Project description:Oral Cavity Cancer Comparative analysis of gene expression using Affymetrix U133 plus 2.0 microarrays to compare differences between oral cancer samples (most HPV-) and adjacent "normal" mucosa. We have 24 matched pairs of tumor and normal from the same patient
Project description:Oral squamous cell carcinoma (OSCC) is a main reason of oral cancer mortality and morbidity. Cancer of oral cavity in central south Asia, ranks among third most common kinds of cancer. The discovery of candidate markers to differentiate normal from malignant cells in clinical diagnosis of OSCC would be of critical importance because this malignancy has poor prognosis. To improve the clinical outcome in OSCC patients, the present study was aimed at identifying robust candidate biomarkers for early OSCC diagnosis and to enhance understanding of the mechanisms of disease progression and pathogenesis. Of particular interest are proteins that can be found in tissue lysates of OSCC tumor vs normal adjacent mucosa samples and secreted in cell line Secretomes of HNSCC for non-invasive detection. We analysed 17 paired human malignant OSCC tissues and normal adjacent tissue in addition to secretomes of 9 HNSCC cell lines. The proteome dataset of OSCC and normal tissues consisted of 5,123 protein groups, including 299 proteins with strong differential expression (p-value <0.01, fold change barrier to ˃+2 and <-2, 205 upregulated and 94 down regulated) and 134 common proteins were also found out of total dataset of 4473 identified proteins of HNSCC cell line secretomes. Functional data analysis revealed that these differential proteins were significantly associated with multiple biological processes. Myogenesis, Fatty Acid Metabolism and KRAS Signaling DN were associated with the proteins downregulated in cancer tissues, while Protein Secretion, Unfolded Protein Response, Spliceosomal complex assembly, Protein localization to endosome and Interferon Gamma Response were enriched in the set of upregulated proteins and these regulated proteins may be classically or non-classically secreted. Furthermore, we found differential enrichment of Creb3L1, ESRRA, YY, ELF2, STAT1 and XBP transcription factors potentially regulating these major pathways.
Project description:Gene Expression Profiling of Oral Squamous Cell Carcinoma (OSCC) was performed to delineate candidate genes clusters with potential to distinguish normal and tumor tissue from oral cavity. All tissue samples were collected after obtaining written informed consent. The RNA profile of 27 OSCC patients was compared with 4 independent controls and 1 pooled control oral cavity tissue from healthy donors. Agilent one-color experiment, Organism: Human, Agilent-014850 Whole Human Genome Microarray 4x44K G4112F
Project description:Samples were prospectively collected from patients with histologically normal surgical resection margins. 96 tissue samples (histologically normal margins, oral carcinoma and adjacent normal tissues) from 24 patients comprised the training set. Our study design was guided by the hypothesis that the expression of genes present in oral squamous cell carcinoma (OSCC) but not in healthy oral tissues would be indicative of recurrence in advance of histological alteration. We used meta-analysis of five published microarray data sets (GEO accession GDS2520, Kuriakose et al. 2004; GDS1584, Toruner et al. 2004; GSE6791, Pyeon et al. 2007; GSE9844, Ye et al. 2008; and GSE10121, Sticht et al. 2008), in conjunction with the current training set, to identify genes reliably over-expressed in OSCC. This reduced gene set was used to train a risk model to predict recurrence based on over-expression of a subset of these genes in histologically normal surgical resection margins. Validation of the risk signature was performed using quantitative real-time reverse-transcription PCR in an independent set of 136 samples from an independent cohort of 30 patients.
Project description:Gene Expression Profiling of Oral Squamous Cell Carcinoma (OSCC) was performed to delineate candidate genes clusters with potential to distinguish normal and tumor tissue from oral cavity. All tissue samples were collected after obtaining written informed consent.
Project description:Samples were prospectively collected from patients with histologically normal surgical resection margins. 96 tissue samples (histologically normal margins, oral carcinoma and adjacent normal tissues) from 24 patients comprised the training set. Our study design was guided by the hypothesis that the expression of genes present in oral squamous cell carcinoma (OSCC) but not in healthy oral tissues would be indicative of recurrence in advance of histological alteration. We used meta-analysis of five published microarray data sets (GEO accession GDS2520, Kuriakose et al. 2004; GDS1584, Toruner et al. 2004; GSE6791, Pyeon et al. 2007; GSE9844, Ye et al. 2008; and GSE10121, Sticht et al. 2008), in conjunction with the current training set, to identify genes reliably over-expressed in OSCC. This reduced gene set was used to train a risk model to predict recurrence based on over-expression of a subset of these genes in histologically normal surgical resection margins. Validation of the risk signature was performed using quantitative real-time reverse-transcription PCR in an independent set of 136 samples from an independent cohort of 30 patients. This was a case-only design involving a training set of 23 tumors and 73 margins from 24 patients with squamous cell carcinoma of the tongue.
Project description:OSCC is associated with substantial mortality and morbidity. To identify potential biomarkers for the early detection of invasive OSCC, we compared the gene expressions of OSCC, oral dysplasia, and normal oral tissue from patients without oral cancer or preneoplastic oral lesions (controls). Results provided models of gene expression to distinguish OSCC from controls. RNA from 167 OSCC, 17 dysplasia and 45 normal oral tissues were extracted and hybridized to Affymetrix U133 2.0 Plus GeneChip arrays. The differentially expressed genes were identified using GenePlus software and the validation was done using RT-PCR, using independent internal and external datasets.
Project description:Laser Capture Microdissected cells from archival FFPE Prostate cancer and normal adjacent tissues from 10 patients (5 CA and 5 AA) were converted to cDNA and analysed by PCR array to identify differentially expressed miRNAs between the groups. Selected differentially expressed miRNAs were further validated in tissues from 40 prostate cancer patients. The miRNAs which were differentially modulated in PCa patients in the validation set were further analysed in 32 urine samples from PCa patients and compared with 12 healthy individuals. Differentially expressed miRNAs were explored to e used as non-invasive biomarker for PCa. qPCR miRNA expression profiling. mRNA from 10 Prostate Cancer patients (5 Caucasian American and 5 African American) and their paired adjacent normal tissue were analysed to identify the differentially expressed miRNA between the groups. Equal amount small RNA from each group was pooled prior to gene expression analysis.