Xin Feng1, Wenqing Ding1, Junhong Ma1, Baijun Liu1, Hongmei Yuan1.
Abstract
BACKGROUND: Lung cancer is the most common malignant cancer worldwide. Targeted therapies have emerged as a promising treatment strategy for lung cancers.
OBJECTIVE: To evaluate the current landscape of targets and find promising targets for future new drug discovery for lung cancers, this research identified the science-technology-clinical development pattern and mapped the interaction network of targets.
METHODS: Targets for cancers were classified into 3 groups based on a paper published in Nature. We searched for scientific pieces of literature, patent documents and clinical trials of targets in Group 1 and Group 2 for lung cancers. Then, a target-target interaction network of Group 1 was constructed, and the science-technology-clinical (S-T-C) development patterns of targets in Group 1 were identified. Finally, based on the cluster distribution and the development pattern of targets in Group 1, interactions between the targets were employed to predict potential targets in Group 2 for drug development.
RESULTS: The target-target interaction (TTI) network of group 1 resulted in 3 clusters with different developmental stages. The potential targets in Group 2 are divided into 3 ranks. Level-1 is the first priority and level-3 is the last. Level-1 includes 16 targets, such as STAT3, CRKL, and PTPN11, that are mostly involved in signaling transduction pathways. Level-2 and level-3 contain 8 and 6 targets, respectively, related to various biological functions.
CONCLUSION: This study will provide references for drug development in lung cancers, emphasizing that priorities should be given to targets in Level-1, whose mechanisms are worth further exploration. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
BACKGROUND: Lung cancer is the most common malignant cancer worldwide. Targeted therapies have emerged as a promising treatment strategy for lung cancers.
OBJECTIVE: To evaluate the current landscape of targets and find promising targets for future new drug discovery for lung cancers, this research identified the science-technology-clinical development pattern and mapped the interaction network of targets.
METHODS: Targets for cancers were classified into 3 groups based on a paper published in Nature. We searched for scientific pieces of literature, patent documents and clinical trials of targets in Group 1 and Group 2 for lung cancers. Then, a target-target interaction network of Group 1 was constructed, and the science-technology-clinical (S-T-C) development patterns of targets in Group 1 were identified. Finally, based on the cluster distribution and the development pattern of targets in Group 1, interactions between the targets were employed to predict potential targets in Group 2 for drug development.
RESULTS: The target-target interaction (TTI) network of group 1 resulted in 3 clusters with different developmental stages. The potential targets in Group 2 are divided into 3 ranks. Level-1 is the first priority and level-3 is the last. Level-1 includes 16 targets, such as STAT3, CRKL, and PTPN11, that are mostly involved in signaling transduction pathways. Level-2 and level-3 contain 8 and 6 targets, respectively, related to various biological functions.
CONCLUSION: This study will provide references for drug development in lung cancers, emphasizing that priorities should be given to targets in Level-1, whose mechanisms are worth further exploration. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Entities:
Keywords:
Lung cancer; progress andzzm321990prospects; science-technology-clinic; signal transduction; target prediction.; target-target interaction network; targeted therapies
Mesh：
Substances：
Year: 2021
PMID： 34132185 DOI： 10.2174/1574892816666210615161501
Source DB: PubMed Journal: Recent Pat Anticancer Drug Discov ISSN： 1574-8928 Impact factor: 4.169