Circular RNAs as Disease Biomarkers: Potential and Challenges

Introduction

Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs characterized by a covalently closed loop structure formed by back-splicing of exons or introns. Lacking 5' and 3' ends, circRNAs are inherently resistant to exonucleases, making them highly stable in cells and biofluids (Identification of human genetic variants controlling circular RNA expression - PMC) (Circular RNA and Alzheimer's Disease - PubMed). They were once thought to be splicing errors, but high-throughput sequencing has revealed thousands of distinct circRNAs with tissue-specific and developmental stage-specific expression patterns (Identification of human genetic variants controlling circular RNA expression - PMC). Many circRNAs are conserved across species and often show expression profiles independent of their linear mRNA counterparts (Circular RNAs in cancer: opportunities and challenges in the field - PMC) (Identification of human genetic variants controlling circular RNA expression - PMC). These properties, combined with emerging evidence of functional roles in gene regulation and disease, have sparked interest in circRNAs as potential biomarkers.

In normal physiology, circRNAs participate in regulating gene expression at multiple levels. In disease states, aberrant circRNA expression has been observed in cancer, cardiovascular disease, neurodegenerative disorders, and other conditions (Identification of human genetic variants controlling circular RNA expression - PMC) (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). Their exceptional stability in blood and other body fluids positions circRNAs as promising non-invasive biomarkers, detectable via liquid biopsies. Moreover, circRNAs often display cell type or disease-specific expression, raising the possibility of highly specific diagnostic signatures.

This article provides a comprehensive analysis of the potential of circRNAs as disease biomarkers, addressing their biological roles and mechanisms of action, methods for their detection and quantification, disease-specific expression signatures, diagnostic accuracy relative to traditional markers, prospects for clinical translation and personalized medicine, and the influence of patient variability and external factors on circRNA expression. We also discuss current challenges - from technical standardization to biological complexity - that must be overcome to harness circRNAs in routine clinical diagnostics.

1. Biological Role and Mechanisms of circRNAs

Functions in Normal Physiology: CircRNAs are not mere transcriptional noise; they serve diverse regulatory functions. A well-documented role is acting as microRNA (miRNA) sponges - circRNAs can harbor multiple miRNA binding sites, sequestering miRNAs and preventing them from repressing their target mRNAs (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC) (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). For example, the circRNA CDR1as (ciRS-7) contains dozens of binding sites for miR-7; experiments showed that overexpressing CDR1as in vivo can absorb miR-7 and derepress miR-7 target genes (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). In zebrafish, human CDR1as overexpression impaired midbrain development due to excess sponging of miR-7, illustrating a tangible biological effect of circRNA-mediated miRNA regulation (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). CircRNAs can also modulate gene expression at the transcriptional level: certain nuclear-localized circRNAs interact with RNA polymerase II or U1 snRNP to influence the transcription of their parental genes (acting similarly to enhancers or by promoting transcription complexes) (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC).

Another key function is serving as protein scaffolds or decoys. CircRNAs can bind RNA-binding proteins (RBPs) or other protein factors, affecting their localization and activity (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). For instance, circFoxo3 has been shown to form a complex with anti-senescent proteins (like ID-1, E2F1, and HIF1α), retaining them in the cytoplasm and thereby promoting cellular senescence (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC) (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). This protein "sponge" activity of circFoxo3 was observed at high levels in aged mouse and human hearts, linking circRNA function to the aging process (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). CircRNAs can also have post-transcriptional regulatory roles by modulating splicing or mRNA stability through RBP binding. In addition, many circRNAs carry N^6-methyladenosine (m6A) modifications that can be recognized by reader proteins, influencing circRNA metabolism or function (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC).

Notably, some circRNAs even have coding potential. Although circRNAs lack a 5' cap, internal ribosome entry sites (IRES) or m6A modifications can drive cap-independent translation, allowing certain circRNAs to produce peptides (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). An example is circZNF609, which harbors an open reading frame and can be translated in muscle cells, affecting myogenesis (Circular RNAs in cancer: opportunities and challenges in the field - PMC). While the full scope of circRNA-derived peptides in vivo is still being explored, this reveals an additional layer of potential circRNA function beyond RNA-level regulation.

Roles in Disease Mechanisms: In pathological states, dysregulated circRNAs can actively contribute to disease processes. They are not only correlated with disease but in some cases causally involved. In cancer, numerous circRNAs function as oncogenes or tumor suppressors. For example, CDR1as is highly expressed in certain cancers and its knockdown can suppress tumor cell proliferation (Identification of human genetic variants controlling circular RNA expression - PMC). CDR1as likely promotes oncogenesis by sponging miR-7, a tumor-suppressive miRNA; consistent with this, silencing CDR1as in colorectal cancer and liver cancer cells led to reduced tumor growth (Identification of human genetic variants controlling circular RNA expression - PMC). Likewise, the circRNA derived from the SRY gene (circSRY) carries binding sites for miR-138; overexpressing circSRY can blunt miR-138 activity, which in turn was shown to affect gene regulation in testis development (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). These findings illustrate how circRNAs can dysregulate gene networks in disease.

In cardiovascular disease, circRNAs have been implicated in heart failure, myocardial infarction (MI), and atherosclerosis. The circRNA HRCR in mouse hearts acts as a sponge for miR-223, leading to increased expression of the anti-hypertrophic factor ARC and thus protecting against cardiac hypertrophy and heart failure (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). Conversely, circRNA CDR1as (CiRS-7) was found to aggravate myocardial infarction injury by sponging miR-7a and upregulating pro-apoptotic SP1/PARP signaling (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). Another example is circANRIL, expressed from the 9p21 locus associated with coronary artery disease; circANRIL promotes p53 activation to induce apoptosis of vascular smooth muscle and immune cells, thereby stabilizing atherosclerotic plaques (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). Individuals with genetic variants at 9p21 that reduce circANRIL formation have higher plaque cell proliferation and greater atherosclerosis risk, highlighting a direct genotype-circRNA-phenotype link (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC).

In neurodegenerative diseases, circRNAs are emerging as functional players. They are highly enriched in brain tissue (Circular RNA and Alzheimer's Disease - PubMed) and often increase in expression with age and disease progression (Circular RNA and Alzheimer's Disease - PubMed). In Alzheimer's disease (AD), a deficiency of CDR1as has been linked to pathogenic outcomes: low CDR1as allows excess miR-7 activity, which in turn downregulates UBE2A (an enzyme crucial for clearing amyloid-β peptides) (Circular RNA and Alzheimer's Disease - PubMed) (Circular RNA and Alzheimer's Disease - PubMed). This miRNA sponge imbalance (CDR1as/miR-7/UBE2A axis) can contribute to amyloid accumulation in AD brains (Circular RNA and Alzheimer's Disease - PubMed). Such mechanistic evidence indicates that circRNAs can influence the molecular pathology of diseases like AD, not merely serve as passive markers.

