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A recent study conducted by the Department of Laboratory Medicine explored the role of abnormal metabolism-related genes in the prognosis of patients with lung adenocarcinoma (LUAD). The researchers analyzed transcriptome data from the Cancer Genome Atlas database and identified 42 abnormal metabolism-related differential genes. After further analysis, they constructed a prognostic model using five metabolism-related genes, including ALG3, COL7A1, KL, MST1, and SLC52A1. The study found that the high-risk subgroup had a lower survival rate, and the risk score of the prognostic model was related to the infiltration of multiple immune cells. The researchers concluded that this model provides a novel perspective for the diagnosis and research of LUAD. [Extracted from the article]

A recent study conducted by the Department of Laboratory Medicine has constructed a prognostic model for esophageal cancer (ESCA) based on prognostic RNA-binding proteins (RBPs). The researchers analyzed RNA-seq data from The Cancer Genome Atlas database and identified 105 RBPs associated with ESCA. They used COX regression analysis to establish a prognostic risk model consisting of 6 RBPs (ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1). The model showed good predictive ability for patients' 5-year survival and may provide new ideas for targeted therapy of ESCA. [Extracted from the article]