Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC DOI

Xiaoli Wang,

Yan Zhou,

Xiaomin Lu

et al.

BioFactors, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 31, 2024

Abstract Most patients with non‐small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk metastasis. Consequently, timely identification biomarkers associated lymph node metastasis is essential for improving clinical management NSCLC patients. In this research, WGCNA algorithm was utilized pinpoint genes linked in NSCLC. A cluster analysis carried out investigate how these correlate prognosis and outcomes immunotherapy Following this, diagnostic prognostic models were created validated through various machine learning methodologies. The random forest technique highlighted importance ARHGAP11A, leading in‐depth examination its role By analyzing 78 tissue chip samples from patients, study confirmed association between ARHGAP11A expression, patient prognosis, Finally, influence on cells assessed function experiments. This research identify 25 that related metastasis, clarifying their connections tumor invasion, growth, activation stemness pathways. Cluster revealed significant associations NSCLC, especially concerning targeted treatments. system combines approaches demonstrated strong efficacy forecasting both diagnosis Importantly, identified as key gene Molecular docking analyses suggested has affinity therapies within Additionally, immunohistochemical assessments higher levels expression unfavorable Experiments showed reducing can hinder proliferation, traits cells. investigation reveals novel insight may potential biomarker connected Moreover, ability diminish characteristics, presenting promising opportunity strategies condition.

Language: Английский

Targeting liver cancer stem cells: the prognostic significance of MRPL17 in immunotherapy response DOI Creative Commons

Jingjing Shao,

Tianye Zhao, Ji‐Bin Liu

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 17, 2024

Liver hepatocellular carcinoma (LIHC) ranks as the foremost cause of cancer-related deaths worldwide, and its early detection poses considerable challenges. Current prognostic indicators, including alpha-fetoprotein, have notable limitations in their clinical utility, thereby underscoring necessity for discovering new biomarkers to improve diagnosis enable personalized treatment options.

Language: Английский

Citations

1
Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC DOI

Xiaoli Wang,

Yan Zhou,

Xiaomin Lu

et al.

BioFactors, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 31, 2024

Abstract Most patients with non‐small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk metastasis. Consequently, timely identification biomarkers associated lymph node metastasis is essential for improving clinical management NSCLC patients. In this research, WGCNA algorithm was utilized pinpoint genes linked in NSCLC. A cluster analysis carried out investigate how these correlate prognosis and outcomes immunotherapy Following this, diagnostic prognostic models were created validated through various machine learning methodologies. The random forest technique highlighted importance ARHGAP11A, leading in‐depth examination its role By analyzing 78 tissue chip samples from patients, study confirmed association between ARHGAP11A expression, patient prognosis, Finally, influence on cells assessed function experiments. This research identify 25 that related metastasis, clarifying their connections tumor invasion, growth, activation stemness pathways. Cluster revealed significant associations NSCLC, especially concerning targeted treatments. system combines approaches demonstrated strong efficacy forecasting both diagnosis Importantly, identified as key gene Molecular docking analyses suggested has affinity therapies within Additionally, immunohistochemical assessments higher levels expression unfavorable Experiments showed reducing can hinder proliferation, traits cells. investigation reveals novel insight may potential biomarker connected Moreover, ability diminish characteristics, presenting promising opportunity strategies condition.

Language: Английский

Citations

0