Skip to main content
SpringerLink
Log in

Research progress and potential application of microRNA and other non-coding RNAs in forensic medicine

  • Review
  • Published:
International Journal of Legal Medicine Aims and scope Submit manuscript

Abstract

At present, epigenetic markers have been extensively studied in various fields and have a high value in forensic medicine due to their unique mode of inheritance, which does not involve DNA sequence alterations. As an epigenetic phenomenon that plays an important role in gene expression, non-coding RNAs (ncRNAs) act as key factors mediating gene silencing, participating in cell division, and regulating immune response and other important biological processes. With the development of molecular biology, genetics, bioinformatics, and next-generation sequencing (NGS) technology, ncRNAs such as microRNA (miRNA), circular RNA (circRNA), long non-coding RNA (lncRNA), and P-element induced wimpy testis (PIWI)-interacting RNA (piRNA) are increasingly been shown to have potential in the practice of forensic medicine. NcRNAs, mainly miRNA, may provide new strategies and methods for the identification of tissues and body fluids, cause-of-death analysis, time-related estimation, age estimation, and the identification of monozygotic twins. In this review, we describe the research progress and application status of ncRNAs, mainly miRNA, and other ncRNAs such as circRNA, lncRNA, and piRNA, in forensic practice, including the identification of tissues and body fluids, cause-of-death analysis, time-related estimation, age estimation, and the identification of monozygotic twins. The close links between ncRNAs and forensic medicine are presented, and their research values and application prospects in forensic medicine are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Gayon J (2016) From Mendel to epigenetics: history of genetics. C R Biol 339:225–230. https://doi.org/10.1016/j.crvi.2016.05.009

    Article  PubMed  Google Scholar 

  2. de Mendoza A, Nguyen TV, Ford E et al (2022) Large-scale manipulation of promoter DNA methylation reveals context-specific transcriptional responses and stability. Genome Biol 23:163. https://doi.org/10.1186/s13059-022-02728-5

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Li J, Xue Y, Amin MT et al (2020) ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types. Nucleic Acids Res 48:D956–D963. https://doi.org/10.1093/nar/gkz711

    Article  PubMed  CAS  Google Scholar 

  4. Zhu Z, Han Z, Halabelian L et al (2021) Identification of lysine isobutyrylation as a new histone modification mark. Nucleic Acids Res 49:177–189. https://doi.org/10.1093/nar/gkaa1176

    Article  PubMed  CAS  Google Scholar 

  5. Werner JM, Ballouz S, Hover J, Gillis J (2022) Variability of cross-tissue X-chromosome inactivation characterizes timing of human embryonic lineage specification events. Dev Cell 57:1995-2008.e5. https://doi.org/10.1016/j.devcel.2022.07.007

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Belk JA, Yao W, Ly N et al (2022) Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence. Cancer Cell 40:768–86.e7. https://doi.org/10.1016/j.ccell.2022.06.001

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Weinberg-Shukron A, Ben-Yair R, Takahashi N et al (2022) Balanced gene dosage control rather than parental origin underpins genomic imprinting. Nat Commun 13:4391. https://doi.org/10.1038/s41467-022-32144-z

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  8. Imani S, Zhang X, Fu S et al (2018) Non-coding RNAs in Cancer. In: Fu J, Imani S (ed) Epigenetics in Cancer, 1st edn. Science Press, Beijing, pp 104–184

  9. Ashrafizadeh M, Zarrabi A, Mostafavi E et al (2022) Non-coding RNA-based regulation of inflammation. Semin Immunol 101606. https://doi.org/10.1016/j.smim.2022.101606

  10. Liu X, Li Y, Jiang X et al (2022) Long non-coding RNA: multiple effects on the differentiation, maturity and cell function of dendritic cells. Clin Immunol 245:109167. https://doi.org/10.1016/j.clim.2022.109167

    Article  PubMed  CAS  Google Scholar 

  11. Shah AM, Giacca M (2022) Small non-coding RNA therapeutics for cardiovascular disease. Eur Heart J 43:4548–4561. https://doi.org/10.1093/eurheartj/ehac463

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Beucher A, Miguel-Escalada I, Balboa D et al (2022) The HASTER lncRNA promoter is a cis-acting transcriptional stabilizer of HNF1A. Nat Cell Biol 24:1528–1540. https://doi.org/10.1038/s41556-022-00996-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Fagan SG, Helm M, Prehn JHM (2021) tRNA-derived fragments: a new class of non-coding RNA with key roles in nervous system function and dysfunction. Prog Neurobiol 205:102118. https://doi.org/10.1016/j.pneurobio.2021.102118

    Article  PubMed  CAS  Google Scholar 

  14. Chen X, Rechavi O (2022) Plant and animal small RNA communications between cells and organisms. Nat Rev Mol Cell Biol 23:185–203. https://doi.org/10.1038/s41580-021-00425-y

    Article  PubMed  CAS  Google Scholar 

  15. Liu N, Xu Y, Li Q et al (2022) A lncRNA fine-tunes salicylic acid biosynthesis to balance plant immunity and growth. Cell Host Microbe 30:1124–38.e8. https://doi.org/10.1016/j.chom.2022.07.001

    Article  PubMed  CAS  Google Scholar 

  16. Bauer M (2007) RNA in forensic science. Forensic Sci Int Genet 1:69–74. https://doi.org/10.1016/j.fsigen.2006.11.002

    Article  PubMed  CAS  Google Scholar 

  17. Haas C, Hanson E, Bar W et al (2011) mRNA profiling for the identification of blood–results of a collaborative EDNAP exercise. Forensic Sci Int Genet 5:21–26. https://doi.org/10.1016/j.fsigen.2010.01.003

