Circular RNA expression profiling in hepatocellular carcinoma tissues and adjacent normal liver

Here is the paradox: circular RNAs (circRNAs) were dismissed as splicing errors for decades, yet they now appear to be among the most stable and abundant RNA species in eukaryotic cells. This research profiled circRNA expression in 24 paired samples of hepatocellular carcinoma (HCC) tissue and adjacent non-tumorous liver using RNA-seq with RNase R enrichment. A total of 14,382 circRNAs were detected, of which 847 were differentially expressed between HCC and adjacent tissue (|log2FC| > 1, FDR < 0.05). Among these, 412 were upregulated and 435 were downregulated in tumors. Eight circRNAs were validated by RT-qPCR. circHIPK3 and circFBLIM1 were consistently upregulated in HCC (3.1- and 2.8-fold respectively), while circSMARCA5 and circMTO1 were downregulated (2.4- and 1.9-fold). Functional prediction analysis identified miRNA sponging as the dominant mechanism (41.3% of DE circRNAs), with circHIPK3 predicted to sequester miR-124, thereby de-repressing downstream oncogenes. ROC curve analysis showed circSMARCA5 achieved an AUC of 0.89 for distinguishing HCC from adjacent tissue. These results expand the catalogue of HCC-associated circRNAs and highlight candidates with biomarker and therapeutic potential.

Illustrations explain the paper; charts are replotted from the paper's own table values.

AI-generated illustration

↗ Enlarge

Mechanism of miRNA SpongingClose ×

AI-generated conceptual illustration (human hepatocellular carcinoma liver tissue cells). It explains the idea and does not show study data.

Open full-size image ↗

Replotted from source data

↗ Enlarge

log2FC: circHIPK3 vs circFBLIM1 vs circSMARCA5 vs circMTO1Close ×

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Replotted from source data

↗ Enlarge

AUC: circHIPK3 vs circFBLIM1 vs circSMARCA5 vs circMTO1 vs circCDYLClose ×

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Introduction

A paradox sits at the heart of circRNA biology: these molecules were first observed in the 1970s but were written off as aberrant splicing byproducts for nearly four decades [1] . It took the RNA-seq revolution to reveal that circRNAs are abundant, conserved, and tissue-specific -properties inconsistent with junk [2] . In the liver, circRNAs are particularly enriched, and their dysregulation in hepatocellular carcinoma has attracted growing attention as a source of diagnostic biomarkers and therapeutic targets [3] . CircRNAs exert their biological effects primarily through three mechanisms: sponging miRNAs to relieve repression of target mRNAs, binding RNA-binding proteins to modulate their activity, and -in rare cases -being translated into peptides via internal ribosome entry sites [4] . In HCC specifically, a handful of circRNAs have been linked to proliferation, invasion, and drug resistance, but comprehensive profiling across large sample sets has been limited [5] . This research used RNase R-enriched RNA-seq to profile the full circRNA landscape in 24 HCC patients, performed at the Himalayan Institute of Biosciences, Dehradun, from January to September 2023.

Material and Methods

Material and Methods

Material

Twenty-four pairs of HCC tumor tissue and matched adjacent non-tumorous liver (>2 cm from tumor margin) were collected from patients undergoing hepatectomy at partnering hospitals in Uttarakhand. Inclusion criteria: primary HCC confirmed by pathology, no prior chemotherapy or radiation. Tissues were snap-frozen in liquid nitrogen within 30 minutes of resection. Patient demographics: median age 57 years, 79.2% male, 62.5% hepatitis B positive, Barcelona Clinic Liver Cancer stage A (n=10) and B (n=14). Written informed consent was obtained from all patients. Ethics approval: Himalayan Institute of Biosciences Human Research Ethics Committee (Protocol HIB-HREC-2022-034, December 2022).

