Research Paper Volume 16, Issue 9 pp 7578—7595
Establishment and verification of a prognostic model based on coagulation and fibrinolysis-related genes in hepatocellular carcinoma
- 1 Department of General Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710003, China
- 2 Department of Medical, Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co., Ltd., Shenzhen 518038, China
Received: July 20, 2023 Accepted: February 7, 2024 Published: April 1, 2024
https://doi.org/10.18632/aging.205699How to Cite
Copyright: © 2024 Fan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Studies have shown that coagulation and fibrinolysis (CFR) are correlated with Hepatocellular carcinoma (HCC) progression and prognosis. We aim to build a model based on CFR-correlated genes for risk assessment and prediction of HCC patient.
Methods: HCC samples were selected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. The Molecular Signatures Database (MSigDB) was used to select the CFR genes. RiskScore model were established by single sample gene set enrichment analysis (ssGSEA), weighted correlation network analysis (WGCNA), multivariate Cox regression analysis, LASSO regression analysis.
Results: PCDH17, PGF, PDE2A, FAM110D, FSCN1, FBLN5 were selected as the key genes and designed a RiskScore model. Those key genes were Differential expressions in HCC cell and patients. Overexpression PDE2A inhibited HCC cell migration and invasion. The higher the RiskScore, the lower the probability of survival. The model has high AUC values in the first, third and fifth year prediction curves, indicating that the model has strong prediction performance. The difference analysis of clinicopathological features found that a great proportion of high clinicopathological grade samples showed higher RiskScore. RiskScore were positively correlated with immune scores and TIDE scores. High levels of immune checkpoints and immunomodulators were observed in high RiskScore group. High RiskScore groups may benefit greatly from taking traditional chemotherapy drugs.
Conclusions: We screened CFR related genes to design a RiskScore model, which could accurately evaluate the prognosis and survival status of HCC patients, providing certain value for optimizing the clinical treatment of cancer in the future.
Abbreviations
HCC: Hepatocellular carcinoma; CFR: Coagulation and fibrinolysis; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; MSigDB: The Molecular Signatures Database; GSEA: Gene set enrichment analysis; ssGSEA: Single sample gene set enrichment analysis; WGCNA: Weighted correlation network analysis; TME: Tumor microenvironments; FGL1: Fibrinogen-like-protein 1; FREP: Fibrinogen-related proteins; DEGs: Differential expressed genes; GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; MAD: Mean absolute deviation; LASSO: Least absolute shrinkage and selection operator; DCA: Decision curve; OS: Overall survival; GSVA: Gene Set Variation Analysis; TIDE: Tumor Immune Dysfunction and Exclusion; TF: Tissue factor; PAR2: Protease activating receptor 2; TAT: Thrombin-Antithrombin; PIC: Plasmin inhibitor complex; FSCN1: Fascin homologous 1.