Evidence map›Paper›PMID 35855425›Full record

ArticlePeerJ2022

Novel lncRNAs with diagnostic or prognostic value screened out from breast cancer via bioinformatics analyses.

Hongxian Wang, Lirong Shu, Nan Niu, Chenyang Zhao, Shuqi Lu, Yanhua Li, Huanyu Wang, Yao Liu, Tianhui Zou, Jiawei Zou and 2 more

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

11 citing papers in PubMed, 13 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

12 authors at 1 institution in 1 country.

Hongxian Wang *Department of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Lirong Shu *Longhua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, P.R. China.
Nan NiuLonghua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, P.R. China.
Chenyang ZhaoLonghua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, P.R. China.
Shuqi LuLonghua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, P.R. China.
Yanhua LiDepartment of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Huanyu WangDepartment of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Yao LiuDepartment of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Tianhui ZouDepartment of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Jiawei ZouDepartment of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Xiaoqin WuDepartment of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Yun WangLonghua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, P.R. China.
Shenzhen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Recent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer. Materials and Methods: The database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, Results: In this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor. Conclusion: A total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).

Indexed as

Breast NeoplasmsRNA, Long NoncodingCohort StudiesComputational BiologyFemaleHumansPrognosisROC CurveRNA, Long NoncodingBiomarkerBreast cancerDiagnosisExosomelncRNAPrognosis

Identifiers

PMID35855425
PMCPMC9288825
OpenAlexW4285389660

What OpenQuestion holds

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LicenceCC BY
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 OpenQuestion graph.