Introduction

Among all types of cancer, lung cancer ranks among the highest in both incidence and mortality rates. According to the latest epidemiological data indicate that lung cancer accounts for 11.6% of total cancer cases and 18.4% of cancer-related deaths1. Based on histopathological classification, lung cancers are categorized into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC)2. NSCLC constitutes approximately 80–85% of all lung cancer cases and includes major subtypes such as squamous cell carcinoma, adenocarcinoma, and large cell carcinoma3. The majority of patients with NSCLC are diagnosed at an advanced stage, and the 5-year survival rate is only 15%4,5. Therefore, accurately evaluating prognosis and treatment efficacy is crucial for managing advanced NSCLC patients.

Assessment of T lymphocyte subsets is a key indicator of cellular immune function and holds value in auxiliary diagnosis, pathogenesis analysis, therapeutic response monitoring, and prognosis assessment of various diseases, including autoimmune disorders, immunodeficiencies, malignancies, hematological diseases, and allergic conditions6. Evidence suggests that monitoring T lymphocyte subsets can serve as objective indicators for predicting prognosis and assessing therapeutic responses in NSCLC patients7. At the mechanistic level, immune checkpoint inhibitors such as Programmed Death 1 (PD1)/Programmed Death Ligand 1 (PD-L1) monoclonal antibodies exert antitumor effects by blocking the PD-1/PD-L1 inhibitory axis, thereby reactivating dysfunctional T cells and restoring immune surveillance8. This therapeutic strategy underscores the centrality of T-cell functionality in cancer immunotherapy. Supporting this, recent clinical observations reveal that the density and activity of tumor-infiltrating CD8 + T lymphocytes within the tumor microenvironment serve as robust predictors of patient survival. This finding reinforces the intricate interplay between immune evasion mechanisms and tumor microenvironment dynamics9. Mitochondria, present in all human cells, are essential hubs for T lymphocyte immune function. Two novel mitochondrial indicators, mitochondrial mass (MM) and low mitochondrial membrane potential (MMP-low), indicate mitochondrial energy metabolism10. MM is the number of protein complexes in the mitochondria’s inner membrane respiratory chain that is related to dynamics of mitochondrial fusion and division10,11. Some studies found that CD8 + tumor infiltrating lymphocytes showed a dramatic reduction of mitochondrial mass and the ability to take up glucose12. MMPlow% is the voltage difference across the mitochondrial membrane, with certain higher MMP levels, which leads to lower MMPlow%, pointing to heightened ATP synthesis, thus reflecting the cell’s metabolic activity. It has been reported that a high MMPlow% of tumor-infiltrating CD4 + T cells was observed in a mouse lung cancer model, suggesting a low ATP production capacity and a weakened functional immunity of CD4 + T cells. Recent studies have found that mitochondrial damage not only exhibits a significant correlation with cancer development, but may also serve as a potential molecular marker for predicting therapeutic efficacy and metastasis progression13,14.

Given emerging evidence of immunometabolic dysregulation in NSCLC, we hypothesized that peripheral blood T lymphocyte subsets and mitochondrial indices could serve as non-invasive biomarkers for predicting metastasis and evaluating therapeutic response in advanced NSCLC. This study aimed to: (1) Quantify differences in these parameters between advanced NSCLC patients and healthy controls; (2) Assess their predictive value for distant metastasis; (3) Determine whether longitudinal changes in these biomarkers correlate with treatment efficacy after therapy.

