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Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods.
Lin, Xiandong; Ma, QingLan; Chen, Lei; Guo, Wei; Huang, Zhiyi; Huang, Tao; Cai, Yu-Dong.
Afiliación
  • Lin X; Laboratory of Radiation Oncology and Radiobiology, Clinical Oncology School of Fujian Medical University and Fujian Cancer Hospital, Fuzhou 350014, China. Electronic address: linxdong1970@fjzlhospital.com.
  • Ma Q; School of Life Sciences, Shanghai University, Shanghai 200444, China.
  • Chen L; College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
  • Guo W; Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200030, China.
  • Huang Z; College of Chemistry, Fuzhou University, Fuzhou 350000, China.
  • Huang T; Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition a
  • Cai YD; School of Life Sciences, Shanghai University, Shanghai 200444, China. Electronic address: caiyudong@staff.shu.edu.cn.
Biochim Biophys Acta Gen Subj ; 1867(12): 130484, 2023 12.
Article en En | MEDLINE | ID: mdl-37805078
BACKGROUND: Targeted therapy has revolutionized cancer treatment, greatly improving patient outcomes and quality of life. Lung cancer, specifically non-small cell lung cancer, is frequently driven by the G12C mutation at the KRAS locus. The development of KRAS inhibitors has been a breakthrough in the field of cancer research, given the crucial role of KRAS mutations in driving tumor growth and progression. However, over half of patients with cancer bypass inhibition show limited response to treatment. The mechanisms underlying tumor cell resistance to this treatment remain poorly understood. METHODS: To address above gap in knowledge, we conducted a study aimed to elucidate the differences between tumor cells that respond positively to KRAS (G12C) inhibitor therapy and those that do not. Specifically, we analyzed single-cell gene expression profiles from KRAS G12C-mutant tumor cell models (H358, H2122, and SW1573) treated with KRAS G12C (ARS-1620) inhibitor, which contained 4297 cells that continued to proliferate under treatment and 3315 cells that became quiescent. Each cell was represented by the expression levels on 8687 genes. We then designed an innovative machine learning based framework, incorporating seven feature ranking algorithms and four classification algorithms to identify essential genes and establish quantitative rules. RESULTS: Our analysis identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, that are known to be associated with the progression of multiple cancers. CONCLUSION: Above genes were relevant to tumor cell resistance to targeted therapy. This study provides important insights into the molecular mechanisms underlying tumor cell resistance to KRAS inhibitor treatment.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Risk_factors_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: Biochim Biophys Acta Gen Subj Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Risk_factors_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: Biochim Biophys Acta Gen Subj Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos