Congenital anomalies cause ~7% of all neonatal deaths, many of which have no identified pathophysiological cause. Because accurate and robust laboratory tests are unavailable for most birth defects, physicians rely on imaging such as ultrasound and MRI. Biomarkers from human body fluids are considered a powerful diagnostic tool to assess human disease and health as it mirrors an individual’s condition. Minimally invasive ‘liquid biopsies’ from blood samples are highly valuable for diagnosis, prognosis, risk assessment, and treatment of many conditions. Recent large-scale analysis (‘omics’) have enabled researchers to identify novel biomarkers in different areas. To accurately facilitate the early detection of congenital anomalies, the identification of biomarkers from maternal plasma should be promoted. This approach will uncover new opportunities in prenatal diagnosing and likely lead to a better understanding of the pathogenesis of congenital anomalies.
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Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544 (2016).
Matthews, T. J., MacDorman, M. F. & Thoma, M. E. National vital statistics reports infant mortality statistics from the 2013 period linked birth / infant death data set. Natl Vital-. Stat. Rep. 64, 2000–2013 (2015).
Boyle, B. et al. Estimating Global Burden of Disease due to congenital anomaly: an analysis of European data. Arch. Dis. Child. Fetal Neonatal Ed. 103, 2016–311845 (2017).
Burgos C. M., et al. Prenatally versus postnatally diagnosed congenital diaphragmatic hernia – Side, stage, and outcome. J. Pediatr. Surg. (2018). https://doi.org/10.1016/j.jpedsurg.2018.04.008
Bradshaw, C. J. et al. Accuracy of prenatal detection of tracheoesophageal fistula and oesophageal atresia. J. Pediatr. Surg. 51, 1268–1272 (2016).
Beta, J., Lesmes-HereDia, C., Bedetti, C. & Akolekar, R. Risk of miscarriage following amniocentesis and chorionic villus sampling: A systematic review of the literature. Minerva Ginecol. 70, 215–219 (2018).
Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).
Kroh, E. M., Parkin, R. K., Mitchell, P. S. & Tewari, M. Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods 50, 298–301 (2010).
Chiu R. W. K., et al. Time profile of appearance and disappearance of cir-culating placenta-derived mRNA in maternal plasma. Clin. Chem. 1–19 (2006). https://doi.org/10.1373/clinchem.2005.059774
Anderson, N. L. et al. The human plasma proteome. Mol. Cell. Proteom. 3, 311–326 (2004).
Geyer, P. E., Holdt, L. M., Teupser, D. & Mann, M. Revisiting biomarker discovery by plasma proteomics. Mol. Syst. Biol. 12, 1–14 (2016).
Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185–195 (2016).
Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).
Dashe, J. S., Twickler, D. M., Santos-Ramos, R., McIntire, D. D. & Ramus, R. M. Alpha-fetoprotein detection of neural tube defects and the impact of standard ultrasound. Am. J. Obstet. Gynecol. 195, 1623–1628 (2006).
An, D. et al. Identification of PCSK9 as a novel serum biomarker for the prenatal diagnosis of neural tube defects using iTRAQ quantitative proteomics. Sci. Rep. 5, 17559 (2015).
Shen, G., He, P., Du, Y. & Zhang, S. Identification of biomarkers by proteomics for prenatal screening for neural tube defects. Tohoku J. Exp. Med. 238, 123–129 (2016).
Chen, L. et al. Comprehensive maternal serum proteomics identifies the cytoskeletal proteins as non-invasive biomarkers in prenatal diagnosis of congenital heart defects. Sci. Rep. 6, 19248 (2016).
Blankley, R. T. et al. A label-free selected reaction monitoring workflow identifies a subset of pregnancy specific glycoproteins as potential predictive markers of early-onset pre-eclampsia. Mol. Cell Proteom. 12, 3148–3159 (2013).
Menon, R., McIntyre, J. O., Matrisian, L. M. & Fortunato, S. J. Salivary proteinase activity: a potential biomarker for preterm premature rupture of the membranes. Am. J. Obstet. Gynecol. 194, 1609–1615 (2006).
Bartel, D. P. MicroRNA Target Recognition and Regulatory Functions. Cell 136, 215–233 (2009).
Sayed D., Abdellatif M. Micrornas in development and disease. 827–887 (2011). https://doi.org/10.1152/physrev.00006.2010
Khoshgoo, N., Kholdebarin, R., Iwasiow, B. M. & Keijzer, R. MicroRNAs and lung development. Pediatr. Pulmonol. 48, 317–323 (2013).
Sadovsky, Y., Mouillet, J. F., Ouyang, Y., Bayer, A. & Coyne, C. B. The function of trophomirs and other micrornas in the human placenta. Cold Spring Harb. Perspect. Med. 5, 1–16 (2015).
Gross, N., Kropp, J. & Khatib, H. MicroRNA signaling in embryo development. Biology 6, 34 (2017).
Gebert L. F. R., MacRae I. J. Regulation of microRNA function in animals. Nat. Rev. Mol. Cell Biol. (2018). https://doi.org/10.1038/s41580-018-0045-7
Floris, I., Kraft, J. D. & Altosaar, I. Roles of microRNA across prenatal and postnatal periods. Int. J. Mol. Sci. 17, 1–12 (2016).
Rodosthenous, R. S. et al. Second trimester extracellular microRNAs in maternal blood and fetal growth: An exploratory study. Epigenetics 12, 804–810 (2017).
Smith, T., Rajakaruna, C., Caputo, M. & Emanueli, C. Micro. Congenit. Heart Dis. 3, 1–10 (2015).
Dong R., Shen Z., Zheng C., Chen G., Zheng S. Serum microRNA microarray analysis identifies miR-4429 and miR-4689 are potential diagnostic biomarkers for biliary atresia. Sci. Rep. 1–11 (2016). https://doi.org/10.1038/srep21084
Pereira-Terra, P. et al. Unique tracheal fluid MicroRNA signature predicts response to feto in patients with congenital diaphragmatic hernia. Ann. Surg. 262, 1130–1140 (2015).
Khoshgoo N., et al. Prenatal microRNA miR-200b therapy improves nitrofen-induced pulmonary hypoplasia associated with congenital diaphragmatic hernia. Ann. Surg. 1 (2017). https://doi.org/10.1097/SLA.0000000000002595
Tsochandaridis, M., Nasca, L., Toga, C. & Levy-Mozziconacci, A. Circulating MicroRNAs as clinical biomarkers in the predictions of pregnancy complications. Biomed. Res. Int. 2015, 294954 (2014).
Winger, E. E., Reed, J. L. & Ji, X. First-trimester maternal cell microRNA is a superior pregnancy marker to immunological testing for predicting adverse pregnancy outcome. J. Reprod. Immunol. 110, 22–35 (2015).
Xie, W. Q., Zhou, L., Chen, Y. & Bin, N. Circulating microRNAs as potential biomarkers for diagnosis of congenital heart defects. World J. Emerg. Med. 7, 85–89 (2017).
Tan, K. et al. Downregulation of miR-199a-5p disrupts the developmental potential of in vitro-fertilized mouse blastocysts. Biol. Reprod. 95, 54–54 (2016).
Lee, Y., Thouas, G. & Gardner, D. Developmental kinetics of cleavage stage mouse embryos are related to their subsequent carbohydrate and amino acid utilization at the blastocyst stage. Hum. Reprod. 30, 543–552 (2015).
Bay B., Lyngsø J., Hohwü L., Kesmodel U. S. Childhood growth of singletons conceived following in vitro fertilisation or intracytoplasmic sperm injection: a systematic review and meta-analysis. BJOG (2018). https://doi.org/10.1111/1471-0528.15456
Carreras-Badosa, G. et al. Dysregulation of placental miRNA in maternal obesity is associated with pre-and postnatal growth. J. Clin. Endocrinol. Metab. 102, 2584–2594 (2017).
Li, X. & Zhao, Z. MicroRNA biomarkers for early detection of embryonic malformations in pregnancy. J. Biomol. Res. Ther. 03, 10–12 (2014).
Song, Y. et al. Clinical significance of circulating microRNAs as markers in detecting and predicting congenital heart defects in children. J. Transl. Med. 16, 1–11 (2018).
Lim, J. H. et al. MicroRNAs as potential biomarkers for noninvasive detection of fetal trisomy 21. J. Assist. Reprod. Genet. 32, 827–837 (2015).
Kan, A. A. et al. Genome-wide microRNA profiling of human temporal lobe epilepsy identifies modulators of the immune response. Cell. Mol. Life Sci. 69, 3127–3145 (2012).
Freischmidt, A. et al. Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers. Brain 137, 2938–2950 (2014).
