2 research outputs found
Doxorubicin-induced mitophagy contributes to drug resistance in cancer stem cells from HCT8 human colorectal cancer cells
No full textNagasaki University (長崎大学)博士(医学)Cancer stem cells (CSCs) are known to be drug resistant. Mitophagy selectively degrades unnecessary or damaged mitochondria by autophagy during cellular stress. To investigate the potential role of mitophagy in drug resistance in CSCs, we purified CD133+/CD44+ CSCs from HCT8 human colorectal cancer cells and then exposed to doxorubicin (DXR). Compared with parental cells, CSCs were more resistant to DXR treatment. Although DXR treatment enhanced autophagy levels in both cell types, the inhibition of autophagy by ATG7 silencing significantly increased the toxicity of DXR only in parental cells, not in CSCs. Interestingly, the level of mitochondrial superoxide was detected to be significantly lower in CSCs than in parental cells after DXR treatment. Furthermore, the mitophagy level and expression of BNIP3L, a mitophagy regulator, were significantly higher in CSCs than in parental cells after DXR treatment. Silencing BNIP3L significantly halted mitophagy and enhanced the sensitivity to DXR in CSCs. Our data suggested that mitophagy, but not non-selective autophagy, likely contributes to drug resistance in CSCs isolated from HCT8 cells. Further studies in other cancer cell lines will be needed to confirm our findings.長崎大学学位論文 学位記番号:博(医歯薬)甲第1065号 学位授与年月日:平成30年3月20日Author: Chen Yan, Lan Luo, Chang-Ying Guo, Shinji Goto, Yoshishige Urata, Jiang-Hua Shao, Tao-Sheng LiCitation: Cancer Letters, 388, pp.34-42; 2017Nagasaki University (長崎大学), 博士(医学) (2018-03-20)doctoral thesi
[[alternative]]The feasibility of applying Latent Semantic Analysis to analyze Item similarity
No full text[[abstract]]The purpose of this study is to apply latent semantic analysis (LSA) to analyze item similarity , and discuss the result of using different score function. The feature of LSA model is “Lexically Co-occur” detection , in other words, LSA model can analyze many documents, and find synonyms , but synonyms rarely exist in the same item , so LSA model needs to be trained by documents which are related to this item . This study revealed that the result using dice measure or inner product measure correlates more closely with expert’s scores. For the items which is more agreeable of expert’s scores than others , the maximum correlation is up to 0.9, and the mean of correlation is up to 0.7, so applying latent semantic analysis to analyze item similarity is a feasible technology.
