Evidence map›Paper›PMID 37180578›Full record

ArticlePeerJ2023

Identifying liver metastasis-related hub genes in breast cancer and characterizing

Mingkuan Chen, Wenfang Zheng, Lin Fang

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2023. 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
0.5field-weighted citation impact, top 33% of its field
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, 2 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Mingkuan Chen *Tongji University School of Medicine, Department of Thyroid and Breast Division of General Surgery Shanghai Tenth People's Hospital, Shanghai, Jing'an District, China.
Wenfang Zheng *Tongji University School of Medicine, Department of Thyroid and Breast Division of General Surgery Shanghai Tenth People's Hospital, Shanghai, Jing'an District, China.
Lin FangTongji University School of Medicine, Department of Thyroid and Breast Division of General Surgery Shanghai Tenth People's Hospital, Shanghai, Jing'an District, China.
Shanghai Tenth People's Hospital · CNTongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The liver is the third most common metastatic site for advanced breast cancer (BC), and liver metastases predict poor prognoses. However, the characteristic biomarkers of BC liver metastases and the biological role of secreted protein acidic and rich in cysteine-like 1 ( Methods: The publicly available GSE124648 dataset was used to identify differentially expressed genes (DEGs) between BC and liver metastases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to annotate these DEGs and understand the biological functions in which they are involved. A protein-protein interaction (PPI) network was constructed to identify metastasis-related hub genes and further validated in a second independent dataset (GSE58708). Clinicopathological correlation of hub gene expression in patients with BC was determined. Gene set enrichment analysis (GSEA) was performed to explore DEG-related signaling pathways. Results: We identified 332 liver metastasis-related DEGs from GSE124648 and 30 hub genes, including Conclusion: We identified

Indexed as

Breast NeoplasmsLiver NeoplasmsBiomarkersCalcium-Binding ProteinsCysteineExtracellular Matrix ProteinsFemaleHumansOsteonectinPrognosisBiomarkersCalcium-Binding ProteinsCysteineExtracellular Matrix ProteinsOsteonectinSPARCL1 protein, humanBiomarkerBreast cancerLiver metastasisSPARCL1

Identifiers

PMID37180578
PMCPMC10174054
OpenAlexW4375866224

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.