Lung cancer is the leading cause of cancer-related mortality worldwide, with significant heterogeneity among its major histological subtypes: adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung cancer (SCLC). These subtypes differ not only in clinical behavior and treatment response but also in their metabolic profiles. Metabolomics has emerged as a powerful tool to decipher cancer metabolic reprogramming, offering new insights into subtype-specific metabolic alterations. This review synthesizes recent advances in metabolomic research across lung cancer subtypes, highlighting the roles of lipid, amino acid, and energy metabolism in tumor biology. Through technologies such as mass spectrometry and liquid chromatography, metabolomics enables effective subtype differentiation and identification of potential biomarkers for early diagnosis and personalized therapy. The integration of metabolomic data with clinical practice holds promise for improving lung cancer management through precision oncology approaches.
Introduction
Lung cancer remains a major global health challenge, with late diagnosis contributing to its high mortality rate. Significant heterogeneity exists among its subtypes: non-small cell lung cancer (NSCLC), which includes ADC and SCC and accounts for 85% of cases, and SCLC, known for its aggressiveness and poor prognosis. Traditional diagnostic methods, including imaging and histopathology, have limitations such as high false-positive rates and interobserver variability. Metabolomics, as a core discipline of systems biology, offers a non-invasive, high-sensitivity approach to characterize tumor metabolism. By analyzing small-molecule metabolites in biofluids and tissues, metabolomics provides a dynamic snapshot of metabolic activity, enabling deeper understanding of tumor biology and improved clinical stratification.
The Development of Metabolomics in Lung Cancer
Metabolomics has evolved into a pivotal tool for studying cancer metabolic reprogramming. It utilizes various biological samples—including blood, urine, saliva, exhaled breath condensate (EBC), and tissue—obtained through non-invasive or minimally invasive methods (Fig. 1). These samples reflect systemic metabolic changes influenced by diet, microbiome, and environmental factors. Compared to traditional tissue biopsies, biofluids allow for repeated sampling, facilitating dynamic monitoring of disease progression and treatment response.
Metabolomic technologies such as nuclear magnetic resonance (NMR) and mass spectrometry (MS)—often coupled with separation techniques like GC-MS, LC-MS, and CE-MS—enable comprehensive metabolite profiling (Table 1). Recent advancements include imaging mass spectrometry (spatial metabolomics), single-cell metabolomics, metabolic flux analysis, and ion mobility spectrometry, each enhancing the resolution and applicability of metabolomic studies.
Several studies have demonstrated the potential of metabolomics in lung cancer diagnostics. For instance, serum-based NMR models can distinguish NSCLC patients from healthy controls and predict survival. Salivary and EBC metabolomics have also shown promise in non-invasive diagnostic model development.
Technical Methods in Metabolomics
Metabolomics relies on advanced analytical platforms to identify and quantify metabolites. Key techniques include:
NMR: Non-destructive, suitable for in vivo analysis, but with lower sensitivity.
GC-MS: High sensitivity and reproducibility, ideal for volatile compounds, but requires derivatization.
LC-MS: Broad applicability to polar and non-volatile compounds, with strong separation capabilities.
CE-MS: Effective for charged metabolites, with minimal sample preparation.
Emerging Technologies: Imaging MS, single-cell metabolomics, fluxomics, UHPLC-MS, and IMS-MS enhance spatial, dynamic, and high-throughput analyses.
These technologies have revealed subtype-specific metabolic alterations, such as enhanced glycolysis in SCC and disrupted lipid metabolism in ADC.
Metabolomic Characteristics of NSCLC and SCLC
Metabolomic Differences Between Subtypes
SCLC exhibits a unique metabolic profile distinct from NSCLC. A 2024 multicenter study identified an 8-metabolite panel—including lipids and amino acids—that effectively discriminates SCLC from NSCLC and healthy controls. These findings underscore the role of metabolic reprogramming in SCLC's aggressive behavior and chemoresistance.
Metabolomic Characteristics of NSCLC
ADC and SCC display distinct metabolic phenotypes:
ADC: Enriched in phospholipid metabolites (e.g., phosphatidylcholine), serine metabolism, and oxidized phosphatidylcholines (oxPCs), which promote angiogenesis via VEGF signaling.
SCC: Shows elevated glycolysis (lactate, glucose), amino acids (glutamate, alanine), and lysophosphatidic acids (LPAs), linked to inflammation and tumor progression.
Studies using tissue and plasma samples have consistently identified these differences, enabling high-accuracy subtype classification via logistic regression models (Table 2).
Clinical Implications
Metabolomics is increasingly applied in clinical settings for:
Early Diagnosis: Metabolic models complement imaging to reduce false positives and enable non-invasive screening.
Subtype Stratification: Plasma-based models achieve high sensitivity and specificity in distinguishing ADC from SCC.
Treatment Guidance: Metabolomics identifies resistance mechanisms (e.g., HIF-1/PI3K-Akt pathway in osimertinib resistance) and suggests targeted therapies (e.g., PHGDH inhibitors in ADC).
Surgical and Postoperative Monitoring: Metabolic tracing intraoperatively and postoperative metabolite changes (e.g., sphingolipids) offer new avenues for precision surgery and recurrence monitoring.
Future Directions and Limitations
Future metabolomics research should focus on:
Technical Standardization: Uniform sample processing and cross-platform data calibration.
Multi-Omics Integration: Combining metabolomics with genomics, proteomics, and imaging.
Large-Scale Validation: Multi-center studies to validate biomarkers and diagnostic models.
Mechanistic Studies: Elucidating the functional roles of metabolic alterations in cancer progression.
Current challenges include sample stability, platform variability, and the need for deeper mechanistic insights.
Conclusions
Metabolomics provides a powerful framework for understanding lung cancer heterogeneity and advancing precision medicine. By revealing subtype-specific metabolic signatures, it enhances early detection, subtype classification, and personalized treatment strategies. Future efforts must address technical and translational challenges to fully realize the clinical potential of metabolomics in lung cancer care.
