Evidence map›Paper›PMID 38882157›Full record

ArticleACS omega2024

Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach.

Ambritha Balasundaram, Tanisha Saurav Mitra, Iftikhar Aslam Tayubi, Hatem Zayed, George Priya C Doss

Abstract read
In one paragraph

Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. The role and therapeutic value of NUSAP1 in human cancers.Journal of translational medicine · 2025
    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

5 authors.

Ambritha BalasundaramLaboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
Tanisha Saurav MitraLaboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
Iftikhar Aslam TayubiDepartment of Computer Science, Faculty of Computing and Information Technology, Rabigh (FCITR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Hatem ZayedDepartment of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar.ORCID https://orcid.org/0000-0001-8838-6638
George Priya C DossLaboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.ORCID https://orcid.org/0000-0002-5971-8290

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.

Identifiers

PMID38882157
PMCPMC11170726

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Registered trials

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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.