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Review
. 2021 Mar 3;13(5):1085.
doi: 10.3390/cancers13051085.

Small Non-Coding-RNA in Gynecological Malignancies

Shailendra Kumar Dhar Dwivedi  1 , Geeta Rao  2 , Anindya Dey  1 , Priyabrata Mukherjee  2   3 , Jonathan D Wren  4   5   6 , Resham Bhattacharya  1   3   7
Affiliations
Review

Small Non-Coding-RNA in Gynecological Malignancies

Shailendra Kumar Dhar Dwivedi et al. Cancers (Basel). .

Abstract

Gynecologic malignancies, which include cancers of the cervix, ovary, uterus, vulva, vagina, and fallopian tube, are among the leading causes of female mortality worldwide, with the most prevalent being endometrial, ovarian, and cervical cancer. Gynecologic malignancies are complex, heterogeneous diseases, and despite extensive research efforts, the molecular mechanisms underlying their development and pathology remain largely unclear. Currently, mechanistic and therapeutic research in cancer is largely focused on protein targets that are encoded by about 1% of the human genome. Our current understanding of 99% of the genome, which includes noncoding RNA, is limited. The discovery of tens of thousands of noncoding RNAs (ncRNAs), possessing either structural or regulatory functions, has fundamentally altered our understanding of genetics, physiology, pathophysiology, and disease treatment as they relate to gynecologic malignancies. In recent years, it has become clear that ncRNAs are relatively stable, and can serve as biomarkers for cancer diagnosis and prognosis, as well as guide therapy choices. Here we discuss the role of small non-coding RNAs, i.e., microRNAs (miRs), P-Element induced wimpy testis interacting (PIWI) RNAs (piRNAs), and tRNA-derived small RNAs in gynecological malignancies, specifically focusing on ovarian, endometrial, and cervical cancer.

Keywords: P-Element induced wimpy testis interacting (PIWI) RNAs (piRNAs); cervical cancer; endometrial cancer; gynecological malignancies; microRNAs (miRs); ovarian cancer; small non-coding-RNA; tRNA-derived small RNAs.

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Conflict of interest statement

Authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Biogenesis and function of piRNAs. (A) Biogenesis of piRNAs: Biogenesis of piRNA occurs through primary and secondary pathways (ping-pong cycle). In the primary pathway, piRNA precursors are transcribed from piRNA clusters by RNA polymerase 2 (RNA Pol2). Antisense primary piRNAs are cleaved, trimmed into short fragments, and their 3′ ends are 2′-O-methylated and then loaded onto PIWI family proteins. In the secondary amplification pathways, also known as the ping-pong cycle, PIWI proteins associate with antisense piRNA and cleave piRNA precursors in the sense strand, or PIWI proteins associate with sense piRNA and cleave antisense piRNA precursors in the sense strand. The incorporated RNA is therefore processed into a mature secondary piRNA by trimming and modification, likely by the same mechanisms that generate a primary piRNA. (B) The biological functions of piRNAs: In the nucleus, PIWI–piRNA complexes can repress the transposon expression by methylation of the transposon region or chromatin modification around the transposon region. In the cytoplasm, piRNAs can facilitate mRNA maturation, cause cleavage of mRNAs through a miRNA-like mechanism, and degrade retrotransposon-associated mRNAs.
Figure 2
Figure 2
Biogenesis and function of tRNA-derived small RNAs: Different types of tRNA-derived RNA fragments produced from either pre-tRNAs or mature tRNAs. The tRF-1 series is produced by RNase Z (or ELAC2) cleavage of the pre-tRNA during tRNA processing. Mature tRNA can be cleaved in the anticodon loop by angiogenin (ANG) to produce the 5′-tiRNA and 3′-tiRNA series under stress conditions. tRF-2 is a tRNA fragment containing an anti-codon loop generated by an unknown cleavage method. Cleavage in the T-loop results in the production of the 3′-tRF series. The 5′-tRF series is derived from the 5′-end of mature tRNAs by endonucleolytic cleavage and exonuclease digestion in the D-loop.
Figure 3
Figure 3
tRF- and tiRNA-mediated gene regulation: (A) Regulation of mRNA stability: The combination of tRF-3 with argonaute 3 (Ago3) and Ago4 binding to mRNA allows mRNA-degrading enzymes to degrade the target mRNA [101]; tRFs function like miRNAs to inhibit cancer-associated gene expression. Binding with the 3′ untranslated region (3′UTR) of target mRNA, Argonaute (Ago) protein and other proteins form an RNA-induced silencing complex (RISC). (B,C) Inhibition of translation: By binding to small ribosomal subunits, tRF inhibits peptidyl transferase activity that results in reduced protein abundance of the target gene [99,102]. 5′tiRNA inhibits translation by forming a RNA G-quadruplex (RG4s) that replaces the translation initiation complex eIF4G/eIF4E on the mRNA cap [103,104].
Figure 4
Figure 4
Network of upregulated miR in ovarian cancer: miR deregulated in ovarian cancer were searched for in the PubMed database using IRIDESCENT. The network of upregulated miR generated using miRNET (degree filter cutoff 10) is shown. The blue squares represent miR, and the red dots represent target genes of these miRs.
Figure 5
Figure 5
Network of downregulated miR in ovarian cancer: miR deregulated in ovarian cancer were searched for in the PubMed database using IRIDESCENT. The network of downregulated miR generated using miRNET (degree filter cutoff 15) is shown. The blue squares represent miR, and the red dots represent target genes of these miRs.
Figure 6
Figure 6
Network of upregulated miR in endometrial cancer: miR deregulated in endometrial cancer were searched in the PubMed database using IRIDESCENT. The network of upregulated miR generated using miRNET (degree filter 5) is shown. The blue squares represent miR, and the red dots represent target genes of these miRs.
Figure 7
Figure 7
Network of downregulated miR in endometrial cancer: miR deregulated in endometrial cancer were searched in the PubMed database using IRIDESCENT. The network of downregulated miR generated using miRNET (degree filter cutoff 10) is shown. The blue squares represent miR, and the red dots represent target genes of these miRs.
Figure 8
Figure 8
Network of upregulated miR in cervical cancer: miRs deregulated in cervical cancer were searched in the PubMed database using IRIDESCENT. The network of upregulated miRs generated using miRNET (degree filter cutoff 10) is shown. The blue squares represent miR, and the red dots represent target genes of these miRs.
Figure 9
Figure 9
Network of downregulated miR in cervical cancer: miR deregulated in cervical cancer were searched in the PubMed database using IRIDESCENT. The network of downregulated miR generated using miRNET (degree filter 10) is shown. The blue squares represent miR, and the red dots represent target genes of these miRs.
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