Khoshgoo, N. et al. MicroRNA-200b regulates distal airway development by maintaining epithelial integrity. Sci. Rep. 7, 1–12 (2017).
Herrera-Rivero, M. et al. Circulating microRNAs are associated with pulmonary hypertension and development of chronic lung disease in congenital diaphragmatic hernia OPEN. Sci. Rep. 8, 10735 (2018).
Kotlabova, K., Doucha, J. & Hromadnikova, I. Placental-specific microRNA in maternal circulation - identification of appropriate pregnancy-associated microRNAs with diagnostic potential. J. Reprod. Immunol. 89, 185–191 (2011).
Kappil, M. & Chen, J. Environmental exposures in utero and microRNA. Curr. Opin. Pediatr. 26, 243–251 (2014).
Balaraman, S. et al. Maternal and neonatal plasma MicroRNA biomarkers for fetal alcohol exposure in an ovine model. Alcohol Clin. Exp. Res. 28, 1390–1400 (2014).
Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).
Geisler, S. & Coller, J. RNA in unexpected places: Long non-coding RNA functions in diverse cellular contexts. Nat. Rev. Mol. Cell Biol. 14, 699–712 (2013).
Fatica, A. & Bozzoni, I. Long non-coding RNAs: new players in cell differentiation and development. Nat. Rev. Genet. 15, 7–21 (2013).
Wang, K. C. & Chang, H. Y. Molecular mechanisms of long noncoding RNAs. Mol. Cell 43, 904–914 (2011).
Batista, P. J. & Chang, H. Y. Long noncoding RNAs: cellular address codes in development and disease. Cell 152, 1298–1307 (2013).
Gutschner, T., Hämmerle, M. & Diederichs, S. MALAT1 - A paradigm for long noncoding RNA function in cancer. J. Mol. Med. 91, 791–801 (2013).
Yamada, A. et al. A RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer. Sci. Rep. 8, 2–11 (2018).
Tang, Q. et al. Three circulating long non-coding RNAs act as biomarkers for predicting NSCLC. Cell. Physiol. Biochem. 37, 1002–1009 (2015).
Gupta, R. A. et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464, 1071–1076 (2010).
Szafranski, P. et al. Small noncoding differentially methylated copy-number variants, including lncRNA genes, cause a lethal lung developmental disorder. Genome Res. 23, 23–33 (2013).
Gu, M. et al. Circulating LncRNAs as novel, non-invasive biomarkers for prenatal detection of fetal congenital heart defects. Cell. Physiol. Biochem. 38, 1459–1471 (2016).
Abu, N. & Jamal, R. Circular RNAs as promising biomarkers: a mini-review. Front. Physiol. 7, 355 (2016).
Lasda, E. & Parker, R. Circular RNAs: diversity of form and function. RNA 20, 1829–1842 (2014).
Memczak S., Papavasileiou P., Peters O., Rajewsky N. Identification and characterization of circular RNAs as a new class of putative biomarkers in human blood. PLoS ONE 10, (2015).
Lasda E., Parker R. Circular RNAs co-precipitate with extracellular vesicles: a possible mechanism for circrna clearance. PLoS ONE 11, (2016).
Hansen, T. B. et al. Natural RNA circles function as efficient microRNA sponges. Nature 495, 384–388 (2013).
Xia, S. et al. Comprehensive characterization of tissue-specific circular RNAs in the human and mouse genomes. Brief. Bioinform. 18, 984–992 (2017).
Liu, Q. et al. Circular RNA related to the chondrocyte ECM regulates MMP13 expression by functioning as a MiR-136 ‘Sponge’ in human cartilage degradation. Sci. Rep. 6, 22572 (2016).
Li, P. et al. Using circular RNA as a novel type of biomarker in the screening of gastric cancer. Clin. Chim. Acta 444, 132–136 (2015).
Kristensen L. S., Hansen T. B., Venø M. T., Kjems J. Circular RNAs in cancer: opportunities and challenges in the field. Oncogene (2017). https://doi.org/10.1038/onc.2017.361
Viereck, J. & Thum, T. Circulating noncoding RNAs as biomarkers of cardiovascular disease and injury. Circ. Res. 120, 381–399 (2017).
Barrett, S. P. & Salzman, J. Circular RNAs: analysis, expression and potential functions. Development 143, 1838–1847 (2016).
Venø, M. T. et al. Spatio-temporal regulation of circular RNA expression during porcine embryonic brain development. Genome Biol. 16, 245 (2015).