Materials and methods

Study population

Between January 2024 and January 2025, whole blood samples and clinical data (including age, sex, routine hematology results, and lung CT imaging) were collected from patients with newly diagnosed advanced NSCLC admitted to our hospital. As controls, a number of healthy individuals were randomly selected from a physical examination cohort, with age and sex distributions frequency-matched to the case group. All controls were medically confirmed to have no active diseases. NSCLC diagnoses followed the Small Cell Lung Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology and the 8th Edition of the International Association for the Study of Lung Cancer (IASLC) TNM Staging System15,16. All participants provided written informed consent prior to enrollment. Inclusion Criteria: (i) Aged 18–75 years; (ii) Histologically or cytologically confirmed NSCLC meeting with either: unresectable stage IIIA disease; or stage IIIB–IV disease (newly diagnosed or recurrent); (iii) Eastern Cooperative Oncology Group (ECOG) performance status 0 or 1 (stable for ≥ 2 weeks). (iv) Life expectancy ≥ 12 weeks. Exclusion Criteria: (i) Patients with underlying immune disorders or receiving long-term immunosuppressive therapy (e.g. corticosteroids). (ii) Individuals experiencing acute exacerbations of chronic respiratory diseases (e.g. COPD, asthma, bronchiectasis). (iii) Patients with active systemic/localized infections or chronic infections requiring ongoing antimicrobial treatment. (iv) Patients with other malignancies. Treatment response was assessed according to RECIST 1.1 criteria using axial imaging (CT/PET-CT) after two cycles of treatment. Key response endpoints included: Complete Response (CR): Disappearance of all target lesions (target nodules: short axis < 10 mm); Partial Response (PR): ≥30% decrease in sum of target lesion diameters; Progressive Disease (PD): ≥20% increase in the sum of diameters (with a minimum 5 mm absolute increase), or the appearance of new malignant lesions, or clear progression of non-target lesions; Stable Disease (SD): Neither meeting PR nor PD criteria. Patients were classified into non-metastatic (M0) or metastatic (M1) groups based on comprehensive clinical staging at enrollment. Distant metastasis was confirmed through cross-sectional imaging (contrast-enhanced CT and/or whole-body PET-CT), interpreted according to the RECIST 1.1 criteria17. Metastatic involvement was defined by radiological evidence of lesions in ≥ 1 organ system beyond the primary tumor site (e.g., skeletal, hepatic, or pulmonary metastases). All imaging data were independently reviewed by two radiologists blinded to the experimental results.

Flow cytometry-based measurements of mitochondrial function and PD-1 expression in peripheral blood T lymphocytes

(1) Peripheral blood samples were collected in EDTA-K2 anticoagulant tubes and processed within 48 h. Samples were immediately processed and maintained at 4 °C to prevent cell damage.(2) The mixture of antibodies was added to the sample, and stained at room temperature for 30 min. (3) Mitochondrial dye (MitoDye) was added at 37 °C and incubated for 30 min. (4) Flow cytometry data were detected by the flow cytometer (FACSCanto II, BD, USA). (5) Flow cytometry data (* .fcs files) were calibrated using the Human Lymphocyte Mitochondrial Function Analysis System (UBBio Technology, Hangzhou, China) and the results were output.

Sample processing included the following steps: (a) Preparation of antibody reagents; (b) Mixing the antibody with peripheral blood, vortexing, and incubating for 15 min in the dark; (c) Adding hemolysin and incubating in the dark for 15 min; (d) Measuring TBNK counts by flow cytometry after mixing; (e) Centrifuging (300 g, 5 min), discarding the supernatant, and re-suspending in 200 µL Phosphate-Buffered Saline (PBS); (f) Adding mitodye and incubating at 37 °C for 30 min; (g) Detecting mitochondrial function indices by flow cytometry after mixing.

Lymphocytes were first selected based on SSC/FSC parameters and CD45 expression. From the lymphocyte gate, T cells were gated based on CD3 expression and further classified into CD4 + T cells and CD8 + T cells. CD279 (PD-1) was used to stratify CD4 + and CD8 + T cells into CD4 + PD + T and CD8 + PD + T cells. A mitochondrial count histogram was established to distinguish the low and high mitochondrial groups of CD3 + T, CD4 + T, and CD8 + T cells. The percentage of the low group represented mitochondrial membrane potential (MMP-low%), with lower values reflecting reduced cell activity and more severe mitochondrial dysfunction. The total group fluorescence intensity represented mitochondrial mass (MM), with higher values indicating greater mitochondrial mass (including volume and quantity).

Lymphocyte control samples with high and low concentrations were used to evaluate precision. The samples were tested on three different flow cytometry instruments, with analysis performed by two people each day for 20 working days. The reagent kit was used for intra-batch and inter-batch precision testing on human peripheral blood samples. The results of the intra-batch/inter-batch precision test met the following requirements: The mitochondrial mass of lymphocyte subsets met the requirement that the coefficient of variation (CV) of the average fluorescence intensity value of the cells should not exceed 15%.

