1Cancer Biology and Precision Medicine Department, Germans Trias i Pujol Research Institute and Hospital, Badalona 08916, Spain.
2Biochemistry, Molecular Biology and Biomedicine Department, Universitat Autónoma de Barcelona, Bellaterra, Barcelona 08193, Spain.
3Universitat Autónoma de Barcelona, Bellaterra, Barcelona 08193, Spain.
Correspondence to: Dr. Carlos Pedraz-Valdunciel, Cancer Biology and Precision Medicine Department, Germans Trias i Pujol Research Institute and Hospital, Camí de les Escoles, s/n, Badalon 08916, Spain. E-mail: email@example.com
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Despite the significant decrease in population-level mortality of lung cancer patients as reflected in the Surveillance Epidemiology and End Results program national database, lung cancer, with non-small cell lung cancer (NSCLC) in the lead, continues to be the most commonly diagnosed cancer and foremost cause of cancer-related death worldwide, primarily due to late-stage diagnosis and ineffective treatment regimens. Although innovative single therapies and their combinations are constantly being tested in clinical trials, the five-year survival rate of late-stage lung cancer remains only 5% (Cancer Research, UK). Henceforth, investigation in the early diagnosis of lung cancer and prediction of treatment response is critical for improving the overall survival of these patients. Circular RNAs (circRNAs) are a re-discovered type of RNAs featuring stable structure and high tissue-specific expression. Evidence has revealed that aberrant circRNA expression plays an important role in carcinogenesis and tumor progression. Further investigation is warranted to assess the value of EV- and platelet-derived circRNAs as liquid biopsy-based readouts for lung cancer detection. This review discusses the origin and biology of circRNAs, and analyzes their present landscape in NSCLC, focusing on liquid biopsies to illustrate the different methodological trends currently available in research. The possible limitations that could be holding back the clinical implementation of circRNAs are also analyzed.
CircRNA, extracellular vesicles lung cancer, NSCLC, liquid biopsies, biomarkers
Lung cancer is the most commonly diagnosed cancer, contributing greatly to cancer incidence and cancer-related deaths worldwide. Of those lung cancers, non-small cell lung cancer (NSCLC) accounts for 85% of the cases; the development of the disease is attributed to multileveled and elusive complex interactions between genetic liabilities, sex, environmental toxins, and imbalanced signaling processes.
Although the mortality rate of NSCLC has decreased in previous years, presumably due to the approval and routinization of targeted therapies and immunotherapies, the prognosis in late-stage lung cancer remains dismal. While the 5-year overall survival (OS) of early-stage lung cancer is 85% (stage IA), these numbers fall to only 5% in late-stage cases (stage IV). In addition to tumor tissue characterization, liquid biopsies have been introduced to overcome, or complement, invasive tissue biopsies.
Not only are they instrumental in achieving early detection of the tumor, but they can also be exploited to monitor therapy resistance and provide a more heterogeneous readout of the tumor burden. This allows the identification of resistance mechanisms and can guide second-line therapy selection.
Different body fluids can be used as liquid biopsies, including blood, urine, and saliva. Circulating molecules, such as cell-free DNA (cfDNA), RNA, or proteins, can either be freely present within these media or can be extracted and analyzed from circulating extracellular vesicles (EVs) or tumor-educated platelets (TEPs).
Lung cancer involves massive changes in RNA metabolism, both in the tumor and circulating EVs and TEPs. Traditional RNA biomarker discovery research for either lung cancer detection or monitoring of treatment response has mainly focused on the expression of mRNA and miRNA[5-7].
Circular RNAs (circRNAs) are a recently re-discovered type of RNA generated by coupling the 5' and 3' ends in a non-canonical process known as back-splicing. This circular structure lacks a poly(A) tail, making most of them resistant to the exonuclease RNase R and, therefore, making them robustly stable molecules compared to lineal mRNA. While thousands of circRNAs have been described thanks to the technological burst of deep sequencing, only the function of a fraction has been elucidated.
Recent investigations have unveiled the role of circRNAs as important players in NCLSC, positioning them as valuable biomarkers for early detection and promising candidates for seeking therapeutic and prevention strategies.
This review analyzes the current state of circRNA research, starting from their biology to their different functions and implications in NSCLC, with a special focus on their not yet fully exploited potential as liquid biopsy biomarkers. We also review the most recently discovered circRNAs, both in solid and liquid specimens.
In addition, we provide a practical and complete guide on the current methodology available for their study, stressing the current limitations that may be preventing their implementation in the clinical setting.
Although circRNAs have been acknowledged for many years as abnormally spliced “scrambled” transcripts, only recently have they been re-defined as biologically active molecules with a significant role in human homeostasis, having a tissue-specific expression profile during the different stages of development.
CircRNAs are originated by an alternative process called “back-splicing”, where the 5' splice donor can stick to the 3' splice acceptor of an upstream exon. This process results in forming a circular structure that can include one or different exonic/intronic regions, depending on the specific mechanism that was inferred during this non-canonical process.
They have arisen as key post-transcriptional regulators throughout different functions [Figure 1], with micro-RNA (miRNA) sponging being the most studied. During this process, the circRNA binds to the argonaute-miRNA complex, and either via miRNA degradation or inhibition of the miRNA-mRNA interaction, it triggers further mRNA expression.
Figure 1. Biosynthesis and molecular functions of circRNAs. CircRNAs are generated by three different mechanisms of back-splicing (via lariat formation, intron pairing or RNA binding proteins). Resultant circRNAs can be formed by only exonic regions (EcircRNAs), intronic regions (IcircRNAs) or both (EIcircRNAs). circRNAs are exported into the cytoplasm in a size-mediated manner by URH49 and UAP56. Once in the cytoplasm, circRNAs will perform their functions including miRNA and protein sponging, protein scaffolding, or even translate into small functional peptides. CircRNAs will be released into the blood stream inside exosomes mediating cellular communication. Most cellular types, including tumor cells, will secrete circRNA-containing EVs. Platelets can modify its content when in contact with the tumor, including their circRNA expression profile.
Recent studies have also revealed that circRNAs could associate with ribosomes and be translated into functional short peptides, in a cap-independent manner. Alternatively, they can also associate with proteins acting as scaffolding for enzymatic reactions. The process of circRNA synthesis generates an imbalance of the canonical splicing; hence, the back-splicing process itself stands as a direct regulator of the circRNA precursor gene at the transcriptional level.
