Project description:P53 mutation is closely associated with the occurrence and progression of colon cancer. In this project, we did crotonylomics sequencing by using human colon cancer homologous cell line pair-HCT116+/+(with wild type p53) and HCT116-/- (with null p53). Crotonylomics sequencing results showed that p53 deficiency regulated crotonylation of non-histone proteins.
Project description:Lung cancer is the deadliest cancer worldwide. In this study, we obtained RNA-sequencing data from 61 lung cancer samples. We hope that this data can improve the understanding of this disease.
Project description:Lung cancer is the leading cause of cancer mortality and early detection is the key to improve survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of early-stage lung cancer and found lipid metabolism was broadly dysregulated in different cell types and glycerophospholipid metabolism is the most significantly altered lipid metabolism-related pathway. Untargeted lipidomics were detected in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we have identified nine lipids as the most important detection features and developed a LC-MS-based targeted assay utilizing multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low dose CT and a prospective clinical cohort containing 109 participants, this assay reached over 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization-mass spectrometry imaging assay confirmed the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. Thus, this method, designated as Lung Cancer Artificial Intelligence Detector (LCAID), may be used for early detection of lung cancer or large-scale screening of high-risk populations in cancer prevention.
Project description:RNA and miRNA sequencing of lung tumors induced by transgenic overexpression of the type-I insulin like growth factor receptor was carried out to examine the molecular changes associated with lung tumorigenesis and explore potential similarities with a collection of mouse lung cancer models and human non-small cell lung cancer.
Project description:Microbiome sequencing model is a Named Entity Recognition (NER) model that identifies and annotates microbiome nucleic acid sequencing method or platform in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with sequencing metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
Project description:To identify aberrant splicing isoforms and potential neoantigens, we performed full-length cDNA sequencing of lung adenocarcinoma cell lines using a long-read sequencer MinION. We constructed a comprehensive catalog of aberrant splicing isoforms and detected isoform-specific peptides using proteome analysis.
Project description:The high-throughput sequencing technology was performed after the treatment of human colorectal cancer cells HCT116 and non-small cell lung cancer cells A549 with the active compound YHM18 designed and synthesized by ourselves, to explore the expression of genes related to cell proliferation, adhesion, migration and invasion of human colorectal cancer cells HCT116 and non-small cell lung cancer cells A549 after the treatment of the active compound and to find an interesting target gene, GTSE1.