    Article  PubMed  CAS  Google Scholar 

  18. Haas C, Hanson E, Anjos MJ et al (2012) RNA/DNA co-analysis from blood stains–results of a second collaborative EDNAP exercise. Forensic Sci Int Genet 6:70–80. https://doi.org/10.1016/j.fsigen.2011.02.004

    Article  PubMed  CAS  Google Scholar 

  19. Haas C, Hanson E, Anjos MJ et al (2013) RNA/DNA co-analysis from human saliva and semen stains–results of a third collaborative EDNAP exercise. Forensic Sci Int Genet 7:230–239. https://doi.org/10.1016/j.fsigen.2012.10.011

    Article  PubMed  CAS  Google Scholar 

  20. Haas C, Hanson E, Anjos MJ et al (2014) RNA/DNA co-analysis from human menstrual blood and vaginal secretion stains: results of a fourth and fifth collaborative EDNAP exercise. Forensic Sci Int Genet 8:203–212. https://doi.org/10.1016/j.fsigen.2013.09.009

    Article  PubMed  CAS  Google Scholar 

  21. Haas C, Hanson E, Banemann R et al (2015) RNA/DNA co-analysis from human skin and contact traces–results of a sixth collaborative EDNAP exercise. Forensic Sci Int Genet 16:139–147. https://doi.org/10.1016/j.fsigen.2015.01.002

    Article  PubMed  CAS  Google Scholar 

  22. Ingold S, Dorum G, Hanson E et al (2018) Body fluid identification using a targeted mRNA massively parallel sequencing approach - results of a EUROFORGEN/EDNAP collaborative exercise. Forensic Sci Int Genet 34:105–115. https://doi.org/10.1016/j.fsigen.2018.01.002

    Article  PubMed  CAS  Google Scholar 

  23. Ingold S, Dorum G, Hanson E et al (2020) Body fluid identification and assignment to donors using a targeted mRNA massively parallel sequencing approach - results of a second EUROFORGEN / EDNAP collaborative exercise. Forensic Sci Int Genet 45:102208. https://doi.org/10.1016/j.fsigen.2019.102208

    Article  PubMed  CAS  Google Scholar 

  24. Courts C, Madea B (2010) Micro-RNA - a potential for forensic science? Forensic Sci Int 203:106–111. https://doi.org/10.1016/j.forsciint.2010.07.002

    Article  PubMed  CAS  Google Scholar 

  25. Rocchi A, Chiti E, Maiese A, Turillazzi E, Spinetti I (2020) MicroRNAs: An Update of Applications in Forensic Science. Diagnostics (Basel) 11. https://doi.org/10.3390/diagnostics11010032

  26. Huttenhofer A, Schattner P, Polacek N (2005) Non-coding RNAs: hope or hype? Trends Genet 21:289–297. https://doi.org/10.1016/j.tig.2005.03.007

    Article  PubMed  CAS  Google Scholar 

  27. Wilusz CJ, Wilusz J (2004) Bringing the role of mRNA decay in the control of gene expression into focus. Trends Genet 20:491–497. https://doi.org/10.1016/j.tig.2004.07.011

    Article  PubMed  CAS  Google Scholar 

  28. Faure G, Ogurtsov AY, Shabalina SA, Koonin EV (2016) Role of mRNA structure in the control of protein folding. Nucleic Acids Res 44:10898–10911. https://doi.org/10.1093/nar/gkw671

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Cerezo M, Robert C, Liu L, Shen S (2021) The role of mRNA translational control in tumor immune escape and immunotherapy resistance. Cancer Res 81:5596–5604. https://doi.org/10.1158/0008-5472.CAN-21-1466

    Article  PubMed  CAS  Google Scholar 

  30. Djebali S, Davis CA, Merkel A et al (2012) Landscape of transcription in human cells. Nature 489:101–108. https://doi.org/10.1038/nature11233

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  31. Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. https://doi.org/10.1038/nature11247

    Article  ADS  CAS  Google Scholar 

  32. Harrow J, Frankish A, Gonzalez JM et al (2012) GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 22:1760–1774. https://doi.org/10.1101/gr.135350.111

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Derrien T, Johnson R, Bussotti G et al (2012) The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res 22:1775–1789. https://doi.org/10.1101/gr.132159.111

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Dahariya S, Paddibhatla I, Kumar S, Raghuwanshi S, Pallepati A, Gutti RK (2019) Long non-coding RNA: Classification, biogenesis and functions in blood cells. Mol Immunol 112:82–92. https://doi.org/10.1016/j.molimm.2019.04.011

    Article  PubMed  CAS  Google Scholar 

  35. Esteller M (2011) Non-coding RNAs in human disease. Nat Rev Genet 12:861–874. https://doi.org/10.1038/nrg3074

    Article  PubMed  CAS  Google Scholar 

  36. Asim MN, Ibrahim MA, Imran Malik M, Dengel A, Ahmed S (2021) Advances in computational methodologies for classification and sub-cellular locality prediction of non-coding RNAs. Int J Mol Sci 22. https://doi.org/10.3390/ijms22168719

  37. Sun P, Li G (2019) CircCode: a powerful tool for identifying circRNA coding ability. Front Genet 10:981. https://doi.org/10.3389/fgene.2019.00981

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Miao Q, Ni B, Tang J (2021) Coding potential of circRNAs: new discoveries and challenges. PeerJ 9:e10718. https://doi.org/10.7717/peerj.10718

    Article  PubMed  PubMed Central  Google Scholar 

  39. Misir S, Wu N, Yang BB (2022) Specific expression and functions of circular RNAs. Cell Death Differ 29:481–491. https://doi.org/10.1038/s41418-022-00948-7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Chen C, Ridzon DA, Broomer AJ et al (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33:e179. https://doi.org/10.1093/nar/gni178

