Predictive Models for HCC Prognosis, Recurrence Risk, and Immune Infiltration Based on Two Exosomal Genes: and .

J Inflamm Res

Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital, School of Medicine, Zhejiang University, Lishui, 323000, People's Republic of China.

Published: August 2021

Introduction: Hepatocellular carcinoma (HCC) is a heterogeneous molecular disease with complex molecular pathogenesis that influences the efficacy of therapies. Exosomes play a crucial role in tumorigenesis and poor disease outcomes in HCC.

Objective: The aim of this study was to identify the optimal gene set derived from exosomes in HCC with substantial predictive value to construct models for determining prognosis, recurrence risk and diagnosis and to identify candidates suitable for immunotherapy and chemotherapy, thereby providing new ideas for the individualized treatment of patients and for improving prognosis.

Methods: Weighted correlation network analysis (WGCNA) and univariate and multivariate Cox PH regression analyses were applied to identify exosome-related signatures in the TCGA and exoRbase databases associated with clinical relevance, immunogenic features and tumor progression in HCC. Cell experiments were performed to further confirm the oncogenic effect of and .

Results: The models for prognosis and recurrence risk prediction were built based on two exosomal genes ( and ) and were confirmed to be independent predictive factors with superior predictive performance. Patients with high prognostic risk had poorer prognosis than patients with low prognostic risk in all HCC datasets, namely, the TCGA cohort (HR=2.5, P<0.001), the ICGC cohort (HR=3.15, P<0.001) and the GSE14520 cohort (HR=1.85, P=0.004). A higher recurrence probability was found in HCC patients with high recurrence risk than in HCC patients with low recurrence risk in the TCGA cohort (HR=2.44, P<0.001) and the GSE14520 cohort (HR=1.54, P=0.025). High prognostic risk patients had higher expression of immune checkpoint genes, such as and (P<0.05). Diagnostic models based on the same two genes were able to accurately distinguish HCC patients from normal individuals and HCC from dysplastic nodules.

Conclusion: Our findings lay the foundation for identifying molecular markers to increase the early detection rate of HCC, improve disease outcomes, and determine more effective individualized treatment options for patients.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403029PMC
http://dx.doi.org/10.2147/JIR.S315957DOI Listing

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