Different back-splicing mechanisms have been reported in the nucleus, including RNA binding protein (RBP)-mediated circularization, circRNA synthesis by intron pairing, or circularization by intron-lariat formation [Figure 1]. The first mechanism is normally executed by associating two adjacent exons and skipping the intronic region during an RBP-assisted circularization process, resulting in an exonic-circRNA (EcircRNA). Numerous RBPs have been described to regulate this mechanism, such is the case of the adenosine deaminase RNA specific-1 protein (ADAR1), NF90/NF110 immune factors, muscleblind transcription factor (MBL), heterogeneous nuclear ribonucleoprotein L, FUS protein, Quaking binding protein (QKI), RNA helicase DHX9, and the RNA-binding motif protein 20.
Exon-intron circRNAs are the result of 2 or more exons circularized along with their corresponding introns via intron-lariat formation. Intron pairing back-splicing is usually the common process in conserved RNAs with high frequency of Alu repeats in flanking sequences. These Alu elements complement each other, promoting the hairpin formation and further back-splicing, creating mono-EcircRNAs as a result. Intronic circRNAs are another type of such a class; however, the mechanism of generation of these molecules remains yet unclear.
After synthesis in the nucleus, circRNAs are exported into the cytoplasm. Recent studies have shown the active role of the UAP56/URH49 helicases in this size-mediated process. UAP55 is required to transfer molecules longer than 1300 nucleotides, while URH49 intervenes only in short transcript exporting. Once in the cytoplasm, circRNAs accumulate and exert their function by regulating transcription, normally via sponging targeted miRNAs.
How circRNA gets degraded still remains unclear; however, recent investigation has shed light on this conundrum, unveiling some intriguing mechanisms that underpin circRNA decay. Hansen et al. describe an Ago2-miR-671-mediated degradation of the circRNA CDR1as (aka ciRS-7). In another study by
A different mechanism was described by Fischer et al. revealing an alternative structure-mediated circRNA regulation process that selectively degrades circRNAs based on 3'-UTR structure complexity via the UPF1/G3BP1 protein complex.
The implication of circRNAs in cancer metabolism has been studied in recent years. Their contribution to mutant glycolysis (via transporter, enzyme, and/or transcription factor regulation), lipogenesis and lipolysis, glutaminolysis, and oxidative respiration has been widely demonstrated.
CircRNAs are becoming a new area of interest within cancer research, including NSCLC, where several authors are contributing by investigating the effect that dysregulated circRNA expression can have on the different cancer stages. Although their implication in NSCLC has not been as intensively investigated as other types of non-coding RNAs, circRNAs have been shown to have a significant role in tumorigenesis, tumor development, proliferation, migration, invasion, and sensitivity to NSCLC therapy. In light of these aforementioned findings, recent publications highlight the potential of these circular transcripts as plausible biomarkers to assess disease status.
The number of studies on circRNA profiling in NSCLC patients has exploded exponentially in the last few years [Table 1].
List of the most relevant recently discovered circRNAs associated with lung cancer
|circRNA||Gene||CircBase ID||Source||Regulation||Target||Downstream pathway|
|ciRS-7||CDR1||-||NSCLC tissues and cell lines||Upregulated||-||-|
|circABCC4||ABCC4||hsa_circ_0030586||LUAC tissues and cell lines||Upregulated||hsa-miR‐3186‐3p||TNRC6B axis|
|circCDR1||CDR1||hsa_circ_0001946||LUAC tissues and cell lines||Upregulated||hsa-miR-135a-5p||SIRT1/Wnt/β-catenin|
|circATXN7||ATXN7||hsa_circ_0007761||LC tissues and cell lines||Upregulated||-||-|
|circATAD3B||ATAD3B||hsa_circ_0000003||NSCLC tissues and cell lines||Upregulated||hsa-miR-338-3p||IRS2|
|circP2RX1||P2RX1||hsa_circ_0000735||NSCLC tissues and cell lines||Upregulated||hsa-miR-1179, miR-1182||-|
|circC16orf62||C16orf62||hsa_circ_0003645||NSCLC tissues and cell lines||Upregulated||hsa-miR-1179||TMEM14A|
|circPDZD8||PDZD8||hsa_circ_0020123||NSCLC tissues and cell lines||Upregulated||hsa-miR-488e3p||ADAM9|
|circTUBA1C||TUBA1C||hsa_circ_0026134||NSCLC tissues and cell lines||Upregulated||hsa-miR-1256, miR-12||TCTN1 and GAGE1|
|circFOXM1||FOXM1||hsa_circ_0025033)||NSCLC tissues and cell lines||Upregulated||hsa-miR-1304-5p||PPDPF and MACC1|
|circMTO1||MTO1||hsa_circ_0007874||LUAD tissues and cell lines||Downregulated||hsa-miR-17||QKI-5|
|circPRMT5||PRMT5||hsa_circ_0031250||NSCLC tissues and cell lines||Upregulated||hsa-miR-377/382/498||EZH2|
|circRAD23B||RAD23B||hsa_circ_0087855||NSCLC tissues and cell lines||Upregulated||hsa-miR-593e3p, hsa-miR-653e5p||CCND2 and TIAM1|
|circZKSCAN1||ZKSCAN1||hsa_circ_0001727||NSCLC tissues and cell lines||Upregulated||hsa-miR-330-5p||FAM83A (MAP signaling)|
|circCRIM1||CRIM1||hsa_circ_0002346||LUAC cell lines||Downregulated||hsa-miR‐182/miR‐93||-|
|circHIPK3||HIPK3||hsa_circ_0000284||A549, H838 cell lines||Upregulated||hsa-miR-124-3p, miR-149||STAT3-PRKAA/AMPKα|
|circPIP5K1A||PIP5K1A||hsa_circ_0014130||NSCLC cell lines||Upregulated||hsa-miR‐600||HIF-1α|
|circPRKCI||PRKCI||hsa_circ_0067934||NSCLC cell lines||Upregulated||hsa-miR-545, hsa-miR-589||E2F7|
|circPTPRA||PTPRA||hsa_circRNA_0102984||NSCLC tissues and cell lines||Downregulated||hsa-miR-96-5p||RASSF8/E-cadherin|
|circPVT1||PVT1||Hsa_circ_0001821||NSCLC tissues and cell lines||Upregulated||hsa-miR-497||-|
|circTP63||TP63||hsa_circ_0068515||LUSC tissues and cell lines||Upregulated||hsa-miR-873-3p||FOXM1/CENPA-CENPB|
|circVANGL1||VANGL1||-||NSCLC tissues and cell lines||Upregulated||hsa-miR-195||Bcl-2|
