Evidence map›Paper›PMID 33991433›Full record

ArticleIET systems biology2021

Long non-coding RNAs and their targets as potential biomarkers in breast cancer.

Maryam Khalid, Rehan Zafar Paracha, Maryum Nisar, Sumaira Malik, Salma Tariq, Iqra Arshad, Amnah Siddiqa, Zamir Hussain, Jamil Ahmad, Amjad Ali

Open access · goldAbstract read
In one paragraph

Article in IET systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 47% 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

6 citing papers in PubMed, 7 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

10 authors at 3 institutions in 2 countries.

Maryam KhalidResearch Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Rehan Zafar ParachaResearch Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology (NUST), Islamabad, Pakistan.ORCID 0000-0003-4227-1354
Maryum NisarResearch Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Sumaira MalikResearch Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Salma TariqResearch Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Iqra ArshadResearch Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Amnah SiddiqaThe Jackson Laboratory for Genomic Medicine, Connecticut, USA.
Zamir HussainResearch Centre for Modeling and Simulation - RCMS, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Jamil AhmadDepartment of Computer Science and Information Technology, University of Malakand, Chakdara, Pakistan.
Amjad AliAtta-ur-Rahman School of Applied Biosciences - ASAB, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
National University of Sciences and Technology · PKInformation Technology University · PKJackson Laboratory · US

Funding

This research was not funded by any organization
6 · The paper itself

Abstract

Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

Indexed as

Breast NeoplasmsRNA, Long NoncodingBiomarkersFemaleGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansRNA, MessengerBiomarkersRNA, Long NoncodingRNA, Messenger

Identifiers

PMID33991433
PMCPMC8675856
OpenAlexW3163646850

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.