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Open Access Publications from the University of California

Scholarly Works (27 results)

  • Article
  • Peer Reviewed
UC San Francisco Previously Published Works (2022)
For the precise treatment of patients with glioblastoma multiforme (GBM), we classified and detected bevacizumab (BVZ)-responsive subtypes of GBM and found their differential expression (DE) of miRNAs and mRNAs, clinical characteristics, and related functional pathways. Based on miR-21 and miR-10b expression z-scores, approximately 30% of GBM patients were classified as having the GBM BVZ-responsive subtype. For this subtype, GBM patients had a significantly shorter survival time than other GBM patients (p = 0.014), and vascular endothelial growth factor A (VEGF) methylation was significantly lower than that in other GBM patients (p = 0.005). It also revealed 14 DE miRNAs and 7 DE mRNAs and revealed functional characteristics between GBM BVZ subgroups. After comparing several machine learning algorithms, the construction and cross-validation of the SVM classifier were performed. For clinical use, miR-197 was optimized and added to the miRNA panel for better classification. Afterwards, we validated the classifier with several GBM datasets and discovered some key related issues. According to this study, GBM BVZ subtypes can be classified and detected by a combination of SVM classifiers and miRNA panels in existing tissue GBM datasets. With certain modifications, the classifier may be used for the classification and detection of GBM BVZ subtypes for future clinical use.
    Cover page: Machine learning and bioinformatics approaches for classification and clinical detection of bevacizumab responsive glioblastoma subtypes based on miRNA expression
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2017)
    The pathogenesis and progression of glioblastoma multiforme (GBM) have been investigated extensively, but the genetic factors involved in the development of the disease remain poorly understood. The outcome of GBM is still grim. Recently, numerous microRNA (miRNA) mediated gene regulation and interactions have been found to be involved in the development of the disease, making the disease more difficult to understand using the traditional methods. This study used a bioinformatics pipeline—the relative R-squared method (RRSM)—to predict cancer related miRNA targets using gene expression profiles and motif complementary sequences, prior to quantitative real-time PCR experiments in human GBM tissues and cancer cell lines. This study predicted and confirmed 25 miRNA candidates associated to GBM in a comprehensive and non-biased manner, and ten miRNA candidates were also investigated as potential GBM biomarkers. The combination of bioinformatics algorithm and molecular techniques may yield clues to the mechanism of transcriptional and post-transcriptional regulation in the development of glioblastoma and new information about the genetic networks related to the disease.
      Cover page: Predicting and Identifying Human Glioblastoma MiRNA Targets Using RRSM and qPCR Methods
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2022)
        Cover page: Traumatic Brain Injury May Lead to Alzheimer’s Disease and Related Dementia
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2020)
        The long noncoding RNA (LncRNA) SNHG7 (small nuclear RNA host gene 7) is a new type of lncRNA, whose function as an oncogene has been studies. However, the role of SNHG7 in Alzheimer’s disease (AD) remains to be revealed. In this study, the expression data of SNHG7 in AD brains (n=7) and normal brains (n = 5) were collected and calculated. The results indicated that low SNHG7 level in AD was correlated with high expressed microRNA-34a (miR 34a), and the decreased expression of Bcl2 (B-cell lymphoma 2), a target of miR-34a. Moreover, previous studies have shown that miR-34a and Bcl2 are involved into the development and progression of AD. Bioinformatic analysis predicted that SNHG7 has miR-34a binding sites. Therefore, it suggests that SNHG7 may regulate neuronal survive in AD brain through miR-34a/Bcl2 axis. In addition, miR-34a may regulate post-transcriptionally estimated hundred mRNAs. Using several bioinformatics tools, we can predict the regulatory pathways that SNHG7 participates in through miR-34a and its targets in AD. These findings indicate that the down-regulated lncRNA SNHG7 in AD may reduce the inhibition of miR-34s’ function, then increase its function and decrease miR-34a target signals, thereby joining in the regulatory network of AD.
          Cover page: Predicted Regulatory Pathways for Long Noncoding RNA-SNHG7 via miR-34a and its Targets in Alzheimer’s Disease
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2016)
          Cancer is a fatal human disease. Early diagnosis of cancer is the most effective method to prevent cancer development and to achieve higher survival rates for patients. Many traditional diagnostic methods for cancer are still not sufficient for early, more convenient and accurate, and noninvasive diagnosis. Recently, the use of microRNAs (miRNAs), such as exosomal microRNA-21(miR-21), as potential biomarkers was widely reported. This initial systematic review analyzes the potential role of exosomal miR-21 as a general biomarker for cancers. A total of 10 studies involving 318 patients and 215 healthy controls have covered 10 types of cancers. The sensitivity and specificity of pooled studies were 75% (0.70-0.80) and 85% (0.81-0.91), with their 95% confidence intervals (CIs), while the area under the summary receiver operating characteristic curve (AUC) was 0.93. Additionally, we examined and evaluated almost all other issues about biomarkers, including cutoff points, internal controls and detection methods, from the literature. This initial meta-analysis indicates that exosomal miR-21 has a strong potential to be used as a universal biomarker to identify cancers, although as a general biomarker the case number for each cancer type is small. Based on the literature, a combination of miRNA panels and other cancer antigens, as well as a selection of appropriate internal controls, has the potential to serve as a more sensitive and accurate cancer diagnosis tool. Additional information on miR-21 would further support its use as a biomarker in cancer.
            Cover page: Considering Exosomal miR-21 as a Biomarker for Cancer
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2024)
            The early 2-factor (E2F) family of transcription factors, including E2F1 through 8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment. Using machine learning algorithms, including TreeBagger and deep neural networks, we systematically predicted and validated GBM patient survival terms based on the expression profiles of E2Fs across BVZ-responsive GBM subtypes. Our bioinformatics analyses suggested that a significant increase in E2F8 post-BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, implicating this factor as a candidate mechanism of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. These insights underscore that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers, therapeutic targets, and recommending precision BVZ treatment to individual GBM patients.
              Cover page: Early 2-Factor Transcription Factors Associated with Progression and Recurrence in Bevacizumab-Responsive Subtypes of Glioblastoma
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2023)
              Microglia and macrophages are pivotal to the brains innate immune response and have garnered considerable attention in the context of glioblastoma (GBM) and Alzheimers disease (AD) research. This review delineates the complex roles of these cells within the neuropathological landscape, focusing on a range of signaling pathways-namely, NF-κB, microRNAs (miRNAs), and TREM2-that regulate the behavior of tumor-associated macrophages (TAMs) in GBM and disease-associated microglia (DAMs) in AD. These pathways are critical to the processes of neuroinflammation, angiogenesis, and apoptosis, which are hallmarks of GBM and AD. We concentrate on the multifaceted regulation of TAMs by NF-κB signaling in GBM, the influence of TREM2 on DAMs responses to amyloid-beta deposition, and the modulation of both TAMs and DAMs by GBM- and AD-related miRNAs. Incorporating recent advancements in molecular biology, immunology, and AI techniques, through a detailed exploration of these molecular mechanisms, we aim to shed light on their distinct and overlapping regulatory functions in GBM and AD. The review culminates with a discussion on how insights into NF-κB, miRNAs, and TREM2 signaling may inform novel therapeutic approaches targeting microglia and macrophages in these neurodegenerative and neoplastic conditions. This comparative analysis underscores the potential for new, targeted treatments, offering a roadmap for future research aimed at mitigating the progression of these complex diseases.
                Cover page: Comparative Insight into Microglia/Macrophages-Associated Pathways in Glioblastoma and Alzheimers Disease.
                • Thesis
                • Peer Reviewed
                UC Santa Barbara Electronic Theses and Dissertations (2017)