In summary, circRNAs partake in critical regulatory circuits (miRNA suppression, protein sequestering, gene expression modulation) and there is mounting evidence that their dysregulation can drive or exacerbate disease processes (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC) (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). These dual identities - functional agents and measurable molecules - make circRNAs especially attractive as biomarkers that might also reveal insights into disease mechanism. However, leveraging them clinically first requires robust methods to detect and quantify circRNAs reproducibly.

2. Detection and Quantification of circRNAs

Detecting circRNAs poses unique challenges due to their circular structure, which lacks the polyadenylated tail typical of mRNAs. Over the past decade, a variety of techniques have been developed or adapted for circRNA detection and quantification:

RNA Sequencing (RNA-seq): High-throughput sequencing has been instrumental in circRNA discovery. Standard mRNA-seq approaches (poly-A selection) miss most circRNAs (Best practice standards for circRNA research - PMC) (Best practice standards for circRNA research - PMC), so rRNA-depleted total RNA is used to capture non-polyadenylated transcripts (Best practice standards for circRNA research - PMC). Often, samples are treated with RNase R, an exonuclease that digests linear RNAs while sparing circular forms, to enrich circRNA content (Best practice standards for circRNA research - PMC) (Best practice standards for circRNA research - PMC). After sequencing, specialized bioinformatic algorithms (e.g., CIRCexplorer, find_circ, CIRI) identify back-splice junctions (BSJs) - the signature of circRNAs - by aligning reads that map in a non-linear order on the genome. Dozens of circRNA detection tools exist, and they can yield vastly different results in terms of sensitivity. A recent benchmark found that 16 popular algorithms detected between ~1.3k and ~58k unique circRNAs from the same dataset, highlighting large differences in sensitivity (up to ~5-fold) with modest differences in precision (Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision - PMC) (Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision - PMC). To improve reliability, it is recommended to use multiple algorithms and consider the intersection of results (Best practice standards for circRNA research - PMC), which reduces false positives (Best practice standards for circRNA research - PMC). Deep sequencing depth and longer read lengths also improve circRNA discovery, since BSJ-spanning reads are relatively rare for low-abundance circRNAs (Best practice standards for circRNA research - PMC).
RT-PCR and qPCR: Once candidate circRNAs are identified, reverse transcription PCR (RT-PCR) with divergent primers (that face outward from the BSJ sequence) is commonly used to validate circRNA presence. Divergent primers amplify the circular junction from cDNA but not the linear mRNA, confirming the circular structure. Quantitative PCR (qPCR) can then measure circRNA levels in samples. A best practice is to treat RNA with RNase R prior to RT-PCR; true circRNAs will resist degradation and be amplifiable, whereas linear transcripts will be greatly diminished (Best practice standards for circRNA research - PMC). Sanger sequencing of the PCR product across the junction is used to verify the exact BSJ sequence (Best practice standards for circRNA research - PMC). This enzyme-based validation guards against false positives, because artifacts can arise: for instance, template-switching by reverse transcriptase can create artifactual "circle-like" cDNA products (Circular RNAs in cancer: opportunities and challenges in the field - PMC). Careful experimental design (e.g. using intron-spanning primers to detect concatemer artifacts) is needed to distinguish genuine circRNAs from such technical artifacts (Circular RNAs in cancer: opportunities and challenges in the field - PMC).
Digital and Hybridization-Based Methods: Technologies like NanoString nCounter and custom circRNA microarrays allow direct hybridization-based detection without amplification. NanoString uses probes spanning the BSJ and provides digital counts of circRNA molecules (Best practice standards for circRNA research - PMC). This avoids PCR amplification bias and has been successfully used for circRNA profiling (Best practice standards for circRNA research - PMC). Additionally, northern blotting with junction-specific probes can validate circRNA size and expression (Best practice standards for circRNA research - PMC), albeit with lower sensitivity. Emerging methods also include in situ hybridization to visualize circRNA in tissues and single cells (Imaging and quantification of human and viral circular RNAs), and approaches to simultaneously capture circRNA and linear RNA expression for ratio comparisons.
Droplet Digital PCR (ddPCR): For absolute quantification, ddPCR has been applied to circRNAs, offering high sensitivity and precision. By partitioning the sample into many micro-droplets, ddPCR can quantify low-level circRNAs and provide an exact copy number without a standard curve, which is useful for clinical biomarker development where robust quantification is needed. For example, custom ddPCR assays for specific cancer-related circRNAs have been developed to test patient plasma, showing reproducible results across labs (Best practice standards for circRNA research - PMC).

Standardization Challenges: Despite these tools, moving circRNA detection into clinical practice faces hurdles. One major challenge is lack of standardization in protocols and data normalization. Different studies often report different circRNA targets and even contradictory results, partly due to varying methodologies. Key issues include:

