Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa

Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012. Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark. We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page

HE4 in the differential diagnosis of ovarian masses

Ovarian masses, a common finding among pre- and post-menopausal women, can be benign or malignant. Ovarian cancer is the leading cause of death from gynecologic malignancy among women living in industrialized countries. According to the current guidelines, measurement of CA125 tumor marker remains the gold standard in the management of ovarian cancer. Recently, HE4 has been proposed as emerging biomarker in the differential diagnosis of adnexal masses and in the early diagnosis of ovarian cancer. Discrimination of benign and malignant ovarian tumors is very important for correct patient referral to institutions specializing in care and management of ovarian cancer. Tumor markers CA125 and HE4 are currently incorporated into the Risk of Ovarian Malignancy Algorithm” (ROMA) with menopausal status for discerning malignant from benign pelvic masses. The availability of a good biomarker such as HE4, closely associated with the differential and early diagnosis of ovarian cancer, could reduce medical costs related to more expensive diagnostic procedures. Finally, it is important to note that HE4 identifies platinum non-responders thus enabling a switch to second line chemotherapy and improved survival

Untargeted lipidomic features associated with colorectal cancer in a prospective cohort.

BackgroundEpidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC.MethodsUsing an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis.ResultsRather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality).ConclusionsOf the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC

Mutational Analysis of Uterine Cervical Cancer That Survived Multiple Rounds of Radiotherapy

学位記番号：医博甲175