Project description:We systematically profiled the genome-wide alternative splicing events in Lung Adenocarcinoma (LUAD) and Lung Squomous Cell Carcinoma (LUSC) against Lung Control through Human Transcriptome Array2.0. Non-invasive Stage IIIA non-small cell lung cancer (NSCLC) is heterogeneous in nature which makes it difficult to predict, diagnose and prognose owing to lower 5-year survival rate and 75-85% brain or bone metastasis. Hence, we hypothesized to develop transcript-based signature to categorize Stage IIIA-NSCLC-LUAD and LUSC, as well as identify markers which could indicate towards prognosis of disease. We were able to molecularly-categorize LUAD- and LUSC-tissue more precisely through HTA array2.0.
Project description:To determine the differentially expressed miRNAs in human cutaneous squamous cell carcinoma biopsies versus normal skin samples using microarray analysis.
Project description:This SuperSeries is composed of the following subset Series: GSE41194: Differentially Expressed Genes Regulating the Progression of Ductal Carcinoma In Situ to Invasive Breast Cancer (Group 1) GSE41196: Differentially Expressed Genes Regulating the Progression of Ductal Carcinoma In Situ to Invasive Breast Cancer (Group 2) GSE41197: Differentially Expressed Genes Regulating the Progression of Ductal Carcinoma In Situ to Invasive Breast Cancer (Group 3) GSE41198: Differentially Expressed Genes Regulating the Progression of Ductal Carcinoma In Situ to Invasive Breast Cancer (Group 4 stroma) GSE41227: Differentially Expressed Genes Regulating the Progression of Ductal Carcinoma In Situ to Invasive Breast Cancer (Group 4 Epithelial) Refer to individual Series
Project description:Lung cancer is the most common malignant tumor and the leading cause of cancer-related deaths worldwide. Because current treatments for advanced non-small cell lung cancer (NSCLC), the most prevalent lung cancer histological subtype, show limited efficacy, screening for tumor-associated biomarkers using bioinformatics reflects the hope to improve early diagnosis and prognosis assessment. In our study, a Gene Expression Omnibus dataset was analyzed to identify genes with prognostic significance in NSCLC. Upon comparison with matched normal tissues, 118 differentially expressed genes (DEGs) were identified in NSCLC, and their functions were explored through bioinformatics analyses. The most significantly upregulated DEGs were TOP2A, SLC2A1, TPX2, and ASPM, all of which were significantly associated with poor overall survival (OS). Further analysis revealed that TOP2A had prognostic significance in early-stage lung cancer patients, and its expression correlated with levels of immune cell infiltration, especially dendritic cells (DCs). Our study provides a dataset of potentially prognostic NSCLC biomarkers, and highlights TOP2A as a valuable survival biomarker to improve prediction of prognosis in NSCLC.