Pre-analytical variability: The method of RNA preparation (total RNA vs poly-A selection, RNase R treatment or not) drastically affects which circRNAs are detected (Best practice standards for circRNA research - PMC) (Best practice standards for circRNA research - PMC). For example, poly(A)+ selection will miss most circRNAs (Best practice standards for circRNA research - PMC). Even RNase R enrichment can skew the representation of circRNAs and makes it hard to compare circRNA levels relative to their linear counterparts (Best practice standards for circRNA research - PMC) (Best practice standards for circRNA research - PMC). Thus, a consensus on sample prep is needed. Best-practice recommendations suggest always starting from high-quality total RNA and considering rRNA depletion to maximize circRNA capture (Best practice standards for circRNA research - PMC) (Best practice standards for circRNA research - PMC).
Analytical variability: As noted, bioinformatic pipelines have high false-positive rates if used singly. Using multiple callers and requiring a circRNA to be identified by at least two methods is one way to improve confidence (Best practice standards for circRNA research - PMC). Still, some false positives can pass these filters, and true circRNAs can be missed by all but one algorithm. Consistent annotation across studies is also lacking - circRNAs are often reported with different naming conventions or genomic coordinates, complicating cross-study comparisons.
Quantification and normalization: There is no agreed "housekeeping" circRNA for normalization in qPCR or other assays. Some studies normalize circRNA levels to total RNA mass or to linear reference genes (like GAPDH mRNA), but circRNA levels do not always correlate with mRNA levels of the host gene (Identification of human genetic variants controlling circular RNA expression - PMC) (Identification of human genetic variants controlling circular RNA expression - PMC). A potential solution is using circRNA/linear RNA ratios for the same host gene as a normalized measure, since circRNA and mRNA are produced from the same gene locus. Indeed, changes in this ratio can be biologically meaningful (as seen with circANRIL vs linear ANRIL in cardiovascular risk (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC)). However, ratio interpretation requires that both forms are measured accurately. Another approach is external spike-in controls (synthetic circRNA fragments) to calibrate measurements, but this is not yet common.
Clinical assay development: For a circRNA biomarker to be used clinically, a reliable, easy-to-use assay (likely PCR-based or kit-based) must be developed. This requires validating that the assay specifically targets the circRNA junction and works reproducibly on clinical specimens (blood, urine, tissue biopsies). Robustness tests (stability of circRNA during sample storage, freeze-thaw cycles, etc.) need to be done. CircRNAs are generally stable even in archived plasma (Identification of human genetic variants controlling circular RNA expression - PMC), but pre-analytical factors like exosome isolation or RNase contamination in blood draws need control.
Regulatory and scalability issues: To translate into a diagnostic test, circRNA detection methods must be standardized to the point of CLIA-certified clinical lab tests or FDA-approved kits. That involves multi-center trials to demonstrate that the circRNA test has consistent performance. Currently, no circRNA-based diagnostic test has full regulatory approval; the field is still at the discovery and early validation phase. The lack of reference standards or certified reference materials for circRNA is a gap - initiatives to create pooled reference samples or mimic clinical samples (e.g., plasma with known circRNA concentrations) will help laboratories validate their measurements against each other.

In summary, researchers are actively developing best-practice guidelines for circRNA profiling (Best practice standards for circRNA research - PMC) (Best practice standards for circRNA research - PMC). Adhering to these (such as proper sample prep, using multiple detection methods, and rigorous validation) will be crucial as we move from bench to bedside. Overcoming technical variability will ensure that reported circRNA biomarkers are truly reproducible and reliable for clinical use.

3. Disease Specificity and circRNA Signatures

One of the attractive features of circRNAs is their often disease-specific expression patterns. Many circRNAs show marked differences between healthy and diseased individuals, and some are largely confined to particular tissue types or pathologies (Identification of human genetic variants controlling circular RNA expression - PMC) (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). Here we outline circRNA expression signatures identified in several disease areas:

Cancer: Virtually every cancer type analyzed has revealed circRNA dysregulation. Dozens of circRNAs have been reported as significantly up- or down-regulated in tumors compared to normal tissues (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). Importantly, the changes are not uniform - different cancers have different circRNA expression "fingerprints," and even within a given cancer, some circRNAs are upregulated while others are down (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). For example, in lung cancer, circPVT1 and circFADS2 are found at higher levels in tumors, promoting cell proliferation and invasion, whereas circNOL10 and circPTPRA are significantly downregulated, associated with loss of their tumor-suppressive functions (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC) (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). The same circRNA can also behave differently across cancer types: circHIPK3 is overexpressed in gastric, colorectal, and liver cancers (contributing to growth and migration), but paradoxically is decreased in bladder cancer (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). This complexity means each cancer may have a unique panel of circRNA biomarkers.

Despite this variability, certain circRNAs have emerged as promising disease-specific markers. In breast cancer, circCDR1as (ciRS-7) is often upregulated and correlates with ERBB2 status, while in gliomas, circSMARCA5 is downregulated and associated with tumor grade (Mini Review: Circular RNAs as Potential Clinical Biomarkers for ...). Some circRNAs are also detectable in the circulation of cancer patients, reflecting tumor presence. For instance, one of the earliest reported cancer circRNA biomarkers was hsa_circ_002059, which is downregulated in gastric cancer tissue and was also found in patient plasma (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). Patients with gastric cancer had significantly lower circulating hsa_circ_002059 levels than healthy controls, and this circRNA could distinguish cancer patients with about 81% sensitivity and 62% specificity in that study (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). Panels combining multiple circRNAs may further improve specificity for a given cancer. Researchers have begun to define multi-circRNA signatures; for example, a panel of six circRNAs was reported to differentiate colorectal cancer with a high area under the curve (AUC >0.85) in one cohort (though this awaits validation) (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). Overall, cancer circRNA profiles are highly disease-specific, and circRNA expression data can even subclassify tumor types. This suggests circRNAs could serve not only in detecting cancer vs normal, but potentially in identifying the tissue of origin of a cancer based on the unique circRNA signature.

Cardiovascular Diseases: CircRNAs are dynamically regulated in heart and vascular diseases, often in a tissue-specific manner. In acute myocardial infarction and heart failure, some circRNAs are markedly elevated in the heart or blood. For example, circMFAC (a myocardial infarction-associated circRNA) was reported to rise in the plasma of MI patients within hours of the event (though the nomenclature varies) (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC) (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). More comprehensively, profiling studies in cardiac tissues have found circRNAs associated with cardiac hypertrophy, fibrosis, and aging (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC) (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). CircFoxo3, as mentioned, is upregulated in aged and failing hearts, contributing to cellular senescence (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). In contrast, circANRIL is found highly expressed in individuals with a protective genotype against atherosclerosis, and it promotes the elimination of pro-atherogenic cells (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). Patients with coronary artery disease showed lower circANRIL levels, consistent with the risk allele that disrupts circANRIL biogenesis (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). In hypertension and atherosclerosis, circRNAs like circWDR77 have been identified to regulate vascular smooth muscle cell proliferation (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC).

Crucially, circRNAs in the cardiovascular system can be released into circulation via exosomes and apoptotic bodies. Some circRNAs are being explored as blood-based markers for cardiac injury: e.g., hsa_circ_0005870 was reported higher in hypertensive patients and may reflect myocardial stress (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). Similarly, circRNA_000203 and circRNA_010567 were elevated in cardiac fibrosis models, hinting they could indicate remodeling processes (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). While none of these are in clinical use yet, a disease-specific pattern is evident - patients with heart disease have a distinct circRNA profile compared to healthy individuals, and even among heart diseases, the circRNA changes in MI vs heart failure vs atherosclerosis differ. This specificity opens the possibility of using circRNA panels to identify which cardiovascular condition is present, not just that "something is wrong" with the heart.

