HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC

Abstract

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCC database (HCCDB v1.0) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

1. Introduction

Hepatocellular carcinoma (HCC), which accounts for the vast majority (75%–85%) of primary liver cancer, is one of the leading digestive system malignancies [1]. The accumulation of transcriptomic data in HCC has facilitated the precise subtyping and biomarker identification [2–4]. However, due to the high heterogeneity of HCC, transcriptomic data from a single cohort frequently generate inconsistent results due to limited sample size. The meta-analysis is an important approach for identifying stable patterns and cohort-specific effects across different datasets [5]. To provide a resource for studying the heterogeneities and dysregulated biological processes in HCC, we developed HCCDB v1.0, which integrated transcriptomes of 3917 samples from 15 bulk datasets and emphasized the centrality of meta-analysis in transcriptomic analysis [6–8]. With the growth of published datasets of HCC clinical samples over the past four years, it is imperative to increase the volume of the database contents. Moreover, it is interesting to assess the stability or reproducibility of the meta-analysis results after adding new datasets.

The bulk transcriptomic data provide important resources for analyzing gene expression variations of tumors in terms of malignancy, aggressiveness, and cell composition. However, bulk data only provide the average gene expression levels of the sample, which consists of multiple cell types and tumor subclones. The latest single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) can decompose gene expression variations at the cellular level and obtain the spatial distribution of intact tissues. The scRNA-seq technique captures the cellular heterogeneity within the same tissue and reveals distinct cell subpopulations [9,10]. ST preserves the spatial location information of tumor tissues by in situ characterization of tissue spots, shedding light on integrating the functional and structural aspects of tumors.