadDepartment of Biology, ShK.C., Islamic Azad University, ShahreKord 88137-33395, Iran.ORCID 0000-0002-1343-9069
Laleh ShariatiDepartment of Biomaterials, Nanotechnology and Tissue Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran.ORCID 0000-0002-1078-5978
Kamran SafaviDepartment of Agricultural Biotechnology and Engineering, Institute of Agriculture, Water, Food and Nutraceutical, Isf.C., Islamic Azad University, Isfahan 81551-39998, Iran.
Navid AbedpoorResearch Center of Medicinal Plants, Isf.C., Islamic Azad University, Isfahan 81551-39998, Iran.ORCID 0000-0003-1676-6951

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PAN-gastrointestinal cancers (PAN-GI cancers), including the oral, esophageal, gastric, hepatocellular, pancreatic=, and colorectal cancers, are the leading cause of cancer-related mortality. Despite recent advances in identifying the molecular mechanisms driving these malignancies, the high incidence and recurrence of the PAN-gastrointestinal cancers and the low survival rates of patients indicate the need to introduce biomarkers for early diagnosis to improve diagnostic and therapeutic approaches. In the present study, using integrated transcriptomics, RNA-Seq and microarray data, from the TCGA and GEO databases, respectively, were combined to discover and validate a global biomarker panel for PAN-gastrointestinal cancers. In order to validate the bioinformatics data, the expression levels of genes in the molecular panel were evaluated using real-time quantitative polymerase chain reaction (qPCR) in tumor tissues of 21 patients with early diagnosis of gastric cancer and colorectal cancer (Stage I and II). By examining the transcriptomic profiles of six types of PAN-gastrointestinal cancers, a network of closely related hub genes (

Indexed as

AURKAbioinformaticsCEP55DTLPAN-gastrointestinal cancersTTK

Identifiers

PMID40723362
PMCPMC12292149

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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.