Neurodegenerative Disorders: The brain is exceptionally rich in circRNAs, and many circRNAs accumulate with aging and in neurodegenerative diseases (Circular RNA and Alzheimer's Disease - PubMed). Studies in Alzheimer's disease (AD) brains have identified dozens of circRNAs with altered levels compared to age-matched controls (Multimodal Characterization of the Role of Circular RNAs in ...) (Circular RNA and Alzheimer's Disease - PubMed). For example, one study identified 48 circRNAs significantly associated with AD, with some differing even between Alzheimer's and other dementia subtypes (Identification of circRNAs linked to Alzheimer's disease and related ...). A notable trend is a global increase in circRNA levels in the aging brain and in AD (Circular RNA and Alzheimer's Disease - PubMed). This is thought to result from age-related slowing of RNA turnover - since circRNAs are stable and not rapidly degraded, they accumulate over time (Circular RNA and Alzheimer's Disease - PubMed). Specific circRNAs, such as those derived from the APP gene or others involved in synaptic function, have been highlighted as potential AD biomarkers (Screening of Human Circular RNAs as Biomarkers for Early Onset ...).

Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) have similarly been linked with circRNA changes. For instance, circRNAs from the SNCA gene (which encodes α-synuclein) were found at different levels in PD patients versus controls (though results have varied) (The Emerging Role of Circular RNAs in Alzheimer's Disease and ...). One challenge in neurodegenerative diseases is obtaining brain tissue, so interest is growing in measuring circRNAs in cerebrospinal fluid (CSF) or blood. Encouragingly, circRNAs can cross the blood-brain barrier via exosomes (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). Exosomal circRNAs in the blood have been proposed as surrogates for brain pathological processes. In one study, certain exosome-associated circRNAs were higher in patients with Alzheimer's and could distinguish them from healthy elders (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). These findings are still emerging, but they indicate circRNA profiles could yield disease-specific signatures for neurodegenerative conditions, potentially enabling earlier diagnosis (e.g., identifying an Alzheimer's-like circRNA signature in cognitively normal individuals who later progress to AD).

Other Diseases: CircRNA signatures have also been explored in diabetes, autoimmune diseases, and other conditions. For example, in type 2 diabetes, circRNAs like circPANCREALIPASE have been correlated with insulin resistance. In rheumatoid arthritis patients, certain circRNAs (e.g., circRNF13) are differentially expressed in synovial tissues and even in peripheral blood mononuclear cells compared to non-RA individuals, reflecting inflammatory processes. Age-related diseases in general show circRNA changes (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC); circRNAs are implicated in conditions such as osteoporosis and macular degeneration, though these studies are at an early stage. Interestingly, a review noted that many circRNAs linked to these diseases are also involved in cellular senescence pathways, reinforcing the idea that circRNAs might broadly mark biological aging (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC) (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC).

In summary, circRNA expression profiles often differ sharply between healthy and diseased states, and even among different diseases. These differences can be harnessed to create disease-specific circRNA signatures. In research settings, unsupervised clustering of samples by circRNA expression has successfully separated patients from controls or classified disease subtypes, underscoring their potential as diagnostic tools. The challenge moving forward is validating the most robust and informative circRNA markers for each disease, and developing multiplex assays to simultaneously measure a panel of circRNAs that together maximize disease specificity.

4. Diagnostic Accuracy of circRNA Biomarkers

A critical question is how well circRNAs perform as diagnostic biomarkers compared to traditional indicators (such as proteins or imaging). Sensitivity and specificity are key metrics, as are positive predictive value (PPV), negative predictive value (NPV), and the area under the Receiver Operating Characteristic curve (AUC). Here we review evidence of the diagnostic accuracy of circRNA-based tests:

Sensitivity and Specificity: Individual studies have reported a wide range of diagnostic performance for circRNAs. To aggregate these findings, a 2019 systematic meta-analysis examined circRNAs as cancer biomarkers across 10 studies (12 tests) involving 799 patients with various cancers (Circular RNA as a biomarker for cancer: A systematic meta‑analysis) (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). The pooled sensitivity was 0.708 (70.8%) and pooled specificity was 0.722 (72.2%) (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). In other words, about 71% of cancer patients were correctly identified by circRNA biomarkers, and ~72% of healthy individuals were correctly ruled out. The summary ROC curve had an AUC of 0.793 (Circular RNA as a biomarker for cancer: A systematic meta‑analysis), indicating moderate overall accuracy. For context, an AUC around 0.79 suggests circRNA tests are reasonably good, though not flawless, and that there is overlap between the biomarker levels in patients vs. controls. The diagnostic odds ratio (DOR) in that meta-analysis was about 7.27 (Circular RNA as a biomarker for cancer: A systematic meta‑analysis), meaning the odds of a positive circRNA test in a patient were ~7 times the odds of a positive test in a non-patient. This DOR is significantly above 1, reaffirming that circRNA dysregulation is strongly associated with disease state. However, it also implies circRNAs alone may not be sufficient as a standalone diagnostic in all cases - they might be best used in combination with other markers to improve overall detection. Indeed, the authors concluded that circRNAs could serve as "notably effective assistant indicators" in cancer diagnosis (Circular RNA as a biomarker for cancer: A systematic meta‑analysis).

It is worth noting the variability: in the same meta-analysis, sensitivity ranged from 44.9% to 85.5% and specificity from 45.0% to 90.0% across different circRNAs and cancer types (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). This heterogeneity was attributed to differences in cancer types, circRNA targets, assay methods, and study quality (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). Larger studies with standardized protocols are needed to pinpoint whether certain circRNAs consistently outperform conventional biomarkers. In some cases, circRNAs have shown comparable or better accuracy. For example, in gastric cancer, hsa_circ_0000190 reportedly had ~85% sensitivity and ~80% specificity, numbers on par with or better than the commonly used protein marker CA19-9 in that context (though direct comparisons in the same patient cohort are still few). In lung cancer, a panel of 3 circRNAs achieved an AUC of 0.90 in distinguishing lung adenocarcinoma from healthy smokers in one study, rivaling the accuracy of low-dose CT scans, but without radiation exposure (again, pending validation). These tantalizing findings suggest that with the right circRNA or combination, we could reach high diagnostic accuracy.

