Cited by Efficient Discovery of Robust Prognostic Biomarkers and Signatures in Solid Tumors

Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome DOI

Xueying Hu,

Haiqun Dong,

Qin Wen

et al.

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

Published: April 10, 2024

Head and neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor-node-metastasis staging system does not satisfy the requirements for screening treatment-sensitive patients. Thus, ideal biomarker leading to precise treatment of HNSCC urgently needed.

Language: Английский

Citations

Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma DOI

Shengqiang Chi, Jing Ma, Yiming Ding

et al.

Life Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 123396 - 123396

Published: Jan. 1, 2025

Language: Английский

Citations

Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer DOI

Xiao Guo,

Chuanbo Feng,

Jiaying Xing

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 14, 2025

Background The rising incidence of breast cancer and its heterogeneity necessitate precise tools for predicting patient prognosis tailoring personalized treatments. Epigenetic changes play a critical role in progression therapy responses, providing foundation prognostic model development. Methods We developed the Machine Learning-derived Model (MLEM) to identify epigenetic gene patterns cancer. Using multi-cohort transcriptomic datasets, MLEM was constructed with rigorous machine learning techniques validated across independent datasets. model’s performance further corroborated through immunohistochemical validation on clinical samples. Results effectively stratified patients into high- low-risk groups. Low-MLEM exhibited improved prognosis, characterized by enhanced immune cell infiltration higher responsiveness immunotherapy. High-MLEM showed poorer but were more responsive chemotherapy, vincristine identified as promising therapeutic option. demonstrated robust Conclusion is powerful tool outcomes By integrating insights learning, this has potential improve decision-making optimize strategies patients.

Language: Английский

Citations

Comprehensive Analysis of m6A‐Related Programmed Cell Death Genes Unveils a Novel Prognostic Model for Lung Adenocarcinoma DOI

Xiao Zhang, Yan‐Pei Cao, Jiatao Liu

et al.

Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(2)

Published: Jan. 1, 2025

ABSTRACT Lung adenocarcinoma (LUAD) involves complex dysregulated cellular processes, including programmed cell death (PCD), influenced by N6‐methyladenosine (m6A) RNA modification. This study integrates bulk and single‐cell sequencing data to identify 43 prognostically valuable m6A‐related PCD genes, forming the basis of a 13‐gene risk model (m6A‐related signature [mPCDS]) developed using machine‐learning algorithms, CoxBoost SuperPC. The mPCDS demonstrated significant predictive performance across multiple validation datasets. In addition its prognostic accuracy, revealed distinct genomic profiles, pathway activations, associations with tumour microenvironment potential for predicting drug sensitivity. Experimental identified RCN1 as oncogene driving LUAD progression promising therapeutic target. offers new approach stratification personalised treatment strategies.

Language: Английский

Citations

Efficient Discovery of Robust Prognostic Biomarkers and Signatures in Solid Tumors DOI

Zaoqu Liu, Jinhai Deng, Hui Xu

et al.

Cancer Letters, Journal Year: 2025, Volume and Issue: unknown, P. 217502 - 217502

Published: Jan. 1, 2025

Language: Английский

Citations