Back

Red blood cell-derived extracellular vesicles with miR-204 mimic loading for pediatric neuroblastoma treatment

Chiangjong, W.; Panachan, J.; Keadsanti, S.; Newburg, D. S.; Morrow, A. L.; Hongeng, S.; Chutipongtanate, S.

2023-05-30 bioengineering
10.1101/2023.05.29.542704 bioRxiv
Show abstract

Neuroblastoma (NB) is the most common extracranial solid tumor in pediatric population with a high degree of heterogeneity in clinical outcomes, ranging from spontaneous remission to rapid progression and death. Upregulation of a tumor suppressor miR-204 in patient-derived neuroblastoma tumors was associated with good prognosis independent of known risk factors. While miR-204 is recognized as a therapeutic candidate, its delivery was unavailable. This study aimed to develop red blood cell-derived extracellular vesicles (RBC-EVs) as the miR-204 carrier and evaluate the inhibitory activity against neuroblastoma cell lines and spheroids. MiR-204 mimics were loaded into RBC-EVs (RBC-EVmiR-204) by electroporation with the optimized parameters of 250 V, 20 ms, 10 pulsing times. RBC-EVmiR-204, but not the native RBC-EVs, could inhibit cell viability, migration and spheroid formation and growth of MYCN-amp and MYCN non-amplification (MYCN-NA) NB cells, even though the suppressive effects were more preferable in MYCN-amp NB. For the mechanistic insight, SWATH-proteomics suggested that RBC-EVmiR-204 induced dysregulation of ribosomal proteins and alterations in RNA metabolism, leading to inhibiting neuroblastoma progression. This study developed RBC-EVmiR-204 as an alternative/adjunct therapy of pediatric neuroblastoma. The therapeutic efficacy of RBC-EVmiR-204 should be further investigated in preclinical models and clinical studies.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.