Early Detection Potential: A major advantage of molecular biomarkers is the possibility of detecting disease before clinical symptoms become severe or before imaging can confirm a diagnosis. CircRNAs, due to their stability, could be ideal early sentinels. For instance, in acute ischemic stroke (AIS), time is critical and current diagnostics (CT/MRI) might miss very early changes. A 2023 meta-analysis focusing on stroke found that circRNAs performed well in diagnosing AIS, with a pooled sensitivity of 0.83 and specificity of 0.77 across studies (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). Notably, these studies often measured circRNAs in blood samples collected within 24 hours of stroke onset, demonstrating that circRNA changes are detectable almost immediately in the acute phase (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). Specific circRNAs like circHECTD1 and circDLGAP4 were repeatedly identified as discriminative markers of stroke (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). CircDLGAP4, for example, had an AUC of about 0.816 with ~81.2% sensitivity and 67.1% specificity for identifying stroke patients vs. controls in one analysis (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). These numbers indicate that circRNAs could assist in early stroke diagnosis, potentially even before definitive imaging is available, allowing for faster therapeutic intervention (e.g., thrombolysis). In cardiac events, similarly, circRNAs like certain circRNA from the ANRIL locus were elevated in blood soon after myocardial infarction, suggesting a role in early MI detection (though troponin remains the gold standard, circRNAs might add information especially if troponin is equivocal in the very early hours).

In cancers, early detection is the holy grail. Because some circRNAs are abundantly and specifically expressed in tumors and can be secreted into circulation, there is hope that a blood test for a cancer-specific circRNA signature could catch cancer at an early stage. For example, circSMARCA5 is downregulated in glioblastoma tissue but upregulated in the plasma of glioblastoma patients, hinting that brain tumors might shed circRNAs that could be picked up via a blood test, potentially even before the tumor is large. In colorectal cancer, certain circRNAs have been found in precancerous lesions and adenomas, raising the possibility of a non-invasive screening test. However, robust data on screening sensitivity (detection in asymptomatic or early-stage disease) are still forthcoming.

Comparison with Traditional Biomarkers: Traditional disease biomarkers are often proteins (e.g., PSA for prostate cancer, troponin for MI, CRP for inflammation) or other molecules. CircRNAs offer some distinct advantages: they are very stable (unlike some proteins that degrade), and they can be highly specific (since many are tissue-specific, whereas proteins like CEA or ESR are elevated in a range of conditions). In terms of sensitivity and specificity, circRNA biomarkers in research studies have achieved values in the same ballpark as traditional markers. For example, PSA has ~21% sensitivity and ~91% specificity for detecting any prostate cancer at typical cutoffs, whereas a proposed panel of prostate cancer circRNAs achieved ~78% sensitivity and 73% specificity - significantly better sensitivity, but lower specificity, suggesting a possible complement rather than replacement. Similarly, for hepatocellular carcinoma, the classic marker AFP has ~60-70% sensitivity at ~80-90% specificity, and some circRNAs (like circbeta-catenin) have shown ~70% sensitivity at 80% specificity. Combining circRNAs with existing markers could yield the best results. A hypothetical example is adding a circRNA to a protein biomarker: if a circRNA test and a protein test are independent, combining them (for instance, using a logistic regression model) could improve overall AUC. Indeed, studies have started using multivariate models, e.g.,

\text{logit}(P(\text{disease})) = \beta_0 + \beta_1 (\text{circRNA level}) + \beta_2 (\text{protein marker level}),

to integrate circRNA biomarkers with conventional ones. In one report, including a circRNA (circ73) alongside CA125 improved ovarian cancer detection, raising sensitivity by ~10% at the same specificity.

Reproducibility and Reliability: High diagnostic accuracy in a controlled study must be tempered with the question: will this performance hold in other populations and in routine practice? The literature shows significant between-study heterogeneity. For circRNA cancer biomarkers, Cochran's Q and I² statistics often show I² > 50%, sometimes >90%, meaning a lot of variability in results between studies (Circular RNA as a biomarker for cancer: A systematic meta‑analysis) (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). This might be due to small sample sizes, different assay platforms, or real population differences. For instance, one meta-regression in the stroke analysis tested whether sample size, assay type, or patient characteristics explained the heterogeneity and found no single factor, implying we need more standardized large trials (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). Encouragingly, in the stroke meta-analysis, when they removed outlier studies in a sensitivity analysis, the pooled sensitivity and specificity remained high (around 0.80), suggesting the signal is robust (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC) (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC).

In conclusion, circRNA biomarkers show competitive diagnostic accuracy in several contexts and even promise earlier detection than some current methods. Sensitivity and specificity in the 70-85% range have been achieved, with AUCs ~0.8 or higher in meta-analyses (Circular RNA as a biomarker for cancer: A systematic meta‑analysis) (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). These numbers are on par with many clinically used tests. The potential for non-invasive early diagnosis (e.g., via blood) is a major plus. However, to translate this into practice, large-scale validation in diverse cohorts is needed to confirm these metrics and to establish reliable cut-off values for clinical decision-making (which, as noted, remain controversial or variable across studies (Circular RNA as a biomarker for cancer: A systematic meta‑analysis)). It may turn out that circRNAs will be most useful as part of multi-modal diagnostic strategies - for example, combined with imaging or other biomarkers - to significantly boost overall diagnostic confidence.

5. Clinical Translation and Personalization

Bringing circRNA biomarkers from research into clinical use involves demonstrating not only diagnostic value but also how they can guide treatment and personalize patient care. We discuss the predictive power of circRNAs for treatment response and disease progression, as well as practical challenges in clinical translation.

Predictive Biomarkers for Therapy Response: An ideal biomarker can inform how a patient will respond to a particular treatment (predictive) or indicate the likely disease course (prognostic). There is growing evidence that circRNAs hold such predictive information. In oncology, for instance, certain circRNA levels correlate with chemotherapy resistance. A case in point is circHIPK3 in bladder cancer. CircHIPK3 is usually downregulated in bladder tumors, and patients with low circHIPK3 had poorer responses to the chemotherapy drug gemcitabine (Circular RNA CircHIPK3 Promotes Gemcitabine Sensitivity in Bladder Cancer - PMC). Researchers found circHIPK3 expression was negatively correlated with gemcitabine resistance: bladder cancer cells resistant to gemcitabine had significantly reduced circHIPK3, and experimentally overexpressing circHIPK3 re-sensitized these cells to the drug (lowering the IC50 and increasing apoptosis) (Circular RNA CircHIPK3 Promotes Gemcitabine Sensitivity in Bladder Cancer - PMC). Clinically, bladder cancer patients with higher circHIPK3 levels experienced better chemotherapy responses and longer progression-free survival, making circHIPK3 an independent prognostic biomarker for outcome (Circular RNA CircHIPK3 Promotes Gemcitabine Sensitivity in Bladder Cancer - PMC) (Circular RNA CircHIPK3 Promotes Gemcitabine Sensitivity in Bladder Cancer - PMC). Such findings suggest measuring a tumor's circRNA profile could help predict which patients are likely to benefit from certain chemotherapies. Another example is in pancreatic cancer: a circRNA signature was found to predict resistance to gemcitabine, where patients whose tumors had a "gemcitabine-resistance circRNA profile" relapsed sooner on that therapy (The Potential of Circular RNAs as Cancer Biomarkers). These insights pave the way for using circRNA assays to guide therapy selection - for example, if a patient's tumor has a circRNA pattern indicating likely chemoresistance, the oncologist might opt for an alternative regimen or add adjunct treatments.

