Neural functions in cancer: Data analyses and database construction.

Recent studies have revealed that neural functions are involved in possibly every aspect of a cancer development, serving as bridges connecting microenvironmental stressors, activities of intracellular subsystems, and cell survival. Elucidation of the functional roles played by the neural system could provide the missing links in developing a systems-level understanding of cancer biology. However, the existing information is highly fragmented and scattered across the literature and internet databases, making it difficult for cancer researchers to use. We have conducted computational analyses of transcriptomic data of cancer tissues in TCGA and tissues of healthy organs in GTEx, aiming to demonstrate how the functional roles by the neural genes could be derived and what non-neural functions they are associated with, across different stages of 26 cancer types. Several novel discoveries are made, including i) the expressions of certain neural genes can predict the prognosis of a cancer patient; ii) cancer metastasis tends to involve specific neural functions; iii) cancers of low survival rates involve more neural interactions than those with high survival rates; iv) more malignant cancers involve more complex neural functions; and v) neural functions are probably induced to alleviate stresses and help the associated cancer cells to survive. A database, called NGC, is developed for organizing such derived neural functions and associations, along with gene expressions and functional annotations collected from public databases, aiming to provide an integrated and publicly available information resource to enable cancer researchers to take full advantage of the relevant information in their research, facilitated by tools provided by NGC.

Brain metastases: It takes two factors for a primary cancer to metastasize to brain.

Brain metastasis of a cancer is a malignant disease with high mortality, but the cause and the molecular mechanism remain largely unknown. Using the samples of primary tumors of 22 cancer types in the TCGA database, we have performed a computational study of their transcriptomic data to investigate the drivers of brain metastases at the basic physics and chemistry level. Our main discoveries are: (i) the physical characteristics, namely electric charge, molecular weight, and the hydrophobicity of the extracellular structures of the expressed transmembrane proteins largely affect a primary cancer cell's ability to cross the blood-brain barrier; and (ii) brain metastasis may require specific functions provided by the activated enzymes in the metastasizing primary cancer cells for survival in the brain micro-environment. Both predictions are supported by published experimental studies. Based on these findings, we have built a classifier to predict if a given primary cancer may have brain metastasis, achieving the accuracy level at AUC = 0.92 on large test sets.

Quantitative estimation of intracellular oxidative stress in human tissues.

Oxidative stress is known to be involved in and possibly a key driver of the development of numerous chronic diseases, including cancer. It is highly desired to have a capability to reliably estimate the level of intracellular oxidative stress as it can help to identify functional changes and disease phenotypes associated with such a stress, but the problem proves to be very challenging. We present a novel computational model for quantitatively estimating the level of oxidative stress in tissues and cells based on their transcriptomic data. The model consists of (i) three sets of marker genes found to be associated with the production of oxidizing molecules, the activated antioxidation programs and the intracellular stress attributed to oxidation, respectively; (ii) three polynomial functions defined over the expression levels of the three gene sets are developed aimed to capture the total oxidizing power, the activated antioxidation capacity and the oxidative stress level, respectively, with their detailed parameters estimated by solving an optimization problem and (iii) the optimization problem is so formulated to capture the relevant known insights such as the oxidative stress level generally goes up from normal to chronic diseases and then to cancer tissues. Systematic assessments on independent datasets indicate that the trained predictor is highly reliable and numerous insights are made based on its application results to samples in the TCGA, GTEx and GEO databases.

Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur.

Data from the SEER reports reveal that the occurrence rate of a cancer type generally follows a unimodal distribution over age, peaking at an age that is cancer-type specific and ranges from 30+ through 70+. Previous studies attribute such bell-shaped distributions to the reduced proliferative potential in senior years but fail to explain why some cancers have their occurrence peak at 30+ or 40+. We present a computational model to offer a new explanation to such distributions. The model uses two factors to explain the observed age-dependent cancer occurrence rates: cancer risk of an organ and the availability level of the growth signals in circulation needed by a cancer type, with the former increasing and the latter decreasing with age. Regression analyses were conducted of known occurrence rates against such factors for triple negative breast cancer, testicular cancer and cervical cancer; and all achieved highly tight fitting results, which were also consistent with clinical, gene-expression and cancer-drug data. These reveal a fundamentally important relationship: while cancer is driven by endogenous stressors, it requires sufficient levels of exogenous growth signals to happen, hence suggesting the realistic possibility for treating cancer via cleaning out the growth signals in circulation needed by a cancer.

Selection of features for patient-independent detection of seizure events using scalp EEG signals.

