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

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

1 Sept 2020

Abstract excerpt

BACKGROUND: Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung...

Read the complete abstract on PubMed

Topics

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

Share to Ferroptosis and cancer prognosis
Share to Lung Cancer Diagnosis and Treatment
Share to Lung Cancer Treatments and Mutations

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 32998690
DOI: 10.1186/s12859-020-03691-3

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinomaPMID 32998690Read ↗

Comprehensive profiling of genomic and transcriptomic differences between risk groups of lung adenocarcinoma and lung squamous cell carcinomaSemantic proximity Read ↗

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell CarcinomaSemantic proximity Read ↗

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

Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.Semantic 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 ↗

Identification of Molecular Subtypes and a Prognostic Signature Based on m6A/m5C/m1A-Related Genes in Lung adenocarcinomaSemantic proximity Read ↗

Survival prediction for patients with lung adenocarcinoma: A prognostic risk model based on gene mutations.Semantic proximity Read ↗

The identification of key biomarkers in patients with lung adenocarcinoma based on bioinformaticsSemantic proximity Read ↗

Systematical identifications of prognostic meaningful lung adenocarcinoma subtypes and the underlying mutational and expressional characters.Semantic proximity Read ↗

Analysis of prognostic model based on immunotherapy related genes in lung adenocarcinomaSemantic proximity Read ↗

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing Data to Establish and Validate a Prognostic Model for Patients With Lung AdenocarcinomaSemantic proximity Read ↗

Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinomaSemantic proximity Read ↗

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