Evidence map›Paper›PMID 40287845›Full record

ArticleCancer medicine2025

Network-Based Integrative Analysis to Identify Key Genes and Corresponding Reporter Biomolecules for Triple-Negative Breast Cancer.

Pooja Singh, Rupesh Chaturvedi, Pallavi Somvanshi

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In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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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.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Pooja SinghSchool of Computational & Sciences (SCIS), Jawaharlal Nehru University, New Delhi, India.
Rupesh ChaturvediSchool of Biotechnology (SBT), Jawaharlal Nehru University, New Delhi, India.ORCID https://orcid.org/0000-0002-8219-8465
Pallavi SomvanshiSchool of Computational & Sciences (SCIS), Jawaharlal Nehru University, New Delhi, India.ORCID https://orcid.org/0000-0003-1214-9374

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe malignant neoplasm of the TNBC is the leading cause of death among Indian women. Recent studies identified the global burden of TNBC affecting approximately more than 40 percent of all BC cases in women worldwide. The absence of expression of receptors such as ER, PR, and HER2 characterizes TNBC.

objectivesDue to the lack of specific targets, standard treatment options for TNBC are limited. This integrative study aims to identify key genes and provide insights into the underlying molecular mechanisms of TNBC, which can potentially lead to the development of more effective therapeutic strategies. MATERIAL AND METHODOLOGY: This study integrates PPI and WGCNA analysis of TNBC-related datasets (GSE52194 and GSE58135) to identify key genes. Subsequently, downstream analysis is conducted to explore potential therapeutic targets for TNBC.

resultsThe present study renders the potential 13 key genes (PLCG2, CXCL10, CDK1, STAT1, IL6, PLK1, CCNB1, AURKA, NDC80, EGFR, 1L1B, FN1, BUB1B), along with their associated 6 TFs and 20 miRNAs, as reporter biomolecules around which the most significant changes occur. There were some miRNAs hsa-mir-449b-5p, hsa-let-7b-5p, hsa-mir-26a-5p, hsa-mir-155-5p, hsa-mir-24-3p, hsa-mir-212-3p, hsa-mir-21-5p, hsa-mir-210-3p and hsa-mir-20a-5p whose association with other cancers and other BC subtypes have been reported but their association with TNBC need to be explored. Further, enrichment and cumulative survival analysis support the disease association of identified key genes with TNBC.

conclusionThis integrative analysis could be regarded for experimental inspection as it provides the platform for future researchers in drug designing and biomarker discovery for TNBC diagnosis and treatment.

Indexed as

Biomarkers, TumorGene Regulatory NetworksTriple Negative Breast NeoplasmsComputational BiologyDatabases, GeneticFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMicroRNAsProtein Interaction MapsBiomarkers, TumorMicroRNAscumulative survival analysiskey genereporter biomoleculetriple negative breast cancer

Identifiers

PMID40287845
PMCPMC12034156

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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.