Epilepsy involves brain abnormalities that may cause sudden seizures or other uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality of life, and its detection heavily relies on tedious and time-consuming manual curation by experienced clinicians, based on EEG signals. Most existing EEG-based seizure detection algorithms are patient-dependent and train a detection model for each patient. A new patient can only be monitored effectively after several episodes of epileptic seizures. This study investigates the patient-independent detection of seizure events using the open dataset CHB-MIT Scalp EEG. First, a novel feature extraction algorithm called MinMaxHist is proposed to measure the topological patterns of the EEG signals. Following this, MinMaxHist and several other feature extraction algorithms are applied to parameterize the EEG signals. Next, a comprehensive series of feature screening and classification optimization experiments are conducted, and finally, an optimized EEG-based seizure detection model is presented that can achieve overall values for accuracy, sensitivity, specificity, Matthews correlation coefficient, and Kappa of 0.8627, 0.8032, 0.9222, 0.7504 and 0.7254, respectively, with only 30 features. The classification accuracy of the method with MinMaxHist features was 0.0464 higher than that without MinMaxHist features. Compared with existing methods, the proposed algorithm achieved higher accuracy and sensitivity, as shown in the experimental results.

Neural Functions Play Different Roles in Triple Negative Breast Cancer (TNBC) and non-TNBC.

Triple negative breast cancer (TNBC) represents the most malignant subtype of breast cancer, and yet our understanding about its unique biology remains elusive. We have conducted a comparative computational analysis of transcriptomic data of TNBC and non-TNBC (NTNBC) tissue samples from the TCGA database, focused on genes involved in neural functions. Our main discoveries are: (1) while both subtypes involve neural functions, TNBC has substantially more up-regulated neural genes than NTNBC, suggesting that TNBC is more complex than NTNBC; (2) non-neural functions related to cell-microenvironment interactions and intracellular damage processing are key inducers of the neural genes in both TNBC and NTNBC, but the inducer-responder relationships are different in the two cancer subtypes; (3) key neural functions such as neural crest formation are predicted to enhance adaptive immunity in TNBC while glia development, along with a few other neural functions, induce both innate and adaptive immunity in NTNBC. These results reveal key differences in the biology between the two cancer subtypes, particularly in terms of the roles that neural functions play. Our findings may open new doors for further investigation of the distinct biology of TNBC vs. NTNBC.

The RNF146 E3 ubiquitin ligase is required for the control of Wnt signaling and body pattern formation in Xenopus.

The RING finger protein Rnf146 encodes an E3 ubiquitin ligase capable of targeting poly-ADP-ribosylated substrates for proteasomal degradation. Rnf146 has been identified as a critical regulator of Axin1 and thus of Wnt/ß-catenin signaling. However its physiological significance in vertebrate embryonic development remains to be demonstrated. In this study, we take advantages of early Xenopus embryos to demonstrate that Rnf146 is essential for embryonic pattern formation. Depletion of zygotic Rnf146 using a translation blocking morpholino oligo (MO) results in anteriorized development and increased expression the anterior marker gene Otx2, consistent the notion that Rnf146 is a positive regulator of Wnt/ß-catenin signaling through negatively regulating Axin1 expression. This notion is further supported by examination of the role of maternal Rnf146 in the context of Spemann organizer formation and dorsal axis development. Depletion of maternal Rnf146 using an antisense oligodeoxynucleic acid (ODN) leads to ventralized development and diminished expression of organizer genes. Together, we have provided evidence for the first time that Rnf146 is a critical regulator of embryonic pattern formation in vertebrates.

NF2/Merlin is required for the axial pattern formation in the Xenopus laevis embryo.

The NF2 gene product Merlin is a FERM-domain protein possessing a broad tumor-suppressing function. NF2/Merlin has been implicated in regulating multiple signaling pathways critical for cell growth and survival. However, it remains unknown whether NF2/Merlin regulates Wnt/ß-catenin signaling during vertebrate embryogenesis. Here we demonstrate that NF2/Merlin is required for body pattern formation in the Xenopus laevis embryo. Depletion of the maternal NF2/Merlin enhances organizer gene expression dependent on the presence of ß-catenin, and causes dorsanteriorized development; Morpholino antisense oligo-mediated knockdown of the zygotic NF2/Merlin shifts posterior genes anteriorwards and reduces the anterior development. We further demonstrate that targeted depletion of NF2 in the presumptive dorsal tissues increases the levels of nuclear ß-catenin in the neural epithelial cells. Biochemical analyses reveal that NF2 depletion promotes the production of active ß-catenin and concurrently decreases the level of N-terminally phosphorylated ß-catenin under the stimulation of the endogenous Wnt signaling. Our findings suggest that NF2/Merlin negatively regulates the Wnt/ß-catenin signaling activity during the pattern formation in early X. laevis embryos.

Sebox regulates mesoderm formation in early amphibian embryos.

