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. 2021 Jan 1;11(1):181-193.
doi: 10.7150/thno.48206. eCollection 2021.

Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer

Yumin Zhu  1   2 , Siqi Wang  1 , Xiaochen Xi  1 , Minfeng Zhang  3 , Xiaofan Liu  1 , Weina Tang  4 , Peng Cai  4 , Shaozhen Xing  1 , Pengfei Bao  1 , Yunfan Jin  1 , Weihao Zhao  1 , Yinghui Chen  1 , Huanan Zhao  1 , Xiaodong Jia  5 , Shanshan Lu  5 , Yinying Lu  5 , Lei Chen  5   6 , Jianhua Yin  4 , Zhi John Lu  1
Affiliations

Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer

Yumin Zhu et al. Theranostics. .

Abstract

Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.

Keywords: RNA biomarker; cancer; circular RNA; extracellular RNA; liquid biopsy; noncoding RNA.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Multiple variations of long exosomal RNAs in different cancer types. (A) Multiple variations of long exRNAs identified in three cancer types (CRC: colorectal cancer, HCC: hepatocellular carcinoma, PAAD: pancreatic adenocarcinoma) using healthy donors as control. The dark blue and light blue indicate the opposite pattern of RNA variations, including differential expression, alternative splicing (AS), alternative polyadenylation (APA), and differential editing. FC: fold-change, FDR: false discovery rate, PSI: percent spliced in index, UTR: untranslated region. (B) Numbers of differentially expressed RNAs for different RNA types. up: up-regulation, dn: down-regulation. (C) Overlap of differential expression, alternative splicing, alternative polyadenylation, differential editing events among three types of cancer. (D) List of common differential expression, alternative splicing, and differential editing events among three cancer types.
Figure 2
Figure 2
The exosomal RNA variations are enriched in cancer genes and cancer related pathways. (A) Cancer gene enrichment (top heatmaps) and non-cancer gene enrichment (bottom heatmaps) of RNA variations for three cancer types. NCG5, PosAGO, PosSomatic, PosUniprotKB, PosTextMine, PosTrans, PosUnionAll are the cancer gene lists, while NegAgoClean, NegAgoFull, and NegDavoli are the non-cancer gene lists (see Methods). (B) Top 20 enriched KEGG pathways of differential expression, alternative splicing, and differential editing events for three cancer types.
Figure 3
Figure 3
Overview of experimental design and integrative analysis. Three discovery sets, exosomal RNA-seq (exoRNA-seq) data from exoRBase, self-profiled cell free RNA-seq (cfRNA-seq) data and tissue RNA-seq data from TCGA, were used to discover candidate biomarkers. Two validation sets (qRT-PCR data of cell free and exosomal RNAs) were used for experimental verification. Different RNA types and variations were assayed. Several experimental methods were applied to validate the candidate biomarkers. CHB: chronic hepatitis B.
Figure 4
Figure 4
Identify recurrent exRNA variations from multiple datasets for liver cancer. (A) Differentially expressed exRNAs of liver cancer idenfied from both exoRNA-seq and cfRNA-seq data, using healthy donor as control. (B) Expression level of seven differentially expressed exRNAs in tissue RNA-seq data (TCGA), exoRNA-seq data (exoRBase), and cfRNA-seq data. ***: P-value < 0.001, **: P-value < 0.01, *: P-value < 0.05, Wilcoxon rank sum test. (C) Alternatively spliced exRNAs idenfied from both exoRNA-seq and cfRNA-seq data. (D) Inclusion level of three alternatively spliced exRNAs in tissue RNA-seq data (TCGA), exoRNA-seq data (exoRBase), and cfRNA-seq data. ***: P-value < 0.001, **: P-value < 0.01, *: P-value < 0.05, Wilcoxon rank sum test. (E) Representative gel electrophoresis image showing alternative splicing of ADD3 (left); primers designed for validating alternative splicing (right). Three kinds of primers were designed for full length, exon skipping (ES), and non-exon skipping/exon inclusion (Non-ES).
Figure 5
Figure 5
Examples of the selected exRNA biomarkers for liver cancer detection. (A) RBP binding profile, RNA secondary structure and reads distribution of exoRNA-seq and cfRNA-seq for SNORD3B-1. (B) The genomic locus of circ-0073052 in POLK gene. The supported unique reads are presented. The expression of circ-0073052 was validated by RT-qPCR followed by sanger sequencing. Arrows represent divergent primers binding to the genome region of circ-0073052. Reads distributions of POLK and circ-0073052 for exoRNA-seq and cfRNA-seq show the differential expression pattern (in the gray box) of circ-0073052 instead of POLK. (C) Enrichment of RNA secondary structure in up-regulated mRNAs, lncRNAs, snoRNAs, and other ncRNAs. Comparison of icSHAPE reactivities (left box-plot) and gini indexes (right box-plot) between up-regulated RNAs identified by exoRNA-seq and shuffled background RNAs. Higher icSHAPE reactivity represents more unpaired bases in a RNA. Higher gini index represents that RNA is more structured. ***: P-value < 0.001, **: P-value < 0.01, *: P-value < 0.05, Wilcoxon rank sum test. (D) Characterizations of exosomes purified from plasma mixtures. The curve indicates the diameter distribution of exosomes by Nanosight. The transmission electron micrograph shows the external morphology of exosomes. (E) Relative expression levels measured by RT-qPCR of the 5 selected long exRNAs in exosome and supernatant isolated from the same samples. No significant difference between exosome and supernatant (Wilcoxon rank sum test).
Figure 6
Figure 6
Performance of the selected long exRNAs and known miRNAs on liver cancer detection. (A) Validation of 5 selected long exRNAs by RT-qPCR in an independent cohort, plasma samples of 38 HCC patients V.S. 37 Healthy Donors (HDs). ***: P-value < 0.001, **: P-value < 0.01, *: P-value < 0.05, Wilcoxon rank sum test. (B) Validation of 6 previously published miRNA biomarkers by RT-qPCR in a subset of the cohort (26 of 38 HCCs; 26 of 37 HDs). ***: P-value < 0.001, **: P-value < 0.01, *: P-value < 0.05, Wilcoxon rank sum test. (C) AUROC values of 5 long exRNAs; 6 miRNAs; and 11, 9, 7, 5, 3 out of all RNAs when classifying HCCs from HDs with Random Forest models. We used 5-fold cross-validation and repeated it 10 times by re-shuffling the data. (D) Average ROC curves of the selected 3 RNAs, 5 long exRNAs, and 6 miRNAs. The AUC values are also labeled under the curves.
Figure 7
Figure 7
Detection panel of 3 noncoding RNAs for the AFP-negative and early-stage liver cancer. (A) Workflow chart of identifying a 3-RNA panel for detecting liver cancer in plasma. Rectangular box indicates the type and quantity of RNA variation. Diamond indicates the screening method and cut-off. (B) Performance of the 3-RNA panel (SNORD3B-1, circ-0080695, miR-122) in training, testing and validation sets (model: Random Forest). Trained on alpha feto-protein (AFP) positive (AFP > 400 ng/ml) patients (HCCs), Chronic hepatitis B patients (CHBs) and healthy donors (HDs); tested and validated on AFP negative (AFP < 400 ng/ml) patients (HCCs). *: early stages (0/A) are labeled in red. NA: Not available. (C) Predicted values of the 3-RNA panel (model: Random Forest). The cutoff of the predicted value is defined by requiring > 95% specifity of healthy donors in the training set. Triangle points represent patients with 20 ng/ml < AFP ≤ 400 ng/ml. Red points represent patients of early stages (0/A).
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