|circZFR||ZFR||hsa_circ_0001649||NSCLC tissues and cell lines||Upregulated||hsa-miR-101-3p||CUL4B|
|circMras||MRAS||hsa_circ_0067512||LUAC samples and NSCLC cell lines||Downregulated||hsa-miR‐567||PTPRG|
|F-circSR||SLC34A2-ROS1||-||HCC78 cell line||Upregulated||-||ROS|
|circCDK6||CDK6||hsa_circ_000984||NSCLC tissues and cell lines||Upregulated||-||Wnt/β-catenin pathway|
|circZNF720||ZNF720||hsa_circ_0007059||LC tissues and cell lines||Downregulated||hsa-miR-378||Wnt/β-catenin and ERK1/2|
|circRNF121||RNF121||hsa_circ_0023404||NSCLC tissues and cell lines||Upregulated||hsa-miR-217||ZEB1|
|circTADA2A||TADA2A||hsa_circ_0043278||NSCLC tissues and cell lines||Upregulated||hsa-miR-520f||ROCK1, CDKN1B and AKT3|
|circLIFR||LIFR||hsa_circ_0072309||NSCLC tissues and cell lines||Downregulated||hsa-miR-580-3p||-|
|circITCH||ITCH||N.A.||LC tissues and cell lines||Downregulated||hsa-miR-7 and hsa-miR-214||(PI3K)/AKT|
|circSMARCA5||SMARCA5||hsa_circ_0001445||NSCLC tissues and cell lines||Downregulated||hsa-miR-19b-3p||HOXA9|
|circRAD23B||RAD23B||hsa_circ_0087862||NSCLC tissues and cell lines||Upregulated||hsa-miR-1253||RAB3D|
|circPIP5K1A||PIP5K1A||hsa_circ_0014130||NSCLC tissues and cell lines||Upregulated||hsa-miR-142-5p, hsa-miR-136-5p||IGF-1 and BCL2|
|circABCB10||ABCB10||hsa_circ_0008717||NSCLC tissues and cell lines||Upregulated||-||KISS1|
|circIGF1R||IGF1R||hsa_circ_0005035||NSCLC tissues and cell lines||Downregulated||hsa-miR-1270||VANGL2|
|circSOX4||SOX4||N.A.||LUAD tissues and cell lines||Upregulated||hsa-miR‑1270||PLAGL2 (WNT signaling)|
|circACACA||ACACA||hsa_circ_0043256||NSCLC tissues and cell lines||Upregulated||hsa-miR-1183||PI3K/PKB pathway|
|circBIRC6||BIRC6||hsa_circ_0003288||NSCLC tissues and cell lines||Upregulated||hsa-miR-145||FSCN1 and S6K1|
|circCCDC66||CCDEC66||N.A.||NSCLC cell lines||Upregulated||hsa-miR-33a-5p||KPNA4/STAT3|
|circGFRA1||GFRA1||hsa_circ_0005239||NSCLC tissues and cell lines||Upregulated||hsa-miR-188-3p||PI3K/AKT|
|circLARP4||LARP4||N.A.||NSCLC tissues and cell lines||Downregulation||-||SMAD7|
|circTCONS||TCONS||hsa_circ_0000326||NSCLC tissues and cell lines||Upregulated||hsa-miR-338-3p||RAB14|
|circDHCR24||DHCR24||hsa_circ_0012673||LC tissues and cells||Upregulated||hsa-miR-320a||LIMK18521|
|circMACF1||MACF1||hsa_circ_0011780||NSCLC tissues and cells||Downregulated||hsa-miR-544a||FBXW7|
|circPANX2||PANX2||hsa_circ_0012515||NSCLC tissues and cells||Upregulated||hsa-miR-98-5p, hsa-miR-615-5p, hsa-let-7a-5p, hsa-let-7b-5p and hsa-let-7c-5p||-|
|circMET||MET||hsa_circ_0082003||NSCLC tissues and cells||Upregulated||miR-145-5p||CXCL3|
ciRS-7 was the first and best characterized circRNA in cancer and served as a foundation stone for current research. Its role in carcinogenesis was first described in hepatocellular carcinoma, following breast and cervical cancer, acting as a competing endogenous RNA for miR-7. A recent study has introduced ciRS-7 as an important player in lung cancer; its expression seems to correlate with tumor size and both lymph and tumor node metastasis stages.
A study by Wang et al. recently demonstrated the involvement of circSOX4 in lung adenocarcinoma by activating the WNT signaling pathway via sponging miR-1270 and following upregulation of PLAL2. CircSOX4 was found overexpressed in all managed lung adenocarcinoma tissue samples, and further validated across different cell-based preclinical experiments.
Circular RNA HIPK3 (circHIPK3) is yet another extensively studied circRNA critical in cell proliferation of different types of cancer. Its specific role in NSCLC has been recently discovered by Xie et al. demonstrating impaired cell proliferation, migration, invasion and autophagy induction via the miR124-3p-STAT3-PRKAA/AMPKa axis upon silencing of the cited circular transcript. Authors also demonstrated that overexpression of circHIPK3 correlates to poor survival, especially in advanced stages.
Another well studied circRNA, circSMARCA5, plays a significant role in NSCLC via the miR-19b-3p/HOXA9 axis, setting the grounds for exploring underlying therapeutic targets. On a similar note, a circular RNA from FGFR3 was reported in NSCLC, promoting cell invasion and proliferation of tumors by sequestering miR-22-3p, thus promoting galectin‐1, p‐AKT, and p‐ERK1/2 expression, and activating downstream pathways.
The oncogenic circ-FOXM1 was first discovered overexpressed in pancreatic tissues upregulating the pancreatic progenitor cell differentiation and proliferation factor (PPDPF) and metastasis-associated in colon cancer 1 (MACC1) proteins via miR-1304-5p sponging. More recently, the same circ-FOXM1/miR-1304-5p/PPDPF/MACC1 axis was found decisive for NSCLC development and progression.
Chromosomal translocations are cancer-associated events that may strike frequently in some genes, like ROS or ALK, leading to activation of downstream signaling pathways upon sustained expression. These events can also generate oncogenic circRNAs, as has been reported with the solute carrier family 34 member 2 (SLC34A2) and ROS proto-oncogene 1 (ROS1), producing two circRNAs (F-circSR1 and F-circSR2) both promoting cell migration in NSCLC.
Precursor mRNA of driver mutations, such as MET, can also lead to the generation of circRNAs. CircMET was first described in hepatocellular carcinoma driving immunosuppression and anti-programmed cell death 1 (PD-1) therapy resistance via the miR-30-5p/snail/DPP4 axis. Its role in NSCLC was recently discovered promoting tumor proliferation via the miR-145-5p/CXCL3 axis.
Although a circRNA from epidermal growth factor receptor (EGFR) has been reported in mouse ovaries during postnatal development with a marked expression profile, the implication of this circRNA in lung cancer has not been studied yet.
