Page 48 - Read Online

Singh et al. Cancer Drug Resist. 2025;8:56 Page 13 of 20

in routine diagnostic settings. Another major limitation lies in the functional characterization of identified
circRNAs . While many studies report associations between specific circRNAs and drug resistance
[52]
phenotypes, few have established causal mechanisms or validated these findings in vivo [113] . This gap
undermines the translational potential of circRNA signatures and limits their incorporation into clinical trial
designs [120] . Moreover, circRNAs often function as competing endogenous RNAs or miRNA sponges, and
their network-level interactions with other non-coding RNAs or mRNAs are complex and still not fully
understood . From a regulatory and ethical standpoint, the introduction of novel biomarkers into clinical
[84]
trials necessitates rigorous validation under Good Clinical Practice and compliance with data safety and
ethical standards, which adds time and logistical complexity to trial initiation and conduct . Additionally,
[121]
the cost and data burden of high-throughput circRNA profiling, especially when integrated with other omics
data, may limit its widespread adoption, particularly in low-resource clinical settings .
[122]

Another issue is the lack of clear clinical utility or guidelines on how circRNA-based resistance detection
would influence therapeutic decisions [123] . Unlike ctDNA, which can reveal actionable mutations leading to
drug switching [e.g., estrogen receptor 1 (ESR1) mutations in breast cancer], circRNAs currently lack such
validated clinical pathways [124] . Their integration into decision-making algorithms and treatment protocols
remains theoretical at this stage. Lastly, patient heterogeneity in terms of tumor type, treatment history, and
genetic background adds another layer of complexity, making it difficult to define universal circRNA panels
for resistance tracking [125] . In conclusion, while the scientific foundation for using circulating circRNAs as
non-invasive biomarkers for drug resistance is strong, the absence of clinical trial validation, methodological
standardization, and real-time clinical utility assessment significantly limits current implementation.
Addressing these limitations through collaborative, multi-center clinical trials and robust bioinformatics
pipelines will be crucial for advancing the role of circRNAs in precision oncology.

FUTURE PROSPECTIVE
The significant potential of circRNAs in liquid biopsy for monitoring cancer drug resistance necessitates
extensive future research, both experimental and clinical . To begin, large-scale multi-center clinical trials
[44]
should be launched to evaluate the clinical utility of circRNAs as predictive biomarkers of therapeutic
resistance in multiple cancer types . These trials should assess circRNA expression levels in solid tumors,
[112]
hematological malignancies, and pediatric hematological malignancies using a longitudinal model [126] . The
expression profiles will be correlated with resistance, progression-free survival (PFS), and therapeutic
response to treatment modifications [120] . To ensure consistency and reproducibility, standardized
pre-analytical and analytical methods are essential . This includes establishing global standards for sample
[44]
collection, circRNA extraction, RNase R-based enrichment, and detection using highly sensitive techniques
(e.g., qRT-PCR, ddPCR, or RNA seq) . Long-term data on circRNA expression will further enhance the
[52]
clinical translatability of circRNAs as predictive biomarkers of therapeutic resistance, with far-reaching
implications in cancer management.

The development of affordable, point-of-care diagnostic platforms for detecting tumor-specific circRNAs in
body fluids could enable real-time monitoring of resistance while facilitating personalized treatment
planning [127] . Future work should also prioritize functional characterization of resistance-associated
circRNAs, including their molecular roles and interactions with miRNAs, mRNAs, and signaling
pathways . Such efforts would establish the biomarker potential of circRNAs and support the feasibility of
[85]
therapeutically targeting oncogenic circRNAs. Combining circRNA profiles with other omics data (e.g.,
proteomics, genomics, metabolomics), using AI and machine learning approaches, may further enhance
predictive accuracy and facilitate the development of comprehensive resistance signatures [128] . Another
important step will be advancing circRNAs as companion diagnostics to guide precision-targeted therapies
or immunotherapies [129] . Close collaboration with regulatory authorities will also be critical to define the

41