The presence or absence alone of miRNA isoforms (isomiRs) successfully discriminate amongst the 32 TCGA cancer types

Telonis, AG; Magee, Rogan; Loher, Phillipe; Chervoneva, Inna; Londin, Eric; Rigoutsos, Isidore

doi:10.1101/082685

Cited by 6 publications

(10 citation statements)

References 70 publications

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“…In fact, a substantial background signal is observed in the 0|0 probe signal, both in synthetic and real cell contexts, before and after transfection of this isomiR ( Figure 2 , Figure 3 and Figure 4 ). If one were to attempt to probe −1|+1 levels using a qPCR probe, in order to confirm its absence from a sample and thereby heighten suspicion of a specific disease (see [ 16 ] for example), one would potentially encounter background signals that render the problem of confirming lack of a specific isomiR non-trivial. The reverse is true as well; in order to confirm the presence of a specific isomiR, one would need to be sure that any signal observed on qPCR captured the abundance of the isomiR itself, and not closely related sequences, which here are shown to produce non-trivial background noise.…”

Section: Discussionmentioning

confidence: 99%

“…We downloaded these data through the online data portal at: http://gdc-portal.nci.nih.gov. We then computed isomiR expression using the distributed miRNA isoform data, as in [11,16], and further subjected the computed reads to Threshold-seq, in order to determine a dynamic threshold for inclusion of isomiRs in downstream analysis [22]. Finally, we built expression matrices for isomiRs from each of the 32 TCGA projects, retaining only those isomiRs within a specific TCGA project that exceed Threshold-seq filtering level in at least one sample and retaining samples of any original type (specifically: tumor, normal, blood based, or metastasis).…”

Section: Methodsmentioning

confidence: 99%

“…Our previous analysis of 452 deep sequencing datasets from the GEUVADIS project [ 15 ] showed that isomiR profiles are able to classify individual samples based on characteristics, such as: sex, race, and population [ 11 ]. More recently, we showed that isomiRs can distinguish amongst tissue type and disease subtypes and that isoforms of the same miRNA can target largely non-overlapping groups of mRNAs [ 10 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of isomiR Discrimination Using Commercial qPCR Methods

Magee

Telonis

Cherlin

et al. 2017

ncRNA

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We sought to determine whether commercial quantitative polymerase chain reaction (qPCR) methods are capable of distinguishing isomiRs: variants of mature microRNAs (miRNAs) with sequence endpoint differences. We used two commercially available miRNA qPCR methods to quantify miR-21-5p in both synthetic and real cell contexts. We find that although these miRNA qPCR methods possess high sensitivity for specific sequences, they also pick up background signals from closely related isomiRs, which influences the reliable quantification of individual isomiRs. We conclude that these methods do not possess the requisite specificity for reliable isomiR quantification.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of isomiR Discrimination Using Commercial qPCR Methods

Magee

Telonis

Cherlin

et al. 2017

ncRNA

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“…The classification accuracy in Liao's study for BRCA and STAD is lower than that in our study, while the accuracy for LUAD is higher than that of our method. Telonis et al [41] evaluated the ability of isomiRs and used the top 20% abundant isomiRs to construct a binary classifier. The classifier could label tumor samples with 93% average sensitivity.…”

Section: Comparison With the State Of The Artmentioning

confidence: 99%

“…Methods Result AUC ACC Pre BRCA Liao [22] N/A 0.87 N/A Guo [20] N/A N/A 0.88 Telonis [41] N/A 0.91 N/A Li [42] 0.89 N/A N/A Sherafatian [43] N/A 0.89 0.90 MLW-gcForest 0.98 0.91 0.92 LUAD Liao [22] N/A 0.91 N/A Guo [20] N/A N/A 0.88 Telonis [41] N/A 0.86 N/A Podolsky [44] 0.92 N/A N/A Cai [19] N/A 0.85 0.86 MLW-gcForest 0.92 0.87 0.86 LIHC Guo [20] N/A N/A 0.82 Telonis [41] N/A 0.90 N/A Tan [45] 0.77 0.83 N/A Friemel [46] N/A 0.87 N/A MLW-gcForest 0.91 0.87 0.85 GBM Guo [20] N/A N/A 0.78 Lu [21] 0.92 0.88 N/A Ryu [47] 0.83 0.80 N/A MLW-gcForest 0.87 0.89 0.86 STAD Liao [22] N/A 0.84 N/A Telonis [41] N/A 0.85 N/A MLW-gcForest 0.88 0.87 0.87…”

Section: Cancermentioning

confidence: 99%

MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

et al. 2019

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Effective cancer treatment requires a clear subtype. Due to the small sample size, high dimensionality, and class imbalances of cancer gene data, classifying cancer subtypes by traditional machine learning methods remains challenging. The gcForest algorithm is a combination of machine learning methods and a deep neural network and has been indicated to achieve better classification of small samples of data. However, the gcForest algorithm still faces many challenges when this method is applied to the classification of cancer subtypes. In this paper, we propose an improved gcForest algorithm (MLW-gcForest) to study the applicability of this method to the small sample sizes, high dimensionality, and class imbalances of genetic data. The main contributions of this algorithm are as follows: (1) Different weights are assigned to different random forests according to the classification ability of the forests. (2) We propose a sorting optimization algorithm that assigns different weights to the feature vectors generated under different sliding windows. The MLW-gcForest model is trained on the methylation data of five data sets from the cancer genome atlas (TCGA). The experimental results show that the MLW-gcForest algorithm achieves high accuracy and area under curve (AUC) values for the classification of cancer subtypes compared with those of traditional machine learning methods and state of the art methods. The results also show that methylation data can be effectively used to diagnose cancer.

