Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Drp1 controls complex II assembly and skeletal muscle metabolism by Sdhaf2 action on mitochondria.

Science advances·2024

Same author

A temporal transcriptome and methylome in human embryonic stem cell-derived cardiomyocytes identifies novel regulators of early cardiac development.

Epigenetics·2018

Same journal

A causal association between Epstein-Barr virus infection and lung cancer: A two-sample Mendelian Randomization study.

Cancer biomarkers : section A of Disease markers·2026

Same journal

In silico analysis suggests ELL2 as a survival-associated cross-tissue biomarker in gastric cancer.

Cancer biomarkers : section A of Disease markers·2026

Same journal

The clinical assessment of miR-34a and miR-135b panel as promising diagnostic biomarkers predict the risk of colorectal cancer: A case-control study.

Cancer biomarkers : section A of Disease markers·2026

Same journal

Integrative multi-omics analysis-based Mendelian randomization identifies association of DNA repair-related genes with oral cancer.

Cancer biomarkers : section A of Disease markers·2026

Same journal

Stage-specific biomarkers in triple-negative breast cancer: Preliminary findings from bioinformatics approaches.

Cancer biomarkers : section A of Disease markers·2026

Same journal

Development and verification of cell cycle-related gene markers for prognosis in bladder cancer.

Cancer biomarkers : section A of Disease markers·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 18, 2026

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

Machine learning-driven gene expression profiling for lung cancer stage determination.

Yinbo Wang¹, Kai Fu²

¹Department of Biostatistics, University of Michigan-Ann Arbor, Ann Arbor, MI, USA.

Cancer Biomarkers : Section a of Disease Markers

|September 12, 2025

Summary

This summary is machine-generated.

This study used machine learning and RNA sequencing data to accurately classify early versus late-stage lung cancer. The developed XGBoost model identified key genes, improving cancer staging for better treatment guidance.

Keywords:

Lung cancer RNA-Seq XGBoost cancer staging gene expression machine learning

More Related Videos

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Related Experiment Videos

Last Updated: Jan 18, 2026

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Lung cancer is a major cause of cancer mortality, necessitating accurate staging for effective treatment.
Next-generation sequencing (NGS) and machine learning (ML) offer advanced methods for precise cancer classification.
Traditional imaging methods have limitations in detailed lung cancer staging.

Purpose of the Study:

To classify early versus late-stage lung cancer using RNA-Seq data.
To apply the XGBoost machine learning algorithm with cross-validation (CV) for improved staging accuracy.
To identify key predictive genes from RNA sequencing data.

Main Methods:

Utilized RNA-Seq data from 993 patients in The Cancer Genome Atlas (TCGA) cohort.
Performed gene selection using the Wilcoxon rank-sum test on training data.
Optimized the XGBoost model through cross-validation and assessed performance using Area Under the Curve (AUC).

Main Results:

The XGBoost model achieved a test AUC of 0.6534.
Identified 40 key genes crucial for predictive accuracy and minimizing overfitting.
Determined optimal classification thresholds of 0.3 and 0.4 for balancing sensitivity and specificity.

Conclusions:

Integrating RNA-Seq data with machine learning enhances lung cancer staging accuracy.
The study highlights the potential of ML-driven genomic analysis in oncology.
Future work should involve larger datasets, model benchmarking, and multi-omics integration for clinical translation.