The combined signatures of hypoxia and cellular landscape provides a prognostic and therapeutic biomarker in hepatitis B virus-related hepatocellular carcinoma

Abstract

Prognosis and treatment options of hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) are generally based on tumor burden and liver function. Yet, tumor growth and therapeutic resistance of HBV-HCC are strongly influenced by intratumoral hypoxia and cells infiltrating the tumor microenvironment (TME). We, therefore, studied whether linking parameters associated with hypoxia and TME cells could have a better prediction of prognosis and therapeutic responses. Quantification of 109 hypoxia-related genes and 64 TME cells was performed in 452 HBV-HCC tumors. Prognostic hypoxia and TME cells signatures were determined based on Cox regression and meta-analysis for generating the Hypoxia-TME classifier. Thereafter, the prognosis, tumor, and immune characteristics as well as the benefit of therapies in Hypoxia-TME defined subgroups were analyzed. Patients in the Hypoxia^low /TME^high subgroup showed a better prognosis and therapeutic responses than any other subgroups, which can be well elucidated based on the differences in terms of immune-related molecules, tumor somatic mutations, and cancer cellular signaling pathways. Notably, our analysis furthermore demonstrated the synergistic influence of hypoxia and TME on tumor metabolism and proliferation. Besides, the classifier allowed a further subdivision of patients with early- and late-HCC stages. In addition, the Hypoxia-TME classifier was validated in another independent HBV-HCC cohort (n = 144) and several pan-cancer cohorts. Overall, the Hypoxia-TME classifier showed a pretreatment predictive value for prognosis and therapeutic responses, which might provide new directions for strategizing patients with optimal therapies.

Keywords: HBV-related hepatocellular carcinoma; hypoxia; prognosis; therapeutic responses; tumor microenvironment.

FIGURE 1

Schematic diagram depicting the establishment and validation of Hypoxia‐TME classifier. HCC patients with complete clinical information and positive HBV infection were enrolled from the four cohorts (GSE14520, ICGC‐LIHC‐JP, Gao et al, GSE 10143), which were used to establish the prognostic TME score and Hypoxia score, respectively. Cox regression analyses of 64 TME cells and 109 hypoxia‐related genes were performed in each of the four cohorts independently, and then meta‐analysis was used for the evaluation of the overall prognostic values of TME cells and genes. Briefly, 19 cell types within tumor microenvironment were selected for the development of TME score. As well, 48 hypoxia‐related genes were used for the establishment of Hypoxia score. The Hypoxia‐TME classifier which integrating the TME score and hypoxia score, divided all the patients into three different subgroups: Hypoxia^low/TME^high, Hypoxia^high/TME^low and the intermediate mixed subgroups. The difference in terms of prognosis, immune‐related molecules, clinical characteristics, tumor mutational burden and tumor cellular pathways were explored in different patients' subgroups based on the Hypoxia‐TME classifier. Furthermore, the classifier's performance was further validated in another independent HBV‐HCC cohort (TCGA‐LIHC), HCV‐HCC, various HCC cohorts and pan‐cancer patient cohorts, respectively. The figure was created with BioRender.com [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Development of the Hypoxia score and TME score based on hypoxia‐related genes and TME cells respectively. (A) Heat map showing the relationships between the Hypoxia score and 48 prognostic hypoxia‐related genes in five HBV‐HCC cohorts. Positive (red) and negative (purple) correlations are indicated. Gray means missing values in that cohort. (B) Heat map showing the relationships between the TME score and 19 prognostic TME cells in five HBV‐HCC cohorts. Positive (red) and negative (purple) correlations are indicated. (C) Kaplan‐Meier overall survival curves of four training cohorts in tumors with high Hypoxia score vs low Hypoxia score. (D) Kaplan‐Meier overall survival curves of four training cohorts in tumors with high TME score vs low TME score. (E) Gene Set Enrichment Analysis (GSEA) of 109 genes represent HIF‐1 signaling pathway reveals the association between Hypoxia score and HIF‐1 signaling pathway. High hypoxia score located in the left approaching the origin of the x‐axis, by contrast, low hypoxia score lay on the right of the x‐axis (GSE14520). (F) Gene Set Enrichment Analysis (GSEA) of 332 marker genes represent immune response display the association between TME score and immune response. High TME score located in the left approaching the origin of the x‐axis, by contrast, low TME score lay on the right of the x‐axis (GSE14520). (G) t‐SNE plot of all 3836 cells from 9 primary liver cancer patients. Cells were annotated based on known lineage‐specific marker genes as CD8 T cells, monocytes/macrophages, plasma cells, fibroblasts, malignant cells, endothelial cells, hepatic progenitor (GSE125449). (H) t‐SNE plot of all the single cells colored by Hypoxia score. (I) t‐SNE plot of all the single cells colored by TME score. (J) Comparisons of Hypoxia score among immune cells, malignant cells and stromal cells. (K) Comparisons of TME score among immune cells, malignant cells and stromal cells [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Prognostic value and tumor cellular signaling pathways analysis based on Hypoxia‐TME classifier. (A) Kaplan‐Meier overall survival curves of four HBV‐HCC training cohorts (n = 452) stratified into four different subgroups based on the Hypoxia‐TME classifier (Hypoxia^low/TME^high, Hypoxia^low/TME^low, Hypoxia^high/TME^high and Hypoxia^high/TME^low). Log‐rank test, P < .001. (B) Kaplan‐Meier overall survival curves of the independent validation HBV‐HCC cohort (n = 144) stratified into three different subgroups based upon the Hypoxia‐TME classifier (Hypoxia^low/TME^high, mixed and Hypoxia^high/TME^low). Log‐rank test, P < .001. (C) Heat map showing the correlations matrix among all Hypoxia‐TME signatures in three different subgroups based upon Hypoxia‐TME classifier. Positive (red) and negative (purple) correlations are indicated. (D) Multivariate cox analysis of the Hypoxia‐TME classifier in five HBV‐HCC cohorts. (E) Cox analysis of the Hypoxia‐TME classifier in three HCV‐HCC cohorts. (F) Compare tumor proliferative signaling pathways and tumor metabolism‐related pathways among four Hypoxia‐TME subgroups based on mRNA expression levels (GSE14520, Gao et al, TCGA cohorts): higher enrichment scores (red), lower enrichment scores (blue) [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 4