There have been no circRNAs derived from the KRAS gene reported either; however, numerous circRNAs have been portrayed as key intermediaries of the classical pathways and may serve as a readout of these foremost altered genes.
Although several studies have unveiled the potential role of circRNAs in lung cancer development and progression, not much has been clarified regarding their contribution to therapeutic resistance, and only a few published studies focus on their involvement in this area [Table 2]. CircRNAs can be classified as promoters, when their high expression enhances resistance to cancer therapy; or suppressors, when their expression limits the progression of the disease during treatment, thus acting as inhibitors of resistance.
List of circRNAs involved in NSCLC treatment resistance
|circSEMA5A||SEMA5A||hsa_circ_0071799||NSCLC cells||Upregulated||Chemotherapy||Taxol||hsa-miR-141-5p; also, hsa-miR-1228-5p, hsa-miR-194-3p, hsa-miR-512-5p, hsa-miR-4-5p||-||Xu et al., 2018|
|circFLNA||FLNA||hsa_circ_0091931||NSCLC cells||Downregulated||Chemotherapy||Taxol||hsa-miR-34c-5p; also, hsa-miR-105-3p, hsa-miR-1268b, hsa-miR-1226-5p, hsa-miR-1180||-||Xu et al., 2018|
|circMTDH.4||SNORD115||-||NSCLC tissue and cell lines||Upregulated||Chemotherapy||5‐FU, cisplatin||hsa-miR-630||AEG‐1||Li et al., 2020|
|circESRP1||ESRP1||hsa_circ_0084927||Lung cancer cells||Downregulated||Chemotherapy||Generic chemotherapy||hsa-miR-93-5p||TGF-β pathway||Huang et al., 2020|
|circARFGEF2||ARFGEF2||hsa_circ_0003998||LUAC cells||Upregulated||Chemotherapy||Docetaxel||hsa-miR-326||-||Yu, 2019|
|circCDR1||CDR1||hsa_circ_0001946||A549 cell line||Downregulated||Chemotherapy||Cisplatin||hsa-miR-7-5p, hsa-miR-671-5p, hsa-miR-1270, hsa-miR-3156-5p||NER signaling||Huang et al., 2019|
|circPGC||PGC||hsa_circ_0076305||NSCLC tissues and cell lines||Upregulated||Chemotherapy||DDP||hsa-miR-296-5p||STAT3||Dong et al., 2019|
|circAKT3||AKT3||hsa_circ_0017252||Lung cancer tissues and cell lines||Upregulated||Chemotherapy||DDP, cisplatin||hsa-miR-516b-5p||STAT3||Xu et al., 2020|
|circPVT1||PVT1||hsa_circ_0001821||LUAC tissues and cell lines||Upregulated||Chemotherapy||Cisplatin, pemetrexed||hsa-miR-145-5p||ABCC1||Zheng et al., 2020|
|circCDK14||CDK14||hsa_circ_0004015||NSCLC cells||Upregulated||Tyrosine Kinase Inhibitors (TKIs)||Gefitinib||hsa-miR-1183||PDPK1 gene||Zhou et al., 2019|
|circKRT17||KRT17||hsa_circ_0043632||AZD9291-resistant NSCLCcell lines||Upregulated||Tyrosine Kinase Inhibitors (TKIs)||Osimertinib||hsa-miR-6861-3p, hsa-miR-492, hsa-miR-4743-5p, hsa-miR-6829-3p, hsa-miR-6778-3p||-||Chen et al., 2019|
|circFXYD3||FXYD3||hsa_circ_0050581||AZD9291-resistant NSCLCcell lines||Downregulated||Tyrosine Kinase Inhibitors (TKIs)||Osimertinib||hsa-miR-6722-5p, hsa-miR-4641, hsa-miR-4707-3p, hsa-miR-4258, hsa-miR-652-3p||-||Chen et al., 2019|
|circFGFR1||FGFR1||hsa_circ_0084003||NSCLC tissues and cells||Upregulated||Immunotherapy||Anti-PD-1 therapy||hsa-miR-381-3p||PD-1||Zhang et al., 2019|
Astrocyte elevated gene-1 (AEG-1) is a key player in development, progression, and metastasis of lung cancer by regulating the Wnt/β-catenin pathway. In a recent publication, Li et al. showed that circMTDH.4 regulates AEG-1 expression by sponging miR-630, leading to chemo- and radio-resistance in NSCLC cells. Sensitivity was restored via the knockdown of the cited circRNA or over expression of its target, miR-630.
Two different works have recently been published describing circRNAs that regulate the expression of STAT3. Dong et al. reported that upregulation of hsa_circ_0076305 confers DDP-resistance to NSCLC cells via sponging miR-296-5p, positively modulating STAT3. Xu et al. introduced the role of circAKT3 inhibiting cisplatin sensitivity by regulating mir-516b-5p/STAT3 axis.
Other important circRNAs described to be involved in chemotherapy resistance are hsa_circ_0071799 via miR-141 (taxol resistance), hsa_circ 0091931 via miR-34c-5p, hsa_circ_0003998 via miR-326, hsa_circ_0001946 via miR-7-5p, miR-671-5p, miR-1270 and miR-3156-5p (NER signaling, cisplatin resistance), circPVT1 via miR-145-5p (ABCC1, cisplatin, and pemetrexed resistance), circNFIX via miR-132 (TMZ-resistant), and cESRP1. Huang et al. recently discovered a suppressor circRNA that, when downregulated, allows major expression of its target miR-93-5p. This process leads to the upregulation of downstream targets, such as Smad7/p21(CDKN1A), enhancing the transforming growth factor-β (TGF-β) pathway. Furthermore, cESRP1 overexpression boosts cisplatin sensitivity by repressing miR-93-5p and TGF-β pathway in SCLC. Related to this pathway, PDPK1, intermediary of the PI3K/AKT/mTOR pathway, has been discovered to be regulated by the hsa_circ_0004015-miR-1183 axis. Overexpression of this circRNA can induce gefitinib resistance in NSCLC cells by sponging the abovementioned miRNA.
Other authors have centered their investigation on the differential expression of circRNAs that confer resistance to this and other tyrosine kinase inhibitor-based therapies. Fu et al. found hsa_circRNA_012515 increased in gefitinib-resistant NSCLC cell lines. Further investigation in patient tissue indicated that high expression correlated with lower OS and shorter progression free survival. Chen et al. found 10 differentially expressed circRNAs in different osimertinib-resistant lung cancer cell lines. Five of them were further validated and proved to correlate with resistance status (hsa_circ_0043632, hsa_circ_0048856, hsa_circ_0043634, hsa_circ_0050581, and hsa_circ_0023302). The authors made use of specific software to predict possible targeted miRNAs; however, the axis or mechanism of action has not yet been elucidated.
