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

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-05-15
DOI
https://doi.org/10.1038/s41598-025-99419-5
Citations
1
Primary Topic
Protein Degradation and Inhibitors
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Qingjun Wei, Kai Tong, Bo-Hua Gao, Lan Li et al.
1 citations
Scientific Reports
Protein Degradation and Inhibitors
0.81
article

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

Qingjun Wei, Kai Tong, Bo-Hua Gao, Lan Li, Yong Hu, Yujian Zhong
article en
1 citations

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.

Scientific ReportsVol. 15(1)
Wuhan University (CN), First Affiliated Hospital of GuangXi Medical University (CN), Hainan General Hospital (CN), Renmin Hospital of Wuhan University (CN), Hainan Medical University (CN)
Good health and well-being
Openalex Percentile: Top 28%
Protein Degradation and Inhibitors
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.