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.

Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas

“…Nevertheless, not only histology but also biomarker interpretation and analysis of omics data (transcriptomics, proteomics, and metabolomics) can benefit from machine learning approaches [15]. A recent example is the identification of PPGL‐specific long intergenic noncoding RNAs and their use for molecular subtyping of PPGL patients [25].…”

Metastatic Paragangliomas and Pheochromocytomas: An Epigenetic View

“…Their tissue-specific and condition-specific expression patterns suggest that lncRNAs could be potential biomarkers. Recent reports described DGCR9, FENDRR, HIF1A-AS2, MIR210HG [71] and BC063866 [21] with significantly elevated expression in metastatic compared to benign PPGLs. Expression of BC063866 was found significantly elevated in SDHx-mutated metastatic PPGLs and, if validated in larger series, could be a novel biomarker to identify potentially metastatic tumors in patients carrying SDHB mutation.…”

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

“…Four ML models, elastic net, LASSO, Ridge, and CART (classification and regression trees) were used to classify samples into five molecular subtypes of PCPG. This model can be used as a potential diagnostic tool for several molecular subtypes and/or aggressive/metastatic PCPGs (Ghosal et al, 2020). Wen et al (2017) combined microarray and RNA data, and clinical information from patients with GBM to study the association between malignant tumor degree and gene methylation level, while logistic regression was used to assess methylated genes associated with the tumor malignant degree of patients.…”