Evidence map›Paper›PMID 39000413›Full record

ArticleInternational journal of molecular sciences2024

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Mostafa Rezapour, Robert Wesolowski, Metin Nafi Gurcan

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
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  3. Article
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  5. Article
  6. Artificial Intelligence in Bulk RNA-Seq: Challenges and Potential Solutions.Computational and structural biotechnology journal · 2026
    Review
  7. Article
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  12. Instruction tuning for colorectal histopathology: a multimodal vision-language assistant on human-evaluated data.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
  13. Article
  14. Article
  15. Article
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

3 authors.

Mostafa RezapourCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0001-9569-118X
Robert WesolowskiDivision of Medical Oncology, James Cancer Hospital and the Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA.
Metin Nafi GurcanCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0002-2421-8229

Funding

Efficient and cost-effective breast cancer risk stratification using whole slide histopathology imagesR21CA273665 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI GURCAN, METIN NAFI · 2023 to 2024
$394k
NCI NIH HHS R21 CA273665
6 · The paper itself

Abstract

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the

Indexed as

Breast NeoplasmsGene Expression Regulation, NeoplasticLymphatic MetastasisAxillaBiomarkers, TumorFemaleGene Expression ProfilingHumansLymph NodesRNA-SeqSequence Analysis, RNABiomarkers, Tumoraxillary lymph node metastasis (ALNM)breast cancergene expression analysisgeneralized linear modelsquasi-likelihood F testRNA sequencing (RNA-Seq)

Identifiers

PMID39000413
PMCPMC11242629

What OpenQuestion holds

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Registered trials

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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.