$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Q-omics: Smart Software for Assisting Oncology and Cancer Research 원문보기

Molecules and cells, v.44 no.11, 2021년, pp.843 - 850  

Lee, Jieun (Department of Biological Sciences, Sookmyung Women's University) ,  Kim, Youngju (Department of Biological Sciences, Sookmyung Women's University) ,  Jin, Seonghee (Department of Biological Sciences, Sookmyung Women's University) ,  Yoo, Heeseung (Department of Biological Sciences, Sookmyung Women's University) ,  Jeong, Sumin (Department of Biological Sciences, Sookmyung Women's University) ,  Jeong, Euna (Research Institute of Women's Health, Sookmyung Women's University) ,  Yoon, Sukjoon (Department of Biological Sciences, Sookmyung Women's University)

Abstract AI-Helper 아이콘AI-Helper

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computationa...

주제어

참고문헌 (29)

  1. Aran, D., Hu, Z., and Butte, A.J. (2017). xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220. 

  2. Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A.A., Kim, S., Wilson, C.J., Lehar, J., Kryukov, G.V., Sonkin, D., et al. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603-607. 

  3. Biswas, A., Haldane, A., Arnold, E., and Levy, R.M. (2019). Epistasis and entrenchment of drug resistance in HIV-1 subtype B. Elife 8, e50524. 

  4. Cancer Genome Atlas Research Network, Weinstein, J.N., Collisson, E.A., Mills, G.B., Shaw, K.R., Ozenberger, B.A., Ellrott, K., Shmulevich, I., Sander, C., and Stuart, J.M. (2013). The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113-1120. 

  5. Cao, R., Yuan, L., Ma, B., Wang, G., Qiu, W., and Tian, Y. (2020). An EMT-related gene signature for the prognosis of human bladder cancer. J. Cell. Mol. Med. 24, 605-617. 

  6. Cerami, E., Gao, J., Dogrusoz, U., Gross, B.E., Sumer, S.O., Aksoy, B.A., Jacobsen, A., Byrne, C.J., Heuer, M.L., Larsson, E., et al. (2012). The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401-404. 

  7. Eckstein, M., Strissel, P., Strick, R., Weyerer, V., Wirtz, R., Pfannstiel, C., Wullweber, A., Lange, F., Erben, P., Stoehr, R., et al. (2020). Cytotoxic T-cell-related gene expression signature predicts improved survival in muscle-invasive urothelial bladder cancer patients after radical cystectomy and adjuvant chemotherapy. J. Immunother. Cancer 8, e000162. 

  8. Garnett, M.J., Edelman, E.J., Heidorn, S.J., Greenman, C.D., Dastur, A., Lau, K.W., Greninger, P., Thompson, I.R., Luo, X., Soares, J., et al. (2012). Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570-575. 

  9. Ghandi, M., Huang, F.W., Jane-Valbuena, J., Kryukov, G.V., Lo, C.C., McDonald, E.R., 3rd, Barretina, J., Gelfand, E.T., Bielski, C.M., Li, H., et al. (2019). Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503-508. 

  10. Guan, N.N., Zhao, Y., Wang, C.C., Li, J.Q., Chen, X., and Piao, X. (2019). Anticancer drug response prediction in cell lines using weighted graph regularized matrix factorization. Mol. Ther. Nucleic Acids 17, 164-174. 

  11. He, N., Kim, N., Song, M., Park, C., Kim, S., Park, E.Y., Yim, H.Y., Kim, K., Park, J.H., Kim, K.I., et al. (2014). Integrated analysis of transcriptomes of cancer cell lines and patient samples reveals STK11/LKB1-driven regulation of cAMP phosphodiesterase-4D. Mol. Cancer Ther. 13, 2463-2473. 

  12. Hong, Y., Kim, N., Li, C., Jeong, E., and Yoon, S. (2017). Patient sample-oriented analysis of gene expression highlights extracellular signatures in breast cancer progression. Biochem. Biophys. Res. Commun. 487, 307-312. 

  13. Iorio, F., Knijnenburg, T.A., Vis, D.J., Bignell, G.R., Menden, M.P., Schubert, M., Aben, N., Goncalves, E., Barthorpe, S., Lightfoot, H., et al. (2016). A landscape of pharmacogenomic interactions in cancer. Cell 166, 740-754. 

