Long intergenic noncoding RNA profiles of pheochromocytoma and paraganglioma: A novel prognostic biomarker

Abstract

Many long intergenic noncoding RNAs (lincRNAs) serve as cancer biomarkers for diagnosis or prognostication. To understand the role of lincRNAs in the rare neuroendocrine tumors pheochromocytoma and paraganglioma (PCPG), we performed first time in-depth characterization of lincRNA expression profiles and correlated findings to clinical outcomes of the disease. RNA-Seq data from patients with PCPGs and 17 other tumor types from The Cancer Genome Atlas and other published sources were obtained. Differential expression analysis and a machine-learning model were used to identify transcripts specific to PCPGs, as well as established PCPG molecular subtypes. Similarly, lincRNAs specific to aggressive PCPGs were identified, and univariate and multivariate analysis was performed for metastasis-free survival. The results were validated in independent samples using RT-PCR. From a pan-cancer context, PCPGs had a specific and unique lincRNA profile. Among PCPGs, five different molecular subtypes were identified corresponding to the established molecular classification. Upregulation of 13 lincRNAs was found to be associated with aggressive/metastatic PCPGs. RT-PCR validation confirmed the overexpression of four lincRNAs in metastatic compared to non-metastatic PCPGs. Kaplan-Meier analysis identified five lincRNAs as prognostic markers for metastasis-free survival of patients in three subtypes of PCPGs. Stratification of PCPG patients with a risk-score formulated using multivariate analysis of lincRNA expression profiles, presence of key driver mutations, tumor location, and hormone secretion profiles showed significant differences in metastasis-free survival. PCPGs thus exhibit a specific lincRNA expression profile that also corresponds to the established molecular subgroups and can be potential marker for the aggressive/metastatic PCPGs.

Keywords: biomarkers; lincRNA; molecular subtypes; paraganglioma; pheochromocytoma.

Figure 1.

(a) lincRNA expression changes (upregulation or downregulation) in presence of different driver alterations in PCPG is calculated from PCPG TCGA tumor samples. For each of the 11 driver alterations shown in the figure, the mutual exclusivity of lincRNA upregulation and downregulation between each of the driver mutation groups are calculated using a hypergeometric test against the null hypothesis that same lincRNAs are upregulated (or downregulated) between different driver alteration groups (e.g., the lincRNAs upregulated (or downregulated) in SDHB mutated vs. non-SDHB mutated groups are compared against the lincRNAs upregulated (or downregulated) in VHL mutated vs. non-VHL mutated groups; and the same comparison is done between all driver mutation groups). For each comparison, a significant hypergeometric p-value (<0.05) reflects the fact that lincRNAs upregulated (or downregulated) between the driver mutation groups are distinct; and a high p-value (close to 1) reflects that the lincRNAs upregulated (or downregulated) between the driver alterations are very similar. In the figure the hypergeometric p-values between each driver mutation groups are plotted as an n*n heatmap; where the rows and columns denote driver mutation groups in PCPG and each cell shows the hypergeometric p-value for common lincRNA up-/down-regulation between the corresponding mutation groups in the respective row and column. The gradient blue to red denotes the hypergeometric p-value in the range 0–1. The driver alteration groups having distinct lincRNA up-/down-regulation signatures between them have significantly low hypergeometric p-values (<0.01), denoted as cells having dark blue color; whereas the driver alteration groups having common lincRNA up-/down-regulation signatures between them have high p-values between them, denoted as cells having red colors. We grouped the PCPG driver alteration groups belonging to four molecular subtypes of PCPG; SDHx-mutated pseudohypoxia (SDHB, SDHD), non-SDHx-mutated pseudohypoxia (VHL, EPAS1, EGLN1), Wnt-altered (MAML3, CSDE1) and kinase-signaling (NF1, RET, HRAS, MAX). (b) We plotted the signature of up-/down-regulation of lincRNA expression among the PCPG driver mutations grouped according to known molecular classification SDHx-mutated pseudohypoxia (SDHB/SDHD), non-SDHx-mutated pseudohypoxia (VHL/EPAS1/EGLN1), Wnt-altered (MAML3/CSDE1), and kinase-signaling (NF1, RET, HRAS, MAX), with minimal overlap between upregulated lincRNAs in each driver mutation group.

Figure 2.

(a) Signature of 106 lincRNAs identified by Elastic Net model as high-precision classifier of PCPG molecular subtypes. Expression heatmap of 106 marker lincRNAs are plotted using R gplots package. (b) Unsupervised clustering of 106 marker lincRNA expression signatures between PCPG molecular subgroups (from the heatmap in panel a) done using spearman correlation coefficient as distance metric. Three distinct clusters corresponding to pseudohypoxia, kinase-signaling and cortical admixture can be clearly identified. The pseudohypoxic cluster is further divided into two clusters; one showing more elevated expression in SDHx-mutated pseudohypoxia than non-SDHx-mutated pseudohypoxia. (c) RT-PCR validation of subtype-specific expression pattern of five lincRNAs, LINC00472, MIR210HG, DGCR9, HIF1A-AS2, FENDRR in PCPG tumor tissue samples from patients. All five lincRNAs showed significantly elevated expression in pseudohypoxic compared to kinase-signaling subtypes. *p < 0.01, **p < 0.001, ***p < 0.0001.

Figure 3.

Four lincRNA expression signatures are confirmed to be marker of metastatic PCPG tumors compared to benign PCPG tumors, validated using RT-PCR in patient tumor samples. *p < 0.01, **p < 0.001, ***p < 0.0001.

Figure 4.

(a) Univariate Kaplan–Meier analysis of progression-free survival identifies lincRNAs associated with good/poor prognosis (event free survival) in PCPG molecular subtypes. (b) Combined prognostic score for 18 lincRNA signature (13 lincRNAs from Table 1 and five lincRNAs from Fig. 4a) and 7 previously reported clinical parameters (SDHB germline mutation, ATRX somatic mutation, extra-adrenal tumor location, TERT expression representing telomerase activation and hormone profiles) for prediction of metastasis-free survival from PCPG clinical samples from TCGA.