ArticleScientific reports2022
An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Monocyte-related markers as predictors of immune checkpoint inhibitor efficacy and immune-related adverse events: a systematic review and meta-analysis.Cancer metastasis reviews · 2025Pooled it
- Association of a gene-expression subtype to outcome and treatment response in patients with recurrent/metastatic head and neck squamous cell carcinoma treated with nivolumab.Journal for immunotherapy of cancer · 2024Trial
- Single-cell and machine learning-based neural regulation signature for prognosis prediction and immunotherapy response in lung adenocarcinoma.Translational oncology · 2026Article
- Advancing Prognosis Prediction and Immunotherapy Efficacy in Lung Adenocarcinoma Through Machine Learning: Novel Insights From Anoikis Regulator Patterns in Single-Cell Multiomics.International journal of genomics · 2026Article
- Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer.Scientific reports · 2025Article
- The Current Roadmap of Lung Cancer Biology, Genomics and Racial Disparity.International journal of molecular sciences · 2025Review
- Review
- Integration of the bulk transcriptome and single-cell transcriptome reveals efferocytosis features in lung adenocarcinoma prognosis and immunotherapy by combining deep learning.Cancer cell international · 2024Article
- Analysis and identification of mRNAsi‑related expression signatures via RNA sequencing in lung cancer.Oncology letters · 2024Article
- Prognostic model of lung adenocarcinoma based on immunoprognosis-related genes and related drug prediction.Journal of thoracic disease · 2024Article
- High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC).Journal for immunotherapy of cancer · 2024Article
- Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology.Journal of translational medicine · 2024Article
- Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response.Respiratory research · 2024Article
- Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024Article
- Article
- Biomarkers for immune checkpoint inhibition in sarcomas - are we close to clinical implementation?Biomarker research · 2023Review
- Pharmacogenomics: Driving Personalized Medicine.Pharmacological reviews · 2023Review
- Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.NPJ precision oncology · 2023Article
- Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.bioRxiv : the preprint server for biology · 2023Article
- Identification of lung adenocarcinoma subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes.Frontiers in cell and developmental biology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung adenocarcinoma is one of the most common malignant tumors worldwide. The purpose of this study was to construct a stable immune gene signature for prediction of prognosis (IGSPP) and response to immune checkpoint inhibitors (ICIs) therapy in LUAD patients. Five genes were screened by weighted gene coexpression network analysis, Cox regression and LASSO regression analyses and were used to construct the IGSPP. The survival rate of the IGSPP low-risk group was higher than that of the IGSPP high-risk group. Multivariate Cox regression analysis showed that IGSPP could be used as an independent prognostic factor for the overall survival of LUAD patients. IGSPP genes were enriched in cell cycle pathways. IGSPP gene mutation rates were higher in the high-risk group. CD4 memory-activated T cells, M0 and M1 macrophages had higher infiltration abundance in the high-risk group, which was associated with poor overall survival. In contrast, the abundance of resting CD4 memory T cells, monocytes, resting dendritic cells and resting mast cells associated with a better prognosis was higher in the low-risk group. TIDE scores and the expressions of different immune checkpoints showed that patients in the high-risk IGSPP group benefited more from ICIs treatment. In short, an IGSPP of LUAD was constructed and characterized. It could be used to predict the prognosis and benefits of ICIs treatment in LUAD patients.
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