  14. Jeong, E., Lee, Y., Kim, Y., Lee, J., and Yoon, S. (2020). Analysis of cross-association between mRNA expression and RNAi efficacy for predictive target discovery in colon cancers. Cancers (Basel) 12, 3091. 

  15. Kim, N., Yim, H.Y., He, N., Lee, C.J., Kim, J.H., Choi, J.S., Lee, H.S., Kim, S., Jeong, E., Song, M., et al. (2016). Cardiac glycosides display selective efficacy for STK11 mutant lung cancer. Sci. Rep. 6, 29721. 

  16. Kitsou, M., Ayiomamitis, G.D., and Zaravinos, A. (2020). High expression of immune checkpoints is associated with the TIL load, mutation rate and patient survival in colorectal cancer. Int. J. Oncol. 57, 237-248. 

  17. Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., Li, B., and Liu, X.S. (2020). TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 48(W1), W509-W514. 

  18. Li, W., Wang, H., Ma, Z., Zhang, J., Ou-Yang, W., Qi, Y., and Liu, J. (2019). Multi-omics analysis of microenvironment characteristics and immune escape mechanisms of hepatocellular carcinoma. Front. Oncol. 9, 1019. 

  19. Li, Y., Umbach, D.M., Krahn, J.M., Shats, I., Li, X., and Li, L. (2021). Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines. BMC Genomics 22, 272. 

  20. McFarland, J.M., Ho, Z.V., Kugener, G., Dempster, J.M., Montgomery, P.G., Bryan, J.G., Krill-Burger, J.M., Green, T.M., Vazquez, F., Boehm, J.S., et al. (2018). Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. Nat. Commun. 9, 4610. 

  21. Meyers, R.M., Bryan, J.G., McFarland, J.M., Weir, B.A., Sizemore, A.E., Xu, H., Dharia, N.V., Montgomery, P.G., Cowley, G.S., Pantel, S., et al. (2017). Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779-1784. 

  22. Monks, A., Zhao, Y., Hose, C., Hamed, H., Krushkal, J., Fang, J., Sonkin, D., Palmisano, A., Polley, E.C., Fogli, L.K., et al. (2018). The NCI Transcriptional Pharmacodynamics Workbench: a tool to examine dynamic expression profiling of therapeutic response in the NCI-60 cell line panel. Cancer Res. 78, 6807-6817. 

  23. Park, C., Lee, Y., Je, S., Chang, S., Kim, N., Jeong, E., and Yoon, S. (2019). Overexpression and selective anticancer efficacy of ENO3 in STK11 mutant lung cancers. Mol. Cells 42, 804-809. 

  24. Rhodes, D.R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., Barrette, T., Pandey, A., and Chinnaiyan, A.M. (2004). ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6, 1-6. 

  25. Shen, Y., Liu, J., Zhang, L., Dong, S., Zhang, J., Liu, Y., Zhou, H., and Dong, W. (2019). Identification of potential biomarkers and survival analysis for head and neck squamous cell carcinoma using bioinformatics strategy: a study based on TCGA and GEO datasets. Biomed Res. Int. 2019, 7376034. 

  26. Shi, B., Ding, J., Qi, J., and Gu, Z. (2021). Characteristics and prognostic value of potential dependency genes in clear cell renal cell carcinoma based on a large-scale CRISPR-Cas9 and RNAi screening database DepMap. Int. J. Med. Sci. 18, 2063-2075. 

  27. Yang, D., Khan, S., Sun, Y., Hess, K., Shmulevich, I., Sood, A.K., and Zhang, W. (2011). Association of BRCA1 and BRCA2 mutations with survival, chemotherapy sensitivity, and gene mutator phenotype in patients with ovarian cancer. JAMA 306, 1557-1565. 

  28. Yang, W., Soares, J., Greninger, P., Edelman, E.J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J.A., Thompson, I.R., et al. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41(Database issue), D955-D961. 

  29. Zhong, Z., Hong, M., Chen, X., Xi, Y., Xu, Y., Kong, D., Deng, J., Li, Y., Hu, R., Sun, C., et al. (2020). Transcriptome analysis reveals the link between lncRNA-mRNA co-expression network and tumor immune microenvironment and overall survival in head and neck squamous cell carcinoma. BMC Med. Genomics 13, 57. 

LOADING...

관련 콘텐츠

원문 보기

원문 URL 링크

*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로