Project description:593 FFPE colorectal cancer samples were used to generate three prediction models: Recurrence prediction, 5FU efficacy prediction, and FOLFOX efficacy prediction The recurrence prediction study has 235 samples , 5FU efficacy prediction study has 192 samples, FOLFOX had 166 samples
Project description:Colorectal Cancer (CRC) is one of most common cancers in the world and a main treatment in postoperative chemotherapy is oxaliplatin and fluorouracil (FOLFOX), But the effect is different among CRC patients. In this study, LC-MS/MS strategy was used to profile the plasma proteome in FOLFOX benefit and futile group. As a result, a panel of plasma proteins by machine learning from our data was verified for a possible prediction tool in postdiagnosis.
Project description:The combination of FOLFOX and bevacizumab (FOLFOX-Bev) is a promising treatment for advanced colorectal cancer (CRC). To gain insights into the cellular changes associated with FOLFOX-Bev treatment, we conducted single-cell transcriptomic analysis of CRC samples derived from a patient before and after treatment. Our results show that cancer cells with high proliferative, metastatic, and pro-angiogenic properties respond better to FOLFOX-Bev treatment. Moreover, FOLFOX-Bev enhances CD8+ T cell cytotoxicity, thereby boosting the anti-tumor immune response. Conversely, FOLFOX-Bev impairs the functionality of tumor-associated macrophages, plasma cells, and cancer-associated fibroblasts, leading to a decrease in VEGFB-mediated angiogenesis. Furthermore, FOLFOX-Bev treatment reset intercellular communication, which could potentially affect the function of non-cancer cells. Our findings provide valuable insights into the molecular mechanisms underlying the response of advanced CRC to FOLFOX-Bev treatment and highlight potential targets for improving the efficacy of this treatment strategy.
Project description:Herein, we assembled a cohort composed of 254 CRC patients, including discovery cohort (N = 124) and validation cohort (N = 130), which received either chemotherapy (mainly FOLFOX therapy), chemoradiotherapy (mainly FOLFOX combined with radiotherapy), or targeted combination therapy (mainly FOLFOX combined with cetuximab).
Project description:The aim of this study is to identify responders to FOLFOX therapy by applying the Random Forests (RF) algorithm to gene expression data. Eighty-three unresectable colorectal cancer (CRC) patients including 42 responders and 41 non-responders were divided into training (54 patients) and test (29 patients) sets. Samples were divided (approximately 2:1 ratio) into training and test sets. As a result, 54 of 83 samples obtained in the first half of this period were selected for the training set, and the remaining 29 samples in the latter half were selected as the test set.