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Shi R, Chiang VL (2005) Facile means for quantifying microRNA expression by real-time PCR. Biotechniques 39:519–525. https://doi.org/10.2144/000112010

    Article  PubMed  CAS  Google Scholar 

  42. Raymond CK, Roberts BS, Garrett-Engele P, Lim LP, Johnson JM (2005) Simple, quantitative primer-extension PCR assay for direct monitoring of microRNAs and short-interfering RNAs. RNA 11:1737–1744. https://doi.org/10.1261/rna.2148705

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Bentley DR, Balasubramanian S, Swerdlow HP et al (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456:53–59. https://doi.org/10.1038/nature07517

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  44. Rothberg JM, Hinz W, Rearick TM et al (2011) An integrated semiconductor device enabling non-optical genome sequencing. Nature 475:348–352. https://doi.org/10.1038/nature10242

    Article  PubMed  CAS  Google Scholar 

  45. Plomin R, Schalkwyk LC (2007) Microarrays. Dev Sci 10:19–23. https://doi.org/10.1111/j.1467-7687.2007.00558.x

    Article  PubMed  PubMed Central  Google Scholar 

  46. Li W, Ruan K (2009) MicroRNA detection by microarray. Anal Bioanal Chem 394:1117–1124. https://doi.org/10.1007/s00216-008-2570-2

    Article  PubMed  CAS  Google Scholar 

  47. Mathew R, Mattei V, Al Hashmi M, Tomei S (2020) Updates on the Current Technologies for microRNA Profiling. Microrna 9:17–24. https://doi.org/10.2174/2211536608666190628112722

    Article  PubMed  Google Scholar 

  48. Li S, Teng S, Xu J et al (2019) Microarray is an efficient tool for circRNA profiling. Brief Bioinform 20:1420–1433. https://doi.org/10.1093/bib/bby006

    Article  PubMed  CAS  Google Scholar 

  49. Glynn CL (2020) Potential applications of microRNA profiling to forensic investigations. RNA 26:1–9. https://doi.org/10.1261/rna.072173.119

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Hanson EK, Lubenow H, Ballantyne J (2009) Identification of forensically relevant body fluids using a panel of differentially expressed microRNAs. Anal Biochem 387:303–314. https://doi.org/10.1016/j.ab.2009.01.037

    Article  PubMed  CAS  Google Scholar 

  51. Hanson EK, Ballantyne J (2013) Circulating microRNA for the identification of forensically relevant body fluids. Methods Mol Biol 1024:221–234. https://doi.org/10.1007/978-1-62703-453-1_18

    Article  PubMed  CAS  Google Scholar 

  52. Zubakov D, Boersma AW, Choi Y, van Kuijk PF, Wiemer EA, Kayser M (2010) MicroRNA markers for forensic body fluid identification obtained from microarray screening and quantitative RT-PCR confirmation. Int J Legal Med 124:217–226. https://doi.org/10.1007/s00414-009-0402-3

    Article  PubMed  PubMed Central  Google Scholar 

  53. Park JL, Park SM, Kwon OH et al (2014) Microarray screening and qRT-PCR evaluation of microRNA markers for forensic body fluid identification. Electrophoresis 35:3062–3068. https://doi.org/10.1002/elps.201400075

    Article  PubMed  CAS  Google Scholar 

  54. Wang Z, Luo H, Pan X, Liao M, Hou Y (2012) A model for data analysis of microRNA expression in forensic body fluid identification. Forensic Sci Int Genet 6:419–423. https://doi.org/10.1016/j.fsigen.2011.08.008

    Article  PubMed  CAS  Google Scholar 

  55. Wang Z, Zhou D, Cao Y et al (2016) Characterization of microRNA expression profiles in blood and saliva using the Ion Personal Genome Machine((R)) System (Ion PGM System). Forensic Sci Int Genet 20:140–146. https://doi.org/10.1016/j.fsigen.2015.10.008

    Article  PubMed  CAS  Google Scholar 

  56. Seashols-Williams S, Lewis C, Calloway C et al (2016) High-throughput miRNA sequencing and identification of biomarkers for forensically relevant biological fluids. Electrophoresis 37:2780–2788. https://doi.org/10.1002/elps.201600258

    Article  PubMed  CAS  Google Scholar 

  57. Sauer E, Reinke AK, Courts C (2016) Differentiation of five body fluids from forensic samples by expression analysis of four microRNAs using quantitative PCR. Forensic Sci Int Genet 22:89–99. https://doi.org/10.1016/j.fsigen.2016.01.018

    Article  PubMed  CAS  Google Scholar 

  58. Sirker M, Fimmers R, Schneider PM, Gomes I (2017) Evaluating the forensic application of 19 target microRNAs as biomarkers in body fluid and tissue identification. Forensic Sci Int Genet 27:41–49. https://doi.org/10.1016/j.fsigen.2016.11.012

    Article  PubMed  CAS  Google Scholar 

  59. Fujimoto S, Manabe S, Morimoto C et al (2019) Distinct spectrum of microRNA expression in forensically relevant body fluids and probabilistic discriminant approach. Sci Rep 9:14332. https://doi.org/10.1038/s41598-019-50796-8

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  60. Dorum G, Ingold S, Hanson E et al (2019) Predicting the origin of stains from whole miRNome massively parallel sequencing data. Forensic Sci Int Genet 40:131–139. https://doi.org/10.1016/j.fsigen.2019.02.015

