Literature DB >> 32570944

Systemic Inflammation and Activation of Haemostasis Predict Poor Prognosis and Response to Chemotherapy in Patients with Advanced Lung Cancer.

Florian Moik1, Sabine Zöchbauer-Müller2, Florian Posch3, Ingrid Pabinger1, Cihan Ay1,4.   

Abstract

Systemic inflammation and activation of haemostasis are common in patients with lung cancer. Both conditions support tumour growth and metastasis. Therefore, inflammatory and haemostatic biomarkers might be useful for prediction of survival and therapy response. Patients with unresectable/metastatic lung cancer initiating 1st-line chemotherapy (n = 277, 83% non-small cell lung cancer) were followed in a prospective observational cohort study. A comprehensive panel of haemostatic biomarkers (D-dimer, prothrombin fragment 1+2, soluble P-selectin, fibrinogen, coagulation factor VIII, peak thrombin generation), blood count parameters (haemoglobin, leucocytes, thrombocytes) and inflammatory markers (neutrophil-lymphocyte ratio, lymphocyte-monocyte ratio, platelet-lymphocyte ratio, C-reactive protein) were measured at baseline. We assessed the association of biomarkers with mortality, progression-free-survival (PFS) and disease-control-rate (DCR). A biomarker-based prognostic model was derived. Selected inflammatory and haemostatic biomarkers were strong and independent predictors of mortality and therapy response. The strongest predictors (D-dimer, LMR, CRP) were incorporated in a unified biomarker-based prognostic model (1-year overall-survival (OS) by risk-quartiles: 79%, 69%, 51%, 24%; 2-year-OS: 53%, 36%, 23%, 8%; log-rank p < 0.001). The biomarker-based model further predicted shorter PFS and lower DCR. In conclusion, inflammatory and haemostatic biomarkers predict poor prognosis and treatment-response in patients with advanced lung cancer. A biomarker-based prognostic score efficiently predicts mortality and disease progression beyond clinical characteristics.

Entities:  

Keywords:  chemotherapy; haemostatic biomarker; lung cancer; prognostic model; systemic inflammation

Year:  2020        PMID: 32570944     DOI: 10.3390/cancers12061619

Source DB:  PubMed          Journal:  Cancers (Basel)        ISSN: 2072-6694            Impact factor:   6.639


  5 in total

1.  Hypothesized Explanations for the Observed Lung Cancer Survival Benefit Among Hispanics/Latinos in the United States.

Authors:  Emily Miao; Madelyn Klugman; Thomas Rohan; H Dean Hosgood
Journal:  J Racial Ethn Health Disparities       Date:  2022-05-06

2.  Application of an elevated plasma D-dimer cut-off value improves prognosis prediction of advanced non-small cell lung cancer.

Authors:  Chong Chen; Jianhua Li; Jing Li; Xu Wang; Xiaoyan Wang; Na Du; Li Ren
Journal:  Ann Transl Med       Date:  2020-09

3.  Identification and Validation of a Tumor Microenvironment-Related Gene Signature for Prognostic Prediction in Advanced-Stage Non-Small-Cell Lung Cancer.

Authors:  Xuening Zhang; Xuezhong Shi; Hao Zhao; Xiaocan Jia; Yongli Yang
Journal:  Biomed Res Int       Date:  2021-03-30       Impact factor: 3.411

4.  Combination of Colchicine and Ticagrelor Inhibits Carrageenan-Induced Thrombi in Mice.

Authors:  BuChun Zhang; Rong Huang; DaiGang Yang; GuiLan Chen; YuanLi Chen; Jihong Han; Shuang Zhang; LiKun Ma; XiaoXiao Yang
Journal:  Oxid Med Cell Longev       Date:  2022-01-17       Impact factor: 6.543

5.  Monitoring early dynamic changes of plasma cell-free DNA and pretreatment pre-albumin to predict chemotherapy effectiveness and survival outcomes in advanced non-small cell lung cancer.

Authors:  Jia Chen; Jingjing Shao; Xunlei Zhang; Sheng Wei; Hongyu Cai; Gaoren Wang
Journal:  Ann Transl Med       Date:  2022-03
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.