Robotics & Machine Learning Daily News2024,Issue(Feb.7) :85-86.

Fourth Hospital of Hebei Medical University Reports Findings in Pancreatic Cancer (Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma)

Robotics & Machine Learning Daily News2024,Issue(Feb.7) :85-86.

Fourth Hospital of Hebei Medical University Reports Findings in Pancreatic Cancer (Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma)

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Abstract

New research on Oncology - Pancreatic Cancer is the subject of a report. According to news reporting from Hebei, People’s Republic of China, by NewsRx journalists, research stated, “MYC has been identified to profoundly influence a wide range of pathologic processes in cancers. However, the prognostic value of MYC-related genes in pancreatic adenocarcinoma (PAAD) remains unclarified.” The news correspondents obtained a quote from the research from the Fourth Hospital of Hebei Medical University, “Gene expression data and clinical information of PAAD patients were obtained from The Cancer Genome Atlas (TCGA) database (training set). Validation sets included GSE57495, GSE62452, and ICGC-PACA databases. LASSO regression analysis was used to develop a risk signature for survival prediction. Single-cell sequencing data from GSE154778 and CRA001160 datasets were analyzed. Functional studies were conducted using siRNA targeting RHOF and ITGB6 in PANC-1 cells. High MYC expression was found to be significantly associated with a poor prognosis in patients with PAAD. Additionally, we identified seven genes (ADGRG6, LINC00941, RHOF, SERPINB5, INSYN2B, ITGB6, and DEPDC1) that exhibited a strong correlation with both MYC expression and patient survival. They were then utilized to establish a risk model (MYCsig), which showed robust predictive ability. Furthermore, MYCsig demonstrated a positive correlation with the expression of HLA genes and immune checkpoints, as well as the chemotherapy response of PAAD. RHOF and ITGB6, expressed mainly in malignant cells, were identified as key oncogenes regulating chemosensitivity through EMT. Downregulation of RHOF and ITGB6 reduced cell proliferation and invasion in PANC-1 cells. The developed MYCsig demonstrates its potential in enhancing the management of patients with PAAD by facilitating risk assessment and predicting response to adjuvant chemotherapy.”

Key words

Hebei/People’s Republic of China/Asia/Adenocarcinoma/Biotechnology/Cancer/Cyborgs/Drugs and Therapies/Emerging Technologies/Gastroenterology/Genetics/Health and Medicine/Machine Learning/Oncology/Pancreas/Pancreatic Cancer

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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