CircRNAs seem to also have a role mediating response to immunotherapy. CircFGFR1 has been described by Zhang et al. to promote progression and anti-PD-1 resistance. By sponging miR-381-3p in NSCLC cells, C-X-C motif chemokine receptor 4 would result upregulated, leading to progression and resistance to therapy.
Non-coding RNA-enriched exosomes are strategic players in different cancer stages, especially regarding malignant tumor metastasis. The assessment of circRNA expression by RNAseq analysis in extracellular vesicles was first reported by Li et al., finding circRNAs enriched at least 2-fold in exosomes compared to producer cells. Although some authors defend the theory that exosomal circRNA enrichment may be a mechanism of cellular circRNA clearance, few investigators have shown that these circRNA are directly involved in cellular communication, henceforth, acting as direct readouts of several human malignancies, including NSCLC.
As a result, circRNAs stand as important liquid biopsy-derived biomarkers, holding potential for NSCLC diagnosis and prediction of treatment response.
In a recent study, Chen et al. performed high throughput sequence of plasma-EV RNA cargo of lung adenocarcinoma patients, finding 182 circRNA dysregulated when compared to cancer-free donors, including 105 up-regulated and 78 downregulated. Four upregulated circRNAs were successfully validated by qRT-PCR (hsa_circ_0001492, hsa_circ_0001346, hsa_circ_0000690, and hsa_circ_0001439). Although authors elucidated the specific circRNA-miRNA-mRNA interaction, not much information about their biological impact was provided.
Fei et al. also presented in a recent study a novel circRNA, hsa_circRNA_005661, that could be found enriched in plasma EVs from lung adenocarcinoma patients with lymph node metastasis, presenting it as a biomarker of such stage.
Not only plasma-EVs, but serum and whole plasma can serve as a good source of circRNAs [Table 3].
List of the most relevant liquid biopsy-based circRNAs associated with NSCLC
|circERBB2IP||ERBB2IP||hsa_circ_0001492||LUAD plasma exosomes||Upregulated||hsa-miR-130b-5p, hsa-miR-5195-3p, hsa-miR-4464, hsa-miR1236-3p, hsa-miR-106a-3p||Chen et al., 2019|
|circRNF13||RNF13||hsa_circ_0001346||LUAD plasma exosomes||Upregulated||hsa-miR-34B-5P, ha-miR-654-3p, hsa-miR-5683, hsa-miR-4452, hsa-miR-4662b|
|circITGAL||ITGAL||hsa_circ_0000690||LUAD plasma exosomes||Upregulated||hsa-miR-7161-3p, hsa-miR-9-5p, hsa-miR-6843-3p, hsa-miR-4502, miR-372-5p|
|circSCLT1||SCLT1||hsa_circ_0001439||LUAD plasma exosomes||Upregulated||hsa-miR-3671, hsa-miR-452-5p, hsa-miR-892c-3p, hsa-miR-223-3p, hsa-miR-4676-3p|
|circCD226||CD226||hsa_circ_0047921||NSCLC serum exosomes||Downregulated||hsa-miR-let-7g||Xian et al., 2020|
|circATXN7||ATXN7||hsa_circ_0007761||NSCLC serum exosomes||Upregulated||-|
|circRALB||RALB||hsa_circ_0056285||NSCLC serum exosomes||Downregulated||-|
|circNPHP4||NPHP4||hsa_circ_0005661||LUAD plasma exosomes||Upregulated||-||He et al., 2020|
|circFARSA||FARSA||hsa_circ_0000896||NSCLC plasma||Upregulated||hsa-miR-330‐5p, hsa-miR-326, hsa-miR-1270||Hang et al., 2018|
|circYWHAZ||YWHAZ||hsa_circ_0005962||LUAD plasma||Upregulated||hsa-miR-369-5p, hsa-miR-626, hsa-miR-326, hsa-miR-330-5p, hsa-miR-1265, and hsa-miR-622||Liu et al., 2019|
|F-circEA||EMLK4-ALK||Lung cancer tissues, plasma and cells||Upregulated||-||Tan et al., 2018|
|circZNF91||ZNF91||hsa_circ_0109320||NSCLC plasma||Downregulated||-||Liu et al., 2019|
Hang et al. explored the use of circRNA found in total plasma of NSCLC patients in order to find some candidates that could correlate to malignancy status. Not only did they find a notorious circRNA coming from the FARSA gene, circFARSA, but they also found a set of differentially expressed circRNAs (hsa_circ_0001495, hsa_circ_0000566, hsa_circ_0001238, hsa_circ_0007037, circ_c1orf116, hsa_circ_0001083, hsa_circ_0006451, hsa_circ_0004458, and hsa_circ_0000847) based on which they were able to discriminate NSCLC patients from healthy individuals. Additionally, they performed in silico investigation of possible targets of circFARSA. Consequently, miR‐330‐5p and miR‐326 emerged as direct target candidates. Both miR‐330‐5p and miR‐326 may interact directly with fatty acid synthase, which has been described as a notorious oncogene in various types of cancer.
Also, directly from plasma Liu et al. found a two circRNA-based signature that could potentially be used to classify lung adenocarcinoma patients. Hsa_circ_0005962 was found upregulated while hsa_circ_0086414 was barely expressed. In addition, they observed that overexpression of hsa_circ_0005962 was correlated to mutant EGFR expression. In vitro experiments suggested that this circRNA could be involved in cancer proliferation.
Moreover, a fusion-gene circRNA has been studied in liquid biopsies. Tan et al. started their line of research exploring the existence of a circRNA derived from the fusion gene EML4-ALK (F-circEA) in the NCI-H2228 cell line. After verification, they observed that overexpression of this circRNA could trigger cell migration and invasion, contributing to tumor development. They validated the existence of this circRNA in plasma of NSCLC patients with the EML4-ALK translocation, suggesting that screening of plasma F-circEA in this type of patients could be a valuable approach to monitor the EML4-ALK translocation, and provide further guidance on targeted therapy.
Alhasan et al. showed for the first time that platelets are enriched in circRNAs when compared to nucleated tissues, and also, that their content is superior to that on mRNA. Preußer et al. demonstrated that platelets are not only a good source of circRNA, but also platelet-derived extracellular vesicles are enriched in these biomolecules, representing yet another source of potential biomarkers that may be involved in different signaling pathways.
Platelets change their RNA profile when in contact with the tumor, enabling them to contribute to the systemic and local responses to tumor growth. As a result, TEP-RNA can be used as a potential biomarker for cancer diagnostics. Although TEPs could also possibly be enriched in circRNAs, and hold potential value for NSCLC diagnosis, nothing yet has been investigated.