    Article  PubMed  CAS  Google Scholar 

  61. Mayes C, Houston R, Seashols-Williams S, LaRue B, Hughes-Stamm S (2019) The stability and persistence of blood and semen mRNA and miRNA targets for body fluid identification in environmentally challenged and laundered samples. Leg Med (Tokyo) 38:45–50. https://doi.org/10.1016/j.legalmed.2019.03.007

    Article  PubMed  CAS  Google Scholar 

  62. Li Z, Chen D, Wang Q et al (2021) mRNA and microRNA stability validation of blood samples under different environmental conditions. Forensic Sci Int Genet 55:102567. https://doi.org/10.1016/j.fsigen.2021.102567

    Article  PubMed  CAS  Google Scholar 

  63. Li Z, Lv M, Peng D et al (2021) Feasibility of using probabilistic methods to analyse microRNA quantitative data in forensically relevant body fluids: a proof-of-principle study. Int J Legal Med 135:2247–2261. https://doi.org/10.1007/s00414-021-02678-w

    Article  PubMed  Google Scholar 

  64. Iroanya OO, Olutunde OT, Egwuatu TF, Igbokwe C (2022) Stability of selected microRNAs in human blood, semen and saliva samples exposed to different environmental conditions. Forensic Sci Int 336:111338. https://doi.org/10.1016/j.forsciint.2022.111338

    Article  PubMed  CAS  Google Scholar 

  65. Sauer E, Extra A, Cachee P, Courts C (2017) Identification of organ tissue types and skin from forensic samples by microRNA expression analysis. Forensic Sci Int Genet 28:99–110. https://doi.org/10.1016/j.fsigen.2017.02.002

    Article  PubMed  CAS  Google Scholar 

  66. He H, Ji A, Zhao Y et al (2020) A stepwise strategy to distinguish menstrual blood from peripheral blood by Fisher’s discriminant function. Int J Legal Med 134:845–851. https://doi.org/10.1007/s00414-019-02196-w

    Article  PubMed  Google Scholar 

  67. He H, Han N, Ji C et al (2020) Identification of five types of forensic body fluids based on stepwise discriminant analysis. Forensic Sci Int Genet 48:102337. https://doi.org/10.1016/j.fsigen.2020.102337

    Article  PubMed  CAS  Google Scholar 

  68. Liu Y, He H, Xiao ZX et al (2021) A systematic analysis of miRNA markers and classification algorithms for forensic body fluid identification. Brief Bioinform 22. https://doi.org/10.1093/bib/bbaa324

  69. Wang G, Wang Z, Wei S et al (2022) A new strategy for distinguishing menstrual blood from peripheral blood by the miR-451a/miR-21-5p ratio. Forensic Sci Int Genet 57:102654. https://doi.org/10.1016/j.fsigen.2021.102654

    Article  PubMed  CAS  Google Scholar 

  70. Bamberg M, Bruder M, Dierig L, Kunz SN, Schwender M, Wiegand P (2022) Best of both: a simultaneous analysis of mRNA and miRNA markers for body fluid identification. Forensic Sci Int Genet 59:102707. https://doi.org/10.1016/j.fsigen.2022.102707

    Article  PubMed  CAS  Google Scholar 

  71. Rhodes C, Lewis C, Szekely J et al (2022) Developmental validation of a microRNA panel using quadratic discriminant analysis for the classification of seven forensically relevant body fluids. Forensic Sci Int Genet 59:102692. https://doi.org/10.1016/j.fsigen.2022.102692

    Article  PubMed  CAS  Google Scholar 

  72. Wei S, Hu S, Han N et al (2023) Screening and evaluation of endogenous reference genes for miRNA expression analysis in forensic body fluid samples. Forensic Sci Int Genet 63:102827. https://doi.org/10.1016/j.fsigen.2023.102827

    Article  PubMed  CAS  Google Scholar 

  73. Memczak S, Jens M, Elefsinioti A et al (2013) Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495:333–338. https://doi.org/10.1038/nature11928

    Article  PubMed  ADS  CAS  Google Scholar 

  74. Jeck WR, Sorrentino JA, Wang K et al (2013) Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19:141–157. https://doi.org/10.1261/rna.035667.112

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Song F, Luo H, Xie M, Zhu H, Hou Y (2017) Microarray expression profile of circular RNAs in human body fluids. Forensic Sci Int: Genet Suppl Ser 6:e55–e56. https://doi.org/10.1016/j.fsigss.2017.09.005

    Article  Google Scholar 

  76. Zhang Y, Liu B, Shao C et al (2018) Evaluation of the inclusion of circular RNAs in mRNA profiling in forensic body fluid identification. Int J Legal Med 132:43–52. https://doi.org/10.1007/s00414-017-1690-7

    Article  PubMed  Google Scholar 

  77. Liu B, Song F, Yang Q et al (2019) Characterization of tissue-specific biomarkers with the expression of circRNAs in forensically relevant body fluids. Int J Legal Med 133:1321–1331. https://doi.org/10.1007/s00414-019-02027-y

    Article  PubMed  Google Scholar 

  78. Liu B, Yang Q, Meng H et al (2020) Development of a multiplex system for the identification of forensically relevant body fluids. Forensic Sci Int Genet 47:102312. https://doi.org/10.1016/j.fsigen.2020.102312

    Article  PubMed  CAS  Google Scholar 

  79. Yang Q, Liu B, Zhou Y et al (2021) Evaluation of one-step RT-PCR multiplex assay for body fluid identification. Int J Legal Med 135:1727–1735. https://doi.org/10.1007/s00414-021-02535-w

    Article  PubMed  Google Scholar 

  80. Ponnusamy M, Yan KW, Liu CY, Li PF, Wang K (2017) PIWI family emerging as a decisive factor of cell fate: an overview. Eur J Cell Biol 96:746–757. https://doi.org/10.1016/j.ejcb.2017.09.004

