Skip to main page content
U.S. flag

An official website of the United States government

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Apr 14;16(4):e0250083.
doi: 10.1371/journal.pone.0250083. eCollection 2021.

MiR-30a and miR-200c differentiate cholangiocarcinomas from gastrointestinal cancer liver metastases

Jun Won Park  1   2 , Jong Min Jeong  1 , Kye Soo Cho  1   3 , Soo Young Cho  1 , Jae Hee Cheon  3 , Dong Ho Choi  4 , Sang Jae Park  1 , Hark Kyun Kim  1
Affiliations

MiR-30a and miR-200c differentiate cholangiocarcinomas from gastrointestinal cancer liver metastases

Jun Won Park et al. PLoS One. .

Abstract

Prior studies have demonstrated the utility of microRNA assays for predicting some cancer tissue origins, but these assays need to be further optimized for predicting the tissue origins of adenocarcinomas of the liver. We performed microRNA profiling on 195 frozen primary tumor samples using 14 types of tumors that were either adenocarcinomas or differentiated from adenocarcinomas. The 1-nearest neighbor method predicted tissue-of-origin in 33 samples of a test set, with an accuracy of 93.9% at feature selection p values ranging from 10-4 to 10-10. According to binary decision tree analyses, the overexpression of miR-30a and the underexpression of miR-200 family members (miR-200c and miR-141) differentiated intrahepatic cholangiocarcinomas from extrahepatic adenocarcinomas. When binary decision tree analyses were performed using the test set, the prediction accuracy was 84.8%. The overexpression of miR-30a and the reduced expressions of miR-200c, miR-141, and miR-425 could distinguish intrahepatic cholangiocarcinomas from liver metastases from the gastrointestinal tract.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. MicroRNAs for tissue origin.
(A) Prediction accuracies of 1-NN analyses on the test set at feature selection p < 10−10, after allocating different numbers of cholangiocarcinomas to the training set according to chronological order (B) Decision tree analysis. MicroRNAs that were differentially expressed at feature selection p cutoff of 0.00005 were used to predict the tissue of origin at each node of the decision tree. Samples for node no. 7 (surrounded by black broken lines) were further evaluated in Fig 1C and 1D. (C) PCA plot for samples in node no. 7 of (B), based on 914 microRNAs. Each sphere represents each sample and ‘1-correlation’ was used as a distance metric. Cholangiocarcinomas (shown in red), gastrointestinal/pancreatic cancers (shown in green), and non-digestive system cancers (shown in blue) clustered separately. (D) Expression profiles for microRNAs in node no. 7 of (B) in the training set. Expression profiles of 3 microRNAs underexpressed in cholangiocarcinomas compared with extrahepatic cancers in digestive and non-digestive systems (upper panel) and 1 overexprexpressed microRNAs (lower panel) at feature selection p < 0.00005. Since the apparent overexpression of miR-122 in cholangiocarcinomas is presumably due to contaminating hepatocytes in the sample, we decided to exclude miR-122 from a set of discriminatory microRNAs comprising the node no. 7 of the decision tree. A heatmap generated using a log2-pseudocolor image with microRNA centering. Red and blue colors denote high and low expression of microRNAs, respectively. A scale bar for the log2-expression is shown at the bottom. (E) Expression profiles of miR-30a and miR-200c between 25 intrahepatic cholangiocarcinomas (in the training and test sets) and 29 colorectal cancer metastases in the test set. Scatter plots display median microarray signal values (***p < 10−14 and p < 10−5; t-test). (F) Real-time RT-PCR profiles of miR-30a and miR-200c in cell lines. Scatter plots display median values of RNU6-normalized–log2 Ct values (p = 0.22 and p = 0.30, respectively; t-test). (G) Expression profiles of cholangiocarcinoma signature in TCGA small RNA sequencing data. Scatter plots display median values of normalized RPKM (*p < 10−10; t-test).
Fig 2
Fig 2. Expression profiles of selective discriminatory microRNAs of each node in the training set.
Discriminatory microRNAs were defined as microRNAs differentially expressed between two branches at each node of the decision tree at p < 0.00005. The tissue of origin was assigned by selecting one of the two branches at each node using these discriminatory microRNAs. Acute leukemia (AL), thyroid cancer (THCA), prostate adenocarcinoma (PRAD), renal cell carcinoma (KIRC), small cell lung carcinoma (SCLC), hepatocellular carcinoma (LIHC), cholangiocarcinoma (CHOL), colorectal adenocarcinoma (COAD), gastric adenocarcinoma (STAD), pancreatic adenocarcinoma (PAAD), lung adenocarcinoma (LUAD), breast adenocarcinoma (BRCA), uterine endometrial carcinoma (UCEC), ovarian cancer (OV).
Fig 3
Fig 3. Discriminatory microRNAs for small cell lung cancer.
Discriminatory microRNAs comprising the node no. 6 of the decision tree that differentiate small cell lung cancer (SCLC) from primary lung adenocarcinoma (LUAD) and extrapulmonary cancers (*p < 10−10; #p = 2 x 10−9; t-test).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al.. MicroRNA expression profiles classify human cancers. Nature. 2005;435: 834–838. 10.1038/nature03702 - DOI - PubMed
    1. Rosenfeld N, Aharonov R, Meiri E, Rosenwald S, Spector Y, Zepeniuk M, et al.. MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol. 2008;26: 462–469. 10.1038/nbt1392 - DOI - PubMed
    1. Meiri E, Mueller WC, Rosenwald S, Zepeniuk M, Klinke E, Edmonston TB, et al.. A Second‐Generation MicroRNA‐Based Assay for Diagnosing Tumor Tissue Origin. Oncologist. 2012;17: 801–812. 10.1634/theoncologist.2011-0466 - DOI - PMC - PubMed
    1. Ferracin M, Pedriali M, Veronese A, Zagatti B, Gafà R, Magri E, et al.. MicroRNA profiling for the identification of cancers with unknown primary tissue-of-origin. J Pathol. 2011;225: 43–53. 10.1002/path.2915 - DOI - PMC - PubMed
    1. Søkilde R, Vincent M, Møller AK, Hansen A, Høiby PE, Blondal T, et al.. Efficient identification of miRNAs for classification of tumor origin. J Mol Diagnostics. 2014;16: 106–115. 10.1016/j.jmoldx.2013.10.001 - DOI - PubMed

Publication types

MeSH terms

Grants and funding

This work was supported by Korea Health Industry Development Institute (HI19C0027), National Research Foundation of Korea(NRF) (2019R1A2C2010523) and National Cancer Center(NCC) (1910021/181095) Grants.

LinkOut - more resources