Little has been elucidated regarding NSCLC treatment resistance based on liquid biopsy-based circRNAs. A study of Yu-Tao et al. comparing gefitinib responder and non-responder NSCLC patients found that higher expression of hsa_circ_0109320 in plasma correlated with longer progression free survival in gefitinib-treated NSCLC patients; however, no information on the potentially affected signaling pathway has been provided.
Although there are different methods currently available for the study of circRNAs [Table 4], no consensus has been reached on which protocol to follow for either tissue or liquid biopsy-based circRNA expression analysis.
Current methods for circRNA study
|Method||Application||Total RNA input||Advantages||Disadvantages||Ref.|
|RNAseq||circRNA discovery||Normally ≥ 1µg is needed; however, 1 ng has been used in liquid biopsies showing good results||- Allows whole transcriptome sequence analysis, including rare and low abundant circRNAs||- Time consuming|
- It involves high quality RNA
- Requires expertise for library preparation, sequencing, and Bioinformatics, for data normalization and analysis
|Cheng et al. |
|Microarrays||circRNA discovery||2 µg||- Highly sensitive and specific for circRNA profiling|
- Easy technology, commercial arrays ready to use
|- Although it may be possible to work with less RNA, recommended input remains rather high |
- circRNA discovery gets restricted to the amount of circRNA included in the panel
- Requires Bioinformatics expertise for data normalization and analysis
|Valladares-Ayerbes et al. |
|nCounter||circRNA discovery and quantification||85 ng||- Allows multiplexed analysis of up to 800 circRNA targets|
- Does not require amplification (if enough RNA input)
- Works well with low quality RNA samples
- Very little hands-on time, with results ready within 24 h
- User-friendly data analysis software reducing the need for Bioinformatics support
|- circRNA discovery gets restricted to the amount of circRNA included in the panel|
- Technology is costly, and constrained by one company
|Zhang et al.|
Dahl et al., 2018
|qRT-PCR||circRNA quantification||250 ng (3 replicas, 1 gene)||- Well-established technology|
|- Does not allow analysis of a large number of genes|
- Susceptible to template switching and rolling circle amplification bias
|SplintQuant||circRNA quantification||2 nM||- Sensitive and specific approach|
- Highly reproducibility rates
- Eludes the template switching and rolling circle amplification bias found with qRT-PCR
|- Novel protocol|
- No tested in liquid biopsies
|RT-PCR + end-point PCR + Sanger Sequencing||circRNA identification and validation||100 ng||- Well-established technology|
- Gold standard for circRNA validation
|- It may require time to test divergent primers |
- Optimization is required for each pair of primers
- Does not allow multiplexing
|Panda et al., 2018|
|Northern Blot||circRNA identification and validation||1-50 µg||- Specific circRNA detection|
- Allows isoform studies
- Solves those problems attained to qRT-PCR such as template switching or rolling amplification biases
|- Low sensitivity|
- It requires a big amount of input which makes it incompatible with most liquid biopsy downstream processes
|Schneider et al.|
The range of possibilities when selecting a bio-source is rather ample. Whilst plasma or serum can provide a higher yield of total RNA, tumor released EVs stand out by providing a more accurate picture of lung cancer at the transcriptional level. Procedures such as ultracentrifugation, ultrafiltration, or size-exclusion chromatography are examples of the range of methods accepted by the International Society for Extracellular Vesicles for the study and purification of these biomarkers.
In the case of EV circRNA investigation, concentration levels may sometimes be the limitation factor that restricts further downstream processes. Therefore, in this case, EV isolation methods should be focused on achieving a higher EV-derived circRNA yield rather than acquiring extra pure EV samples, which are mainly attained by compromising RNA concentration.
Full-length RNA sequencing emerged as the first method proving beneficial for de novo circRNA identification. By processing total RNA, unmatched reads are selected and assembled by remapping to custom databases containing all human intragenic exon-exon junctions. This protocol first introduced by Salzman et al. has since been improved with new procedures including ribosomal RNA depletion and non-polyadenylated RNA exonuclease-mediated enrichment (RNase R). Further validation of novel identified targets requires use of specific bioinformatic tools that allow junction site identification from deep-sequencing data. The rise of newly developed bioinformatic methods have boosted the discovery and analysis of thousands of circRNA [Table 5]. However, sensitivity may be a limitation when using next-generation sequencing for circRNA discovery since library preparation is frequently associated with the loss of low-expressed molecules. Other methodologies such as microarrays or the nCounter platform have emerged to overcome this issue; however, circRNA discovery in these cases gets restricted to the candidates included either in the array or the gene panel.