    Article  PubMed  CAS  Google Scholar 

  81. Ross RJ, Weiner MM, Lin H (2014) PIWI proteins and PIWI-interacting RNAs in the soma. Nature 505:353–359. https://doi.org/10.1038/nature12987

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  82. Fu A, Jacobs DI, Zhu Y (2014) Epigenome-wide analysis of piRNAs in gene-specific DNA methylation. RNA Biol 11:1301–1312. https://doi.org/10.1080/15476286.2014.996091

    Article  PubMed  Google Scholar 

  83. Simon B, Kirkpatrick JP, Eckhardt S et al (2011) Recognition of 2’-O-methylated 3’-end of piRNA by the PAZ domain of a Piwi protein. Structure 19:172–180. https://doi.org/10.1016/j.str.2010.11.015

    Article  PubMed  CAS  Google Scholar 

  84. Wang S, Wang Z, Tao R et al (2019) The potential use of Piwi-interacting RNA biomarkers in forensic body fluid identification: a proof-of-principle study. Forensic Sci Int Genet 39:129–135. https://doi.org/10.1016/j.fsigen.2019.01.002

    Article  PubMed  CAS  Google Scholar 

  85. Nallamshetty S, Chan SY, Loscalzo J (2013) Hypoxia: a master regulator of microRNA biogenesis and activity. Free Radic Biol Med 64:20–30. https://doi.org/10.1016/j.freeradbiomed.2013.05.022

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Schober K, Ondruschka B, Dressler J, Abend M (2015) Detection of hypoxia markers in the cerebellum after a traumatic frontal cortex injury: a human postmortem gene expression analysis. Int J Legal Med 129:701–707. https://doi.org/10.1007/s00414-014-1129-3

    Article  PubMed  CAS  Google Scholar 

  87. Zeng Y, Lv Y, Tao L et al (2016) G6PC3, ALDOA and CS induction accompanies mir-122 down-regulation in the mechanical asphyxia and can serve as hypoxia biomarkers. Oncotarget 7:74526–36. https://doi.org/10.18632/oncotarget.12931

    Article  PubMed  PubMed Central  Google Scholar 

  88. Han L, Zhang H, Zeng Y et al (2020) Identification of the miRNA-3185/CYP4A11 axis in cardiac tissue as a biomarker for mechanical asphyxia. Forensic Sci Int 311:110293. https://doi.org/10.1016/j.forsciint.2020.110293

    Article  PubMed  CAS  Google Scholar 

  89. Han L, Li W, Hu Y et al (2021) Model for the prediction of mechanical asphyxia as the cause of death based on four biological indexes in human cardiac tissue. Sci Justice 61:221–226. https://doi.org/10.1016/j.scijus.2021.02.003

    Article  PubMed  Google Scholar 

  90. Liu CX, Chen LL (2022) Circular RNAs: Characterization, cellular roles, and applications. Cell 185:2016–2034. https://doi.org/10.1016/j.cell.2022.04.021

    Article  PubMed  CAS  Google Scholar 

  91. Huang Q, Yang J, Goh RMW, You M, Wang L, Ma Z (2022) Hypoxia-induced circRNAs in human diseases: from mechanisms to potential applications. Cells 11. https://doi.org/10.3390/cells11091381

  92. Barwari T, Joshi A, Mayr M (2016) MicroRNAs in cardiovascular disease. J Am Coll Cardiol 68:2577–2584. https://doi.org/10.1016/j.jacc.2016.09.945

    Article  PubMed  CAS  Google Scholar 

  93. Kakimoto Y, Tanaka M, Hayashi H, Yokoyama K, Osawa M (2018) Overexpression of miR-221 in sudden death with cardiac hypertrophy patients. Heliyon 4:e00639. https://doi.org/10.1016/j.heliyon.2018.e00639

    Article  PubMed  PubMed Central  Google Scholar 

  94. Pinchi E, Frati P, Aromatario M et al (2019) miR-1, miR-499 and miR-208 are sensitive markers to diagnose sudden death due to early acute myocardial infarction. J Cell Mol Med 23:6005–6016. https://doi.org/10.1111/jcmm.14463

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Yan F, Chen Y, Ye X et al (2021) miR-3113-5p, miR-223-3p, miR-133a-3p, and miR-499a-5p are sensitive biomarkers to diagnose sudden cardiac death. Diagn Pathol 16:67. https://doi.org/10.1186/s13000-021-01127-x

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Li L, He X, Liu M, Yun L, Cong B (2022) Diagnostic value of cardiac miR-126-5p, miR-134-5p, and miR-499a-5p in coronary artery disease-induced sudden cardiac death. Front Cardiovasc Med 9:944317. https://doi.org/10.3389/fcvm.2022.944317

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Wang W, Wang Y, Piao H et al (2019) Circular RNAs as potential biomarkers and therapeutics for cardiovascular disease. PeerJ 7:e6831. https://doi.org/10.7717/peerj.6831

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Tian M, Xue J, Dai C, Jiang E, Zhu B, Pang H (2021) CircSLC8A1 and circNFIX can be used as auxiliary diagnostic markers for sudden cardiac death caused by acute ischemic heart disease. Sci Rep 11:4695. https://doi.org/10.1038/s41598-021-84056-5

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  99. Cui X, Niu W, Kong L et al (2017) Long noncoding RNA expression in peripheral blood mononuclear cells and suicide risk in Chinese patients with major depressive disorder. Brain Behav 7:e00711. https://doi.org/10.1002/brb3.711

