Evidence map›Paper›PMID 38757377›Full record

ArticleMolecular oncology2024

Benign breast tumors may arise on different immunological backgrounds.

Lilly Anne Torland, Xiaoran Lai, Surendra Kumar, Margit H Riis, Jürgen Geisler, Torben Lüders, Xavier Tekpli, Vessela Kristensen, Kristine Sahlberg, Andliena Tahiri

Abstract read
In one paragraph

Article in Molecular oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

10 authors.

Lilly Anne TorlandDepartment of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway.ORCID 0009-0003-4425-7676
Xiaoran LaiOslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Norway.
Surendra KumarDepartment of Ocean Sciences, Memorial University of Newfoundland, St. John's, Canada.
Margit H RiisDepartment of Breast and Endocrine Surgery, Clinic of Cancer, Oslo University Hospital, Norway.
Jürgen GeislerInstitute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway.
Torben LüdersDepartment of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway.
Xavier TekpliDepartment of Medical Genetics, Oslo University Hospital, Oslo, Norway.
Vessela KristensenInstitute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway.
Kristine SahlbergInstitute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway.
Andliena TahiriDepartment of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway.ORCID 0000-0002-9221-1424

Funding

Akershus University Hospital through internal strategic moneyVestre Viken Health Trust (HT) 32/21
6 · The paper itself

Abstract

Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding of tumor biology. In this study, we have used two pathway analysis algorithms, Pathifier and gene set variation analysis (GSVA), to identify biological differences between normal breast tissue, benign tumors and malignant tumors in our clinical dataset. Our results revealed that one-third of all pathways that were significantly different between benign and malignant tumors were immune-related pathways, and 227 of them were validated by both methods and in the METABRIC dataset. Furthermore, five of these pathways (all including genes involved in cytokine and interferon signaling) were related to overall survival in cancer patients in both datasets. The cellular moieties that contribute to immune differences in malignant and benign tumors were analyzed using the deconvolution tool, CIBERSORT. The results showed that levels of some immune cells were specifically higher in benign than in malignant tumors, and this was especially the case for resting dendritic cells and follicular T-helper cells. Understanding the distinct immune profiles of benign and malignant breast tumors may aid in developing noninvasive diagnostic methods to differentiate between them in the future.

Indexed as

Breast NeoplasmsAlgorithmsFemaleGene Expression Regulation, NeoplasticHumansSignal Transductionbenign breast tumorsbreast cancerimmunitypathways

Identifiers

PMID38757377
PMCPMC11459044

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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.