Background: Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in the world with a five-year survival rate of less than 5%. Not all PDAC are the same, because there exist intra-tumoral heterogeneity between PDAC, which poses a great challenge to personalized treatments for PDAC.
Methods: To dissect the molecular heterogeneity of PDAC, we performed a retrospective meta-analysis on whole transcriptome data from more than 1200 PDAC patients. Subtypes were identified based on non-negative matrix factorization (NMF) biclustering method. We used the gene set enrichment analysis (GSEA) and survival analysis to conduct the molecular and clinical characterization of the identified subtypes, respectively.
Results: Six molecular and clinical distinct subtypes of PDAC: L1-L6, are identified and grouped into tumor-specific (L1, L2 and L6) and stroma-specific subtypes (L3, L4 and L5). For tumor-specific subtypes, L1 (~ 22%) has enriched carbohydrate metabolism-related gene sets and has intermediate survival. L2 (~ 22%) has the worst clinical outcomes, and is enriched for cell proliferation-related gene sets. About 23% patients can be classified into L6, which leads to intermediate survival and is enriched for lipid and protein metabolism-related gene sets. Stroma-specific subtypes may contain high non-epithelial contents such as collagen, immune and islet cells, respectively. For instance, L3 (~ 12%) has poor survival and is enriched for collagen-associated gene sets. L4 (~ 14%) is enriched for various immune-related gene sets and has relatively good survival. And L5 (~ 7%) has good clinical outcomes and is enriched for neurotransmitter and insulin secretion related gene sets. In the meantime, we identified 160 subtype-specific markers and built a deep learning-based classifier for PDAC. We also applied our classification system on validation datasets and observed much similar molecular and clinical characteristics between subtypes.
Conclusions: Our study is the largest cohort of PDAC gene expression profiles investigated so far, which greatly increased the statistical power and provided more robust results. We identified six molecular and clinical distinct subtypes to describe a more complete picture of the PDAC heterogeneity. The 160 subtype-specific markers and a deep learning based classification system may be used to better stratify PDAC patients for personalized treatments.
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http://dx.doi.org/10.1186/s12885-018-4546-8 | DOI Listing |
ISME Commun
January 2025
Key Laboratory of Water and Sediment Sciences, Ministry of Education, Department of Environmental Engineering, Peking University, Beijing 100871, China.
Rivers serve important functions for human society and are significantly impacted by anthropogenic nutrient inputs (e.g. organic and sulfur compounds).
View Article and Find Full Text PDFAm J Clin Exp Urol
December 2024
Department of Urology, General Hospital of Northern Theater Command Shenyang 110016, Liaoning, China.
Objective: To investigate the expression of metabolism-related genes (MRGs) in kidney renal clear cell carcinoma (KIRC) and their association with patient prognosis, and to identify potential targets for intervention.
Methods: Bioinformatics methods were employed to mine the KIRC transcriptome data in The Cancer Genome Atlas Program (TCGA) database in order to identify MRGs that are aberrantly expressed in cancerous tissues. Subsequently, a prognostic risk score model was constructed and its predictive capacity was evaluated.
Front Med (Lausanne)
January 2025
Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Background: The lysosome plays a vitally crucial role in tumor development and is a major participant in the cell death process, involving aberrant functional and structural changes. However, there are few studies on lysosome-associated genes (LAGs) in lung adenocarcinoma (LUAD).
Methods: Bulk RNA-seq of LUAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).
Med Chem
January 2025
Department of Neurosurgery, The 940th Hospital of Joint Logistics Support force of Chinese People's Liberation Army, Lanzhou, China.
Background: Neurodegenerative diseases are a group of disorders characterized by progressive neuronal degeneration and death, of which Alzheimer's disease and Parkinson's disease are the most common. These diseases are closely associated with increased expression of monoamine oxidase B (MAO-B), an important enzyme that regulates neurotransmitter concentration, and its overactivity leads to oxidative stress and neurotoxicity, accelerating the progression of neurodegenerative diseases. Therefore, the development of effective MAO-B inhibitors is important for the treatment of neurodegenerative diseases.
View Article and Find Full Text PDFEnviron Mol Mutagen
January 2025
Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kanagawa, Japan.
There is growing recognition across broad sectors of the toxicology community that gene expression biomarkers have the potential to identify genotoxic and nongenotoxic carcinogens through a weight-of-evidence approach, providing opportunities to reduce reliance on the 2-year bioassay to identify carcinogens. In August 2022, a workshop within the International Workshops on Genotoxicity Testing (IWGT) was held to critically review current methods to identify genotoxicants using various 'omics profiling methods. Here, we describe the findings of a workshop subgroup focused on the state of the science regarding the use of biomarkers to identify chemicals that act as genotoxicants in vivo.
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