ArticleCancer medicine2020
Development and validation of an immune-related prognostic signature in lung adenocarcinoma.
Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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94 citing papers in PubMed, 170 citations in OpenAlex.
- Machine learning-based coagulation-related signature for predicting the prognosis and therapy benefits in lung adenocarcinoma.Discover oncology · 2025Article
- Prognostic value and biological role of STING-related genes GAB3 and IL16 in lung adenocarcinoma: implications for immune evasion and treatment.Scientific reports · 2025Article
- Toward the Best Generalizable Performance of Machine Learning in Modeling Omic and Clinical Data.Laboratory investigation; a journal of technical methods and pathology · 2025Article
- A Mendelian randomization-based risk model using aging-related genes for prognosis in lung adenocarcinoma.Discover oncology · 2025Article
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- Impact of tertiary lymphoid structure-associated biomarkers on pancreatic cancer via a dual-disease analysis of psoriasis and pancreatic cancer.Discover oncology · 2025Article
- Zinc Finger Protein-Based Prognostic Signature Predicts Survival in Lung Adenocarcinoma.Cancers · 2025Article
- A predictive model based on immune related genes for diffuse large B cell lymphoma (DLBCL).Translational cancer research · 2025Article
- Integration of cuproptosis-related gene signatures in stomach adenocarcinoma: implications for prognostic prediction and therapeutic strategies in cancer drug resistance.Discover oncology · 2025Article
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- Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.Scientific reports · 2025Article
- Integrative multi-omics and machine learning identify a robust signature for discriminating prognosis and therapeutic targets in bladder cancer.Journal of Cancer · 2025Article
- Identification and comprehensive analysis of an immune-related gene prognostic model for indicating tumor immune microenvironment features in soft tissue sarcoma.Frontiers in oncology · 2025Article
- AKR1C2 silencing promotes ferroptosis and inhibits proliferation, migration, and invasion in lung cancer cells.PloS one · 2025Article
- Analysis of the expression of ARNTL2 and miR-204-5p and their correlation with clinical pathological features in NSCLC patients.American journal of clinical and experimental immunology · 2025Article
- Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.Frontiers in oncology · 2025Article
- Development and validation of a prognostic model based on m6A-related lncRNAs to predict prognosis for papillary renal cell cancer patients.Scientific reports · 2024Article
- Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer.World journal of surgical oncology · 2024Article
- Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types.Biology · 2024Article
34 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung adenocarcinomas (LUAD) is the most common histological subtype of lung cancers. Tumor immune microenvironment (TIME) is involved in tumorigeneses, progressions, and metastases. This study is aimed to develop a robust immune-related signature of LUAD.
methodsA total of 1774 LUAD cases sourced from public databases were included in this study. Immune scores were calculated through ESTIMATE algorithm and weighted gene co-expression network analysis (WGCNA) was applied to identify immune-related genes. Stability selections and Lasso COX regressions were implemented to construct prognostic signatures. Validations and comparisons with other immune-related signatures were conducted in independent Gene Expression Omnibus (GEO) cohorts. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ImmuCellAI and gene set enrichment analysis (GSEA).
resultsIn Cancer Genome Atlas (TCGA) LUAD cohorts, immune scores of higher levels were significantly associated with better prognoses (P < .05). Yellow (n = 270) and Blue (n = 764) colored genes were selected as immune-related genes, and after univariate Cox regression analysis (P < .005), a total of 133 genes were screened out for subsequent model constructions. A four-gene signature (ARNTL2, ECT2, PPIA, and TUBA4A) named IPSLUAD was developed through stability selection and Lasso COX regression. It was suggested by multivariate and subgroup analyses that IPSLUAD was an independent prognostic factor. It was suggested by Kaplan-Meier survival analysis that eight out of nine patients in high-risk groups had significantly worse prognoses in validation data sets (P < .05). IPSLUAD outperformed other signatures in two independent cohorts.
conclusionsA robust immune-related prognostic signature with great performances in multiple LUAD cohorts was developed in this study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.