    Article  PubMed  PubMed Central  Google Scholar 

  100. Wang Q, Roy B, Turecki G, Shelton RC, Dwivedi Y (2018) Role of Complex epigenetic switching in tumor necrosis factor-alpha upregulation in the prefrontal cortex of suicide subjects. Am J Psychiatry 175:262–274. https://doi.org/10.1176/appi.ajp.2017.16070759

    Article  PubMed  PubMed Central  Google Scholar 

  101. Yoshino Y, Dwivedi Y (2020) Non-coding RNAs in psychiatric disorders and suicidal behavior. Front Psychiatry 11:543893. https://doi.org/10.3389/fpsyt.2020.543893

    Article  PubMed  PubMed Central  Google Scholar 

  102. Punzi G, Ursini G, Shin JH, Kleinman JE, Hyde TM, Weinberger DR (2014) Increased expression of MARCKS in post-mortem brain of violent suicide completers is related to transcription of a long, noncoding, antisense RNA. Mol Psychiatry 19:1057–1059. https://doi.org/10.1038/mp.2014.41

    Article  PubMed  CAS  Google Scholar 

  103. Punzi G, Ursini G, Viscanti G et al (2019) Association of a noncoding RNA postmortem with suicide by violent means and in vivo with aggressive phenotypes. Biol Psychiatry 85:417–424. https://doi.org/10.1016/j.biopsych.2018.11.002

    Article  PubMed  CAS  Google Scholar 

  104. Zhou Y, Lutz PE, Wang YC, Ragoussis J, Turecki G (2018) Global long non-coding RNA expression in the rostral anterior cingulate cortex of depressed suicides. Transl Psychiatry 8:224. https://doi.org/10.1038/s41398-018-0267-7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Smalheiser NR, Lugli G, Rizavi HS, Torvik VI, Turecki G, Dwivedi Y (2012) MicroRNA expression is down-regulated and reorganized in prefrontal cortex of depressed suicide subjects. PLoS One 7:e33201. https://doi.org/10.1371/journal.pone.0033201

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  106. Smalheiser NR, Lugli G, Zhang H, Rizavi H, Cook EH, Dwivedi Y (2014) Expression of microRNAs and other small RNAs in prefrontal cortex in schizophrenia, bipolar disorder and depressed subjects. PLoS One 9:e86469. https://doi.org/10.1371/journal.pone.0086469

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  107. Lopez JP, Fiori LM, Gross JA et al (2014) Regulatory role of miRNAs in polyamine gene expression in the prefrontal cortex of depressed suicide completers. Int J Neuropsychopharmacol 17:23–32. https://doi.org/10.1017/S1461145713000941

    Article  PubMed  CAS  Google Scholar 

  108. Lopez JP, Fiori LM, Cruceanu C et al (2017) MicroRNAs 146a/b-5 and 425–3p and 24–3p are markers of antidepressant response and regulate MAPK/Wnt-system genes. Nat Commun 8:15497. https://doi.org/10.1038/ncomms15497

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  109. Courts C, Grabmuller M, Madea B (2013) Dysregulation of heart and brain specific micro-RNA in sudden infant death syndrome. Forensic Sci Int 228:70–74. https://doi.org/10.1016/j.forsciint.2013.02.032

    Article  PubMed  CAS  Google Scholar 

  110. Yu S, Na JY, Lee YJ, Kim KT, Park JT, Kim HS (2015) Forensic application of microRNA-706 as a biomarker for drowning pattern identification. Forensic Sci Int 255:96–101. https://doi.org/10.1016/j.forsciint.2015.06.011

    Article  PubMed  CAS  Google Scholar 

  111. Pinchi E, Frati A, Cantatore S et al (2019) Acute spinal cord injury: a systematic review investigating miRNA families involved. Int J Mol Sci 20. https://doi.org/10.3390/ijms20081841

  112. Wang H, Mao J, Li Y et al (2013) 5 miRNA expression analyze in post-mortem interval (PMI) within 48h. Forensic Sci Int: Genet Suppl Ser 4:e190–e191. https://doi.org/10.1016/j.fsigss.2013.10.098

    Article  Google Scholar 

  113. Lv YH, Ma KJ, Zhang H et al (2014) A time course study demonstrating mRNA, microRNA, 18S rRNA, and U6 snRNA changes to estimate PMI in deceased rat’s spleen. J Forensic Sci 59:1286–1294. https://doi.org/10.1111/1556-4029.12447

    Article  PubMed  CAS  Google Scholar 

  114. Lv YH, Ma JL, Pan H et al (2016) RNA degradation as described by a mathematical model for postmortem interval determination. J Forensic Leg Med 44:43–52. https://doi.org/10.1016/j.jflm.2016.08.015

    Article  PubMed  Google Scholar 

  115. Lv YH, Ma JL, Pan H et al (2017) Estimation of the human postmortem interval using an established rat mathematical model and multi-RNA markers. Forensic Sci Med Pathol 13:20–27. https://doi.org/10.1007/s12024-016-9827-4

    Article  PubMed  CAS  Google Scholar 

  116. Pasaribu RS, Auerkari EI, Suhartono AW, Auerkari P (2023) A small RNA, microRNA as a potential biomolecular marker to estimate post mortem interval in forensic science: a systematic review. Int J Legal Med. https://doi.org/10.1007/s00414-023-03015-z

    Article  PubMed  PubMed Central  Google Scholar 

  117. Tu C, Du T, Shao C, Liu Z, Li L, Shen Y (2018) Evaluating the potential of housekeeping genes, rRNAs, snRNAs, microRNAs and circRNAs as reference genes for the estimation of PMI. Forensic Sci Med Pathol 14:194–201. https://doi.org/10.1007/s12024-018-9973-y

