Use of consensus clustering to identify subtypes of clinical early-stage non-small cell lung cancer and its association with lymph node metastasis.

Limited studies have investigated the metabolic heterogeneity of patients with clinical early-stage non-small cell lung cancer (NSCLC). Consensus clustering analysis has the potential to reveal distinct metabolic subgroups within clinical early-stage NSCLC patients. A total of 3324 clinical early-stage NSCLC patients who underwent surgery were included in this comprehensive evaluation. The evaluation encompassed 26 serum assessments related to metabolism and histopathological examination of the lymph nodes. By utilizing consensus clustering analysis, three clusters were identified based on various measurements, including blood glucose levels, blood uric acid, blood lipids, renal and liver function, and tumor markers. The differences in characteristics and lymph node metastasis (LNM) prevalence between the clusters were investigated and compared. The patients were classified into three distinct clusters that exhibited different patterns defined by the highest or lowest levels of metabolic feature variables. NSCLC cluster 1 had the lowest rates of LNM, while cluster 3 showed a significantly higher prevalence of LNM (1.6-fold increase, 95% CI: 1.21, 2.13) compared to cluster 1. Moreover, cluster 2 had the highest odds ratio (OR) of 1.78 (95% CI: 1.37, 2.33) for LNM prevalence. In subsequent sensitivity analysis, metabolic heterogeneity was observed among patients with a tumor measuring less than 2 cm in the long axis, along with similar differences in the prevalence of lymph node metastasis. This present study successfully categorized clinical early-stage NSCLC into three distinct subgroups, each with unique characteristics that reflect metabolic heterogeneity and significant disparities in the prevalence of LNM. Such an approach holds potential implications for clinical early-stage interventions targeting risk factors.