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Systems-level Analysis of 32 TCGA Cancers Reveals Disease-dependent tRNA Fragmentation Patterns and Very Selective Associations with Messenger RNAs and Repeat Elements

Rigoutsos¹,

Telonis²,

Loher³

et al. 2017

Preprint

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We mined 10,274 datasets from The Cancer Genome Atlas (TCGA) for tRNA fragments (tRFs) that overlap nuclear and mitochondrial (MT) mature tRNAs. Across 32 cancer types, we identified 20,722 distinct tRFs, a third of which arise from MT tRNAs. Most of the fragments belong to the novel category of i-tRFs, i.e. they are wholly internal to the mature tRNAs. The abundances and cleavage patterns of the identified tRFs depend strongly on cancer type. Of note, in all 32 cancer types, we find that tRNAHisGTG produces multiple and abundant 5´-tRFs with a uracil at the -1 position, instead of the expected post-transcriptionally-added guanosine. Strikingly, these -1U His 5´tRFs are produced in ratios that remain constant across all analyzed normal and cancer samples, a property that makes tRNAHisGTG unique among all tRNAs. We also found numerous tRFs to be negatively correlated with many messenger RNAs (mRNAs) that belong primarily to four universal biological processes: transcription, cell adhesion, chromatin organization and development/morphogenesis. However, the identities of the mRNAs that belong to these processes and are negatively correlated with tRFs differ from cancer to cancer. Notably, the protein products of these mRNAs localize to specific cellular compartments, and do so in a cancer-dependent manner. Moreover, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in multiple categories of repeat elements. Conversely, the genomic span of mRNAs that are positively correlated with tRFs are depleted in repeat elements. These findings suggest novel and far-reaching roles for tRFs and indicate their involvement in system-wide interconnections in the cell. All discovered tRFs from TCGA can be downloaded from https://cm.jefferson.edu/tcga-mintmap-profiles or studied interactively through the newly-designed version 2.0 of MINTbase at https://cm.jefferson.edu/MINTbase.NOTE: while the manuscript is under review, the content on the page https://cm.jefferson.edu/tcgamintmap-profiles is password protected and available only to Reviewers.Key PointsComplexity: tRNAs exhibit a complex fragmentation pattern into a multitude of tRFs that are conserved within the samples of a given cancer but differ across cancers.Very extensive mitochondrial contributions: the 22 tRNAs of the mitochondrion (MT) contribute 1/3rd of all tRFs found across cancers, a disproportionately high number compared to the tRFs from the 610 nuclear tRNAs.Uridylated (not guanylated) 5´-His tRFs: in all human tissues analyzed, tRNAHisGTG produces many abundant modified 5´-tRFs with a U at their “-1” position (-1U 5´-tRFs), instead of a G.Likely central roles for tRNAHisGTG: the relative abundances of the -1U 5´-tRFs from tRNAHisGTG remain strikingly conserved across the 32 cancers, a property that makes tRNAHisGTG unique among all tRNAs and isoacceptors.Selective tRF-mRNA networks: tRFs are negatively correlated with mRNAs that differ characteristically from cancer to cancer.Mitochondrion-encoded tRFs are associated with nuclear proteins: in nearly all cancers, and in a cancer-specific manner, tRFs produced by the 22 mitochondrial tRNAs are negatively correlated with mRNAs whose protein products localize to the nucleus.tRFs are associated with membrane proteins: in all cancers, and in a cancer-specific manner, nucleus-encoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products localize to the cell’s membrane.tRFs are associated with secreted proteins: in all cancers, and in a cancer-specific manner, nucleusencoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products are secreted from the cell.tRFs are associated with numerous mRNAs through repeat elements: in all cancers, and in a cancerspecific manner, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in specific categories of repeat elements.intra-cancer tRF networks can depend on sex and population origin: within a cancer, positive and negative tRF-tRF correlations can be modulated by patient attributes such as sex and population origin.web-enabled exploration of an “Atlas for tRFs”: we released a new version of MINTbase to provide users with the ability to study 26,531 tRFs compiled by mining 11,719 public datasets (TCGA and other sources).

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The presence or absence alone of miRNA isoforms (isomiRs) successfully discriminate amongst the 32 TCGA cancer types

Cited by 6 publications

References 70 publications

Assessment of isomiR Discrimination Using Commercial qPCR Methods

Assessment of isomiR Discrimination Using Commercial qPCR Methods

MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data

Systems-level Analysis of 32 TCGA Cancers Reveals Disease-dependent tRNA Fragmentation Patterns and Very Selective Associations with Messenger RNAs and Repeat Elements

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