Characteristics of online accessible circRNA resources
|circBase||Database||One of the main resources with updated information discovered circRNAs. Provides a useful blat tool for circRNA alignment against the human genome||http://www.circbase.org||Garcia-Contreras et al., 2014|
|circBank||Database||Along with circBase, is one of the most important resources available including a database with most discovered circRNAs along with usegul information||http://www.circbank.cn||Liu et al., 2019|
|circInteratome||Database||Complete database with different features that allow binding site prediction and knock-down experiment designing||https://circinteractome.nia.nih.gov||Dudekula et al., 2016|
|CIRCpedia||Database||Database for the identification of tissue specific circRNAs||http://www.picb.ac.cn/rnomics/circpedia||Dong et al., 2018|
|circRNADb||Database||Searching tool for the identification of EcircRNAs.||http://reprod.njmu.edu.cn/circrnadb||Chen et al., 2016|
|circRNABase||Database||Allows circRNA network prediction||http://www.hzrna.com/circrn-shujuku/circrnabase||circRNABase, 2016|
|circR2Disease||Database||Serves for the identification of circRNA-miRNA interactions associated to different diseases||http://bioinfo.snnu.edu.cn/CircR2Disease/||Fan et al., 2018|
|starBase||Database||Serves for the identification of circRNA-miRNA interactions||http://starbase.sysu.edu.cn/||Li et al., 2014|
|circAtlas||Database||Databased with annotation of circRNAs and with tools that allow identification of circRNA-miRNA interactions||http://circatlas.bols.ac.cn/||Wu et al., 2020|
|circFunBase||Database||A database for functional circRNAs||http://bis.zju.edu.cn/CircFunBase||Meng et al., 2019|
|circad||Database||Serves for the identification of circRNA-miRNA interactions associated to different diseases||http://clingen.igib.res.in/circad/||Rophina et al., 2020|
|circView||Visualization tool||Identification circRNA associated miRNAs and RBPs||http://gb.whu.edu.cn/CircView/||Feng et al., 2018|
|CSCD||Bioinformatic tool||Identification circRNA associated miRNAs and RBPs, with a focus on circRNA with transcription potential||http://gb.whu.edu.cn/CSCD/||Xia et al., 2018|
|cirRNAPL||Bionformatic tool||Identification of circRNA based on extreme learning machine||http://server.malab.cn/CirRNAPL/index.html||Niu et al., 2020|
|nSolver||Program-Bioinformatic tool||Analysis of RNA expression data generated by the nCounter platform||www.nanostring.com||-|
|circ2Traits||Pipeline||Serves for the identification of circRNA-miRNA interactions associated to different diseases||http://gyanxetbeta.com/circdb/||Ghosal et al., 2013|
|circMeta||Pipeline||Genomic feature annotation and differential expression analysis of circular RNAs||https://github.com/lichenlab/circMeta||Chen et al., 2020|
|circRNAwrap||Pipeline||Pipeline designed for circRNA identification, transcript prediction, and abundance estimation||https://github.com/liaoscience/circRNAwrap||Li et al., 2019|
|SpliceV||Pipeline||Analysis and publication quality|
printing of linear and circular RNA splicing, expression and regulation
|https://github.com/flemingtonlab/SpliceV||Ungerleider et al., 2019|
|CIRCexplorer3||Pipeline||Pipeline for the direct comparison of circular and linear RNA expression||https://github.com/YangLab/CLEAR||Ma et al., 2019|
|circDeep||Pipeline||Permits circular RNA classification from other long non-coding RNA||https://github.com/UofLBioinformatics/circDeep||Chaabane et al., 2020|
|Segemehl||Pipeline||Pipeline for the identification of fusion reads ||http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_0_2_0.tar.gz||Hoffmann et al., 2014|
|MapSplice||Pipeline||Application for small segment mapping||http://www.netlab.uky.edu/p/bioinfo/MapSpliceDownload||-|
|DCC||Pipeline||Identification of circRNA from fusion reads||https://github.com/dieterichlab/DCC||Cheng et al., 2016|
|UROBORUS||Pipeline||Allows identification of EcircRNAs||https://github.com/WGLab/uroborus/||Song et al., 2016|
|NCLscan||Pipeline||Identification of non-coding transcripts||https://github.com/TreesLab/NCLscan||Chuang et al., 2016|
Data analysis tool
|Allows the prediction of circRNA-transcription factor regulatory networks||http://www.licpathway.net/TRCirc/||Tang et al., 2018|
Microarrays are useful tools for high-throughput analysis and expression studies of circRNAs where probes are designed to bind specifically to the junction site, getting immobilized, incubated, and further sequenced. Samples may normally be subject to RNase R to reduce background noise and enhance detection. This systematically expression profiling process is quite sensitive and straight forward. Current methodology developed by Arraystar includes all necessary tools in order to get detailed annotation specific to circRNA biology, such as miRNA binding sites or conservation status, to reveal all possible functional roles as miRNA sponges.
The nCounter platform allows multiplex analysis of up to 800 circRNA transcripts by direct capturing and counting of individual targets. This qualitative and quantitative process is rather simple and requires minimal hands on, providing results in less than 48 h. Although nCounter is routinely used for RNA expression assessment in both FFPE and fresh tissues, only few studies have investigated its potential when it comes to liquid biopsies. EV-DNA and EV-miRNA profiles have been examined with this platform obtaining different success rates; however, investigation with circRNA remains restricted to tumor and cultured cells, and in no case this platform has been explored for lung cancer research so far.
For circRNA validation, end-point PCR has been established as the most extended practice using divergent primers spanning the junction site and followed by further Sanger sequencing.
RNase R treatment is still a debate whether it is beneficial or not to use it in liquid biopsy samples. RNase R has been widely used for the study of circRNAs since it has the property of affecting mostly linear RNA, henceforth, enriching our samples with circRNAs. However, some circRNAs have demonstrated to be sensitive to the effect of this exonuclease. The often-long incubation periods can compromise the quality of our RNA samples. In addition, RNase treatment has been proved to not be 100% effective towards mRNA depletion which could lead to a circRNA overestimation if quantification by qPCR is the next downstream process and convergent primers are used. Xiao et al. proved that standard RNase R protocols result in up to 20% of highly expressed mRNAs being unaffected. Therefore, the correct design of divergent primers is instrumental for the study of circRNAs, regardless of whether RNase R treatment is applied to the samples or not. Authors also described that RNase R protocol could be enhanced by replacing K+ by Li+ in the reaction buffer so enzyme can digest complex structured linear transcripts; however, this is a convoluted process that, even though scientifically relevant, may not result practical in the laboratory routine.
Northern blot analysis has arisen as another common methodology for the study of circRNAs. Following standard protocols, once the RNA is transferred from the gel onto a blotting membrane, circRNAs are then hybridized with short probes normally designed spanning the junction site, hence, allowing circRNA identification. This method also allows studies on size, isoforms, sequence, and abundance of these circular transcripts. However, the usual high amounts of RNA required for this method is rather high, so investigations get restricted mostly to RNA from either tissue or cell lines.
Nowadays, different methodologies are being used for the quantification of circRNAs both in solid and liquid biopsies. qRT-PCR has been broadly established as one of the easiest and predilected mechanisms of quantification; however, different aspects may need to be taken into consideration.
Contrary to tissue, circRNAs are enriched in plasma exosomes. In this case, RNase R treatment may not be recommended due to the low overall RNA concentration that is expected in these vesicles, however, sometimes its use is necessary to validate primer specificity or due to the nature of specific experiments. In this respect, it is important to stress the need of designing divergent primers as previously cited, along with a probe spanning the junction site. Furthermore, throughout this procedure, the expression of classical reference genes, such as beta-actin or GADPH, will result altered; hence, ruling out the possibility of performing circRNA expression evaluation by using classical normalization procedures. In this case, the selection of circular RNA housekeeping genes is crucial for the correct assessment of circRNA expression.
CircRNA amplification via reverse transcription PCR (RT-PCR) often leads to extended concatemeric transcript amplification from a single priming of the reverse transcriptase. This process, triggered by the circular architecture of these molecules, is known as rolling circle amplification, and was first described by You et al. while studying circRNA expression in brain tissues. This event is not problematic if de novo circRNA discovery is intentional and direct comparison with canonical transcripts is not envisioned (in fact, it can be beneficial for the study of circRNA splice variants). However, this does not apply to transcript abundant studies, in which this mechanism can introduce biases leading to an overestimation of circRNA expression.
