You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Identification of miRNA modulators to PARP inhibitor response. Based on the principle of synthetic lethality, PARP inhibitors have been shown to be very effective in killing cells deficient in homologous recombination (HR), such as those bearing mutations in BRCA1/2. However, questions regarding their wider use persist and other determinants of responsiveness to PARP inhibitor remain to be fully explored. MicroRNAs (miRNAs) are small non-coding RNAs, which serve as post-transcriptional regulators of gene expression and are involved in a wide variety of cellular processes, including the DNA damage response (DDR). However, little is known about whether miRNAs might influence sensitivity to PARP inhibitors. To investigate this, we performed a high throughput miRNA mimetic screen, which identified several miRNAs whose over-expression results in sensitization to the clinical PARP inhibitor olaparib. In particular, our findings indicate that hsa-miR-107 and hsa-miR-222 regulate the DDR and sensitise tumour cells to olaparib by repressing expression of RAD51, thus impairing DSB repair by HR. Moreover, elevated expression of hsa-miR-107 has been observed in a subset of ovarian clear cell carcinomas, which correlates with PARP inhibitor sensitivity and reduced RAD51 expression. Taken together, these observations raise the possibility that these miRNAs could be used as biomarkers to identify patients that may benefit from treatment with PARP inhibitors.
{"Chemical": []}