Association between Hypoxia‐TME classifier and other HCC‐related molecular signatures. (A) The association between Hypoxia‐TME classifier and clinical characteristics/other HCC‐related molecular signatures. AVR‐CC, active viral replication chronic carrier; CC, chronic carrier; AFP (high) means >400 ng/mL; AFP (low) means <400 ng/mL; tumor size (large) >5 cm, tumor size (small) <5 cm. Higher enrichment scores (red), lower enrichment scores (purple) (GSE14520). (B) Kaplan‐Meier overall survival curves of an HBV‐HCC cohort stratified into three different subgroups based on the Hypoxia‐TME classifier. Log‐rank test, P < .001. (C) ROC curves for the 1‐, 2‐, 3‐, 5‐year survival according to the Hypoxia‐TME classifier. (D) Comparisons of Hypoxia‐TME classifier with other clinical prognostic staging systems/biomarkers under the time‐dependent ROC for the incidence of overall survival. TNM staging system (light green), Hypoxia‐TME classifier (brown), BCLC staging system (purple), tumor size (dark green), AFP (brick‐red). (E) Kaplan‐Meier overall survival curves of HBV‐HCC patients, divided by the combination of Hypoxia‐TME classifier and TNM staging system. Log‐rank test, P < .001 [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 5

Comparison of immune‐related markers in three subgroups based upon Hypoxia‐TME classifier. Comparison of the expression of immune‐related genes in defined three subgroups based on Hypoxia‐TME classifier. Box and whisker plots showing normalized expression of mRNA for selected markers. The Hypoxia^low/TME^high, intermediate mixed and Hypoxia^high/TME^low subgroups are represented as yellow, blue and red, respectively (GSE14520) [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 6

Association between tumor somatic mutations and Hypoxia‐TME classifier. (A) The OncoPrint was constructed by the top 20 mutation genes between Hypoxia^low/TME^high and Hypoxia^high/TME^low subgroups. Each liver tumor from an individual patient was represented in each column (TCGA‐LIHC). (B) Comparison of tumor mutational burden among defined subgroups based on Hypoxia‐TME classifier. (C) Comparison of TP53 expression among defined subgroups according to Hypoxia‐TME classifier. (D) Kaplan‐Meier overall survival curves of HBV‐HCC patients with or without TP53 gene mutation. (E) Kaplan‐Meier overall survival curves of HBV‐HCC patients divided by TP53 mutation status and Hypoxia‐TME classifier [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 7

Therapy responses prediction based on Hypoxia‐TME classifier. (A) Comparison of Hypoxia scores between sorafenib responder and nonresponder. NR (nonresponder), RE (responder). (B) Comparison of TME scores between sorafenib responder and nonresponder. (C) The different percentages of sorafenib responder among subgroups based on Hypoxia‐TME classifier. (D) Comparison of Hypoxia scores among patients with different MAGE‐A3 immunotherapy response status. NE (not evaluable), NR (nonresponder), RE (responder). (E) Comparison of TME scores among patients with different MAGE‐A3 immunotherapy response status. (F) The different percentage of MAGE‐A3 immunotherapy responder among subgroups based on Hypoxia‐TME classifier. (G) Functional analysis in Hypoxia^low/TME^high (left) and Hypoxia^high/TME^low (right) of patients under MAGE‐A3 immunotherapy illustrated using Proteomaps. Each small polygon corresponds to a single KEGG pathway, and the size correlates with the ratio between the subgroups. (H) Functional analysis in responder (left) and nonresponder (right) of patients under MAGE‐A3 immunotherapy illustrated using Proteomaps. Each small polygon corresponds to a single KEGG pathway, and the size correlates with the ratio between the subgroups [Color figure can be viewed at wileyonlinelibrary.com]