Evidence map›Paper›PMID 39271768›Full record

ArticleScientific reports2024

Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer.

Qiaonan Guo, Pengjun Qiu, Kelun Pan, Huikai Liang, Zundong Liu, Jianqing Lin

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 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.

Qiaonan Guo *Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Pengjun Qiu *Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Kelun Pan *Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Huikai LiangDepartment of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Zundong LiuStem Cell Laboratory, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. harryblood1989@126.com.
Jianqing LinDepartment of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. ljq13905977336@163.com.

Funding

Doctoral Special Foundation of the Second Affiliated Hospital of Fujian Medical University 2022BD0905Natural Science Foundation of Fujian Province 2023J01103
6 · The paper itself

Abstract

Cancer stem cells (CSCs) have the potential to self-renew and induce cancer, which may contribute to a poor prognosis by enabling metastasis, recurrence, and therapy resistance. Hence, this study was performed to identify the association between CSC-related genes and triple-negative breast cancer (TNBC) development. Stemness gene sets were downloaded from StemChecker. Based on the online databases, a consensus clustering algorithm was conducted for unsupervised classification of TNBC samples. The variations between subtypes were assessed with regard to prognosis, tumor immune microenvironment (TIME), and chemotherapeutic sensitivity. The stemness-related gene signature was established and random survival forest analysis was employed to identify the core gene for validation experiments and tumor sphere formation assays. 499 patients with TNBC were classified into three subgroups and the Cluster 1 had a better OS than others. After that, WGCNA study was performed to identify genes important for Cluster 1 subtype. Out of all 8 modules, the subtype of Cluster 1 and the yellow module with 103 genes demonstrated the largest positive association. After that, a four-gene stemness-related signature was established. Based on the yellow module, the 39 potential pivotal genes were subjected to the random forest survival analysis to find out the gene that was relatively important for OS. KIF15 was confirmed as the targeted gene by LASSO and random survival forest analyses. In vitro experiments, the downregulation of KIF15 promoted the stemness of TNBC cells. The expression levels of stem cell markers Nanog, SOX2, and OCT4 were found to be elevated in TNBC cell lines after KIF15 inhibition. A stemness-associated risk model was constructed to forecast the clinical outcomes of TNBC patients. The downregulation of KIF15 expression in a subpopulation of TNBC stem cells may promote stemness and possibly TNBC progression.

Indexed as

Biomarkers, TumorGene Expression Regulation, NeoplasticKinesinsMachine LearningNeoplastic Stem CellsTriple Negative Breast NeoplasmsAlgorithmsCell Line, TumorFemaleGene Expression ProfilingHumansPrognosisTumor MicroenvironmentBiomarkers, TumorKIF15 protein, humanKinesinsCancer stem cell (CSC)KIF15PrognosisRisk modelTriple-negative breast cancer (TNBC)Tumor microenvironment (TME)

Identifiers

PMID39271768
PMCPMC11399402

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