Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination
Abstract Background Lung cancer is a highly aggressive and lethal cancer requiring prognostic and predictive biomarkers for improving patient outcomes. Here, a prognostic signature for lung cancer was developed and prognostic programmed cell death (PCD)-related genes were identified. Methods In this...
Saved in:
| Main Authors: | Han Ning, Ying Jiang, Mengli Zheng, Gao Yang, Lianjun Ma, Yachao Zhao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Oncology |
| Online Access: | https://doi.org/10.1007/s12672-025-02793-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
In-depth exploration of programmed cell death-related subtypes and development of a prognostic signature model in lung adenocarcinoma
by: Desheng Zhou, et al.
Published: (2025-07-01) -
Immunogenic cell death signature predicts survival and reveals the role of VEGFA + Mast cells in lung adenocarcinoma
by: Meng Zhang, et al.
Published: (2025-02-01) -
Feasibility study of in vitro drug sensitivity assay of advanced non-small cell lung adenocarcinomas
by: Emoke Papp, et al.
Published: (2020-09-01) -
Cell Death and Senescence‐Based Molecular Classification and an Individualized Prediction Model for Lung Adenocarcinoma
by: Pan Wang, et al.
Published: (2025-06-01) -
Molecular Subtyping and Therapeutic Targeting of IFNG‐Driven Immunogenic Cell Death in Lung Adenocarcinoma
by: Lifeng Li, et al.
Published: (2025-02-01)