T lymphocyte subsets and mitochondrial index predict outcomes in advanced non small cell lung cancer therapy

Currently, accessible objective indicators for predicting the prognosis and treatment outcomes of non-small cell lung cancer (NSCLC) are lacking. This study investigates the potential of T lymphocyte subsets and mitochondrial biomarkers as predictors of metastasis and evaluators of therapy response in advanced NSCLC. We analyzed clinical data from 24 advanced NSCLC patients, including T-cell counts and mitochondrial mass (MM)/membrane potential (MMP) measurements. Group comparisons used parametric t-tests and non-parametric Mann-Whitney U tests, with False Discovery Rate (FDR) correction for multiple comparisons. Predictive performance was evaluated via receiver operating characteristic (ROC) curves (area under curve, AUC). CD8 + T cells with low mitochondrial membrane potential (MMP-Low) and CD8 + T-cell mitochondrial mass (MM) showed promising potential in predicting metastasis (AUC = 0.85 and 0.87). Three T-cell biomarkers correlated with therapeutic outcomes (p < 0.05): PD-1 + CD8 + T-cell percentage, MMP-Low CD8 + T-cell proportion, CD8 + T-cell MM levels. Our exploratory findings suggest that T lymphocyte subsets and mitochondrial indices show potential as biomarkers to evaluate therapy response and predict metastasis risk in advanced NSCLC, which may facilitate individualized treatment and timely regimen adjustment for patients pending further validation.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-08-31
DOI
https://doi.org/10.1038/s41598-025-17735-2
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

T lymphocyte subsets and mitochondrial index predict outcomes in advanced non small cell lung cancer therapy

Chiqing Ying, Dan Zhu, Zheng‐Ming Huang
Scientific Reports
Lung Cancer Diagnosis and Treatment
article

T lymphocyte subsets and mitochondrial index predict outcomes in advanced non small cell lung cancer therapy

Chiqing Ying, Dan Zhu, Zheng‐Ming Huang
article en

Abstract

Currently, accessible objective indicators for predicting the prognosis and treatment outcomes of non-small cell lung cancer (NSCLC) are lacking. This study investigates the potential of T lymphocyte subsets and mitochondrial biomarkers as predictors of metastasis and evaluators of therapy response in advanced NSCLC. We analyzed clinical data from 24 advanced NSCLC patients, including T-cell counts and mitochondrial mass (MM)/membrane potential (MMP) measurements. Group comparisons used parametric t-tests and non-parametric Mann-Whitney U tests, with False Discovery Rate (FDR) correction for multiple comparisons. Predictive performance was evaluated via receiver operating characteristic (ROC) curves (area under curve, AUC). CD8 + T cells with low mitochondrial membrane potential (MMP-Low) and CD8 + T-cell mitochondrial mass (MM) showed promising potential in predicting metastasis (AUC = 0.85 and 0.87). Three T-cell biomarkers correlated with therapeutic outcomes (p < 0.05): PD-1 + CD8 + T-cell percentage, MMP-Low CD8 + T-cell proportion, CD8 + T-cell MM levels. Our exploratory findings suggest that T lymphocyte subsets and mitochondrial indices show potential as biomarkers to evaluate therapy response and predict metastasis risk in advanced NSCLC, which may facilitate individualized treatment and timely regimen adjustment for patients pending further validation.

Scientific ReportsVol. 15(1)
Jinhua Central Hospital (CN)
Good health and well-being
Openalex Percentile: Top 66%
Lung Cancer Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.