Flow cytometry in this study relies on a mitochondrial-specific dye, which is a green fluorescent dye that specifically binds to the mitochondria of living cells. The median fluorescence intensity (MFI) reflects mitochondrial mass (MM), and the relative ratio of fluorescence peaks reflects the percentage of high mitochondrial membrane potential to low mitochondrial membrane potential. Low mitochondrial membrane potential (MMP-low%) refers to a cell population with incomplete mitochondrial membranes or relatively low membrane potential18,19.

Flow cytometry assays were performed using the NovoCyte flow cytometer (Agilent Technologies, USA), and the flow cytometry antibodies (CD3/CD8/CD4/CD45) and mitochondrial dye were produced by UBBIO LTD (Zhejiang, China).

Statistical analysis

Statistical analyses were performed using Python 3.10.9. For normally distributed data with homogeneous variance (verified by Shapiro-Wilk normality and Levene’s equality tests), parametric independent t-tests or Welch’s t-tests were applied. When these assumptions were violated, non-parametric Mann-Whitney U tests were used for group comparisons (e.g., cancer vs. healthy controls; metastatic vs. non-metastatic subgroups). False Discovery Rate (FDR) correction was employed to adjust p-values for both t-tests and Mann-Whitney U tests to account for multiple comparisons. Associations involving ordinal categorical variables (e.g., treatment response) were assessed using non-parametric methods. Predictive performance of continuous biomarkers was evaluated via receiver operating characteristic (ROC) curves, with classification accuracy quantified by the area under the curve (AUC). Bootstrap resampling estimated 95% confidence intervals (CIs) for AUCs. All tests were two-tailed with statistical significance defined at p < 0.05.

Results

Clinical characteristics of patients with advanced NSCLC

As shown in Table 1, this study enrolled 64 healthy controls (median age = 65 years; Male: Female = 59:5) and 24 patients with advanced non-small cell lung cancer (NSCLC) (median age = 68 years; Male: Female = 22:2), with comparable baseline demographics. The NSCLC cohort had a smoking history distribution of 9 never-smokers, 5 former smokers, and 10 current smokers. The disease staging included 12 stage III and 12 stage IV cases, with histological subtypes as follows: adenocarcinoma (n = 11), squamous cell carcinoma (n = 12), and large-cell neuroendocrine carcinoma (n = 1). Metastatic stratification revealed 11 M1 and 13 M0 patients. After two treatment cycles, assessment by independent specialists using RECIST 1.1 criteria categorized 14 patients as having significant treatment effects (1 CR, 13 PR), while 10 had non-significant effects (9 SD, 1 PD)17. Treatment regimens included 15 patients receiving immunotherapy-based therapies (9 immunotherapy-chemotherapy combinations, 6 with adjunct procedures such as surgery, radiotherapy, or digital subtraction angiography (DSA)), 8 patients on non-immunotherapy regimens (2 chemotherapy alone, 2 targeted monotherapy, 2 chemotherapy-targeted combinations, and 2 chemotherapy plus adjunct procedures), and 1 patient who received no systemic anticancer therapy.

Table 1 Clinical characteristics of patients with advanced NSCLC and healthy controls.
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Changes in T lymphocyte counts and mitochondrial index in patients with advanced NSCLC

As shown in Fig. 1, patients with advanced NSCLC showed significant differences in CD45 + lymphocyte percentage, CD3 + CD8 + PD-1 + T cells percentage, CD3 + MMP-Low T cell percentage, CD3 + CD4 + MMP-Low T cells percentage, CD3 + CD8 + MMP-Low T cell percentage, CD3 + T cell MM, CD3 + CD4 + T cell MM, CD3 + CD8 + T cell MM, absolute count of CD3 + CD4 + PD-1 + T cells, absolute count of CD3 + CD8 + PD-1 + T cell compared to healthy controls.

Fig. 1
figure 1

Changes in T lymphocyte counts and mitochondrial index in patients with advanced NSCLC. The figure presents a series of 10 box plots comparing healthy individuals and cancer patients across ten significantly different metrics. Each subplot corresponds to a specific metric, with the line inside the box, the box itself, and the whiskers representing the median, interquartile range (IQR), and data range (minimum to maximum), respectively. Displayed p-values are adjusted using the False Discovery Rate (FDR) method to control false positives from multiple comparisons.

