Project description:Proteomic sequencing was performed on 5 prostate cancer patient tissues and 5 benign prostatic hyperplasia patient tissues collected from Beijing Tongren Hospital Affiliated to Capital Medical University. Differential genes related to the prognosis of prostate cancer were identified, and enrichment analysis was conducted on prostate cancer patients based on these genes to clarify the biological functions in prostate cancer cells that significantly affect cancer cell progression. Lactylation modification proteomic sequencing revealed the sites in prostate cancer that promote prostate cancer progression due to lactylation modification, providing potential therapeutic methods for the treatment of prostate 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: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 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:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Project description:The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC) is not well understood. Comprehensive molecular analyses were performed including high-pass whole-genome sequencing, targeted deep DNA sequencing, RNA sequencing, reverse-phase protein arrays, mass spectrometry-based proteomics and phosphoproteomics, and immune profiling on primary and metastatic sites from highly clinically annotated HGSC samples. Samples were obtained pre-treatment based on a laparoscopic triage algorithm from patients who underwent R0 tumor debulking or received neoadjuvant chemotherapy (NACT) with excellent or poor response.
Project description:Molecular profiling of breast cancer has achieved great depth in defining the mutational landscapes and molecular profiles of primary tumors, though little is still known regarding cancer evolution into a recurrence. Proteogenomic workflows are particularly useful in defining the multi-layered biology of complex diseases by combining genomic, transcriptomic, and proteomic technologies so to inform not only on mutational processes, but also on their repercussion at the effector level, proteins. We employed our recently developed proteogenomic workflow to analyze a cohort of 27 primary breast cancers and their matched loco-regional recurrences by whole genome sequencing, RNA sequencing, and mass spectrometry.