A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 197

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 317
Function: require_once

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

Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081908PMC
http://dx.doi.org/10.1038/s41598-025-99419-5DOI Listing

Publication Analysis

Top Keywords

prognostic model
8
model osteosarcoma
8
genes associated
8
cox regression
8
model performance
8
set auc
8
high low-risk
8
immune microenvironment
8
unfavorable prognoses
8
high-risk individuals
8

Similar Publications