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Comprehensive analysis of genomic alterations and novel prognostic biomarkers, and establishment of prediction models of metastasis in metastatic non-small cell lung cancer

转移 肺癌 医学 癌症 肿瘤科 癌症转移 癌症研究 病理 内科学 计算生物学 生物
作者
Kangwei Wang,Meifeng Ye,Zexun Mo,Xiaomei Huang,Yujun Li,Shuquan Wei
出处
期刊:Journal of Cancer [Ivyspring International Publisher]
卷期号:16 (1): 339-350
标识
DOI:10.7150/jca.97070
摘要

Introduction: Most patients with non-small cell lung cancer (NSCLC) have metastases at initial diagnosis. However, the comprehensive molecular characteristics and factors associated with its metastases are still needed. Methods: Tumor sequencing of 556 cancer-related genes was performed on 114 Chinese NSCLC patients. A distinct genomic profile was identified in metastatic patients compared to those without metastases. Kaplan-Meier method was used to analyze the associations between clinical outcomes, clinical characteristics, and mutated genes. The Fisher test and Lasso logistic regression analysis were employed to identify factors related to metastasis and to develop prediction models. Results: Male, squamous cell lung carcinoma, and smokers showed strikingly higher TMB levels in all NSCLCs. The metastatic group had a significantly higher proportion of patients aged ≥ 70 years and in stage III-IV. TP53 was the most frequent mutation in both groups, and EGFR tended to be higher in the metastatic group. The copy number variation events occurred more frequently in the metastatic group. Additionally, predictive models for metastasis (AUC = 0.828), pleural metastasis (AUC = 0.582), and multisite metastasis (AUC = 0.559) were established. Females, and EGFR +, ASXL2-, and STK11- cases had better overall survival (OS). Lung adenocarcinoma, and KMT2D- and STK11- cases had better progression-free survival (PFS). NSCLC metastasis was associated with poor OS and poor PFS. Conclusions: Our study provided a comprehensive analysis of genomic alterations in metastatic NSCLCs, identified novel prognostic biomarkers, and provided three predictive models for metastasis, which may have potential implications for personalized treatment strategies.
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