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T lymphocyte counts and mitochondrial index predicted tumor metastasis

Comparative analysis of biomarkers between M1 (distant metastasis) and M0 (no distant metastasis) groups revealed statistically significant differences in CD3 + CD8 + MMP-Low T cell percentage (P = 0.043, FDR-corrected) and CD3 + CD8 + T cell mitochondrial mass (MM) (p = 0.026, FDR-corrected). Notably, lower CD3 + CD8 + MMP-Low T cell percentage or higher CD3 + CD8 + T cell MM levels were strongly associated with an increased likelihood of metastasis. As shown in Fig. 2, ROC curve analysis further demonstrated their predictive potential for metastatic progression, with AUC values of 0.85 and 0.87, respectively. This exploratory analysis suggests their potential as predictors for metastatic progression.

Fig. 2
figure 2

T lymphocyte counts and mitochondrial index predicted tumor metastasis. (A) Inverted ROC curve for CD3 + CD8 + MMP-Low T cell percentage. The area under the curve (AUC) is 0.853, with a 95% confidence interval of (0.643, 1.0). An inverted curve indicates the inverse relationship between the predicted probabilities and the actual outcomes. (B) ROC curve for CD3 + CD8 + T cell mitochondrial mass. The AUC is 0.871, with a 95% confidence interval of (0.714, 0.987), suggesting strong diagnostic performance. Each curve represents the relationship between sensitivity and specificity for distinguishing between the specified groups. A higher AUC value, particularly one approaching 1.0, indicates better discriminatory performance.

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Evaluating tumor therapy efficacy using T lymphocyte counts and mitochondrial index

In the longitudinal analysis comparing pre- and post-treatment differences, 5 out of the initial 24 advanced NSCLC cases were excluded due to incomplete post-treatment follow-up data. To ensure data comparability, only 19 cases with complete baseline and post-treatment paired datasets were retained for final statistical analysis.

Quantitative biomarker changes (pre- to post-treatment) were stratified into three ordinal categories:

Class 1 (Deterioration):

  • Baseline values within normal range but shifted to abnormal post-treatment, or.

  • Baseline abnormal values exhibiting further deviation from normal range post-treatment.

Class 2 (Stability):

Values persistently within normal range at both baseline and post-treatment.

Class 3 (Improvement):

  • Transition from abnormal baseline to normal post-treatment, or.

  • Residual abnormal values with directional shifts toward normalization.

As shown in Table 2, comparative analysis revealed significant associations between these dynamic biomarker patterns and therapeutic efficacy. Specifically, CD3 + CD8 + PD-1 + T cell percentage (p = 0.0295, d=-0.5), CD3 + CD8 + MMP-Low T cell percentage (p = 0.0122, d = 1), and CD3 + CD8 + T cell MM (p = 0.0276, d = 2) demonstrated robust correlations with evaluation of treatment effect.

Table 2 T lymphocyte count and mitochondrial index predict the therapeutic effect of tumor.
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In Fig. 3, the treatment effect is positively correlated with the CD3 + CD8 + MMP-Low T cell percentage and CD3 + CD8 + T cell MM, while negatively correlated with the CD3 + CD8 + PD-1 + T cell percentage.

Fig. 3
figure 3

Correlation of Treatment effect with T lymphocyte count and mitochondrial index. The value within each cell denotes the correlation coefficient between two variables, ranging from − 1 (perfect negative correlation) to + 1 (perfect positive correlation). Color intensity represents the absolute strength of the correlation, with darker red hues indicating stronger correlations. Abbreviations: MM = mitochondrial mass, MMP-low = low mitochondrial membrane potential, PD-1 = Programmed Death 1.

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Discussion

NSCLC is a major global health challenge, constituting a substantial disease burden worldwide. With continuous advancements in detection methods and therapeutic strategies, the clinical management of NSCLC has evolved beyond traditional modalities (e.g., chemotherapy, radiotherapy, or combined regimens) toward increasingly diversified precision approaches. For advanced patients, individualized treatment is of significant importance. Baseline patient condition prior to treatment, coupled with timely and sensitive objective indicators during therapy, are crucial for evaluating prognosis and adjusting treatment plans in advanced NSCLC. The inflammatory cytokines and immune cells within the tumor microenvironment are closely associated with lung cancer prognosis and are a central focus of oncology research, with particular attention given to T lymphocyte subsets20,21. Many studies have shown that monitoring T lymphocyte subsets has significant prognostic value for NSCLC and can further promote the precision medicine approach for this disease22.