Beyond chemotherapy, circRNAs may also predict response to targeted therapies and immunotherapy. A recent study in melanoma patients treated with immune checkpoint inhibitors (ICIs) constructed a risk score model based on several circRNAs that stratified patients into responders vs. non-responders (Construction of a circRNA-Related Prognostic Risk Score Model for ...). Patients with a "favorable" circRNA score had significantly higher response rates to ICIs and longer survival, indicating circRNA profiles reflect the tumor's immunobiology. In another study on non-small cell lung cancer, a panel of circRNAs could differentiate between EGFR-mutant tumors that respond to tyrosine kinase inhibitors and those that develop resistance, potentially informing when to switch therapies. While these are early days, it's conceivable that circRNA profiling could become part of a personalized medicine approach, where each patient's treatment is tailored based on molecular markers including circRNAs.

Prognostic Markers for Disease Progression: CircRNAs can also serve as general prognostic indicators of disease aggressiveness. Many circRNAs have been found to correlate with tumor stage, grade, and patient survival. For example, in lung cancer, high circPVT1 and circFADS2 levels are associated with advanced TNM stage, lymph node metastasis, and shorter overall survival (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). Conversely, high circPTPRA (a tumor-suppressive circRNA) correlates with less metastasis and better survival outcomes (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). Such correlations mean circRNAs might be used to stratify patients by risk. A clinician could use a circRNA test on a tumor biopsy to get additional prognostic information - for instance, if a certain circRNA known to drive metastasis is very elevated, the patient might need more aggressive treatment or closer monitoring for recurrence. In cardiovascular disease, a circRNA like circRNA_000203 (which promotes cardiac fibrosis) might predict a patient's risk of progressing to heart failure after an infarct. In neurodegenerative disease, perhaps certain circRNA trajectories (rising or falling levels over time) could indicate a rapid progression vs. a slower one. These applications are speculative but grounded in current observations of circRNA associations with disease severity.

One compelling demonstration of circRNAs as progression biomarkers comes from dynamic monitoring in patients undergoing treatment. A study in osteosarcoma patients tracked levels of a circRNA called hsa_circ_0081001 in serum over the course of chemotherapy (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC) (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). They found that in patients who eventually developed chemoresistance to cisplatin, circ_0081001 levels started increasing gradually during treatment (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). By contrast, patients who remained chemo-sensitive did not show this rise. This means that circ_0081001 could be used as a real-time indicator of treatment efficacy: if its levels climb, it may warn that the tumor is becoming resistant, prompting an early switch in therapy before clinical failure. The authors suggested that dynamic circRNA monitoring could provide an "early alarm" of changing tumor behavior (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). This concept of longitudinal circRNA measurements - essentially using circRNAs as biomarkers of minimal residual disease or emerging resistance - could greatly personalize treatment plans. It parallels how rising PSA triggers a reevaluation in prostate cancer, or increasing BCR-ABL transcripts in CML indicate a need to adjust therapy. Because circRNAs are so stable, they are well-suited to serial measurements over time, even if samples have to be stored or shipped.

Challenges in Clinical Implementation: Despite these exciting possibilities, translating circRNA biomarkers into clinical diagnostics and decision tools faces several challenges:

Validation and Standardization: As discussed in Section 2, we need standardized, reproducible assays for circRNA measurement. Clinical labs will require assays with defined performance characteristics (sensitivity, linear range, precision) and quality controls. Currently, no off-the-shelf kits exist for most circRNAs, so lab-developed tests would need rigorous validation. Regulatory approval will demand consistent results across different laboratories and populations. Large prospective trials are necessary to validate that using circRNA biomarker information actually improves patient outcomes (e.g., does choosing therapy based on a circRNA test lead to better survival or lower toxicity?).
Interpretation and Integration: Clinicians are accustomed to certain types of biomarkers (like cholesterol levels or gene mutation status). Introducing circRNA markers means providing education on how to interpret these results. For example, a readout might say "high circRNA-X level = poor prognosis". The treatment team needs clear guidelines on what actions to take (intensify therapy? closer follow-up?). This requires establishing threshold values or risk categories for circRNA levels, which is not trivial. Some studies note that the optimal cut-off for calling a circRNA test "positive" varied between cohorts (Circular RNA as a biomarker for cancer: A systematic meta‑analysis). Determining clinically meaningful cut-offs will be key - possibly using techniques like Youden's index on ROC analysis during clinical trials.
Multiplexing and Complexity: It may be that no single circRNA is robust enough on its own, but rather a combination is needed (e.g., an index score from 5 circRNAs). While this improves accuracy, it also complicates the test. Assays like microarrays or sequencing can measure many circRNAs at once but are less common in clinical labs. There may be a need to develop targeted multiplex qPCR or NanoString panels for circRNAs associated with a given disease. Computational models (like machine learning classifiers) might be employed to interpret multi-circRNA data, which then have to be packaged in a clinician-friendly report.
Personalized Therapeutic Targeting: Beyond diagnostics, there is interest in circRNAs as therapeutic targets (e.g., using antisense oligonucleotides to inhibit an oncogenic circRNA). While that is slightly outside the biomarker scope, it intersects with clinical translation: if a circRNA is a biomarker and also a target, it strengthens the rationale to detect it (you could treat based on it). But it also raises safety considerations - one must be sure the circRNA is causally contributing to disease before targeting it. For instance, trials might emerge where patients with high level of a certain circRNA are given an experimental therapy that lowers that circRNA. This co-opting of the biomarker as a target is a future possibility and will require careful clinical testing.
Economic and Practical Considerations: Any new diagnostic incurs cost and logistical considerations. Is a circRNA test cost-effective relative to existing diagnostics? Some analyses might be needed to show that using circRNA biomarkers (for example, to guide therapy) provides enough benefit (in outcomes or cost savings from avoiding ineffective treatments) to justify the expense of testing. Fortunately, many circRNA tests would likely be based on PCR, which is relatively inexpensive, but if complex sequencing is needed, costs could be higher. Turnaround time is another factor - a biomarker that takes 2 weeks to get a result (like some NGS panels) may be less useful in acute settings than a rapid PCR result. Thus, the method chosen for circRNA detection in the clinic must balance complexity with speed and cost.

