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Validation of HepScope Across Spatial and Bulk Transcriptomics for Enhanced Diagnosis and Prognosis of Hepatocellular Carcinoma

초록/요약

Background: Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer- related deaths worldwide, with limited diagnostic and prognostic biomarkers available for effective clinical management. Traditional biomarkers like alpha-fetoprotein (AFP) and glypican-3 (GPC3) exhibit inconsistent specificity and sensitivity, underscoring the need for more reliable molecular tools. Previously, we developed HepScope, a novel gene panel derived from single-cell RNA sequencing (scRNA-seq) data, which demonstrated superior accuracy in distinguishing malignant hepatocytes from non-malignant hepatocytes at the single-cell transcriptomics level. Objective: This study aims to validate the diagnostic and prognostic utility of HepScope by assessing its applicability across spatial transcriptomics (stRNA-seq) and bulk transcriptomics (bulk RNA-seq) data in HCC. Methods: I analyzed 27 tissue sections from three different 10x Visium stRNA-seq datasets, encompassing adjacent non-tumor (ADJ_HCC), leading edge, and HCC tissue samples. After data preprocessing, integration, clustering, and annotation based on established marker genes and inferCNV analysis, I evaluated HepScope's discriminatory capability using module scores. For bulk RNA-seq analysis, I performed single-sample gene set enrichment analysis (ssGSEA) on 1,309 samples to compare HepScope's performance with existing HCC-related gene sets. Functional enrichment analyses were conducted to explore the biological pathways associated with HepScope genes. The prognostic value of HepScope was assessed using Cox regression models in two independent cohorts (TCGA and GSE148355), totaling 406 patients. Finally, I validated cell–cell interactions (CCIs) observed in our previous single-cell study within the tumor immune microenvironment (TIME) using stRNA-seq data. Results: HepScope module scores were significantly higher in HCC hepatocytes and monocyte-infiltrated HCC regions compared to non-HCC hepatocytes in stRNA-seq data (p < 0.05), consistently across individual tissue samples. HepScope outperformed traditional biomarkers such as AFP and GPC3, which were expressed in less than 60% of HCC hepatocyte spots and failed to reliably distinguish malignant regions. In bulk RNA-seq data, HepScope maintained its effectiveness, with HCC samples exhibiting significantly higher ssGSEA scores than ADJ_HCC and normal tissues (p < 0.01). Functional enrichment analysis revealed that HepScope genes are involved in protein folding, mitochondrial function, and oxidative phosphorylation, indicating a metabolic reprogramming in HCC cells. The HepScope risk score stratified patients into high- and low-risk groups with significant differences in overall survival (OS) and disease-free survival (DFS) in both cohorts (hazard ratio > 3.0, p < 0.01). Multivariate Cox regression confirmed the HepScope risk score as an independent prognostic factor. Validation of CCIs showed that interactions such as MMP12-PLAUR, SEMA4D-PLXNB2, TIMP1-CD63, and lymphotoxin beta signaling pathways (LTB-LTBR, LTB-TNFRSF1A) were more prevalent in HepScopehigh tissues, aligning with our previous single-cell findings. Conclusions: HepScope is a robust and reliable gene panel for the diagnosis and prognosis of HCC across single-cell, spatial, and bulk transcriptomic platforms. It outperforms traditional biomarkers by effectively distinguishing malignant hepatocytes and providing significant prognostic insights. By capturing key molecular features of malignant cells and their interactions within the TIME, HepScope offers valuable potential for clinical translation. Further validation and adaptation into practical clinical assay formats are warranted to fully realize its utility in improving outcomes for patients with HCC.

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목차

I. INTRODUCTION 1
II. MATERIALS AND METHODS 3
A. SPATIAL TRANSCRIPTOMICS DATA COLLECTION AND PROCESSING 3
B. BULK TRANSCRIPTOMICS DATA COLLECTION AND PROCESSING 5
C. BULK PROTEOMICS DATA COLLECTION AND PROCESSING 7
D. INFERCNV 7
E. HEPSCOPE 7
F. HCC GENE SETS COLLECTION 7
G. GENE SET ENRICHMENT ANALYSIS (GSEA) 10
H. OVER-REPRESENTATION ANALYSIS (ORA) 10
I. SURVIVAL ANALYSIS 10
J. CELL-CELL INTERACTION ANALYSIS IN STRNA-SEQ DATA 11
III. RESULTS 12
A. COMPREHENSIVE CELL TYPE ANNOTATION OF HCC TISSUES 12
B. HEPSCOPE DISCRIMINATORY CAPABILITY IN SPATIAL TRANSCRIPTOMICS. 16
C. HEPSCOPE DISCRIMINATORY CAPABILITY IN BULK TRANSCRIPTOMICS 19
D. FUNCTIONAL ANALYSIS OF HEPSCOPE 22
E. PROGNOSITC CAPABILITY OF HEPSCOPE 24
F. SPATIAL ANALYSIS OF CELL–CELL INTERACTIONS IN HCC TUMOUR IMMUNE MICRO-ENVIRONMENT USING HEPSCOPE 31
IV. DISCUSSION 35
V. REFERENCES 40

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