    Article  PubMed  CAS  Google Scholar 

  118. Tu C, Du T, Ye X, Shao C, Xie J, Shen Y (2019) Using miRNAs and circRNAs to estimate PMI in advanced stage. Leg Med (Tokyo) 38:51–57. https://doi.org/10.1016/j.legalmed.2019.04.002

    Article  PubMed  CAS  Google Scholar 

  119. Na JY (2020) Estimation of the post-mortem interval using microRNA in the bones. J Forensic Leg Med 75:102049. https://doi.org/10.1016/j.jflm.2020.102049

    Article  PubMed  Google Scholar 

  120. Kim SY, Jang SJ, Jung YH, Na JY (2021) Difference in microRNA levels in the post-mortem blood from different sampling sites: a proof of concept. J Forensic Leg Med 78:102124. https://doi.org/10.1016/j.jflm.2021.102124

    Article  PubMed  Google Scholar 

  121. Lang H, Zhao F, Zhang T et al (2017) MicroRNA-149 contributes to scarless wound healing by attenuating inflammatory response. Mol Med Rep 16:2156–2162. https://doi.org/10.3892/mmr.2017.6796

    Article  PubMed  CAS  Google Scholar 

  122. De Simone S, Giacani E, Bosco MA et al (2021) The role of miRNAs as new molecular biomarkers for dating the age of wound production: a systematic review. Front Med (Lausanne) 8:803067. https://doi.org/10.3389/fmed.2021.803067

    Article  PubMed  Google Scholar 

  123. Neri M, Fabbri M, D’Errico S et al (2019) Regulation of miRNAs as new tool for cutaneous vitality lesions demonstration in ligature marks in deaths by hanging. Sci Rep 9:20011. https://doi.org/10.1038/s41598-019-56682-7

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  124. Maiese A, Manetti AC, Iacoponi N et al (2022) State-of-the-art on wound vitality evaluation: a systematic review. Int J Mol Sci 23. https://doi.org/10.3390/ijms23136881

  125. Bertero T, Gastaldi C, Bourget-Ponzio I et al (2011) miR-483-3p controls proliferation in wounded epithelial cells. FASEB J 25:3092–3105. https://doi.org/10.1096/fj.10-168401

    Article  PubMed  CAS  Google Scholar 

  126. Wang T, Feng Y, Sun H et al (2012) miR-21 regulates skin wound healing by targeting multiple aspects of the healing process. Am J Pathol 181:1911–1920. https://doi.org/10.1016/j.ajpath.2012.08.022

    Article  PubMed  Google Scholar 

  127. Etich J, Bergmeier V, Pitzler L, Brachvogel B (2017) Identification of a reference gene for the quantification of mRNA and miRNA expression during skin wound healing. Connect Tissue Res 58:196–207. https://doi.org/10.1080/03008207.2016.1210606

    Article  PubMed  CAS  Google Scholar 

  128. Chang L, Liang J, Xia X, Chen X (2019) miRNA-126 enhances viability, colony formation, and migration of keratinocytes HaCaT cells by regulating PI3 K/AKT signaling pathway. Cell Biol Int 43:182–191. https://doi.org/10.1002/cbin.11088

    Article  PubMed  CAS  Google Scholar 

  129. Lyu HP, Cheng M, Liu JC et al (2018) Differentially expressed microRNAs as potential markers for vital reaction of burned skin. J Forensic Sci Med 4:15

    Google Scholar 

  130. Zhang K, Cheng M, Xu J et al (2022) MiR-711 and miR-183-3p as potential markers for vital reaction of burned skin. Forensic Sci Res 7:503–509. https://doi.org/10.1080/20961790.2020.1719454

    Article  PubMed  Google Scholar 

  131. Liu W, Li L, Rong Y et al (2020) Hypoxic mesenchymal stem cell-derived exosomes promote bone fracture healing by the transfer of miR-126. Acta Biomater 103:196–212. https://doi.org/10.1016/j.actbio.2019.12.020

    Article  PubMed  CAS  Google Scholar 

  132. Li X, Zhong Z, Ma E, Wu X (2021) Identification of miRNA regulatory networks and candidate markers for fracture healing in mice. Comput Math Methods Med 2021:2866475. https://doi.org/10.1155/2021/2866475

    Article  PubMed  PubMed Central  Google Scholar 

  133. Manetti AC, Maiese A, Baronti A et al (2021) MiRNAs as new tools in lesion vitality evaluation: a systematic review and their forensic applications. Biomedicines 9. https://doi.org/10.3390/biomedicines9111731

  134. Anderson S, Howard B, Hobbs GR, Bishop CP (2005) A method for determining the age of a bloodstain. Forensic Sci Int 148:37–45. https://doi.org/10.1016/j.forsciint.2004.04.071

    Article  PubMed  CAS  Google Scholar 

  135. Lech K, Ackermann K, Wollstein A, Revell VL, Skene DJ, Kayser M (2014) Assessing the suitability of miRNA-142-5p and miRNA-541 for bloodstain deposition timing. Forensic Sci Int Genet 12:181–184. https://doi.org/10.1016/j.fsigen.2014.06.008

    Article  PubMed  CAS  Google Scholar 

  136. Alshehhi S, Haddrill PR (2019) Estimating time since deposition using quantification of RNA degradation in body fluid-specific markers. Forensic Sci Int 298:58–63. https://doi.org/10.1016/j.forsciint.2019.02.046

    Article  PubMed  CAS  Google Scholar 

  137. Wei Y, Wang J, Wang Q, Cong B, Li S (2022) The estimation of bloodstain age utilizing circRNAs and mRNAs biomarkers. Forensic Sci Int 338:111408. https://doi.org/10.1016/j.forsciint.2022.111408

