Evaluating Distribution and Prognostic Value of New Tumor-Infiltrating Lymphocytes in HCC Based on a scRNA-Seq Study With CIBERSORTx.

Lixing Li,Lu Shen,Ting Wang,Mo Li,Hao Wu,Shengying Qin,Jingsong Ma,Muyun Wei,Qiang Zhou,Di Zhang,Tonghai Xing

doi:10.3389/fmed.2020.00451

Abstract

Hepatocellular carcinoma (HCC) is a commonly diagnosed cancer with high mortality rates. The immune response plays an important role in the progression of HCC. Immunotherapies are becoming an increasingly promising tool for treating cancers. Advancements in scRNA-seq (single-cell RNA sequencing) have allowed us to identify new subsets in the immune microenvironment of HCC. Yet, distribution of these new cell types and their potential prognostic value in bulk samples from large cohorts remained unclear. This study aimed to investigate the tumor-infiltration and prognostic value of new cell subsets identified by a previous scRNA-seq study in a TCGA HCC cohort using CIBERSORTx, a machine learning method to estimate cell proportion and infer cell-type-specific gene expression profiles. We observed different distributions of tumor-infiltrating lymphocytes between tumor and normal cells. Among these, the CD4-GZMA cell subset showed association with prognosis (log-rank test, p < 0.05). We further analyzed CD4-GZMA cell specific gene expression with CIBERSORTx, and found 19 prognostic genes (univariable cox regression, p < 0.05). Finally, we applied Least absolute shrinkage and selection operator (LASSO) Cox regression to construct an immune risk score model and performed a prognostic assessment of our model in TCGA and ICGC cohorts. Taken together, the immune landscape in HCC bulk samples may be more complex than assumed, with heterogeneity and different tumor-infiltration relative to scRNA-seq results. Additionally, CD4-GZMA cells and their characteristics may yield therapeutic benefits in the immune treatment of HCC.

Highlights

Liver cancer is the sixth most commonly diagnosed cancer and the fourth leading cause of cancer mortality worldwide [1]
We acquired 4,070 cell samples clustered from 11 immune cell subsets isolated from peripheral blood, tumor, and adjacent normal tissues from hepatocellular carcinoma patients (Table 1)
We aimed to explore the tumor-infiltration and prognostic value of new cell subsets identified by a recent Hepatocellular carcinoma (HCC) scRNA-seq study, using the state-of-the-art deconvolution algorithm CIBERSORTx [7, 11]

Summary

Introduction

Liver cancer is the sixth most commonly diagnosed cancer and the fourth leading cause of cancer mortality worldwide [1]. Hepatocellular carcinoma (HCC) accounts for highest proportion of primary liver cancers and can be caused by chronic hepatitis C virus (HCV) or hepatitis B virus (HBV), heavy alcohol drinking, and metabolic syndromes related to diabetes and obesity [2]. Immunotherapies have made little progress in clinical practice and the characteristics of HCC tumors that may predict the response to immunotherapies remain largely unknown [4, 5]. New subsets of tumor-infiltrating lymphocytes (TILs) related to HCC have been identified, such as exhausted CD8+ T cells, exhausted Tregs, LAMP3+ dendritic cells (DCs), and tumor-associated macrophages (TAMs), gradually unraveling the immune landscape of hepatocellular carcinoma [7, 8]. Though scRNA-seq technique is a powerful method resolving cellular heterogeneity, it remains impractical for large-scale analyses [9]

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