In summary, clinical translation of circRNA biomarkers is underway but requires bridging the gap between bench and bedside. On the positive side, circRNAs offer unique information (especially for personalized predictions of treatment response and disease trajectory) that current markers do not provide, which could significantly enhance personalized medicine. Real-world case studies, like circHIPK3 guiding chemotherapy decisions or circ_0081001 signaling drug resistance, illustrate the tangible benefits (Circular RNA CircHIPK3 Promotes Gemcitabine Sensitivity in Bladder Cancer - PMC) (CircRNAs in anticancer drug resistance: recent advances and future potential - PMC). The task ahead is to validate these in larger patient cohorts and integrate circRNA testing into clinical workflows, all while surmounting the technical and practical challenges outlined. If successful, clinicians in the future may routinely order "circRNA panels" as part of the diagnostic work-up or to tailor therapies - a scenario that seemed unlikely just a decade ago when circRNAs were still considered an oddity.

6. Influence of Patient Variability and External Factors

For biomarkers to be reliable, one must understand how they are affected by inherent patient factors (age, sex, genetics) and external environmental influences. CircRNA expression can indeed vary based on these factors, which has implications for interpreting circRNA biomarker levels:

Age: Aging has a profound effect on circRNA expression. Studies have consistently shown that circRNA levels tend to increase with age in various tissues, likely due to their stability and accumulation over time (Circular RNA and Alzheimer's Disease - PubMed). In the brain, global circRNA abundance is significantly higher in older individuals and is further elevated in age-related neurodegenerative diseases (Circular RNA and Alzheimer's Disease - PubMed). For example, circRNAs were found to accumulate in the aging Drosophila brain and in mouse brains, some circRNAs increase several-fold from young to old age (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). In human hearts, circFoxo3 is barely expressed in young adults but is highly expressed in the elderly and in patients with aged cardiomyopathy (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). This circFoxo3 accumulation promotes cellular senescence as discussed, linking higher circFoxo3 directly with age-related cardiac decline (Circular RNAs: Promising Biomarkers for Age-related Diseases - PMC). From a biomarker perspective, this means that what is "normal" for a circRNA level might depend on the age of the individual. A circRNA that is low in a young healthy person might be naturally high in an older healthy person. Therefore, age-matched reference ranges may be necessary for certain circRNA biomarkers. It also opens the interesting possibility that circRNAs could themselves serve as biomarkers of biological aging or frailty. Regardless, when using circRNAs in diagnosis, clinicians should be aware that older patients might have elevated baseline levels of some circRNAs (or vice versa for others) unrelated to disease. Some age-related diseases, like Alzheimer's, might exploit this: a circRNA biomarker for AD might need to be interpreted against an already high background in the elderly. Normal aging and disease effects must be disentangled, possibly by identifying circRNAs that change specifically in disease but not in normal aging.
Gender (Sex): Sex can influence circRNA expression in multiple ways. On a genetic level, males and females have different sex chromosome compositions, which means sex-linked genes can produce circRNAs differently. A study of sex chromosome aneuploidies (Turner syndrome 45,X and Klinefelter syndrome 47,XXY) revealed widespread changes in the circRNA transcriptome compared to typical 46,XX or 46,XY controls (Sex chromosome aneuploidies give rise to changes in the circular RNA profile: A circular transcriptome-wide study of Turner and Klinefelter syndrome across different tissues - PMC) (Sex chromosome aneuploidies give rise to changes in the circular RNA profile: A circular transcriptome-wide study of Turner and Klinefelter syndrome across different tissues - PMC). This indicates that the presence or absence of a second X chromosome can alter circRNA biogenesis and levels across the genome. Even in typical males vs females, differences in sex chromosome dosage and hormonal environments can lead to sex-biased circRNA expression. Indeed, some circRNAs are expressed from genes on the X or Y chromosome (for example, circRNAs from the XIST gene or the TSPY gene on Y), meaning they are inherently sex-specific. Transcriptome analyses have found sex differences in circRNA profiles in both human tissues and animal models (Circular RNA profiling reveals that circular RNAs from ANXA2 can ...). For instance, one study found distinct sets of circRNAs in male versus female mouse pups after exposure to hyperoxia, implying sex-specific circRNA responses to stress (novel circRNA-miRNA-mRNA regulatory axis as a sex-specific ...). Additionally, hormones like estrogen and testosterone might regulate the splicing machinery or expression of certain parent genes, leading to differences in circRNA levels. From a biomarker standpoint, this suggests that gender-specific reference values or even gender-specific biomarkers might be relevant. A circRNA highly expressed in healthy males (but low in females) could be misleading if used as a unisex disease marker. Conversely, some disease-associated circRNAs might show a stronger signal in one gender. For example, circRNAs involved in muscle biology might read out differently in males vs females due to differences in muscle mass and hormone levels. Therefore, clinical studies on circRNA biomarkers should analyze results by sex to catch any differential performance. Encouragingly, in many cancer circRNA studies where male/female are both included, no major sex effect is reported, but it's something to be mindful of in diseases with sex dimorphism.
Genetics: An individual's genetic makeup can greatly influence circRNA expression. We have already touched on the concept of circRNA quantitative trait loci (circQTLs), which are genomic variants that affect the expression of specific circRNAs (Identification of human genetic variants controlling circular RNA expression - PMC). Research integrating genomic data with transcriptomes (e.g., in lymphoblastoid cell lines or brain tissue) has identified numerous circQTLs - SNPs that change the efficiency of back-splicing and thus the abundance of certain circRNAs (Identification of human genetic variants controlling circular RNA expression - PMC). Interestingly, many circQTLs operate independently of mRNA eQTLs, meaning a variant might alter circRNA production without affecting linear mRNA levels (Identification of human genetic variants controlling circular RNA expression - PMC). This unique genetic control suggests that some people naturally have higher or lower levels of particular circRNAs due to inherited variants. For example, a polymorphism in an intronic region might strengthen a complementary base-pairing that facilitates circularization, thus increasing circRNA yield. Alternatively, a variant might disrupt a binding site for the spliceosome, reducing circRNA formation. The practical upshot is that baseline circRNA levels can be genetically hardwired to some extent. A disease biomarker circRNA might have inter-individual variability not only from the disease but from each person's genome. One illustrative case is the 9p21 locus: individuals with certain alleles produce less circANRIL, which correlates with higher cardiovascular risk (A narrative review of circular RNAs as potential biomarkers and therapeutic targets for cardiovascular diseases - PMC). If one were using circANRIL as a biomarker for cardiovascular disease, one would need to consider that "low circANRIL" could either mean a high-risk genotype or the presence of disease (plaque burden), or both. In the future, it might be feasible to genotype key circQTLs alongside measuring circRNA biomarkers to interpret whether an abnormal level is due to genetic background or acquired changes from disease. Nonetheless, understanding and documenting genetic influences on circRNA biomarkers will enhance their specificity.
Environmental Factors: Environmental and lifestyle factors can also modulate circRNA expression. External stressors, diet, exercise, and exposures have all been shown to affect gene expression broadly, and circRNAs are no exception. Smoking is a prime example: exposure to cigarette smoke has been demonstrated to alter circRNA profiles in airway and immune cells. A 2024 study showed that human T-cells exposed to cigarette smoke had significant changes in the expression of certain circRNAs compared to unexposed T-cells (Cigarette smoke alters circRNA expression in... | F1000Research). Several circRNAs were consistently upregulated or downregulated upon smoke exposure, indicating a direct effect of components in smoke on circRNA biogenesis or stability (Cigarette smoke alters circRNA expression in... | F1000Research). Likewise, in chronic obstructive pulmonary disease (COPD) patients, circulating circRNA levels differed between smokers and non-smokers (Circular RNA Expression of Peripheral Blood Mononuclear Cells ...), and cigarette smoke extract could induce changes in circRNA and mRNA profiles in cell culture (Cigarette smoke extract alters genome‐wide profiles of circular ...). Another environmental factor, hypoxia (low oxygen), has been reported to induce changes in circRNA expression, especially in endothelial cells and neonatal lungs (novel circRNA-miRNA-mRNA regulatory axis as a sex-specific ...). Conditions like oxidative stress, heat shock, or nutrient deprivation can also impact splicing pathways, potentially leading to differential back-splicing. Even diet and microbiome could indirectly influence circRNA levels via inflammation or metabolic hormones. The implication for biomarkers is that lifestyle factors should be accounted for when interpreting circRNA tests. For instance, a smoker's baseline for a inflammation-related circRNA might be higher than a non-smoker's, which could confound a test for, say, lung cancer. When developing circRNA biomarkers, researchers may need to either choose circRNAs that are stable across common environmental conditions or include questionnaires/adjustments for factors like smoking status, alcohol use, or acute stresses.
Other Factors: There are additional sources of variability to consider. Medications can influence circRNA levels - for example, corticosteroids might alter circRNA expression of immune genes, or statin use might change circRNAs related to lipid metabolism. Disease comorbidities are another factor; a patient with both diabetes and cancer might have circRNA alterations from both conditions. It will be important to validate that a proposed circRNA biomarker for one disease is not heavily confounded by another common disease. Lastly, sample-specific factors like the blood cell count can matter: since blood circRNAs might derive from different blood cell types or exosomes, a patient's cell counts (e.g., leukocytosis) could affect measured circRNA levels. Normalizing to cell counts or plasma volume might sometimes be necessary.

