Prioritizing gut microbial SNPs linked to immunotherapy outcomes in NSCLC patients by integrative bioinformatics analysis
The integration of metagenomics, machine learning, and structural bioinformatics provides a robust framework for understanding the association between gut microbial variations and treatment response, paving the way for personalized therapies for NSCLC in the future. These findings emphasize the potential clinical implications of microbiome-based biomarkers in guiding patient-specific treatment strategies and improving immunotherapy outcomes.
Authors
- Shahzad Rasool (ORCID: https://orcid.org/0000-0001-5504-3727)
- Lisu Huang (ORCID: https://orcid.org/0000-0001-8193-7353)
- Mehak Rafiq (ORCID: https://orcid.org/0000-0003-1084-6782)
- Masood Ur Rehman Kayani (ORCID: https://orcid.org/0000-0002-7425-1756)
- Nadeem Khan
- Hina Tariq (ORCID: https://orcid.org/0000-0002-6273-5921)
- Haseeb Manzoor (ORCID: https://orcid.org/0009-0006-0644-4376)
- Muhammad Faheem Raziq
Institutions
- Children's Hospital of Zhejiang University (CN)
- National University of Sciences and Technology (PK)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1186/s12967-025-06370-0
- Citations
- 2
- Primary Topic
- Gut microbiota and health
- Type
- article
- Field-Weighted Citation Impact
- 1.61