A cross-cancer metastasis signature in the microRNA–mRNA axis of paired tissue samples
In the progression of cancer, cells acquire genetic mutations that cause uncontrolled growth. Over time, the primary tumour may undergo additional mutations that allow for the cancerous cells to spread throughout the body as metastases. Since metastatic development typically results in markedly worse patient outcomes, research into the identity and function of metastasis-associated biomarkers could eventually translate into clinical diagnostics or novel therapeutics. Although the general processes underpinning metastatic progression are understood, no clear cross-cancer biomarker profile has emerged. However, the literature suggests that some microRNAs (miRNAs) may play an important role in the metastatic progression of several cancer types. Using a subset of The Cancer Genome Atlas (TCGA) data, we performed an integrated analysis of mRNA and miRNA expression with paired metastatic and primary tumour samples to interrogate how the miRNA–mRNA regulatory axis influences metastatic progression. From this, we successfully built mRNA- and miRNA-specific classifiers that can discriminate pairs of metastatic and primary samples across 11 cancer types. In addition, we identified a number of miRNAs whose metastasis-associated dysregulation could predict mRNA metastasis-associated dysregulation. Among the most predictive miRNAs, we found several previously implicated in cancer progression, including miR-301b, miR-1296, and miR-423. Taken together, our results suggest that metastatic samples have a common cross-cancer signature when compared with their primary tumour pair, and that these miRNA biomarkers can be used to predict metastatic status as well as mRNA expression.
KeywordsTanscriptomics Machine-learning Cancer MicroRNA
SCL and TPQ reviewed the literature, designed the project, performed the analyses, and drafted the manuscript. AQ reviewed the literature and helped draft the manuscript. All authors edited and approved the final manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interests
- 3.van ’t Veer LJ, Dai He, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536CrossRefPubMedGoogle Scholar
- 11.Robert RJ, van den Braak C, Sieuwerts AM, Lalmahomed ZS, Smid M, Wilting SM, Bril SI, Xiang S, van der Vlugt-Daane M, de Weerd V, van Galen A, Biermann K, Han J, van Krieken JM, Kloosterman WP, Foekens JA, Martens JWM, IJzermans. JNM (2018) Confirmation of a metastasis-specific microRNA signature in primary colon cancer. Scientific Reports 8(1):5242CrossRefGoogle Scholar
- 25.Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 102(43):15545–15550CrossRefPubMedGoogle Scholar
- 30.Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
- 31.Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2017) e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU WienGoogle Scholar
- 35.Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57(1):289–300Google Scholar
- 39.Hamilton MP, Rajapakshe K, Hartig SM, Reva B, McLellan MD, Kandoth C, Ding L, Zack TI, Gunaratne PH, Wheeler DA, Coarfa C, McGuire SE (2013) Identification of a pan-cancer oncogenic microRNA superfamily anchored by a central core seed motif. Nat Commun 4:2730CrossRefPubMedPubMedCentralGoogle Scholar
- 40.Shan Xia, Wen Wei, Zhu Danxia, Yan Ting, Cheng Wenfang, Huang Zebo, Zhang Lan, Zhang Huo, Wang Tongshan, Zhu Wei, Zhu Yichao, Zhu Jun (2017) miR 1296–5p inhibits the migration and invasion of gastric cancer cells by repressing ERBB2 expression. PLOS ONE 12(1):e0170298CrossRefPubMedPubMedCentralGoogle Scholar
- 43.Riquelme I, Tapia O, Leal P, Sandoval A, Varga MG, Letelier P, Buchegger K, Bizama C, Espinoza JA, Peek RM, Araya JC, Roa JC (2016) miR-101-2, miR-125b-2 and miR-451a act as potential tumor suppressors in gastric cancer through regulation of the PI3k/AKT/mTOR pathway. Cell Oncol 39(1):23–33CrossRefGoogle Scholar