RESUMO
Tanshinone IIA is a lipophilic organic compound from the root of Danshen (Salvia miltiorrhiza) and is one of the most well-known Tanshinone molecules by pharmacologists. In recent years, in addition to effects of anti-cardiovascular and neurological diseases, Tanshinone IIA has also shown some degrees of anti-prostate cancer potential. Although they do have some studies focusing on the molecular mechanism of Tanshinone IIA's anti-prostate cancer effects, a further understanding on the transcriptomic and structural level is still lacking. In this study, transcriptomic sequencing technology and computer technology were employed to illustrate the effects of Tanshinone IIA on prostate cancer through bioinformatic analysis and molecular dynamics simulation, and PPARG was considered to be one of the targets for Tanshinone IIA according to docking scoring and dynamic calculation. Our study provides a novel direction to further understand the mechanism of the effects of Tanshinone IIA on prostate cancer, and further molecular biological studies need to be carried on to further investigate the molecular mechanism of Tanshinone IIA's anti-prostate cancer effect through PPARG.
Assuntos
PPAR gama , Neoplasias da Próstata , Humanos , Masculino , Simulação de Acoplamento Molecular , TranscriptomaRESUMO
Chinese herbal medicine (CHM), which includes herbal slices and proprietary products, is widely used in China. Shenqi Dihuang (SQDH) is a traditional Chinese medicine (TCM) formula with ingredients that affect tumor growth. Despite recent advances in prognosis, patients with renal cell carcinoma (RCC) cannot currently receive curative treatment. The present study aimed to explore the potential target genes closely associated with SQDH. The gene expression data for SQDH and RCC were obtained from the TCMSP and TCGA databases. The SQDH-based prognostic prediction model reveals a strong correlation between RCC and SQDH. In addition, the immune cell infiltration analysis indicated that SQDH might be associated with the immune response of RCC patients. Based on this, we successfully built the prognostic prediction model using SQDH-related genes. The results demonstrated that CCND1 and NR3C2 are closely associated with the prognosis of RCC patients. Finally, the pathways enrichment analysis revealed that response to oxidative stress, cyclin binding, programmed cell death, and immune response are the most enriched pathways in CCND1. Furthermore, transcription regulator activity, regulation of cell population proliferation, and cyclin binding are closely associated with the NR3C2.