br Although cancer cell membranes can enhance the
Although cancer cell membranes can enhance the biocompatibility,
the MSN itself used in our work is non-degradable, which might be a serious concern for clinical usage. Therefore, based on the present na-nosystem, further incorporation with other biodegradable nanopaticle, such as mesoporous organo-silica nanoparticles [42,43], will be more valuable for cancer treatment.
In conclusion, we have successfully constructed a biocompatible, simple structured and tumor acidic environment responsive drug de-livery system by successive capping the CaCO3 and cloaking cancer cell membrane on mesoporous silica nanoparticles (DOX/MSN/ CaCO3@CM). The synthesized MSNs are monodisperse nanoparticles with a size of about 100 nm, and the modification of the surface does not aﬀect the morphology of nanoparticles. The in vitro investigations demonstrated that the drug release from the obtained DOX/MSN/ CaCO3@CM was confined by CaCO3 layer, but would be triggered by tumor microenvironment and intracellular endosome/lysosomes for delivering DOX into cancer cells like LNCaP-AI cells, and ultimately induced cell death. Moreover, DOX/MSN/CaCO3@CM also showed a favorable anti-tumor eﬀect in the LNCaP-AI tumor model, evidenced by the significant tumor growth delay, tumor cells destruction, as well as reduced tumor cells proliferation. Overall, this study provides a pro-mising alternative as Oxaliplatin responsive drug delivery for eﬃcient cancer therapy.
Appendix A. Supplementary data
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Journal of Biomedical Informatics
journal homepage: www.elsevier.com/locate/yjbin
Cancer classification and pathway discovery using non-negative matrix factorization
Zexian Zenga, Andy H. Vob, Chengsheng Maoa, Susan E. Clarec, , Seema A. Khanc, , Yuan Luoa,
a Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA b Committee on Developmental Biology and Regenerative Medicine, The University of Chicago, Chicago, IL, USA c Department of Surgery, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
Non-negative matrix factorization
Objectives: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type. Design: We applied non-smooth non-negative matrix factorization (nsNMF) and support vector machine (SVM) to utilize the full range of sequencing data, aiming to better aggregate genetic mutations and improve their power to predict disease type. More specifically, we introduce a novel classifier to distinguish cancer types using somatic mutations obtained from whole-exome sequencing data. Mutations were identified from multiple can-cers and scored using SIFT, PP2, and CADD, and collapsed at the individual gene level. nsNMF was then applied to reduce dimensionality and obtain coefficient and basis matrices. A feature matrix was derived from the obtained matrices to train a classifier for cancer type classification with the SVM model.
Results: We have demonstrated that the classifier was able to distinguish four cancer types with reasonable accuracy. In five-fold cross-validations using mutation counts as features, the average prediction accuracy was 80% (SEM = 0.1%), significantly outperforming baselines and outperforming models using mutation scores as features.
Conclusion: Using the factor matrices derived from the nsNMF, we identified multiple genes and pathways that are significantly associated with each cancer type. This study presents a generic and complete pipeline to study the associations between somatic mutations and cancers. The proposed method can be adapted to other studies for disease status classification and pathway discovery.