The D-dimer level predicts the prognosis in patients with lung cancer：A systematic review and meta-analysis

Ma, Mingsheng; Cao, Run; Wang, Wei; Wang, Biying; Yang, Yichen; Huang, Yunchao; Zhao, Guangqian; Ye, Lianhua

doi:10.37766/inplasy2021.7.0096

Cited by 1 publication

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“…A study revealed that cancer patients, particularly those with a favorable prognosis, experienced signi cantly prolonged survival with the administration of anticoagulant therapy (8). Several biomarkers associated with coagulation disorders have been unequivocally linked to prognosis in diverse cancer types (9)(10)(11).Furthermore, studies have highlighted the substantial involvement of the coagulation cascade in shaping the tumor immune microenvironment (TME) (12). Consequently, the impact of coagulation on tumor biology has emerged as a topic of profound research interest.…”

Section: Introductionmentioning

confidence: 99%

Development of a Coagulation-Related Genes Model for Prognostication, Immune Response, and Treatment Prediction in Lung Adenocarcinoma

Zhu,

LI,

Sun

et al. 2023

Preprint

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Backgroud: Lung adenocarcinoma(LUAD) is the most prevalent form of lung cancer worldwide. But the diagnosis and prognosis of LUAD patients remain poor. Studies have shown that LUAD patients with tumors tend to have abnormal coagulation factors.Therefore, the objective of this study is to develop a biomolecular model focusing on coagulation-related factors in LUAD. Methods： In this study, we obtained LUAD patients gene expression information and clinical information from The Cancer Genome Atlas (TCGA) database and coagulation-related genes through The Molecular Signature Database (MsigDB), thereby obtaining differentially expressed coagulation-related genes. Predictive models were constructed through LASSO Cox regression. The risk score from the model was used to build high risk set versus low risk set.Additionally, We verify the accuracy of prognostic models through a range of methods. Finally,we applied tumor immune dysfunction and exclusion (TIDE) algorithms to assess immune escape and immunotherapy in relation to coagulation-related genes. Result: We developed a prognosis model using four genes to estimate the survival rate of patients with LUAD. High risk patients exhibited lower overall survival (OS) rates compared to low risk patients. Kaplan-Meier(K-M) curves,progression-free survival curves (PFS),ROC curves,principal component analysis (PCA) and nomograms can verify the accuracy of the model.Furthermore,the dual effects of high risk and low tumor mutation burden (TMB) led to poorer survival in patients with LUAD. TIDE analysis revealed a higher likelihood of immune evasion in individuals classified as high risk. Conclusion： The prognosis model can accurately predict the prognosis of LUAD patients and provide ideas for future immunotherapy.

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