Conn, S. J. et al. The RNA binding protein quaking regulates formation of circRNAs. Cell 160, 1125–1134 (2015).
Peng, L. et al. Circular RNA ZNF609 functions as a competitive endogenous RNA to regulate AKT3 expression by sponging miR-150-5p in Hirschsprungs disease. Oncotarget 8, 808–818 (2017).
Liu, H. et al. Differential expression of CircRNAs in embryonic heart tissue associated with ventricular septal defect. Int. J. Med. Sci. 15, 703–712 (2018).
Lo, Y. et al. Presence of fetal DNA in maternal plasma and serum. Lancet 350, 485–487 (1997).
Taglauer, E. S., Bianchi, D. W. & Street, W. Review: Cell-free fetal DNA in the maternal circulation as an indication of placental health and disease. Placenta 35, 1–13 (2014).
Fan, H. C. et al. Non-invasive prenatal measurement of the fetal genome. Nature 487, 320–324 (2012).
Bianchi, D. W. & Chiu, R. W. K. Sequencing of Circulating Cell-free DNA during Pregnancy. N. Engl. J. Med. 379, 464–473 (2018).
Snyder H. L., Curnow K. J., Bhatt S., Bianchi D. W. Follow-up of multiple aneuploidies and single monosomies detected by noninvasive prenatal testing: implications for management and counseling. 203–209 (2016).https://doi.org/10.1002/pd.4778
Polin R. A., Fox W. W., Abman S. H. Fetal and Neonatal Physiology (Elsevier, 2011).
Malone, F. D. et al. First-trimester or second-trimester screening, or both, for Down’s Syndrome Fergal. N. Engl. J. Med. 353, 2001–2011 (2005).
Taylor-Phillips S., et al. Accuracy of non-invasive prenatal testing using cell-free DNA for detection of Down, Edwards and Patau syndromes: a systematic review and meta-analysis. (2016). https://doi.org/10.1136/bmjopen-2015-010002
McCullough R. M., et al. Non-invasive prenatal chromosomal aneuploidy testing - clinical experience: 100, 000 clinical samples 9 (2014).
Florkowski, C. et al. Critical Reviews in Clinical Laboratory Sciences Point-of-care testing (POCT) and evidence-based laboratory medicine (EBLM) – does it leverage any advantage in clinical decision making? Crit. Rev. Clin. Lab. Sci. 54, 471–494 (2017).
Vashist, S. K. Point-of-care diagnostics: recent advances and trends. Biosensors 7, 10–13 (2017).
National Intitutes of Health. Point-of-Care Diagnostic Testing. (2010).
Hasin Y., Seldin M., Lusis A. Multi-omics approaches to disease. 1–15 (2017). https://doi.org/10.1186/s13059-017-1215-1
Huang, S., Chaudhary, K. & Garmire, L. X. More is better: recent progress in multi-omics data integration methods. Front Genet. 8, 1–12 (2017).
Lin E., Lane H. Machine learning and systems genomics approaches for multi-omics data. Biomark. Res. 1–6 (2017). https://doi.org/10.1186/s40364-017-0082-y
Lafleur, J. P., Jönsson, A., Senkbeil, S. & Kutter, J. P. Recent advances in lab-on-a-chip for biosensing applications. Biosens. Bioelectron. 76, 213–233 (2016).
Dincer, C., Bruch, R., Kling, A., Dittrich, S. & Urban, G. A. Multiplexed point-of-care testing – xPOCT. Trends Biotechnol. 35, 728–742 (2017).
Lal, C. V., Bhandari, V. & Ambalavanan, N. Genomics, microbiomics, proteomics, and metabolomics in bronchopulmonary dysplasia. Semin. Perinatol. 42, 425–431 (2018).
Ngo, T. T. M. et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 360, 1133–1136 (2018).
Liang H., et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat. Med. (2019). https://doi.org/10.1038/s41591-018-0335-9
Choolani, M., Narasimhan, K., Kolla, V. & Hahn, S. Proteomic technologies for prenatal diagnostics: advances and challenges ahead. Expert Rev. Proteom. 6, 87–101 (2009).
Zeng, I. S. L. & Lumley, T. Review of statistical learning methods in integrated omics studies (An Integrated Information Science). Bioinform. Biol. Insights 12, 1–6 (2018).
We thank Clara Moy Tam for her help with the figures. R.K. holds the Thorlakson Chair in Surgical Research that supported this work.