| Chapter title |
Progress in Cancer Immunotherapy
|
|---|---|
| Chapter number | 2 |
| Book title |
Progress in Cancer Immunotherapy
|
| Published in |
Advances in experimental medicine and biology, January 2016
|
| DOI | 10.1007/978-94-017-7555-7_2 |
| Pubmed ID | |
| Book ISBNs |
978-9-40-177553-3, 978-9-40-177555-7
|
| Authors |
Liu, Ronghua, Luo, Feifei, Liu, Xiaoming, Wang, Luman, Yang, Jiao, Deng, Yuting, Huang, Enyu, Qian, Jiawen, Lu, Zhou, Jiang, Xuechao, Zhang, Dan, Chu, Yiwei, Ronghua Liu, Feifei Luo, Xiaoming Liu, Luman Wang, Jiao Yang, Yuting Deng, Enyu Huang, Jiawen Qian, Zhou Lu, Xuechao Jiang, Dan Zhang, Yiwei Chu |
| Abstract |
Biological response modifiers (BRMs) emerge as a lay of new compounds or approaches used in improving cancer immunotherapy. Evidences highlight that cytokines, Toll-like receptor (TLR) signaling, and noncoding RNAs are of crucial roles in modulating antitumor immune response and cancer-related chronic inflammation, and BRMs based on them have been explored. In particular, besides some cytokines like IFN-α and IL-2, several Toll-like receptor (TLR) agonists like BCG, MPL, and imiquimod are also licensed to be used in patients with several malignancies nowadays, and the first artificial small noncoding RNA (microRNA) mimic, MXR34, has entered phase I clinical study against liver cancer, implying their potential application in cancer therapy. According to amounts of original data, this chapter will review the regulatory roles of TLR signaling, some noncoding RNAs, and several key cytokines in cancer and cancer-related immune response, as well as the clinical cases in cancer therapy based on them. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 17% |
| Student > Bachelor | 5 | 14% |
| Student > Doctoral Student | 4 | 11% |
| Professor > Associate Professor | 4 | 11% |
| Student > Master | 3 | 8% |
| Other | 5 | 14% |
| Unknown | 9 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 33% |
| Agricultural and Biological Sciences | 4 | 11% |
| Immunology and Microbiology | 4 | 11% |
| Nursing and Health Professions | 2 | 6% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 10 | 28% |