Pan-cancer analyses of human nuclear receptors reveal transcriptome diversity and prognostic value across cancer types

Abstract

The human nuclear receptor (NR) superfamily comprises 48 ligand-dependent transcription factors that play regulatory roles in physiology and pathophysiology. In cancer, NRs have long served as predictors of disease stratification, treatment response, and clinical outcome. The Cancer Genome Atlas (TCGA) Pan-Cancer project provides a wealth of genetic data for a large number of human cancer types. Here, we examined NR transcriptional activity in 8,526 patient samples from 33 TCGA 'Pan-Cancer' diseases and 11 'Pan-Cancer' organ systems using RNA sequencing data. The web-based Kaplan-Meier (KM) plotter tool was then used to evaluate the prognostic potential of NR gene expression in 21/33 cancer types. Although, most NRs were significantly underexpressed in cancer, NR expression (moderate to high expression levels) was predominantly restricted (46%) to specific tissues, particularly cancers representing gynecologic, urologic, and gastrointestinal 'Pan-Cancer' organ systems. Intriguingly, a relationship emerged between recurrent positive pairwise correlation of Class IV NRs in most cancers. NR expression was also revealed to play a profound effect on patient overall survival rates, with ≥5 prognostic NRs identified per cancer type. Taken together, these findings highlighted the complexity of NR transcriptional networks in cancer and identified novel therapeutic targets for specific cancer types.

Figure 1

Human nuclear receptors display relatively similar expression patterns across ‘Pan-Cancer’ diseases. Heatmap depicting RNA-seq gene expression for 48 human NRs in 8,526 TCGA samples representing 33 ‘Pan-Cancer’ diseases. Hierarchical clustering was performed using the Manhattan distance metric and Ward’s minimum variance method (Ward.D2). Gene expression is shown in log10 normalized RSEM.

Figure 2

NRs are differentially expressed in normal and cancer tissue. (A) Heatmap of Benjamini-Hochberg adjusted p-values using the Wilcoxon test depicting differences in RNA-seq gene expression levels for 16 ‘Pan-Cancer’ forms and corresponding normal tissue. Hierarchical clustering was performed using the Manhattan distance metric and Ward’s minimum variance method (Ward.D2). Statistical significance is shown in −log10[adjusted p-value], where P < 0.05 corresponds to −log10[adjusted p-value] >1.3 (light green), P ≤ 0.01 corresponds to −log10[adjusted p-value] >2 (blue green), P ≤ 0.001 corresponds to −log10[adjusted p-value] >3 (green), and P ≤ 0.0001 corresponds to −log10[adjusted p-value] >4 (dark blue). (B) Bar chart depicting the number of differentially expressed NRs (cancer vs normal) that were identified per cancer type (corresponds to the number of green to blue colored rows in the heatmap). (C) Bar chart depicting the number of cancer types associated with over- (blue bars) and underexpression (yellow bars) of each NR in cancer compared with normal tissue (corresponds to the number of green to blue colored columns in the heatmap).

Figure 3

Strong association between NR gene expression and the LUSC cancer form. The highest number of differentially expressed NRs (42/48 NRs) was found in the LUSC cancer form. Box plots showing differences in NR gene expression levels between cancer and corresponding normal tissue for the LUSC cancer form. The Wilcoxon test was used to calculate statistical significance (Benjamini-Hochberg adjusted p-values). ns = not significant (P > 0.05); *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001.

Figure 4

Pairwise Pearson correlation plots between NR gene expression in different ‘Pan-Cancer’ diseases. Correlation matrices for (A) the 21 ‘Pan-Cancer’ diseases, (B) BRCA, (C) ESCA, and (D) PAAD, with genes ordered using hierarchical clustering with the Ward’s minimum variance method (Ward.D2). Positive correlation coefficients are displayed in blue and negative correlation coefficients in red color. The color intensity and circle size are proportional to the correlation coefficients (P < 0.05), while correlation coefficients with P > 0.05 are blank.

Figure 5

NRs are associated with clinical outcome for several ‘Pan-Cancer’ forms. (A) Heatmap of log-rank test p-values depicting the effect of NR gene expression on overall survival for 21 ‘Pan-Cancer’ forms. The ESCA ‘Pan-Cancer’ disease is shown as ESCA_A (esophageal adenocarcinoma) and ESCA_S (esophageal squamous cell carcinoma). Hierarchical clustering was performed using the Manhattan distance metric and Ward’s minimum variance method (Ward.D2). Statistical significance is shown in –log10[p-value], where P < 0.05 corresponds to −log10[p-value] >1.3 (light green), P ≤ 0.01 corresponds to −log10[p-value] >2 (blue green), P ≤ 0.001 corresponds to −log10[p-value] >3 (green), and P ≤ 0.0001 corresponds to −log10[p-value] >4 (dark blue). (B) Bar chart depicting the number of identified prognostic NRs per cancer type (corresponds to the number of green to blue colored rows in the heatmap). (C) Bar chart depicting the number of cancer types associated with high (blue bars) and low expression (yellow bars) for each prognostic NR (corresponds to the number of green to blue colored columns in the heatmap).

Figure 6

Gene expression of the PPARG nuclear receptor is significantly associated with overall survival in cancer. (A,B) Kaplan–Meier analysis of PPARG expression in the BLCA and LIHC cohorts. Estimates of the probability of overall survival according to quantile expression (low or high expression). P-values, hazard ratios (HR), and 95% confidence intervals (95% CI) were calculated using the log-rank test and Cox proportional hazards regression, respectively. The x-axes depict months after initial diagnosis and the y-axes depict overall survival. (C) Forest plots illustrating univariate Cox regression analysis of the prognostic impact of PPARG expression on overall survival in 19 ‘Pan-Cancer’ forms. The x-axis is in log scale. HR <1 depicts the association between high PPARG expression and decreased risk, whereas HR >1 illustrates the association between high PPARG expression and increased risk.