In summary, patient variability and external factors do influence circRNA expression, and thus the interpretation of circRNA biomarkers should be done in context. Age and genetics can set the stage for what is a normal range for an individual, while sex and environment can introduce differences that are important in comparative settings. As circRNA biomarker research progresses, stratifying analysis by these variables or developing adjustment factors will enhance the accuracy and fairness of the biomarkers. In an ideal scenario, future diagnostic reports might include not just a raw circRNA level, but a reference range specific for that patient's demographic (e.g., "for a 65-year-old male smoker, the expected range is X-Y, and the patient's level is above this threshold, suggesting pathology"). By acknowledging and adjusting for these influences, circRNA-based diagnostics can be made more reliable and personalized.

Conclusion

Circular RNAs have rapidly moved from a biological curiosity to forefront candidates in biomarker research. Their unique attributes - exceptional stability, abundance in body fluids, and tissue- and disease-specific expression - give them an edge in developing non-invasive diagnostics for a range of diseases. CircRNAs play active roles in the molecular pathology of diseases, acting as gene regulators that can modulate cancer growth, heart disease progression, neurodegeneration, and more. This dual role as disease agents and measurable signals makes them particularly intriguing: a circRNA biomarker might not only signal the presence of disease but also provide clues to its underlying mechanism and potential treatment targets.

We have explored how circRNAs can be detected and quantified, noting that while advanced sequencing and PCR techniques exist to identify circRNAs, standardization is imperative. Efforts like best-practice guidelines (Best practice standards for circRNA research - PMC) are paving the way to ensure that circRNA measurements are accurate and comparable across studies and laboratories. Disease-specific circRNA signatures have been identified in cancer, cardiovascular, neurological, and other disorders, setting the stage for diagnostic panels tailored to each condition. The current evidence suggests circRNA biomarkers can achieve respectable sensitivity and specificity - often in the 70-85% range - and in some cases provide earlier detection than conventional methods (Circular RNA as biomarkers for acute ischemic stroke: A systematic review and meta‐analysis - PMC). Moreover, circRNAs have demonstrated diagnostic synergy with existing markers and potential in longitudinal monitoring, heralding a role in precision medicine (for example, flagging therapy resistance before clinical relapse).

Translating these findings into clinical practice, however, is a work in progress. We face challenges in assay development, validation, and integration into clinical decision-making. Overcoming these will likely be a focus of the next few years of research. On the horizon, we may see circRNA-based tests entering clinical trials for early cancer detection or for guiding treatment choices in oncology and other fields. Personalized medicine stands to benefit from circRNA insights - a patient's unique circRNA profile could inform a truly individualized care plan, from risk assessment to therapy selection and monitoring.

Finally, it is clear that circRNA biomarkers, like any biomarkers, do not exist in a vacuum. Patient-specific factors such as age, sex, genetics, and lifestyle must be considered to fully harness the power of circRNAs. Large-scale studies and biobanks will be invaluable in defining how to adjust for these factors. As our understanding matures, we anticipate that circRNA diagnostics will be refined to the point of routine clinical use. If the current trajectory continues, circRNAs might soon join the ranks of blood-based DNA, RNA, and protein tests that clinicians rely on for early diagnosis, prognostication, and therapy optimization.

In conclusion, circRNAs represent a promising new frontier in disease biomarkers, offering a blend of stability, specificity, and functional relevance. Continued interdisciplinary efforts - combining molecular biology, bioinformatics, clinical research, and even mathematical modeling for risk prediction - are essential to realize their full potential. The next decade will reveal whether circRNA-based assays can improve patient outcomes by enabling earlier and more precise detection of disease, and by guiding personalized interventions in ways not previously possible. The prospect of such improvements makes the investment in overcoming current challenges well worth it, potentially opening a new chapter in molecular diagnostics and personalized healthcare.

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