Thrombosis research · 2026
LY96-CCNT1-IFI44L core molecular features characterize VTE-associated inflammatory prothrombotic risk in elderly cancer patients.
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Wu Jie, Hu Baoji, Shao Lingyi, Wang Jinhuo, Wang Yujia, Wang Lukun, Ju Yue, Guo Jianrong
Аннотация
BACKGROUND: Venous thromboembolism (VTE) is an important complication in elderly patients with cancer, yet traditional clinical scoring systems insufficiently capture its molecular heterogeneity. This study aimed to construct a VTE-related molecular risk stratification framework for elderly cancer patients, identify molecular features that remain stable across datasets, and clarify the potential inflammation-coagulation interaction mechanisms underlying high-risk states.
METHODS: The peripheral-blood discovery cohort GSE19151 and the validation cohort GSE48000 from the Gene Expression Omnibus (GEO) database were integrated to identify stably differentially expressed genes (SDEGs). In The Cancer Genome Atlas (TCGA) elderly pan-cancer cohort, SDEGs and related pathway features were then used to construct a VTE-related molecular risk stratification framework. LASSO and random forest recursive feature elimination were applied for feature selection, multiple machine-learning models were compared, and SHAP analysis was used to evaluate feature contributions. GO/KEGG enrichment analysis, GSVA and weighted gene co-expression network analysis (WGCNA) were performed to characterize the biological basis of the high molecular-risk state. Kaplan-Meier analysis, time-dependent ROC curves and Cox regression were used to assess associations between the molecular risk score and prognosis.
RESULTS: A total of 427 VTE-related differentially expressed genes were identified in the GSE19151 discovery cohort, and 38 SDEGs were retained after cross-cohort validation in GSE48000. Machine-learning analysis further identified 11 robust core features. Among them, LY96, CCNT1 and IFI44L showed dominant contributions in feature importance ranking, SHAP analysis and interaction analysis. Model comparison indicated that XGBoost and random forest achieved favorable classification performance. Functional enrichment analysis showed that SDEGs were mainly enriched in innate immunity, inflammatory responses, cytokine signaling and complement-coagulation-related processes. GSVA and WGCNA further revealed significant activation of complement-coagulation cascades, IL-6/JAK/STAT signaling and immune-inflammatory networks in the high VTE molecular-risk group. Survival analysis showed that the high-risk group had significantly poorer overall survival and progression-free interval, and the VTE molecular risk score retained independent prognostic value in multivariable Cox models.
CONCLUSIONS: This study identified elderly cancer VTE-related core molecular features represented by LY96-CCNT1-IFI44L and established an interpretable molecular risk stratification framework. The framework highlights the coordinated activation of immune inflammation and coagulation pathways in high-risk states and provides a basis for evaluating VTE-related molecular heterogeneity and prognosis in elderly cancer patients.
METHODS: The peripheral-blood discovery cohort GSE19151 and the validation cohort GSE48000 from the Gene Expression Omnibus (GEO) database were integrated to identify stably differentially expressed genes (SDEGs). In The Cancer Genome Atlas (TCGA) elderly pan-cancer cohort, SDEGs and related pathway features were then used to construct a VTE-related molecular risk stratification framework. LASSO and random forest recursive feature elimination were applied for feature selection, multiple machine-learning models were compared, and SHAP analysis was used to evaluate feature contributions. GO/KEGG enrichment analysis, GSVA and weighted gene co-expression network analysis (WGCNA) were performed to characterize the biological basis of the high molecular-risk state. Kaplan-Meier analysis, time-dependent ROC curves and Cox regression were used to assess associations between the molecular risk score and prognosis.
RESULTS: A total of 427 VTE-related differentially expressed genes were identified in the GSE19151 discovery cohort, and 38 SDEGs were retained after cross-cohort validation in GSE48000. Machine-learning analysis further identified 11 robust core features. Among them, LY96, CCNT1 and IFI44L showed dominant contributions in feature importance ranking, SHAP analysis and interaction analysis. Model comparison indicated that XGBoost and random forest achieved favorable classification performance. Functional enrichment analysis showed that SDEGs were mainly enriched in innate immunity, inflammatory responses, cytokine signaling and complement-coagulation-related processes. GSVA and WGCNA further revealed significant activation of complement-coagulation cascades, IL-6/JAK/STAT signaling and immune-inflammatory networks in the high VTE molecular-risk group. Survival analysis showed that the high-risk group had significantly poorer overall survival and progression-free interval, and the VTE molecular risk score retained independent prognostic value in multivariable Cox models.
CONCLUSIONS: This study identified elderly cancer VTE-related core molecular features represented by LY96-CCNT1-IFI44L and established an interpretable molecular risk stratification framework. The framework highlights the coordinated activation of immune inflammation and coagulation pathways in high-risk states and provides a basis for evaluating VTE-related molecular heterogeneity and prognosis in elderly cancer patients.