Project description:The Virochip microarray (version 4.0) was used to detect viruses in patients from North America with unexplained influenza-like illness at the onset of the 2009 H1N1 pandemic. We used metagenomics-based technologies (the Virochip microarray) and deep sequencing to analyze nasal swab samples from individuals with 2009 H1N1 infection. This Series includes the Virochip microarray data only.
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
Project description:Background: Cutaneous squamous cell carcinoma (cSCC) is a common type of skin cancer but there are no comprehensive proteomic studies on this entity. Materials and Methods: We employed liquid chromatography coupled with tandem mass spectrometry (MS/MS) using formalin-fixed paraffin-embedded (FFPE) cSCC material to study the tumor and normal skin tissue proteomes. Ingenuity Pathway Analysis (IPA) was used to interpret the role of altered proteins in cSCC pathophysiology. Results were validated using the Human Protein Atlas and Oncomine database in silico. Results: Of 1,310 unique proteins identified, expression of an average of 144 and 88 proteins were significantly (p<0.05) increased and decreased, respectively, in the tumor samples compared to their normal counterparts. IPA analysis revealed disruptions in proteins associated with cell proliferation, apoptosis, and migration. In silico analysis confirmed that proteins corresponding to 12 antibodies, and genes corresponding to 18 proteins were differentially expressed between the two categories, validating our proteomic measurements. Conclusion: Label-free MS-based proteomics is useful for analyzing FFPE cSCC tissues.
