Evidence map›Paper›PMID 40374892›Full record

ArticleScientific reports2025

Constructing a prognostic model for osteosarcoma based on centrosome-related genes and identifying potential therapeutic targets of paclitaxel.

Yujian Zhong, Bohua Gao, Kai Tong, Lan Li, Qingjun Wei, Yong Hu

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Article in Scientific reports, 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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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yujian Zhong *Department of Orthopedics, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuchang District, Wuhan, 430060, China.
Bohua Gao *Department of Orthopedics Trauma, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, Hainan, China.
Kai Tong *Department of Orthopedics, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuchang District, Wuhan, 430060, China.
Lan LiDepartment of Orthopedics, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuchang District, Wuhan, 430060, China.
Qingjun WeiDepartment of Orthopedics, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, Guangxi, China. weiqingjun@gxmu.edu.cn.
Yong HuDepartment of Orthopedics, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuchang District, Wuhan, 430060, China. hyrm2017@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The centrosome, a vital component in mitosis in eukaryotes, plays a pivotal role in cancer progression by influencing the proliferation and differentiation of malignant cells, making it a significant therapeutic target. We collected genes associated with centrosomes from existing literature and established a prognostic model for 85 osteosarcoma patients from the TARGET database. Genes associated with prognosis were identified through univariate Cox regression. We then mitigated overfitting by addressing collinearity using LASSO regression. Ultimately, a set of five genes was selected for the model through multivariable Cox regression. Model performance was assessed using ROC curves, which yielded a training set AUC of 0.965 and a validation set AUC of 0.770, indicating satisfactory model performance. We further identified genes with differential expression in high and low-risk groups and conducted functional enrichment analysis using KEGG, GO, Progeny, GSVA, and GSEA. Results revealed significant variances in various immune-related pathways between high and low-risk cohorts. Analysis of the immune microenvironment using ssGSEA and ESTIMATE indicated that individuals with unfavorable prognoses had lower immune scores, stromal scores, and ESTIMATE scores, coupled with higher tumor purity. This suggests that high-risk individuals have compromised immune microenvironments, potentially contributing to their unfavorable prognoses. Additionally, drug sensitivity and molecular docking analysis revealed increased responsiveness to paclitaxel in high-risk individuals, implying its prognostic value. The JTB-encoded protein exhibited a negative binding energy of - 5.5 kcal/mol when interacting with paclitaxel, indicating its potential to enhance the patient's immune microenvironment. This framework enables patient prognosis prediction and sheds light on paclitaxel's mechanism in osteosarcoma treatment, facilitating personalized treatment approaches.

Indexed as

Antineoplastic Agents, PhytogenicBone NeoplasmsCentrosomeOsteosarcomaPaclitaxelBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansMolecular Docking SimulationPrognosisROC CurveTumor MicroenvironmentAntineoplastic Agents, PhytogenicBiomarkers, TumorPaclitaxelCentrosomeIndividualized therapyOsteosarcomaPrognostic modelTumor immune microenvironment

Identifiers

PMID40374892
PMCPMC12081908

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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.