Evidence map›Paper›PMID 40612866›Full record

ArticleOncology research2025

Developing a prognostic signature and characterizing the tumor microenvironment based on centrosome-related genes in lung adenocarcinoma.

Lingjie Xu, Yiqin Xia, Qin Qin, Guiqun Wang, Kai Tao, Wei Wei

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Article in Oncology research, 2025. 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Lingjie XuDepartment of Emergency, West China Hospital, Sichuan University, Chengdu, 610041, China.
Yiqin XiaDepartment of Emergency, West China Hospital, Sichuan University, Chengdu, 610041, China.
Qin QinDepartment of Emergency, West China Hospital, Sichuan University, Chengdu, 610041, China.
Guiqun WangDepartment of Emergency, West China Hospital, Sichuan University, Chengdu, 610041, China.
Kai TaoWest China School of Medicine, Sichuan University, Chengdu, 610041, China.
Wei WeiDepartment of Emergency, West China Hospital, Sichuan University, Chengdu, 610041, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The centrosome, a crucial cellular structure involved in the mitotic process of eukaryotic cells, plays a significant role in tumor progression by regulating the growth and differentiation of neoplastic cells. This makes the centrosome a promising target for therapeutic strategies in cancer treatment. Methods: Utilizing data from the TCGA database, we identified centrosome-related genes and constructed a prognostic model for 518 lung adenocarcinoma patients. Prognosis-associated genes were initially screened using univariate Cox regression, with overfitting minimized by applying LASSO regression to remove collinearity. Finally, a set of 12 genes was selected through multivariable Cox regression for inclusion in the prognostic model. Results: The model's performance was assessed using ROC curve analysis, demonstrating a robust predictive ability with an AUC of 0.728 in the training group and 0.695 in the validation group. Differential expression analysis between high-risk (HRLAs) and low-risk (LRLAs) individuals was performed, followed by enrichment analyses using KEGG, GO, Progeny, GSVA, and GSEA. These analyses revealed significant differences in immune-related pathways between the two groups. Immune microenvironment assessment through ssGSEA and ESTIMATE indicated that individuals with poor prognosis exhibited lower immune, stromal, and ESTIMATE scores, along with higher tumor purity, suggesting an impaired immune microenvironment in HRLAs patients. Drug susceptibility analysis and molecular docking showed that HRLAs individuals were more responsive to docetaxel, emphasizing the therapeutic relevance of paclitaxel in this cohort. Conclusion: We successfully developed and validated a centrosome-associated gene-based prognostic model, offering clinicians valuable insights for improved decision-making and personalized treatment strategies. This model may facilitate the identification of high-risk patients and guide therapeutic interventions in lung adenocarcinoma.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorCentrosomeLung NeoplasmsTumor MicroenvironmentFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisBiomarkers, TumorCentrosomeLung adenocarcinomaNeoplastic microenvironmentPersonalized treatmentPrognostic model

Identifiers

PMID40612866
PMCPMC12215551

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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.