Abstract

Hepatocellular carcinoma (HCC) is a rapidly developing digestive tract carcinoma. The prognosis of patients and side effects caused by clinical treatment should be better improved. Nonnegative matrix factorization (NMF) clustering was performed using 109 homologous recombination deficiency (HRD)-related of HCC genes from The Cancer Genome Atlas (TCGA) database. Limma was applied to analyze subtype differences. Immune scores and clinical characteristics of different subtypes were compared. An HRD signature were built with least absolute shrinkage operator (LASSO) and multivariate Cox analysis. Performance of the signature system was then assessed by Kaplan–Meier curves and receiver operating characteristic (ROC) curves. We identified two molecular subtypes (C1 and C2), with C2 showing a significantly better prognosis than C1. C1 contained 3623 differentially expressed genes. A 4-gene prognostic signature for HCC was established, and showed a high predicting accuracy in validation sets, entire TCGA data set, HCCDB18 and GSE14520 queues. Moreover, the risk score was validated as an independent prognostic marker for HCC. Our research identified two molecular subtypes of HCC, and proposed a novel scoring system for evaluating the prognosis of HCC in clinical practice.

Highlights

  • IntroductionThe prognosis of patients and side effects caused by clinical treatment should be better improved

  • Hepatocellular carcinoma (HCC) is a rapidly developing digestive tract carcinoma

  • To explore the immune cell infiltration in HCC, the relationship between prognosis and immune cells was analyzed according to MCP Counter, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA)

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Summary

Introduction

The prognosis of patients and side effects caused by clinical treatment should be better improved. Nonnegative matrix factorization (NMF) clustering was performed using 109 homologous recombination deficiency (HRD)related of HCC genes from The Cancer Genome Atlas (TCGA) database. A 4-gene prognostic signature for HCC was established, and showed a high predicting accuracy in validation sets, entire TCGA data set, HCCDB18 and GSE14520 queues. Our research identified two molecular subtypes of HCC, and proposed a novel scoring system for evaluating the prognosis of HCC in clinical practice. Hepatocellular carcinoma (HCC), which accounts for 90% of all liver cancer types, is characterized by high mortality and poor p­ rognosis[2].Only 5% to 15% of HCC tumors can be surgically removed after diagnosis. Improving the prognosis of patients and reducing side effects are currently the major problems to be solved in clinical practice. The relationship between HRD and HCC prognosis has not been fully characterized

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