Evidence map›Paper›PMID 41824870›Full record

ArticleMedicine2026

AURKB as a key driver of esophageal cancer progression: Molecular mechanisms and potential nursing implications based on multi-omics analysis.

Xiaoling Qu, Lihua Lu, Xuan Dong

Abstract read
In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Xiaoling QuGastrointestinal Rehabilitation Center, Beijing Rehabilitation Hospital Affiliated to Capital Medical University, Shijingshan District, Beijing, China.
Lihua LuSouthern Medical Branch of PLA General Hospital, Haidian District, Beijing, China.
Xuan DongSouthern Medical Branch of PLA General Hospital, Fengtai District, Beijing, China.ORCID 0009-0006-9832-4530

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Esophageal cancer is a highly heterogeneous malignant tumor with a high global incidence and poor prognosis. Its molecular mechanisms are complex and have not yet been fully clarified. The protein kinase encoded by AURKB plays a key role in cell cycle regulation and mitosis; however, its expression characteristics, functional mechanisms, nursing care in esophageal cancer have not yet been clarified, thus urgently require in-depth research. This study integrates bioinformatics analysis and validation with public datasets. Based on GSE164158 and GSE17351 datasets from gene expression omnibus database, differentially expressed genes (DEGs) between esophageal cancer and normal tissues were screened. Functional enrichment analysis was performed using GO, KEGG, GSEA, Metascape to analyze biological pathways involved in DEGs. Protein-protein interaction networks were constructed using STRING database and Cytoscape, core modules were identified by combining MCODE algorithm. Key gene modules related to esophageal cancer phenotypes were screened through weighted gene co-expression network analysis, core genes were further screened by integrating DEGs results. Core genes were identified using multiple algorithms (CLUSTER ONE and MCC), immune infiltration characteristics were analyzed via CIBERSORT. A total of 1500 DEGs were screened out, which were significantly enriched in biological processes and pathways such as cell cycle, DNA replication, mitosis. Metascape analysis further verified that DEGs are involved in key processes including cell cycle regulation and DNA repair. Integrated analysis of the PPI network and weighted gene co-expression network analysis showed that three hundred forty nine genes were present in both key modules and DEGs; combined with screening via multiple algorithms, 4 core genes were finally identified. AURKB was highly expressed in esophageal cancer tissues (validated by heatmap); Comparative Toxicogenomics Database analysis indicated that it was associated with clinical features such as aggressive tumors. Immune infiltration analysis revealed significant heterogeneity in the composition of immune cells in esophageal cancer samples. AURKB is a key driver gene in esophageal cancer, promoting tumor progression through dual mechanisms of cell cycle regulation and ferroptosis resistance. This study not only provides a novel theoretical basis and potential molecular targets for targeted therapy, but also offers valuable insights for precision nursing interventions, particularly in perioperative care, clinical risk assessment, and postoperative rehabilitation of patients with esophageal cancer.

Indexed as

Aurora Kinase BEsophageal NeoplasmsComputational BiologyDatabases, GeneticDisease ProgressionGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMultiomicsProtein Interaction MapsAURKB protein, humanAurora Kinase BAURKBbioinformatics analysisdifferentially expressed genesesophageal cancernursing careprecision nursing

Identifiers

PMID41824870
PMCPMC12991449

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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.