ABSTRACT
BACKGROUND: Increasing evidence suggests that the metabolism of lipids plays a crucial role in the progression of gastric cancer. However, the expression of lipid metabolism-related genes (LMGs) still does not serve as a prognostic biomarker in gastric cancer. METHODS: We obtained transcriptome data for 751 LMGs and divided STAD patients into two subtypes based on differences in LMGs expression. Then, we analyzed genetic changes in two subtypes as well as immune features to determine their differences. We also constructed a prognostic risk model related to LMGs for individualized comprehensive evaluations. RESULTS: In this study, two lipid metabolic (LM) subtypes were identified anchored in the expression profiles of LMGs. Clinical information, genomic alterations, immune features, and immunotherapy response varied significantly between the two LM subtypes. A risk model based on LMGs was also developed to assess prognosis and distinguish patients with high risk from those at low risk. The prognosis differed significantly between the two risk groups of patients. In STAD patients, the risk score was strongly correlated with genomic alterations and immune profile scores. Also, the risk score was an excellent predictor of immune checkpoint inhibitors (ICIs) response. Anchored in preliminary results derived from the aforementioned bioinformatic analysis, we chose CYP19A1 as our target gene and the expression of CYP19A1 was verified in several common gastric cancer cell lines. Then, we carried out the Western blotting, CCK-8 assay, colony formation assay, wound healing assay, and transwell assay to explore the effects of CYP19A1 on malignant biological behavior, and positive consequences were obtained. CONCLUSIONS: In this study, STAD patients were divided into two subtypes based on LMGs expression. It is possible to assess the prognosis of a patient and the response to immunotherapy using the established prognostic risk model. A series of basic laboratory experiments also verified the functional role of CYP19A1 in gastric cancer.
Subject(s)
Lipid Metabolism , Stomach Neoplasms , Humans , Lipid Metabolism/genetics , Stomach Neoplasms/genetics , Stomach Neoplasms/therapy , Prognosis , Immunotherapy , Tumor Microenvironment , AromataseABSTRACT
Disulfiram (DSF), which is an inhibitor of aldehyde dehydrogenase (ALDH) and approved by the FDA for the treatment of alcoholism previously, has been repurposed for use as a cancer treatment because of its potent effect in preclinical studies. In this study, we found that disulfiram forms potent complexes with copper (DSF/Cu) inhibited cell proliferation, induced apoptosis in human pancreatic cancer cells, which was detected by flow cytometry and western blotting. Meanwhile, autophagy and autophagic flux also clearly observed by transmission electron microscopy, confocal microscopy and flow cytometry. Our results also showed that DSF/Cu induced transcription factor p8 upregulation and PI3K/mTOR signaling pathway activation detected by real-time PCR and western blotting. Additionally, suppression of p8 inactivated the mTOR signaling pathway and autophagic flux maintained. Furthermore, mechanism study indicated that autophagy induced by DSF/Cu was regulated by p8 and was related to PI3K/mTOR/p70S6K signaling pathway in pancreatic cancer cells. Our findings provide insights into the role of p8 in regulating autophagy induced by DSF/Cu effects in pancreatic cancer cells.
Subject(s)
Disulfiram , Pancreatic Neoplasms , Apoptosis , Autophagy/genetics , Cell Line, Tumor , Disulfiram/pharmacology , Humans , Pancreatic Neoplasms/drug therapy , Pancreatic Neoplasms/genetics , Phosphatidylinositol 3-Kinases , Ribosomal Protein S6 Kinases, 70-kDa , Signal Transduction/genetics , TOR Serine-Threonine Kinases , Transcription Factors , Pancreatic NeoplasmsABSTRACT
This study is to analyze differentially expressed genes (DEGs) and mutation signatures of pancreatic head cancer and pancreatic body/tail cancer. Pancreatic Adenocarcinoma (PAAD) RNA-seq data, mutation data and clinical data were downloaded and collected from The Cancer Genome Atlas (TCGA), FireHose and CBioPortal. According to the anatomic location, the patients were divided into 146 cases of pancreatic head cancer and 28 cases of pancreatic body/tail cancer. Then survival analysis was performed by Kaplan-Meier and log-rank test. Furthermore, DEGs were screened by R package Deseq2. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) were then carried out by DAVID and String. Online tool TIMER was used to analyze the immune cells infiltration. R package maftools and GenVisR were applied to analyze frequently mutated genes and mutant-allele tumor heterogeneity (MATH) of PAAD. Survival of patients with pancreatic body/tail cancer was better than those with pancreatic head cancer (median survival, 24.05 vs 19.45 months, p = 0.048). And 496 significant DEGs (|log2 FoldChange| > 1.5,false discovery rate (FDR) < 0.05) were identified, including 253 downregulated genes and 243 upregulated genes. And there were 13 Go terms (4 biological processes, 6 cellular components and 3 molecular functions) and 3 KEGG pathways (Pancreatic secretion, Fat digestion and absorption, Protein digestion and absorption) (FDR < 0.05). B cells and CD4 + T cells infiltration were more significant in pancreatic head cancer. MATH scores of pancreatic body/tail cancer were higher than pancreatic head cancer, while χ2 test of top 10 frequently mutated genes showed little difference between them. There were prognostic and genetic differences between pancreatic head cancer and pancreatic body/tail cancer. PAAD originated from different location may have different biology natures and should not be treated with same strategy.
