Extracellular Vesicle-derived circular RNAs confers chemoresistance in Colorectal cancer.
Kha Wai HonNurul Syakima Ab MutalibNik Muhd Aslan AbdullahRahman JamalNadiah AbuPublished in: Scientific reports (2019)
Chemo-resistance is associated with poor prognosis in colorectal cancer (CRC), with the absence of early biomarker. Exosomes are microvesicles released by body cells for intercellular communication. Circular RNAs (circRNAs) are non-coding RNAs with covalently closed loops and enriched in exosomes. Crosstalk between circRNAs in exosomes and chemo-resistance in CRC remains unknown. This research aims to identify exosomal circRNAs associated with FOLFOX-resistance in CRC. FOLFOX-resistant HCT116 CRC cells (HCT116-R) were generated from parental HCT116 cells (HCT116-P) using periodic drug induction. Exosomes were characterized using transmission electron microscopy (TEM), Zetasizer and Western blot. Our exosomes were translucent cup-shaped structures under TEM with differential expression of TSG101, CD9, and CD63. We performed circRNAs microarray using exosomal RNAs from HCT116-R and HCT116-P cells. We validated our microarray data using serum samples. We performed drug sensitivity assay and cell cycle analysis to characterize selected circRNA after siRNA-knockdown. Using fold change >2 and p < 0.05, we identified 105 significantly upregulated and 34 downregulated circRNAs in HCT116-R exosomes. Knockdown of circ_0000338 improved the chemo-resistance of CRC cells. We have proposed that circ_0000338 may have dual regulatory roles in chemo-resistant CRC. Exosomal circ_0000338 could be a potential biomarker for further validation in CRC.
Keyphrases
- cell cycle arrest
- induced apoptosis
- cell death
- mesenchymal stem cells
- pi k akt
- stem cells
- cell cycle
- poor prognosis
- photodynamic therapy
- cancer therapy
- emergency department
- long non coding rna
- endoplasmic reticulum stress
- combination therapy
- high throughput
- locally advanced
- south africa
- drug induced
- mass spectrometry
- artificial intelligence
- electron microscopy
- deep learning