Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer

Vu, Trung Nghia; Pramana, Setia; Calza, Stefano; Suo, Chen; Lee, Donghwan; Pawitan, Yudi

doi:10.18632/oncotarget.11998

Cited by 19 publications

(24 citation statements)

References 49 publications

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“…For example, HOTAIR is a lncRNA that is highly expressed in HER2+ breast cancers whereas HOTAIRM1 is highly expressed in basal-like subgroup of breast cancers (Su et al, 2014). LuminalA types showed over expression of LINC00160 and abundance of DSCAM-AS1 was reported in luminalB subtypes of breast cancer (Jonsson et al, 2015;Vu et al, 2016). MALAT, lncRNA-ATB, BC200, XIST, H19…”

Section: Discussionmentioning

confidence: 99%

Identification of lncRNAs associated with early stage breast cancer and their prognostic implications

Kuha

Patel

et al. 2019

Preprint

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KeywordsLong non-coding RNAs; Breast cancer; ADAMTS9-AS2; FAM83H-AS1; RNA sequencing; ncRNAs Abbreviations DCIS -ductal carcinoma in situ IDC -invasive ductal carcinoma NGS -Next generation sequencing lncRNA -long non-coding RNAs lincRNA -long intergenic non-coding RNA TNBC -Triple negative breast cancers BH -Bonferroni and Benjamini-Hochberg PCA -Principal component analysis PCC -Pearson's correlation coefficient PCG-Protein coding genes AbstractBreast cancer is a common malignancy among women with the highest incidence rate worldwide. Dysregulation of long non-coding RNAs occurring in the preliminary stages of breast carcinogenesis is poorly understood. In this study, RNA sequencing was done to identify long non-coding RNA expression profiles associated with early-stage breast cancer.RNA sequencing was done in 6 invasive ductal carcinoma (IDC) tissues along with paired normal tissue samples, 7 ductal carcinoma in situ (DCIS) tissues and 5 apparently normal breast tissues. We identified 375 differentially expressed lncRNAs (DElncRNAs) in IDC tissues compared to paired normal tissues. Antisense transcripts (~58%) were the largest subtype among DElncRNAs. About 20% of the 375 DElncRNAs were supported by typical split readings leveraging their detection confidence. Validation was done in n=52 IDC and paired normal tissue by qRT-PCR for the identified targets (ADAMTS9-AS2, EPB41L4A-AS1, WDFY3-AS2, RP11-295M3.4, RP11-161M6.2, RP11-490M8.1, CTB-92J24.3 and FAM83H-AS)1. We evaluated the prognostic significance of DElncRNAs based on TCGA datasets and overexpression of FAM83H-AS1 was associated with patient poor survival. We confirmed that the down-regulation of ADAMTS9-AS2 in breast cancer was due to promoter hypermethylation through in-vitro silencing experiments and pyrosequencing.

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Section: Discussionmentioning

confidence: 99%

Identification of lncRNAs associated with early stage breast cancer and their prognostic implications

Kuha

Patel

et al. 2019

Preprint

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“…Considering the great importance of lncRNAs in tumor tumorigenesis and progression, lncRNA dysregulation may serve as an important indicator of the characteristics of tumors. It has been documented that altered lncRNAs can exist in many cancer types, and are tightly associated with the outcome of cancers [ 32 , 33 ]. Many works have focused on whether aberrant expression of specific lncRNAs in cancers can serve as independent markers for diagnosis and prognosis [ 17 , 18 , 31 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

A five-long non-coding RNA signature to improve prognosis prediction of clear cell renal cell carcinoma

Shi

Chang

et al. 2017

Oncotarget

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Recent works have reported that long non-coding RNAs (lncRNAs) play critical roles in tumorigenesis and prognosis of cancers, suggesting the potential utility of lncRNAs as cancer prognostic markers. However, lncRNA signatures in predicting the survival of patients with clear cell renal cell carcinoma (ccRCC) remain unknown. In this study, we attempted to identify lncRNA signatures and their prognostic values in ccRCC. Using lncRNA expression profiling data in 440 ccRCC tumors from The Cancer Genome Atlas (TCGA) data, a five-lncRNA signature (AC069513.4, AC003092.1, CTC-205M6.2, RP11-507K2.3, U91328.21) has been identified to be significantly associated with ccRCC patients’ overall survival in both training set and testing set. Based on the lncRNA signature, ccRCC patients could be divided into high-risk and low-risk group with significantly different survival rate. Further multivariable Cox regression analysis suggested that the prognostic value of this signature was independent of clinical factors. Functional enrichment analyses showed the potential functional roles of the five prognostic lncRNAs in ccRCC oncogenesis. These results indicated that this five-lncRNA signature could be used as an independent prognostic biomarker in the prediction of ccRCC patients’ survival.

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“…Our results provide more information for further biological validation. (2) Although other papers have identified lncRNAs, none of these papers have evaluated the subtype-specific gene signatures from a prognostic perspective, though the prognosis of individual lncRNA has been evaluated [ 41 , 42 , 43 ]. (3) Our novel integrative method is flexible in balancing the tradeoff of prediction accuracy and the number of selected genes, which will provide biologists with more options.…”

Section: Discussionmentioning

confidence: 99%

lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

Zhang

Wang

Ghoshal

et al. 2018

Genes

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Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA )RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC) method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes) that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

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Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer

Cited by 19 publications

References 49 publications

Identification of lncRNAs associated with early stage breast cancer and their prognostic implications

Identification of lncRNAs associated with early stage breast cancer and their prognostic implications

A five-long non-coding RNA signature to improve prognosis prediction of clear cell renal cell carcinoma

lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

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