Unraveling the role of ADAMs in clinical heterogeneity and the immune microenvironment of hepatocellular carcinoma: insights from single-cell, spatial transcriptomics, and bulk RNA sequencing

Abstract

Background: Hepatocellular carcinoma (HCC) is a prevalent and heterogeneous tumor with limited treatment options and unfavorable prognosis. The crucial role of a disintegrin and metalloprotease (ADAM) gene family in the tumor microenvironment of HCC remains unclear.

Methods: This study employed a novel multi-omics integration strategy to investigate the potential roles of ADAM family signals in HCC. A series of single-cell and spatial omics algorithms were utilized to uncover the molecular characteristics of ADAM family genes within HCC. The GSVA package was utilized to compute the scores for ADAM family signals, subsequently stratified into three categories: high, medium, and low ADAM signal levels through unsupervised clustering. Furthermore, we developed and rigorously validated an innovative and robust clinical prognosis assessment model by employing 99 mainstream machine learning algorithms in conjunction with co-expression feature spectra of ADAM family genes. To validate our findings, we conducted PCR and IHC experiments to confirm differential expression patterns within the ADAM family genes.

Results: Gene signals from the ADAM family were notably abundant in endothelial cells, liver cells, and monocyte macrophages. Single-cell sequencing and spatial transcriptomics analyses have both revealed the molecular heterogeneity of the ADAM gene family, further emphasizing its significant impact on the development and progression of HCC. In HCC tissues, the expression levels of ADAM9, ADAM10, ADAM15, and ADAM17 were markedly elevated. Elevated ADAM family signal scores were linked to adverse clinical outcomes and disruptions in the immune microenvironment and metabolic reprogramming. An ADAM prognosis signal, developed through the utilization of 99 machine learning algorithms, could accurately forecast the survival duration of HCC, achieving an AUC value of approximately 0.9.

Conclusions: This study represented the inaugural report on the deleterious impact and prognostic significance of ADAM family signals within the tumor microenvironment of HCC.

Keywords: ADAM family; bulk RNA sequencing; clinical heterogeneity; hepatocellular carcinoma; immune microenvironment; single-cell sequencing; spatial transcriptome sequencing.

Figure 1

Single-cell overview of HCC in our self-generated cohort. (A) Pathology imaging of HCC patients (B) Determination of appropriate resolution. (C) Cell type annotation. (D) Overview of t-SNE dimensionality reduction. (E) t-SNE dimensionality reduction features of normal tissue and tumor tissue.

Figure 2

Single-cell distribution of ADAM family signals in our self-generated cohort. (A) Bubble chart displays the ADAM signals for each type of cell. (B) Violin plot displays the ADAM signals based on six algorithms. (C–H) t-SNE dimensionality reduction displays the single-cell distribution of ADAM signals. Six algorithms used for assessing ADAM signals involve AUCell, UCell, Add, singscore, ssgsea, and Scoring. (*:p<0.05,**:p<0.01,***:p<0.001,****:p<0.0001; p value was calculated by wilcox.test).

Figure 3

Copykat results and cell cycle analysis of single-cell data in our self-generated cohort. (A) t-SNE dimensionality reduction of copykat results. (B) t-SNE dimensionality reduction of cell cycle analysis. (C) The proportion of G1, S, and G2M in HCC, and proportion of aneuploid and diploid in HCC. (D) Single-cell distribution of ADAM signals in aneuploid and diploid. (E) The violin plot displays the discrepancies in ADAM signals between aneuploid and diploid. (F) The violin plot displays the discrepancies in expression of ADAM9, ADAM10, ADAM15, and ADAM17 between aneuploid and diploid. (****:p<0.0001; p value was calculated by wilcox.test).

Figure 4

Spatial transcriptomics overview of 4 HCC samples.

Figure 5

Pan-cancer overview of ADAM family members. (A, B) SNV characteristics of ADAM family members in multiple human cancers. (C) CNV characteristics of ADAM family members in multiple human cancers. (D) mRNA expression characteristics of ADAM family members in multiple human cancers. (E) Methylation characteristics of ADAM family members in multiple human cancers.

Figure 6

Identification of molecular characteristics of ADAM signals in HCC. (A) Cluster analysis for HCC patients based on ADAM signals. (B) Survival analysis of three clusters. (C) The violin plot displays the discrepancies in ADAM signals between three clusters. (D) The heatmap displays the discrepancies in metabolism traits between three clusters. (E) The heatmap displays the discrepancies in immune traits between three clusters. (*:p<0.05,**:p<0.01,***:p<0.001,****:p<0.0001; p value was calculated by kruskal.test).

Figure 7

Machine learning determination of a robust prognostic signature associated with ADAM family members. (A) Find out the best prognostic model based on multiple machine learning algorithms. (B) Survival analysis of prognostic model. (C) ROC curves of prognostic model. (D) Expression traits of ADAM9, ADAM10, ADAM15, and ADAM17 based on GEPIA2 platform. qPCR experiments validated the expression of ADAM9, ADAM10, ADAM15, and ADAM17. (*:p<0.05, ***:p<0.001).

Figure 8

Immunohistochemical experiments on HCC tissue microarray of ADAM10. (A) Immunohistochemical profiles of 48 pairs of liver cancer patients. (B) Examples of low expression of ADAM10 in cancer tissue. (C) Examples of high expression of ADAM10 in cancer tissue. (D) Statistical quantitative analysis of ADAM10 protein levels by immunohistochemistry.