Identification of novel T cell proliferation patterns, potential biomarkers and therapeutic drugs in colorectal cancer
- Xu Wang 1, Shixin Chan 1, Longfei Dai 1, Yuanmin Xu 1, Qi Yang 2, Ming Wang 1, Qijun Han 1, Jiajie Chen 3, Xiaomin Zuo 1, Zhenglin Wang 1, Yang Yang 1, Hu Zhao 1, Guihong Zhang 4, Huabing Zhang 5,6, Wei Chen 1
- Xu Wang 1, Shixin Chan 1, Longfei Dai 1
- 1Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China.
- 2Department of Gastroenterology, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241000, China.
- 3Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China.
- 4The Pathology Department of Anhui Medical University, Hefei 230032, Anhui, China.
- 5Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei 230032, Anhui, China.
- 6The First Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou 239000, Anhui, China.
- 0Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies T cell proliferation-related genes (TRGs) impacting colorectal cancer (CRC) prognosis and treatment. A predictive signature based on TRGs helps stratify patients and guides therapeutic strategies for better CRC outcomes.
Area Of Science
- Immunology
- Oncology
- Bioinformatics
Background
- T cells are vital for antitumor immunity.
- The role of T cell proliferation-related genes (TRGs) in colorectal cancer (CRC) prognosis and treatment response is not well understood.
Purpose Of The Study
- To investigate the impact of TRGs on colorectal cancer (CRC) patient prognosis and therapeutic responses.
- To develop a predictive signature for CRC based on TRGs.
Main Methods
- Bioinformatic analysis of 33 TRGs and clinical data from multiple CRC patient datasets.
- Consensus clustering to identify molecular subtypes and Lasso-Cox regression to build a predictive signature.
- Tumor immune microenvironment analysis, biomarker screening, and in vitro/in vivo validation of therapeutic drugs.
Main Results
- Two TRG clusters and three gene clusters were identified in CRC patients, correlating with survival, immune cells, and functions.
- A validated predictive signature based on prognosis-associated DEGs was developed to assess patient risk.
- Key TRGs (CDK1, BATF, IL1RN, ITM2A) were identified, and 7,8-benzoflavone demonstrated significant suppression of CRC cell proliferation and migration.
Conclusions
- T cell proliferation-based molecular subtypes and predictive signatures can predict CRC patient outcomes, immune landscape, and treatment response.
- Novel biomarker candidates and potential therapeutic agents, like 7,8-benzoflavone, were identified and validated for CRC treatment.
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