    Article  PubMed  CAS  Google Scholar 

  138. Freire-Aradas A, Phillips C, Lareu MV (2017) Forensic individual age estimation with DNA: from initial approaches to methylation tests. Forensic Sci Rev 29:121–144

    PubMed  CAS  Google Scholar 

  139. Vidaki A, Kayser M (2018) Recent progress, methods and perspectives in forensic epigenetics. Forensic Sci Int Genet 37:180–195. https://doi.org/10.1016/j.fsigen.2018.08.008

    Article  PubMed  CAS  Google Scholar 

  140. Noren Hooten N, Abdelmohsen K, Gorospe M, Ejiogu N, Zonderman AB, Evans MK (2010) microRNA expression patterns reveal differential expression of target genes with age. PLoS One 5:e10724. https://doi.org/10.1371/journal.pone.0010724

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  141. Rubie C, Kolsch K, Halajda B et al (2016) microRNA-496 - a new, potentially aging-relevant regulator of mTOR. Cell Cycle 15:1108–1116. https://doi.org/10.1080/15384101.2016.1158360

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. Huan T, Chen G, Liu C et al (2018) Age-associated microRNA expression in human peripheral blood is associated with all-cause mortality and age-related traits. Aging Cell 17. https://doi.org/10.1111/acel.12687

  143. Wang J, Wang C, Wei Y et al (2022) Circular RNA as a potential biomarker for forensic age prediction. Front Genet 13:825443. https://doi.org/10.3389/fgene.2022.825443

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Nikolajevic J, Ariaee N, Liew A, Abbasnia S, Fazeli B, Sabovic M (2022) The role of microRNAs in endothelial cell senescence. Cells 11. https://doi.org/10.3390/cells11071185

  145. Lettieri-Barbato D, Aquilano K, Punziano C, Minopoli G, Faraonio R (2022) MicroRNAs, long non-coding RNAs, and circular RNAs in the redox control of cell senescence. Antioxidants (Basel) 11. https://doi.org/10.3390/antiox11030480

  146. Abu-Halima M, Weidinger J, Poryo M et al (2019) Micro-RNA signatures in monozygotic twins discordant for congenital heart defects. PLoS One 14:e0226164. https://doi.org/10.1371/journal.pone.0226164

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  147. Tuncer SB, Erdogan OS, Erciyas SK et al (2020) miRNA expression profile changes in the peripheral blood of monozygotic discordant twins for epithelial ovarian carcinoma: potential new biomarkers for early diagnosis and prognosis of ovarian carcinoma. J Ovarian Res 13:99. https://doi.org/10.1186/s13048-020-00706-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  148. Bresciani E, Squillace N, Orsini V et al (2022) miRNA expression profiling in subcutaneous adipose tissue of monozygotic twins discordant for HIV Infection: validation of differentially expressed miRNA and bioinformatic analysis. Int J Mol Sci 23. https://doi.org/10.3390/ijms23073486

  149. Fang C, Zhao J, Liu X et al (2019) MicroRNA profile analysis for discrimination of monozygotic twins using massively parallel sequencing and real-time PCR. Forensic Sci Int Genet 38:23–31. https://doi.org/10.1016/j.fsigen.2018.09.011

    Article  PubMed  CAS  Google Scholar 

  150. Xiao C, Pan C, Liu E et al (2019) Differences of microRNA expression profiles between monozygotic twins’ blood samples. Forensic Sci Int Genet 41:152–158. https://doi.org/10.1016/j.fsigen.2019.05.003

    Article  PubMed  CAS  Google Scholar 

  151. Wu H, Kirita Y, Donnelly EL, Humphreys BD (2019) Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol 30:23–32. https://doi.org/10.1681/ASN.2018090912

    Article  PubMed  CAS  Google Scholar 

  152. Slyper M, Porter CBM, Ashenberg O et al (2020) A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med 26:792–802. https://doi.org/10.1038/s41591-020-0844-1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  153. Ding J, Adiconis X, Simmons SK et al (2020) Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38:737–746. https://doi.org/10.1038/s41587-020-0465-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank people in the Research Center for Preclinical Medicine, Southwest Medical University.

Funding

This work was funded by the Technology Project Foundation of Luzhou City (No. 2021-SYF-37), the Teaching Reform Project of Postgraduate Education in Southwest Medical University (No. YJG202222), and partially funded by the National Natural Science Foundation of China (Nos. 81672887).

Author information

Authors and Affiliations

  1. Key Laboratory of Epigenetics and Oncology, the Research Center for Preclinical Medicine, Southwest Medical University, Luzhou, 646000, Sichuan, China

    Binghui Song, Jie Qian & Junjiang Fu

  2. Laboratory of Precision Medicine and DNA Forensic Medicine, the Research Center for Preclinical Medicine, Southwest Medical University, Luzhou, 646000, Sichuan, China

    Binghui Song, Jie Qian & Junjiang Fu

  3. Laboratory of Forensic DNA, the Judicial Authentication Center, Southwest Medical University, Luzhou, 646000, Sichuan, China

    Junjiang Fu

Authors
  1. Binghui Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junjiang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junjiang Fu.

Ethics declarations

Research involving human participants and/or animals

Not applicable.

Informed consent

Not applicable.

Ethical approval

Not applicable.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, B., Qian, J. & Fu, J. Research progress and potential application of microRNA and other non-coding RNAs in forensic medicine. Int J Legal Med 138, 329–350 (2024). https://doi.org/10.1007/s00414-023-03091-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00414-023-03091-1

Keywords

Search

Navigation