Cancer stands as a leading global cause of fatalities. The principal hallmark of cancer is the rapid and uncontrolled division of cells due to genetic mutations. A harmonic balance between mitogenic and anti-proliferative signals is essential for maintaining tight control of cell division in mammalian cells. Aberrations in signaling pathways perturbing cell division can frequently trigger oncogenesis. With advancements in molecular and clinical research, researchers now have the tools to systematically investigate and identify the genetic alterations responsible for driving oncogenic transformations by modulating the cell signaling pathways. Traditionally, the classical approach for comprehending signaling pathways has been the identification of protein-coding genes. However, microRNAs (miRNAs), a class of small non-coding RNAs, have garnered significant attention for their role in regulating gene expression. The significance of miRNAs in diverse physiological processes including proliferation, development and apoptosis, and their implications in pathogenesis of various diseases including cancers has now been widely recognized. miRNAs are small ∼22 nucleotide(nt) single-stranded RNA molecules that are known to regulate the expression of target genes by primarily binding to the 3′UTR of messenger RNA (mRNA). Although miRNAs are expressed from only less than one percent of the human genome, they exert their regulatory influence over nearly all of our developmental and pathological processes (Ha & Kim, 2014). Remarkably, more than 60% of human protein-coding genes contain at least one conserved and various non-conserved miRNA-binding sites underscoring the extensive regulatory potential wielded by miRNAs (Ebert & Sharp, 2012). This chapter aims to elucidate the intricate interplay between miRNAs and signaling pathways in human cancers, as well as to explore their potential therapeutic implications. In light of this, it becomes essential to initially delve into the fundamentals of miRNA generation and provide a concise overview of how miRNAs regulate gene expression. The upcoming sections will therefore establish a foundational understanding of the biological mechanism of functioning and importance of miRNAs in the context of cancer.

miRNA precursors are transcribed as monocistronic or polycistronic units from both the intergenic and the intragenic regions of human genome (Fazi & Nervi, 2008). Transcription of miRNA genes is mediated by RNA polymerase II, however, the possibility that a few miRNAs might be transcribed by other RNA polymerases cannot be excluded (Borchert, Lanier, & Davidson, 2006; Lee et al., 2004). The primary transcripts (pri-miRNA) containing miRNA sequences embedded in local stem-loop structure undergo a stepwise process for genesis of a functionally mature miRNA (Yoontae Lee, Jeon, Lee, & Narry Kim, 2002). To begin with, primary miRNAs (pri-miRNAs) undergo cleavage into approximately 70 nucleotide precursor miRNAs (pre-miRNAs) through the action of the Drosha-DGCR8 microprocessor complex (Denli, Tops, Plasterk, Ketting, & Hannon, 2004; Han et al., 2004). Following the initial cleavage by Drosha, pre-miRNAs are exported to the cytoplasm by Exportin 5 (Yi, Qin, Macara, & Cullen, 2003) and then cleaved into ∼22 nt mature miRNA duplex by RNase III, Dicer (Yoontae Lee et al., 2002). The mature miRNA with a relatively unstable 5′ end (guide strand) is incorporated into the miRNA-induced silencing complex (miRISC) while the complementary strand (passenger strand) is quickly degraded (Gregory, Chendrimada, Cooch, & Shiekhattar, 2005). In some cases, both strands of miRNA duplex have the potential of being incorporated into the RISC complex and are referred to as miR-5p or miR-3p, based on their proximity to the 5′ or 3′ end of pre-miRNA, respectively.

The recognition of a target mRNA by miRNA relies heavily on base pairing between the seed sequence (residues 2–8 at the 5′end) of miRNA guide and miRNA recognition elements (MREs), that lie usually within the 3′UTR of the target mRNAs. The mature 22-nt miRNA recognizes the MREs in the target mRNAs and guides the activated RISC complex to suppress gene expression by inhibiting translation, promoting mRNA decay or both (Bartel, 2009). The gene silencing mechanism is contingent on the strength and nature of the complementarity between a miRNA and its target site (Brennecke, Stark, Russell, & Cohen, 2005). An extensive base-pairing between the miRNA and target generally leads to Ago2 mediated endonucleolytic cleavage of the target (Gunter, & Thomas, 2004), whereas the presence of multiple complementary sites with only moderate (limited) base-pairing per site commonly results in translation inhibition. Presence of a G:U wobble also affects the specificity and activity of miRNA (Doench & Sharp, 2004; Macfarlane & Murphy, 2010).

In order to understand the functional role of miRNAs, the foremost requirement is to determine their direct target genes. Each miRNA may regulate multiple genes in response different developmental or environmental cues. Target prediction of miRNAs is classically performed by computational algorithms based on seed pairing i.e. complementarity between miRNA seed region and 3′ UTR of target mRNA (Bartel, 2009; John et al., 2004) (Lewis, Burge, & Bartel, 2005). Evidence of miRNA-mRNA interactions lacking absolute seed pairing (Lal et al., 2009; Grimson et al., 2007) and the presence of MREs in the transcript regions other than 3′ UTR including the coding sequence, 5′ UTR as well as promoter sequences (Hausser et al., 2013, Kim et al., 2011, Xu et al., 2014, Xu et al., 2014) have founded the development of more efficient strategies for miRNA target prediction. Updated algorithms for in silico miRNA target prediction focus variably on multiple features of miRNA–mRNA interaction, including seed pairing, conservation of target site, free energy of miRNA–mRNA heteroduplex, G:U wobble and MRE sites (Thomas, Lieberman, & Lal, 2010). Functional classification of predicted candidate targets by using gene ontology and interactome analysis can help to define miRNA function and pinpoint biologically relevant target genes from hundreds of identified candidates (Lal et al., 2009). Strategies for in vitro identification of miRNA-mRNA binding based on immunoprecipitation of miRISC associated proteins, followed by subsequent analysis of mRNAs precipitated with RISC proteins, have also emerged as strong tools for deciphering miRNA targets (Hendrickson et al., 2009; Karginov et al., 2007; Easow, Teleman, & Cohen, 2007). High-throughput sequencing of RNA isolated by crosslinking immunoprecipitation and photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation have further advanced the identification of miRNA-mRNA interactions in-vitro (Chi, Zang, Mele, & Darnell, 2009; Hafner et al., 2010). In addition to prediction of a miRNA target gene using the target prediction algorithms or a high-throughput genome-wide approach, individual experiments analyzing the effect of miRNA perturbation on target gene expression are considered as the most significant validation for determining a gene as the target of a particular miRNA (Thomson, Bracken, & Goodall, 2011).