Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma.

World journal of surgical oncology - 19 Sept 2020

Li Guofeng, Wang Guangsuo, Guo Yanhua, Li Shixuan, Zhang Youlong, Li Jialu, Peng Bin

Abstract excerpt

BACKGROUND: Integrating phenotypic and genotypic information to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS) and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression, and mutation data. METHODS: We enrolled a total of 408 patients from...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Adenocarcinoma of Lung
Share to Gene Expression

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Lung Neoplasms

Share to Mutation

Share to Prognosis

Source

Open in PubMed Open full text

Identifiers

PMID: 32950055
DOI: 10.1186/s12957-020-02025-0
PMCID: PMC7502202

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma.PMID 32950055Read ↗

Development of a Novel Prognostic Score Combining Clinicopathologic Variables, Gene Expression and Mutationprofiles for Lung AdenocarcinomaSemantic proximity Read ↗

Identification of an immune signature predicting prognosis risk of patients in lung adenocarcinomaSemantic proximity Read ↗

Integration of Clinicopathological And Genomic Features To Predict The Risk Stratification of TCGA Lung Adenocarcinoma And Lung Squamous Cell Carcinoma PatientsSemantic proximity Read ↗

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genesSemantic proximity Read ↗

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosisSemantic proximity Read ↗

Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinomaSemantic proximity Read ↗

Analysis of Clinical Implications and Immune Landscape in Prognostic Model based on Immune Cell Signatures for Lung Adenocarcinoma PatientsSemantic proximity Read ↗

Mast cell-based molecular subtypes and signature associated with clinical outcome in early-stage lung adenocarcinoma.Semantic proximity Read ↗

Individualized Prognostic Score for Lung Adenocarcinoma Based on Immune StatusSemantic proximity Read ↗

Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinomaSemantic proximity Read ↗

Building a Risk Scoring Model for ARDS in Lung Adenocarcinoma Patients Using Machine Learning Algorithms.Semantic proximity Read ↗

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene setsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.