Project description:Ovarian cancer is one of the leading causes of death among patients with gynecological malignancies worldwide. To identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. To predict the prognosis of HGSC patients, we created machine learning-based models and identified RPL23 and USP19 as candidate prognostic markers. Specifically, patients with higher RPL23 mRNA levels had a worse prognosis and patients with higher USP19 mRNA levels had a better prognosis. This model was then used to analyze HGSC patient data hosted on The Cancer Genome Atlas; this exercise validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of RPL23 and USP19 determined using a machine-learning model could serve as prognostic markers in HGSC patients receiving conventional therapy.
Project description:This SuperSeries is composed of the following subset Series: GSE22216: microRNA expression profiling of early primary breast cancer to identify prognostic markers and associated pathways GSE22219: Gene expression profiling of early primary breast cancer to identify prognostic markers and associated pathways Refer to individual Series. Supplementary file: Shows correspondence between mRNA and miRNA samples.
Project description:In managing patients with coronavirus disease 2019 (COVID-19), early identification of those at high risk and real-time monitoring of disease progression to severe COVID-19 is a major challenge. We aimed to identify early prognostic protein markers and to discover surrogate protein markers that effectively reflect the clinical progression of the disease. We performed in-depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID-19 (non-severe patients, n = 13; patients who progressed to severe COVID-19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3-19 and BNC2, as early prognostic indicators of severe COVID-19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID-19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. This study supports the development of protein biomarkers, which might enable better predicting and monitoring progression to severe COVID-19.
Project description:Ovarian carcinoma has the highest mortality rate among gynecological malignancies. In this project, we investigated the hypothesis that molecular markers are able to predict outcome of ovarian cancer independently of classical clinical predictors, and that these molecular markers can be validated using independent data sets. We applied a semi-supervised method for prediction of patient survival. Microarrays from a cohort of 80 ovarian carcinomas (TOC cohort) were used for the development of a predictive model, which was then evaluated in an entirely independent cohort of 118 carcinomas (Duke cohort). A 300 gene ovarian prognostic index (OPI) was generated and validated in a leave-one-out approach in the TOC cohort (Kaplan-Meier analysis, p=0.0087). In a second validation step the prognostic power of the OPI was confirmed in an independent data set (Duke cohort, p=0.0063). In multivariate analysis, the OPI was independent of the postoperative residual tumour, the main clinico-pathological prognostic parameter with an adjusted hazard ratio of 6.4 (TOC cohort, CI 1.8 – 23.5, p=0.0049) and 1.9 (Duke cohort, CI 1.2 – 3.0, p=0.0068). We constructed a combined score of molecular data (OPI) and clinical parameters (residual tumour), which was able to define patient groups with highly significant differences in survival. The integrated analysis of gene expression data as well as residual tumour can be used for optimised assessment of prognosis. As traditional treatment options are limited, this analysis may be able to optimise clinical management and to identify those patients that would be candidates for new therapeutic strategies. Keywords: disease state analysis RNA from 80 frozen ovarian cancer samples was analysed with oligonucleotide microarrays
Project description:Ovarian carcinoma has the highest mortality rate among gynecological malignancies. In this project, we investigated the hypothesis that molecular markers are able to predict outcome of ovarian cancer independently of classical clinical predictors, and that these molecular markers can be validated using independent data sets. We applied a semi-supervised method for prediction of patient survival. Microarrays from a cohort of 80 ovarian carcinomas (TOC cohort) were used for the development of a predictive model, which was then evaluated in an entirely independent cohort of 118 carcinomas (Duke cohort). A 300 gene ovarian prognostic index (OPI) was generated and validated in a leave-one-out approach in the TOC cohort (Kaplan-Meier analysis, p=0.0087). In a second validation step the prognostic power of the OPI was confirmed in an independent data set (Duke cohort, p=0.0063). In multivariate analysis, the OPI was independent of the postoperative residual tumour, the main clinico-pathological prognostic parameter with an adjusted hazard ratio of 6.4 (TOC cohort, CI 1.8 – 23.5, p=0.0049) and 1.9 (Duke cohort, CI 1.2 – 3.0, p=0.0068). We constructed a combined score of molecular data (OPI) and clinical parameters (residual tumour), which was able to define patient groups with highly significant differences in survival. The integrated analysis of gene expression data as well as residual tumour can be used for optimised assessment of prognosis. As traditional treatment options are limited, this analysis may be able to optimise clinical management and to identify those patients that would be candidates for new therapeutic strategies. Keywords: disease state analysis
Project description:Prostate cancer is the second leading cause of cancer death in the United States and Europe. Diagnosis and risk estimation of cancer recurrence is often critical with the common clinicopathologic parameters of prostate-specific antigen, tumor stage and grade. Therefore it is mandatory to develop new diagnostic and prognostic markers for prostate cancer. miRNAs have been shown to be novel markers in a series of other cancer types. We show for the first time, that good overall classification of normal and malignant prostate tissue was possible with combination of just two miRNAs (hsa-miR-205, hsa-miR-183). Further, hsa-miR-96 is shown to be associated with the recurrence-free interval after radical prostatectomy.
Project description:Epithelial ovarian cancer (EOC) has the highest mortality among gynecological carcinomas. The lack of specific markers for prognostic determination of EOC progression hinders the search for novel effective therapies. The aim of the present study was (i) to explore differences in expressions of ATP-binding cassette and solute carrier transporter genes, genes associated with drug metabolism and cell cycle regulation between control ovarian tissues, primary epithelial ovarian carcinomas (EOC) and intraperitoneal EOC metastases; (ii) to investigate associations of gene expression level with prognosis of patients with intraperitoneal metastases.
Project description:Purpose: upper tract urothelial carcinoma (UTUC) is the predominant subtype of the renal pelvis carcinoma but current knowledge about the molecular properties and prognostic markers is sparse. In this study, we examined the genome-wide mRNA expression spectrum of UTUC aiming to characterize the molecular basis of this cancer, and identify potential prognostic markers and thus facilitate the clinical practices. Experimental Design: we compared the whole mRNA expression spectrum of cancer and matched normal tissues in 10 patients with UTUC using massively parallel sequencing, thereafter the protein levels and prognostic roles of ALDH2, CCNE1 and SMAD3 were evaluated under an independent validation set comprising of 104 patients. Results: mRNA down-regulation of ALDH2 and up-regulation of SMAD3 and CCNE1 in UTUC were revealed by expression profiling. And low protein expression of ALDH2 was associated with an adverse outcome for patients (p < 0.0001). Whereas high CCNE1 and SMAD3 were associated with adverse clinical outcome (p < 0.0001). And multivariate analysis revealed that all these three molecular markers were independent prognostic predictors. Besides, compared to the pathological TNM classification, All ALDH2, CCNE1 and SMAD3 were more competent in identifying patient subgroup with high mortality risk, and the molecular markers were able to predict the survival difference in the patients of T2 and T3 subgroup (p < 0.001), which could not be achieved by TNM staging. Conclusions: This is the first prospective study that characterizes genome-wide mRNA expression profile of UTUC. We revealed the prognostic significance of ALDH2, CCNE1 and SMAD3, and these molecular marker were more robust than TNM system in clinical outcome prediction.
Project description:The aim of our study is to develop a miRNA marker panel prognostic of 5-year survival in early-stage OSCC patients. We assessed differential expression of miRNAs genome-wide via deep sequencing in 100 tumor tissue samples. We also attempted to identify deregulated miRNA expression signatures that may serve as the prognostic markers.