Novel visualized quantitative epigenetic imprinted gene biomarkers diagnose the malignancy of ten cancer types

Abstract

Background: Epigenetic alterations are involved in most cancers, but its application in cancer diagnosis is still limited. More practical and intuitive methods to detect the aberrant expressions from clinical samples using highly sensitive biomarkers are needed. In this study, we developed a novel approach in identifying, visualizing, and quantifying the biallelic and multiallelic expressions of an imprinted gene panel associated with cancer status. We evaluated the normal and aberrant expressions measured using the imprinted gene panel to formulate diagnostic models which could accurately distinguish the imprinting differences of normal and benign cases from cancerous tissues for each of the ten cancer types. Results: The Quantitative Chromogenic Imprinted Gene In Situ Hybridization (QCIGISH) method developed from a 1013-case study which provides a visual and quantitative analysis of non-coding RNA allelic expressions identified the guanine nucleotide-binding protein, alpha-stimulating complex locus (GNAS), growth factor receptor-bound protein (GRB10), and small nuclear ribonucleoprotein polypeptide N (SNRPN) out of five tested imprinted genes as efficient epigenetic biomarkers for the early-stage detection of ten cancer types. A binary algorithm developed for cancer diagnosis showed that elevated biallelic expression (BAE), multiallelic expression (MAE), and total expression (TE) measurements for the imprinted gene panel were associated with cell carcinogenesis, with the formulated diagnostic models achieving consistently high sensitivities (91-98%) and specificities (86-98%) across the different cancer types.

Epigenetic imprinting alterations as effective diagnostic biomarkers for early-stage lung cancer and small pulmonary nodules

“…2 A). Using the expression status of the imprinted genes GNAS , GRB10 and SNRPN , malignancy predictions for the samples were obtained using the QCIGISH binary classification model developed in our previous study [ 30 ]. The receiver operating characteristic (ROC) areas under the curve (AUC) of the BAE, MAE and TE measurements for the imprinted gene HM13 were individually compared to the ROC AUC of the binary classification model.…”

“…We subsequently developed the classification model for distinguishing lung malignancy on the basis of the MAE imprinting alteration signatures from the prior analysis. We adopted the decision tree ensemble model structure from our previous study which combined individual gene classifiers to create more robust diagnostic predictions [ 30 ] but upgraded the malignancy classification system from two to five levels and only used the MAE status for each gene (Additional file 1 : Fig. S6 and Fig.…”

“…The nascent RNA or pre-mRNA in situ hybridization (ISH) method targeting the short-lived introns can be used to visualize and label these transcription sites [ 20 – 23 ], and have been widely applied to study the transcriptional regulations of both imprinted genes [ 24 – 27 ] and non-imprinted genes [ 28 , 29 ]. In our previous study, we have adopted this intron-targeted labeling approach and developed an objective quantification of epigenetic imprinting alterations through the biallelic (BAE), multiallelic (MAE) and total (TE) expression measures which we termed as quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) [ 30 ]. Based on the elevated BAE, MAE and TE signatures observed for various cancers over benign lesions, we formulated a statistical malignancy prediction model and identified GNAS , GRB10 and SNRPN as effective diagnostic biomarkers in ten different cancer types, including lung cancer [ 30 ].…”

“…In our previous study, we have adopted this intron-targeted labeling approach and developed an objective quantification of epigenetic imprinting alterations through the biallelic (BAE), multiallelic (MAE) and total (TE) expression measures which we termed as quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) [ 30 ]. Based on the elevated BAE, MAE and TE signatures observed for various cancers over benign lesions, we formulated a statistical malignancy prediction model and identified GNAS , GRB10 and SNRPN as effective diagnostic biomarkers in ten different cancer types, including lung cancer [ 30 ]. Despite the preliminary model achieving 92% sensitivity and 88% specificity for lung cancer diagnosis, opportunities to further advance the diagnostic performance of QCIGISH in clinical applications need to be explored.…”

Epigenetic Contribution and Genomic Imprinting Dlk1-Dio3 miRNAs in Systemic Lupus Erythematosus

“…The disruption of genomic imprinting and imprinting gene expression have been implicated in the pathogenesis of human diseases including genetic imprinting disorders [ 136 , 137 ], cancers [ 138 ], neurological disorders [ 139 ], and autoimmune disorders [ 117 , 140 , 141 ]. The epigenetic dysregulation of genomic imprinting Dlk1-Dio3 miRNAs in autoimmune diseases such as MS [ 103 , 117 , 141 ], lupus [ 98 ], and T2DM [ 22 ] has been reported [ 98 , 117 ].…”