Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
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:Ctcf heterozygous knockout mice are susceptible to neoplasia in a broad range of tissues, including lymphoma, endometrial cancer, and non-small cell lung cancer. Retention of the wild type Ctcf allele in these tumors establishes CTCF as a haploinsufficient tumor suppressor gene. Both human tumors and normal murine tissues with CTCF disruption are characterized by genome-wide differences in DNA methylation relative to CTCF wild type tissues, indicating even modest disruption of CTCF broadly destabilizes DNA methylation in vivo. This cross species functional analysis identifies CTCF as a commonly mutated tumor suppressor gene and establishes a central role for DNA methylation stability in tumor suppression. RRBS sequencing of transgenic Ctcf heterozygous mice and wild-type litter mate whole lung tissue.
Project description:In order to identify divergent transcripts involved in cancer development, next-generation sequencing analysis of RNA extracted from cancer and normal lung tissue was performed.