Methods

Total RNA was extracted using TRIzol (Thermo Fisher) and treated with RNase R (Epicentre, 3 U/μg RNA, 37°C, 15 min) to degrade linear RNAs and enrich circular species. Libraries were prepared using the NEBNext Ultra II RNA Kit and sequenced on Illumina NovaSeq 6000 (2×150 bp, ~80 million reads per sample). CircRNA identification used CIRI2 and CIRCexplorer2 pipelines, requiring ≥2 backsplice junction reads. Differential expression analysis used DESeq2 (|log2FC| > 1, FDR < 0.05). Eight candidates were validated by RT-qPCR using divergent primers and Sanger sequencing of back-splice junctions. miRNA interaction prediction used CircInteractome and miRanda databases. ROC curves for diagnostic potential were constructed in R. Correlations with clinicopathological features used Spearman's rank test. CircSMARCA5 showed the strongest diagnostic performance with an AUC of 0.89 (Table 1). Its downregulation in HCC correlated with tumor size (r = -0.47, p = 0.021) and vascular invasion (p = 0.008), consistent with a tumor-suppressor role through sponging of oncogenic miR-17.

circRNA	log2FC	FDR	Direction	Host gene	AUC	Mechanism
circHIPK3	3.14	<0.001	Up	HIPK3	0.84	miR-124 sponge
circFBLIM1	2.81	<0.001	Up	FBLIM1	0.79	miR-346 sponge
circSMARCA5	-2.43	<0.001	Down	SMARCA5	0.89	miR-17 sponge
circMTO1	-1.91	0.003	Down	MTO1	0.82	miR-9 sponge
circCDYL	2.38	<0.001	Up	CDYL	0.76	Protein binding

Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.

View original table image

↗ Enlarge

Table 1Close ×

Top differentially expressed circRNAs in HCC versus adjacent liver (n = 24 pairs)

Open full-size image ↗

Original paper tableSource PDF · page 2 ↗

Results

Results

Discussion

The identification of 847 differentially expressed circRNAs in HCC substantially expands the known catalogue, which previously comprised fewer than 200 validated candidates [6,7] . The dominance of miRNA sponging as the predicted functional mechanism (41.3%) is consistent with the established model of circRNA function, though the growing recognition of protein-binding and translational roles (22.8% and 14.6% respectively) suggests the picture is more complex than simple sponging [8] . CircSMARCA5's diagnostic AUC of 0.89 is competitive with established HCC biomarkers like AFP (AUC typically 0.70-0.85) and could complement existing markers in a multi-analyte panel [9] . Its stability in blood -a general property of circRNAs due to their resistance to exonuclease degradation -makes it an attractive candidate for liquid biopsy development [10] .

Conclusion

CircRNA profiling in 24 HCC-adjacent tissue pairs identified 847 differentially expressed species, with circSMARCA5 emerging as the strongest diagnostic candidate (AUC 0.89). CircHIPK3 and circFBLIM1 were consistently upregulated and may promote HCC progression through miRNA sponging. These findings position circRNAs as a rich source of HCC biomarkers and potential therapeutic targets warranting validation in larger, multicenter cohorts [11,12,13] .

Figures and tables from the paper

↗ Enlarge

Figure 1Close ×

Heatmap of eight validated circRNAs showing differential expression between HCC tumor samples (HCC-1 to HCC-4) and adjacent

Open full-size image ↗

Original paper figureSource PDF · page 2 ↗

↗ Enlarge

Figure 2Close ×

Predicted functional categories of the 847 differentially expressed circRNAs in HCC, with miRNA sponging as the dominant

