Project description:Background: Robust prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is important for developing individualized treatment plans. This study was conducted to develop and validate a clinically feasible prognostic classifier based on transcriptome-wide gene expression profiles. Methods: Tumor tissues were collected from 208 OPSCC patients treated at Washington University in St. Louis and 115 OPSCC patients treated at Vanderbilt University, used for model training and validation, respectively. OPSCC patients (n = 70) from the TCGA cohort were also included for independent validation. Based on RNA-seq profiling data, Cox proportional hazards regression analysis was performed to identify genes associated with disease outcomes. Then, Lasso-penalized multivariate survival models were constructed to identify biomarker genes for developing a prognostic gene signature. Findings: A 60-gene signature was identified by RNA-seq profiling analysis. Computed risk score of the gene signature was significantly predictive of 5-year overall survival of the training cohort (Hazard ratio (HR) 28.32, P = 4.3E-41). Subgroup analysis stratified by HPV status revealed that the signature was prognostic in HPV-positive OPSCC patients (HR 30.55, P = 7.0E-37) and was independent of clinical features. Importantly, the gene signature was validated in two independent patient cohorts, including the TCGA cohort (HR 3.94, P = 0.0018) and the Vanderbilt cohort (HR 8.50, P = 5.7E-09) for overall survival. Conclusions: The prognostic gene signature is a robust tool for risk stratification of OPSCC patients. The signature remains prognostic among HPV-positive OPSCC patients.
Project description:To investigate the differences of transcriptional activities between oropharyngeal squamous cell caricnoma and small cell carcinoma, we performed a transcriptomic analysis using high throoughput RNA sequencing (RNA-seq).
Project description:Esophageal cancer is a highly malignant and prevalent cancer worldwide. Current TNM staging system is insufficient for prognosis of esophagus squamous cell carcinoma (ESCC) patients. The aim of this study is to evaluate miRNA expression profile of ESCC and identify a miRNA signature which robustly predict the survival of ESCC patients. MiRNA expression profiles of paired frozen tissues from 119 ESCC patients were assessed by microarray. After normalization of microarray data, the patients were randomly divided into a training set (n=60) and a test set (n=59). From the training set, we identified a four-miRNA prognostic signature (including hsa-miR-218-5p, hsa-miR-142-3p, hsa-miR-150-5p, and hsa-miR-205-5p) using random forest supervised classification algorithm and nearest shrunken centroid algorithm. This signature distinguished the patients into high-risk or low-risk groups whose overall survival differed significantly (5-year survival 7.4% vs. 66.7%, p<0.001). Prognostic value of this signature was validated in the test set (5-year survival 18.8% vs. 46.5%, p=0.025) and further in an independent cohort of 58 patients assessed by a different platform (5-year survival 11.4% vs. 56.7%, p=0.003). Furthermore, multivariable Cox regression analysis revealed that this signature is an independent prognostic factor for ESCC patients. Moreover, stratified analysis showed that this signature was able to predict survival within TNM stages. The expression level of the four miRNAs measured by microarray was verified by qRT-PCR and showed high level of positive correlation (Pearson correlation coefficient>0.75, p<0.001 for all). Our results suggest that the four-miRNA signature can serve as a reliable biomarker to predict the survival of ESCC patients. the miRNA expression profiles of cancer and adjacent normal tissues form 119 ESCC patients were used to identify a miRNA signature that can perdict the survival of ESCC patients.
Project description:Esophageal cancer is a highly malignant and prevalent cancer worldwide. Current TNM staging system is insufficient for prognosis of esophagus squamous cell carcinoma (ESCC) patients. The aim of this study is to evaluate miRNA expression profile of ESCC and identify a miRNA signature which robustly predict the survival of ESCC patients. MiRNA expression profiles of paired frozen tissues from 119 ESCC patients were assessed by microarray. After normalization of microarray data, the patients were randomly divided into a training set (n=60) and a test set (n=59). From the training set, we identified a four-miRNA prognostic signature (including hsa-miR-218-5p, hsa-miR-142-3p, hsa-miR-150-5p, and hsa-miR-205-5p) using random forest supervised classification algorithm and nearest shrunken centroid algorithm. This signature distinguished the patients into high-risk or low-risk groups whose overall survival differed significantly (5-year survival 7.4% vs. 66.7%, p<0.001). Prognostic value of this signature was validated in the test set (5-year survival 18.8% vs. 46.5%, p=0.025) and further in an independent cohort of 58 patients assessed by a different platform (5-year survival 11.4% vs. 56.7%, p=0.003). Furthermore, multivariable Cox regression analysis revealed that this signature is an independent prognostic factor for ESCC patients. Moreover, stratified analysis showed that this signature was able to predict survival within TNM stages. The expression level of the four miRNAs measured by microarray was verified by qRT-PCR and showed high level of positive correlation (Pearson correlation coefficient>0.75, p<0.001 for all). Our results suggest that the four-miRNA signature can serve as a reliable biomarker to predict the survival of ESCC patients.
Project description:Oncogenic human papillomaviruses (HPVs) are associated with nearly all carcinomas of the uterine cervix and have also become an increasingly important factor in the etiology of a subset of oropharyngeal tumors. HPV-associated head and neck cancers (HNSCCs) have a distinct risk profile and appreciate a prognostic advantage compared to HPV-negative HNSCC. We analyzed the genome-wide expression patterns in two HPV(+) and two HPV(-) squamous cell carcinoma (SCC) cell lines.
Project description:DNA methylation analysis in oropharyngeal squamous carcinoma (OPSCC) samples and oropharyngeal non-cancerous mucosa samples. Infinium HumanMethylation450 BeadChip was used to obtain DNA methylation profiles across 485,577 CpG sites. Total samples included 89 OPSCC samples and 5 non-cancerous mucosa samples.
Project description:DNA methylation analysis in oropharyngeal squamous carcinoma (OPSCC) samples and oropharyngeal non-cancerous mucosa samples. Infinium MethylationEPIC BeadChip Kit was used to obtain DNA methylation profiles across more than 850,000 CpG sites. Total samples included 89 OPSCC samples and 5 non-cancerous mucosa samples.
Project description:Lung cancer remains the leading cause of cancer death worldwide. Overall 5-year survival is about 10-15% and despite curative intent surgery, treatment failure is primarily due to recurrent disease. Conventional prognostic markers are unable to determine which patients with completely resected disease within each stage group are likely to relapse. To identify a gene signature associated with recurrent squamous cell carcinoma (SCC) of lung, we analyzed primary tumour gene expression for a total of fifty-one SCCs (stage I-III) on 22,323 element microarrays, comparing expression profiles for individuals who remained disease-free for a minimum of 36 months with those from individuals whose disease recurred within 18 months of complete resection. Cox proportional hazards modeling with leave-one-out cross-validation identified a 70-gene capable of predicting the likelihood of tumor recurrence and a 79-gene signature predictive for overall survival. These two signatures were pooled to generate a 111-gene classifier which achieved an overall predictive accuracy for disease recurrence of 72% (77% sensitivity, 67% specificity) in an independent set of fifty-eight stage I-III SCCs. This classifier also predicted differences in survival (log-rank P=0.0008, hazard ratio (HR), 3.8 [95% confidence interval, 1.6-8.7]), and was superior to conventional prognostic markers such as TNM stage or N stage in predicting patient outcome. Genome-wide profiling has revealed a distinct gene expression profile for recurrent lung SCC which may be clinically useful as a prognostic tool. Expression profiling using 22K element microarrays of 51 primary lung squamous cell carcinomas.