Conn et al. demonstrate this in a study with synthetic circRNAs, resulting in a five-fold increase of circRNAs compared to the expected expression upon RT-PCR and further qPCR amplification. This is a factor to take into consideration in the experimental design.
The same group has developed a cutting-edge tool to avoid the bias introduced by normal qRT-PCR quantification throughout their newly designed SplintQuant method. This technology is based on the inclusion of custom DNA oligonucleotides that complement target circRNAs, and making use of the PBCV-1 DNA ligase, synthesize cDNA skipping reverse transcription. The system is sensitive, specific and reproducible, allowing the identification and quantification of canonical and non-canonical RNA transcripts including gene fusions and alternative splice variants.
nCounter technology stands out as a very effective and sensitive option for circRNA quantification. Its application for the analysis and quantification of circRNAs has been systematically studied by Dahl et al. in different solid biosources (including formalin fixed paraffin-embedded specimens) for the study of B-cell malignancies, becoming the first group to use this technology for the study of circRNA expression.
Identification of circRNAs can be a straight-forward process when using microarray or nCounter data where the exploratory approach gets restricted to a specific panel of genes. However, detection of circRNA can be a much more complex in the case of deep-sequencing data analysis due to the complexity on the computational workflows. For this purpose, different pipelines and computational analysis tools have been created to facilitate this process [Table 5]. Different publicly available databases such as circBank, circBase, or circView have proved useful to simplify the study of circRNA throwing light on specific features such as miRNA binding sites, m6A modifications, mutations, or unveiling protein-coding potential [Table 5]. These databases also allow browsing and download of FASTA files based on specific searching criteria.
The recent impact of circRNAs in lung cancer research has become undeniable. Since ciRS-7 was introduced as the first circRNA ever described to play a role in hepatocellular carcinoma, many others have followed, extending to different types of cancer, henceforth, consolidating their position as active players in cancer development and progression of malignancy. Recently, publications exploring the biomarker potential of these molecules in NSCLC have remarkably increased, with an exponential growth in the last five years. Nevertheless, despite the patent progress in this field, current research is predominantly restricted to expression analysis of circRNA in tumor samples, with very little information regarding validation in liquid specimens.
EVs, including exosomes, are released by most cells in the body and can be easily isolated from plasma. Tumor EVs can mediate intercellular communication between tumor cells and tumor microenvironment; therefore, the study of these molecules via their molecular identification can offer a valuable spatiotemporal snapshot of the state of the disease. However, while several publications have widely demonstrated that EV cargo is enriched in circRNAs, not many investigators have focused on this line of research, delaying the development of novel liquid biopsy-based tools for NSCLC detection. While the potential value of liquid biopsies in the clinic has been recognized as beneficial, in the research context, liquid bio-sources can be rather challenging, including plasma circRNA investigation.
With a superior relative expression and stability in EVs than the canonical mRNA, the extent of circRNA in EVs still remains very low, frequently limiting further downstream analysis. This is unlikely to be an issue in solid tumors; while circRNA overall expression is frequently low (1%-10%), RNA concentration is rarely a limitation. Furthermore, very often the study of circRNA expression relies on enzymatic amplification - qPCR. This course fueled by the circular architecture of these molecules can sometimes lead to the not-so-well-known rolling cycle amplification events, resulting in an inaccurate yet overestimated circRNA quantification, frequently leading to untruthful and irreproducible results.
On addition to the above exposed, there is not a general consensus about other fundamental matters such as EV isolation method (if we target the study of the EV circRNA cargo), potential use of RNase R, or readout assessment, among others. As a result, standardization of protocols for the study of circRNA has become instrumental for the study and implementation of these novel biomarkers into the liquid biopsy setting.
Some technologies have arisen as incipient alternatives such as the nCounter platform or the newly developed SplintQuant. Both of them rely on very low RNA input and can overcome the deviation issues that enzymatic qPCR may create.
Additionally, platelets, especially tumor educated platelets, hold a great unexplored potential as a source of circRNAs, not only due to their higher concentration in RNA when compared to EVs, but also due to the high enrichment they present towards these circular biomolecules. To elucidate wheater platelet derived circRNA signatures could be of better, equal, or complementary value of the ones from EVs, additional investigation will be required.
Nowadays, most studies aim to exploit the biomarker potential of lung cancer circRNAs, frequently leaving aside any additional examination of their inherent biology. Further research elucidating the different molecular functions of these molecules is greatly needed in order to achieve a future circRNA-based liquid biopsy test.
The rediscovered role of circRNAs as lung cancer biomarkers has the potential to reshape the landscape of liquid biopsies. They count on most features needed to be considered a good biomarker: they can be measured in blood, including plasma, serum, and urine; they are reasonably robust and very stable due to their circular architecture; and do not require special handling protocols other than those required for the rest of RNA types. Due to the diverse implications in cancer progression and development of resistance, circRNAs could provide additional information improving diagnosis and treatment guidance by either generating new signatures, or complimenting existing ones.
Circulating tumor DNA is the most commonly explored liquid biopsy for NSCLC, counting with few tests already clinically implemented for the detection of classical mutations such as EGFR Del19 and p. L858R mutation. However, many lung cancer cases are not linked to a specific driver mutation; therefore, research on new biomarkers, including circRNAs, and further development of multi-omic signatures of tumor microenvironment could provide additional diagnostic opportunities for these patients.
However, as mentioned above, several circRNA quantification methods have limitations, and a clear protocol needs first to be established in order to develop any clinically applicable assay. In addition, clinical utility should be demonstrated by providing convincing evidence of the new biomarker performance (in comparison to currently accepted cfDNA/mRNA liquid biopsy tests), and so far, no circRNA biomarker has achieved that status, probably due to the difficulty of recruiting large patient cohorts required to prove biomarker utility.
Further studies in biomarker discovery, molecular biology, and protocol standardization are warranted in the upcoming years to achieve the implementation of these novel biomarkers in the clinical setting.
We would like to thank Stephanie Davis for language editing assistance, and Dr. Cristina Aguado-Esteban for her deep insights. Figure 1 created with BioRender.com.Authors’ contributions
Authors contributed equally to the article.Availability of data and materials
Not applicable.Financial support and sponsorship
This project has received funding from a European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement ELBA No 765492.Conflicts of interest
Both authors declared that there are no conflicts of interest.Ethical approval and consent to participate
Not applicable.Consent for publication
© The Author(s) 2021.
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Pedraz-Valdunciel C, Rosell R. Defining the landscape of circRNAs in non-small cell lung cancer and their potential as liquid biopsy biomarkers: a complete review including current methods. Extracell Vesicles Circ Nucleic Acids 2021;2:179-201. http://dx.doi.org/10.20517/evcna.2020.07
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