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Browsing by Subject "Bioinformatics analysis"
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Item Integrative analysis of deoxyribonuclease 1-like 3 as a potential biomarker in renal cell carcinoma(AME, 2023) Ge, Minghuan; Zhu, Hengcheng; Song, Huajie; Schmeusser, Benjamin N.; Ng, Keng Lim; Zeng, Yan; Liu, Ting; Yang, Kang; Urology, School of MedicineBackground: Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma (RCC), is insensitive to radiotherapy and chemotherapy after surgery. Deoxyribonuclease 1-like 3 (DNASE1L3), an endonuclease that cleaves both membrane-encapsulated single- and double-stranded DNA, suppresses cell cycle progression, proliferation and metabolism in hepatocellular carcinoma cells. There is currently no established link between DNASE1L3 and RCC inhibition. We are gonging to explored the mechanism underlying the relationship between DNASEL1L3 and RCC. Methods: RNA sequencing data for RCC tissue and peritumoral tissue were downloaded from The Cancer Genome Atlas database and analyzed. The expression levels of DNASE1L3 in RCC and normal samples were verified using the Gene Expression Omnibus (GEO) database, Human Protein Atlas database and western blotting. The role and potential mechanism of DNASE1L3 were investigated by analysis of immune-related databases and wound healing, invasion, cell counting kit 8 and immunofluorescence assays. Results: We revealed that DNASE1L3 expression was downregulated in RCC group compared with control group [The Cancer Genome Atlas (TCGA): 7.98 vs. 10.87, P<0.001]. Meanwhile, DNASE1L3 expression correlated with the clinical characteristics of patients. Patients with low DNASE1L3 expression had worse survival (P<0.001) and larger (r=-0.32, P<0.001) and heavier tumors (r=-0.17, P<0.001). DNASE1L3 overexpression inhibited the proliferation (786-O: 0.135±0.014 vs. 0.322±0.027, P<0.001) and invasion (786-O: 1,479±134 vs. 832±67, P<0.05) of RCC cells. The expression of DNASE1L3 was significantly correlated with the tumor immune microenvironment and drug sensitivity in ccRCC. Moreover, the level of the key phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway protein P-AKT was decreased in the group of cells transfected with DNASE1L3. Conclusions: This study strongly suggest that DNASE1L3 may be a promising potential biomarker for the diagnosis and treatment of ccRCC patients.Item Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy(Frontiers Media, 2021-04-06) Su, Jing; Song, Qianqian; Qasem, Shadi; O’Neill, Stacey; Lee, Jingyun; Furdui, Cristina M.; Pasche, Boris; Metheny-Barlow, Linda; Masters, Adrianna H.; Lo, Hui-Wen; Xing, Fei; Watabe, Kounosuke; Miller, Lance D.; Tatter, Stephen B.; Laxton, Adrian W.; Whitlow, Christopher T.; Chan, Michael D.; Soike, Michael H.; Ruiz, Jimmy; Biostatistics, School of Public HealthBackground: The incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death. Methods: We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization. Results: We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference. Conclusion: A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.