Open full-size image ↗

Original paper figureSource PDF · page 2 ↗

1. Sanger HL, Klotz G, Riesner D, Gross HJ, Kleinschmidt AK. Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures. Proceedings of the National Academy of Sciences. 1976;73(11):38523856.
2. Salzman J, Gawad C, Wang PL, Lacayo N, Brown PO. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS ONE. 2012;7(2):e30733.
3. Yu J, Xu QG, Wang ZG, Yang Y, Zhang L, Ma JZ, Bhagat C, Bhagat D, Bhagat K, Bhagat L, Sun SH. Circular RNA cSMARCA5 inhibits growth and metastasis in hepatocellular carcinoma. Journal of Hepatology. 2018;68(6):1214-1227.
4. Kristensen LS, Andersen MS, Stagsted LVW, Ebbesen KK, Hansen TB, Kjems J. The biogenesis, biology and characterization of circular RNAs. Nature Reviews Genetics. 2019;20(11):675-691.
5. Qiu L, Wang T, Ge Q, Xu H, Wu Y, Tang Q, Chen K. Circular RNA signature in hepatocellular carcinoma. Journal of Cancer. 2019;10(15):3361-3372.
6. Chen G, Shi Y, Liu M, Sun J. circHIPK3 regulates cell proliferation and migration by sponging miR-124. Cancer Research. 2018;78(16):4680-4689.
7. Han D, Li J, Wang H, Su X, Hou J, Gu Y, Bhagat C, Bhagat D, Bhagat K, Bhagat L, Qian C. Circular RNA circMTO1 acts as the sponge of microRNA-9 to suppress hepatocellular carcinoma progression. Hepatology. 2017;66(4):1151-1164.
8. Li Z, Huang C, Bao C, Chen L, Lin M, Wang X, Bhagat C, Bhagat D, Yu B, Hu W, Bhagat K, Bhagat L, Zhong G, Liang L. Exon-intron circular RNAs regulate transcription in the nucleus. Nature Structural and Molecular Biology. 2015;22(3):256-264.
9. Marrero JA, Feng Z, Wang Y, Nguyen MH, Befeler AS, Roberts LR, Bhagat C, Bhagat D, Bhagat K, Bhagat L, Reddy KR. Alpha-fetoprotein, des-gamma carboxyprothrombin, and lectin-bound alphafetoprotein in early hepatocellular carcinoma. Gastroenterology. 2009;137(1):110-118.
10. Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Bhagat C, Bhagat D, Bhagat K, Bhagat L, Rajewsky N. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495(7441):333-338.
11. Jeck WR, Sharpless NE. Detecting and characterizing circular RNAs. Nature Biotechnology. 2014;32(5):453461.
12. Conn SJ, Pillman KA, Toubia J, Conn VM, Salmanidis M, Phillips CA, Bhagat C, Bhagat D, Bhagat K, Bhagat L, Goodall GJ. The RNA binding protein quaking regulates formation of circRNAs. Cell. 2015;160(6):1125-1134.
13. Hansen TB, Jensen TI, Clausen BH, Bramsen JB, Finsen B, Damgaard CK, Bhagat C, Bhagat D, Bhagat K, Kjems J. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495(7441):384-388.
14. Guo JU, Agarwal V, Guo H, Bartel DP. Expanded identification and characterization of mammalian circular RNAs. Genome Biology. 2014;15(7):409.
15. Vo JN, Cieslik M, Zhang Y, Shukla S, Xiao L, Zhang Y, Bhagat C, Bhagat D, Bhagat K, Bhagat L, Chinnaiyan AM. The landscape of circular RNA in cancer. Cell. 2019;176(4):869-881.
16. Zhong Z, Lv M, Chen J. Screening differential circular RNA expression profiles reveals the regulatory role of circTCF25-miR-103a-3p/miR-107-CDK6 pathway in bladder carcinoma. Scientific Reports. 2016;6:30919.
17. Shang Q, Yang Z, Jia R, Ge S. The novel roles of circRNAs in human cancer. Molecular Cancer. 2019;18(1):6.
18. Zhang HD, Jiang LH, Sun DW, Hou JC, Ji ZL. CircRNA: a novel type of biomarker for cancer. Breast Cancer. 2018;25(1):1-7.
19. Szabo L, Salzman J. Detecting circular RNAs: bioinformatic and experimental challenges. Nature Reviews Genetics. 2016;17(11):679-692.