Evidence map›Paper›PMID 32322170›Full record

ArticleCancer cell international2020

Overexpression of kinesin superfamily members as prognostic biomarkers of breast cancer.

Tian-Fu Li, Hui-Juan Zeng, Zhen Shan, Run-Yi Ye, Tuck-Yun Cheang, Yun-Jian Zhang, Si-Hong Lu, Qi Zhang, Nan Shao, Ying Lin

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In one paragraph

Article in Cancer cell international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
91citing papers in PubMed, 2 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

91 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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31 more citing papers are in PubMed but not listed here.

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.

Tian-Fu Li *1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Hui-Juan Zeng *1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Zhen Shan1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Run-Yi Ye1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Tuck-Yun Cheang1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Yun-Jian Zhang1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Si-Hong Lu1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Qi Zhang1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Nan Shao1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.
Ying Lin1Breast Disease Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKinesin superfamily (KIFs) has a long-reported significant influence on the initiation, development, and progress of breast cancer. However, the prognostic value of whole family members was poorly done. Our study intends to demonstrate the value of kinesin superfamily members as prognostic biomarkers as well as a therapeutic target of breast cancer.

methodsComprehensive bioinformatics analyses were done using data from TCGA, GEO, METABRIC, and GTEx. LASSO regression was done to select tumor-related members. Nomogram was constructed to predict the overall survival (OS) of breast cancer patients. Expression profiles were testified by quantitative RT-PCR and immunohistochemistry. Transcription factor, GO and KEGG enrichments were done to explore regulatory mechanism and functions.

resultsA total of 20 differentially expressed KIFs were identified between breast cancer and normal tissue with 4 (KIF17, KIF26A, KIF7, KIFC3) downregulated and 16 (KIF10, KIF11, KIF14, KIF15, KIF18A, KIF18B, KIF20A, KIF20B, KIF22, KIF23, KIF24, KIF26B, KIF2C, KIF3B, KIF4A, KIFC1) overexpressed. Among which, 11 overexpressed KIFs (KIF10, KIF11, KIF14, KIF15, KIF18A, KIF18B, KIF20A, KIF23, KIF2C, KIF4A, KIFC1) significantly correlated with worse OS, relapse-free survival (RFS) and distant metastasis-free survival (DMFS) of breast cancer. A 6-KIFs-based risk score (KIF10, KIF15, KIF18A, KIF18B, KIF20A, KIF4A) was generated by LASSO regression with a nomogram validated an accurate predictive efficacy. Both mRNA and protein expression of KIFs are experimentally demonstrated upregulated in breast cancer patients. Msh Homeobox 1 (MSX1) was identified as transcription factors of KIFs in breast cancer. GO and KEGG enrichments revealed functions and pathways affected in breast cancer.

conclusionOverexpression of tumor-related KIFs correlate with worse outcomes of breast cancer patients and can work as potential prognostic biomarkers.

Indexed as

Bioinformatics analysisBreast cancerKinesin superfamilyMSX1Prognostic biomarker

Identifiers

PMID32322170
PMCPMC7161125

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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.