BACKGROUND: Mix/Bix genes are important regulators of mesendoderm formation during vertebrate embryogenesis. Sebox, an additional member of this gene family, has been implicated in endoderm formation during early embryogenesis in zebrafish. However, it remains unclear whether Sebox plays a unique role in early Xenopus embryos. RESULTS: In this study, we provide evidence that Sebox is uniquely required for the formation of mesoderm during early Xenopus embryogenesis. Sebox is dynamically expressed in the involuted mesoderm during gastrulation. It is activated by Nodal/Activin signaling and modulated by zygotic Wnt/ß-catenin signaling. Overexpression of Sebox perturbs movements during convergent extension and inhibits the expression of mesodermal, but not endodermal, genes induced by Nodal/Activin signaling. Depletion of Sebox using a specific morpholino increases the expression of noncanonical wnt5a, wnt5b, and wnt11b. Depletion of Sebox also up-regulates the expression of pcdh8.2, a paraxial mesoderm-specific protocadherin, in a Wnt11B-dependent manner. Sebox morphants display reduced development of the head and notochord. CONCLUSIONS: Our findings illustrate that Sebox, a unique member of the Mix/Bix gene family, functions downstream of Nodal/Activin signaling and is required for the proper expression of noncanonical Wnt ligands and the normal development of mesoderm in Xenopus.

Helical differential X-ray phase-contrast computed tomography.

We report on the first experimental results of helical differential phase-contrast computed tomography (helical DPC-CT) with a laboratory X-ray tube source and a Talbot-Lau grating interferometer. The results experimentally verify the feasibility of helical data acquisition and reconstruction in phase-contrast imaging, in analogy to its use in clinical CT systems. This allows fast and continuous volumetric scans for long objects with lengths exceeding the dimension of the detector. Since helical CT revolutionized the field of medical CT several years ago, we anticipate that this method will bring the same significant impact on the future medical and industrial applications of X-ray DPC-CT.

In-line phase contrast micro-CT reconstruction for biomedical specimens.

X-ray phase contrast micro computed tomography (micro-CT) can non-destructively provide the internal structure information of soft tissues and low atomic number materials. It has become an invaluable analysis tool for biomedical specimens. Here an in-line phase contrast micro-CT reconstruction technique is reported, which consists of a projection extraction method and the conventional filter back-projection (FBP) reconstruction algorithm. The projection extraction is implemented by applying the Fourier transform to the forward projections of in-line phase contrast micro-CT. This work comprises a numerical study of the method and its experimental verification using a biomedical specimen dataset measured at an X-ray tube source micro-CT setup. The numerical and experimental results demonstrate that the presented technique can improve the imaging contrast of biomedical specimens. It will be of interest for a wide range of in-line phase contrast micro-CT applications in medicine and biology.

Analysis and accurate reconstruction of incomplete data in X-ray differential phase-contrast computed tomography.

X-ray differential phase-contrast computed tomography (DPC-CT) is a powerful physical and biochemical analysis tool. In practical applications, there are often challenges for DPC-CT due to insufficient data caused by few-view, bad or missing detector channels, or limited scanning angular range. They occur quite frequently because of experimental constraints from imaging hardware, scanning geometry, and the exposure dose delivered to living specimens. In this work, we analyze the influence of incomplete data on DPC-CT image reconstruction. Then, a reconstruction method is developed and investigated for incomplete data DPC-CT. It is based on an algebraic iteration reconstruction technique, which minimizes the image total variation and permits accurate tomographic imaging with less data. This work comprises a numerical study of the method and its experimental verification using a dataset measured at the W2 beamline of the storage ring DORIS III equipped with a Talbot-Lau interferometer. The numerical and experimental results demonstrate that the presented method can handle incomplete data. It will be of interest for a wide range of DPC-CT applications in medicine, biology, and nondestructive testing.

An algebraic iterative reconstruction technique for differential X-ray phase-contrast computed tomography.

Iterative reconstruction has a wide spectrum of proven advantages in the field of conventional X-ray absorption-based computed tomography (CT). In this paper, we report on an algebraic iterative reconstruction technique for grating-based differential phase-contrast CT (DPC-CT). Due to the differential nature of DPC-CT projections, a differential operator and a smoothing operator are added to the iterative reconstruction, compared to the one commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured at a two-grating interferometer setup. Since the algorithm is easy to implement and allows for the extension to various regularization possibilities, we expect a significant impact of the method for improving future medical and industrial DPC-CT applications.

A reconstruction method for cone-beam differential x-ray phase-contrast computed tomography.

Most existing differential phase-contrast computed tomography (DPC-CT) approaches are based on three kinds of scanning geometries, described by parallel-beam, fan-beam and cone-beam. Due to the potential of compact imaging systems with magnified spatial resolution, cone-beam DPC-CT has attracted significant interest. In this paper, we report a reconstruction method based on a back-projection filtration (BPF) algorithm for cone-beam DPC-CT. Due to the differential nature of phase contrast projections, the algorithm restrains from differentiation of the projection data prior to back-projection, unlike BPF algorithms commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a micro-focus x-ray tube source. Moreover, the numerical simulation and experimental results demonstrate that the proposed method can deal with several classes of truncated cone-beam datasets. We believe that this feature is of particular interest for future medical cone-beam phase-contrast CT imaging applications.