Mitochondria consist of a double-membrane structure and matrix components, playing a crucial role in cellular energy production, metabolism, and signal transduction23. Mitochondrial Mass (MM) refers to the effective protein content (complexes) in the respiratory chain of the mitochondrial inner membrane. Within a certain range, a higher MM value correlates with a higher ATP production rate, representing the upper limit of the cell’s metabolic capacity. Mitochondrial Membrane Potential (MMP) refers to the voltage difference across the mitochondrial membrane. Within a certain range, a higher MMP value (or lower MMP-low%) indicates a higher ATP production rate, reflecting the immediate activity of cell metabolism24. In our study data, significant differences were observed in T lymphocyte subsets between advanced NSCLC patients and healthy controls. Moreover, more pronounced changes were found in the mitochondrial indexes of CD3 + CD4 + T cells and CD3 + CD8 + T cells.

In previous studies of tumor mice, it was found that the MM of CD8 + T cells increased and MMP-Low% decreased, indicating immune activation. Additionally, inflammatory indicators were normal, and no other infections were obvious. These findings suggest the formation of new metastasis25. Our results are consistent with this preclinical conclusion. Additionally, in our cohort, a lower CD3 + CD8 + MMP-Low T cell percentage or a higher CD3 + CD8 + T cell MM level was associated with an increased likelihood of metastasis. ROC curve analysis further supported their potential to predict metastasis progression in this patient group. In our study, the classification of “significant effect” (CR + PR) vs. “non-significant effect” (SD + PD) was made based on both methodological and clinical considerations: (1) Sample size constraints: As shown in Table 1, the non-response group included only 1 patient with PD and 9 with SD. Given the very small number of PD cases (n = 1), separate statistical analysis for PD was not feasible. Combining SD and PD ensured sufficient group size for meaningful statistical comparison and improved analytical stability when evaluating biomarker associations. (2) Clinical rationale: According to RECIST criteria, PD reflects unequivocal disease progression requiring clinical intervention, whereas SD indicates no significant tumor shrinkage17. Therefore, we think grouping CR with PR as “significant effect”, SD with PD as “non-significant effect” were considered appropriate for the purpose of early response evaluation. In this exploratory analysis, changes in CD3 + CD8 + PD-1 + T cell percentage, CD3 + CD8 + MMP-Low T cell percentage, and CD3 + CD8 + T cell MM were associated with therapeutic efficacy and showed potential for predicting prognosis in patients with advanced NSCLC.

The translational implications of these findings are significant. Unlike tissue-based biomarkers that require invasive biopsies, peripheral blood T-cell mitochondrial profiling offers a minimally invasive approach for serial monitoring. The observed metabolic shifts in CD8 + T cells often precede radiographic changes, potentially enabling earlier detection of treatment failure or metastatic spread. This could facilitate more timely therapeutic adjustments, such as switching to immune checkpoint inhibitors for patients with increasing PD-1 + percentages. Such strategies align with the growing interest in optimizing cancer immunotherapy through mitochondrial metabolism.

However, this study has important limitations, primarily the small sample size. Our relatively small sample size limited more nuanced subgroup analyses, such as the separate evaluation of SD patients, who might represent a distinct clinical phenotype. This limitation also hindered comprehensive multivariate adjustments for potential confounders such as tumor stage, ECOG performance status, or treatment modality through logistic regression modeling. Consequently, while the observed biomarker associations are biologically plausible and consistent with existing literature, they must be interpreted as exploratory findings requiring rigorous validation in larger, independent cohorts to establish their robustness and independence from clinical covariates.

In terms of technical considerations, standardizing flow cytometry protocols across institutions and determining clinically relevant MM/MMP cutoff values are critical for improving the robustness and applicability of these findings.

Looking ahead, prospective multi-center studies should validate these mitochondrial biomarkers in broader NSCLC populations. Combining additional biological markers with radiological characteristics may lead to the development of a composite predictive model with higher accuracy, thereby improving clinical outcomes in this challenging disease.

Conclusion

In summary, this exploratory study provides preliminary evidence that peripheral blood T lymphocyte counts and mitochondrial indices show potential as biomarkers in advanced NSCLC. Monitoring changes in these indices, particularly CD8 + T-cell MMP-low and MM, may aid in predicting therapy response and metastasis risk. However, given the small sample size, these findings are preliminary. Future large-scale prospective studies are essential to validate the clinical utility of these markers.