Mitochondrial DNA Methylation as a Predictor of Immunotherapy Outcomes and Prognosis in Lung Adenocarcinoma: Insights from Single-Cell RNA Sequencing and Machine Learning Approaches - Scorecard - MDSpire

Mitochondrial DNA Methylation as a Predictor of Immunotherapy Outcomes and Prognosis in Lung Adenocarcinoma: Insights from Single-Cell RNA Sequencing and Machine Learning Approaches

  • By

  • Jian Ding

  • Gang Cheng

  • Qian Xue

  • Weizhen Guo

  • Yikun Cheng

  • Cheng Yang

  • Jiabing Tong

  • Zegeng Li

  • Yating Gao

  • February 10, 2026

  • 0 min

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Clinical Scorecard: Mitochondrial DNA Methylation as a Predictor of Immunotherapy Outcomes and Prognosis in Lung Adenocarcinoma: Insights from Single-Cell RNA Sequencing and Machine Learning Approaches

At a Glance

CategoryDetail
ConditionLung Adenocarcinoma (LUAD)
Key MechanismsMitochondrial DNA methylation (MTDM) influences gene expression and mitochondrial function.
Target PopulationPatients diagnosed with lung adenocarcinoma.
Care SettingOncology clinics and research settings utilizing genomic and transcriptomic analyses.

Key Highlights

  • MTDM is a critical regulator of cellular function and disease pathogenesis in LUAD.
  • Single-cell RNA sequencing (scRNA-Seq) provides insights into tumor microenvironment and cellular heterogeneity.
  • Differentially expressed MTDM-related genes (DEMTDMRGs) are associated with overall survival in LUAD.

Guideline-Based Recommendations

Diagnosis

  • Utilize single-cell RNA sequencing to assess cellular heterogeneity in LUAD.

Management

  • Incorporate mitochondrial DNA methylation profiles into prognostic models for LUAD.

Monitoring & Follow-up

  • Monitor changes in MTDM scores to evaluate treatment response and prognosis.

Risks

  • Consider genetic predispositions and treatment resistance factors in LUAD management.

Patient & Prescribing Data

Patients with lung adenocarcinoma, particularly those with genetic predispositions.

Mitochondrial DNA methylation patterns may guide personalized immunotherapy approaches.

Clinical Best Practices

  • Employ machine learning methods to integrate mitochondrial gene expression with clinical features for prognostic modeling.
  • Validate prognostic models using independent datasets to ensure generalizability.

References

Original Source(s)

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