Evidence map›Paper›PMID 39574053›Full record

ArticleBMC cancer2024

A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway.

Jun-Luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-Shun Liang, Hong-Hao Zhou, Zhao-Qian Liu, Wen-Xu Hong, Ji-Ye Yin

Abstract read
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Jun-Luan MoDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410078, P. R. China.
Xi LiDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410078, P. R. China.
Lin LeiShenzhen Center for Chronic Disease Control, Shenzhen, 518020, P. R. China.
Ji PengShenzhen Center for Chronic Disease Control, Shenzhen, 518020, P. R. China.
Xiong-Shun LiangShenzhen Center for Chronic Disease Control, Shenzhen, 518020, P. R. China.
Hong-Hao ZhouDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410078, P. R. China.
Zhao-Qian LiuDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410078, P. R. China.
Wen-Xu HongShenzhen Center for Chronic Disease Control, Shenzhen, 518020, P. R. China. szbloodcenter@hotmail.com.
Ji-Ye YinDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410078, P. R. China. yinjiye@csu.edu.cn.

Funding

Guangdong Medical Science and Technology Research Fund B2021240Sanming Project of Medicine in Shenzen Municipality SZSM201811057Shenzhen Science and Technology Program JCYJ20230807120859030
6 · The paper itself

Abstract

backgroundExosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them.

methodPeripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs.

resultsThe machine learning model incorporates six small RNAs: piR-36,340, piR-33,161, miR-484, miR-548ah-5p, miR-4282, and miR-6853-3p. The area under the ROC curve (AUC) of the machine learning model in the training set was 0.985 (95% CI = 0.948-1), while the AUC in the test set was 0.972 (95% CI = 0.882-0.995). RT-qPCR was used to detect the expression levels of these key small RNAs in the validation samples, and the results revealed that their expression levels were significantly different between the two groups (P < 0.05). Through target gene prediction and functional enrichment analysis, it was found that the functions of the target genes were enriched mainly in the chemokine signaling pathway.

conclusionThe combination of six plasma exosome small RNAs has good prognostic value for women with positive breast cancer by imaging screening. The chemokine signaling pathway may be involved in the early stage of breast cancer. It is worth further exploring whether small RNAs mediate chemokine signaling pathways in the pathogenesis of breast cancer through the delivery of exosomes.

Indexed as

Breast NeoplasmsExosomesMachine LearningSignal TransductionAdultBiomarkers, TumorChemokinesEarly Detection of CancerFemaleGene Expression Regulation, NeoplasticHumansMicroRNAsMiddle AgedROC CurveBiomarkers, TumorChemokinesMicroRNAsBreast cancerBreast nodulesExosomesmiRNApiRNASmall RNAs

Identifiers

PMID39574053
PMCPMC11580650

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.