Frontiers in nutrition · 2026
Clinical phenotype identification based on inflammation-nutrition-coagulation biomarkers in advanced non-small cell lung cancer.
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Zhao Huanhuan, Wang Yan, Zhao Shaoyang
Аннотация
OBJECTIVE: This study aimed to identify clinically distinct host phenotype subgroups based on inflammation-nutrition-coagulation biomarkers in patients with advanced non-small cell lung cancer (NSCLC). It also evaluated their association with conventional Tumor-Node-Metastasis (TNM) staging.
METHODS: A total of 1,644 patients with advanced NSCLC were retrospectively included. Twenty-two baseline laboratory indicators spanning inflammation, nutrition, and coagulation were collected. After data cleaning and Z-score standardization, principal component analysis (PCA) was performed for dimensionality reduction. The optimal number of clusters was determined using the elbow method, silhouette coefficient, and Calinski-Harabasz index.
RESULTS: The Kaiser-Meyer-Olkin value was 0.631, and Bartlett's test of sphericity was significant (p < 0.001), confirming data suitability for PCA. Nine principal components were extracted, explaining 80.18% of the cumulative variance. K-means clustering identified three phenotype subgroups. Subgroup 1 (n = 113, 6.87%) showed high-risk pattern characterized by severe inflammation, poor nutritional status, and hypercoagulability. Subgroup 3 (n = 521, 31.69%) represented a favorable phenotype with low inflammation, adequate nutrition, and low coagulation activity. Subgroup 2 (n = 1,010, 61.44%) showed intermediate characteristics. This classification was not associated with TNM staging (p = 0.578).
CONCLUSION: PCA combined with K-means clustering identified three distinct host phenotype subgroups along the inflammation-nutrition-coagulation axis in advanced NSCLC. These subgroups were independent of TNM staging and provide a new framework for individualized risk stratification and clinical management.
METHODS: A total of 1,644 patients with advanced NSCLC were retrospectively included. Twenty-two baseline laboratory indicators spanning inflammation, nutrition, and coagulation were collected. After data cleaning and Z-score standardization, principal component analysis (PCA) was performed for dimensionality reduction. The optimal number of clusters was determined using the elbow method, silhouette coefficient, and Calinski-Harabasz index.
RESULTS: The Kaiser-Meyer-Olkin value was 0.631, and Bartlett's test of sphericity was significant (p < 0.001), confirming data suitability for PCA. Nine principal components were extracted, explaining 80.18% of the cumulative variance. K-means clustering identified three phenotype subgroups. Subgroup 1 (n = 113, 6.87%) showed high-risk pattern characterized by severe inflammation, poor nutritional status, and hypercoagulability. Subgroup 3 (n = 521, 31.69%) represented a favorable phenotype with low inflammation, adequate nutrition, and low coagulation activity. Subgroup 2 (n = 1,010, 61.44%) showed intermediate characteristics. This classification was not associated with TNM staging (p = 0.578).
CONCLUSION: PCA combined with K-means clustering identified three distinct host phenotype subgroups along the inflammation-nutrition-coagulation axis in advanced NSCLC. These subgroups were independent of TNM staging and provide a new framework for individualized risk stratification and clinical management.