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您的检索式：作者名="Qingqing Sang"
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| 1 | Specific generation of nitric oxide in mitochondria of cancer cell for selective oncotherapy显示文摘Nitric oxide(NO)gas therapy,especially,L-arginine(L-Arg)-based NO treatment strategies have attracted extensive attention in the field of oncotherapy.However,current strategies are unable to differentiate well between normal cells and cancer cells,which may lead to unpredictable toxicity.Motivated by the fact that mitochondria of cancer cells can express excessive nitric oxide synthetase(NOS),herein,a nanozyme-based NO generator,cerium oxide(CeO_(2))-AT,is fabricated to specifically catalyze the production of NO in cancer cells for selective tumor treatment.In this system,after being endocytosed into cancer cells,the generator can produce a number of NO under the catalysis of NOS in mitochondria of cancer cells,which can disrupt the mitochondrial respiratory chain of tumor cells and further induce cell apoptosis.In addition,the generator with catalase(CAT)-like activity can catalyze H_(2)O_(2)to produce O_(2),which can promote the generation of NO and improve the performance of NO gas therapy.What is more,our system has no obvious impact on the viability of normal cells owing to the less production of NO.Our work paves a new way for the development of highly selective NO-based treatment particularly useful for the safe and specific cancer therapy. | Mengyu Sun Yanjuan Sang Qingqing Deng Zhengwei Liu Jinsong Ren Xiaogang Qu | 2022 | Nano Research2022，15，6： | 1 |
| 2 | Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer显示文摘The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits.Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability.Mechanism-driven strategy has recently emerged,aiming to build signatures with both predictive power and explanatory power.Driven by this strategy,we developed a robust gene dysregulation analysis framework with machine learning algorithms,which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration.We then applied the framework to a colorectal cancer(CRC)cohort from The Cancer Genome Atlas.The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect.By choosing dysregulations with greedy strategy,we built a four-dysregulation(4-DysReg)signature,which has the capability of predicting prognosis and adjuvant chemotherapy benefit.4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation.These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine,and furthermore,elucidate the mechanisms of carcinogenesis. | Quanxue Li Wentao Dai jixiang Liu Qingqing Sang Yi-Xue Li Yuan-Yuan Li | 2020 | Journal of Molecular Cell Biology2020，12，11： | 0 |
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