                Density estimation plays a fundamental role in many areas including statistics and machine learning. The estimated density functions are useful for model building and diagnostics, inference, prediction, classification and clustering. The goal of our research is to develop new methods for density estimation and inference. This dissertation consists of three projects involving smoothing spline density estimation and inference.

                In the first project, we apply smoothing spline density estimation method to test for the normality under both univariate (Chapter 2) and multivariate (Chapter 3) settings. Using the fact that the null hypothesis is equivalent to the logistic density function belonging to the null space of a quintic spline, we construct new test statistics based on quintic polynomial spline and thin-plate spline estimates of the density function. We compare these new tests with some existing normality tests using simulations.

                In the second project, we propose model-based penalties for smoothing spline density estimation and inference. These model-based penalties incorporate indefinite prior knowledge that the density is close to, but not necessarily in a family of distributions. The Pearson and generalization of the generalized inverse Gaussian families are used to illustrate the derivation of penalties and reproducing kernels. We also propose new inference procedures to test the hypothesis that the density belongs to a specific family of distributions.

                Maximum likelihood estimation within a parametric family and nonparametric estimation are two traditional approaches for density estimation. Often in practice it is desirable to model some components of the density function parametrically while leaving other components unspecified. In the third project, we study a general semiparametric density model, which contains many existing semiparametric density models as special cases. We develop computational procedures for different cases, and study the theoretical properties including consistency and asymptotic distribution for the semiparametric linear case. Extensive simulations show that the proposed computational methods perform well and the semiparametric model can outperform many existing nonparametric and semiparametric density estimation methods. Real data applications are also provided.

                  Cover page: Some Contributions to Smoothing Spline Density Estimation
                  • Article
                  • Peer Reviewed
                  UC San Francisco Previously Published Works (2022)
                    Cover page: Microglia-Related Differences in Inflammation Between Genders after Traumatic Brain Injury
                    • Article
                    • Peer Reviewed
                    UC Berkeley Previously Published Works (2010)

                    Objectives

                    To identify key economic issues involved in raising the tobacco tax and to recommend possible options for tobacco tax reform in China.

                    Methods

                    Estimated price elasticities of the demand for cigarettes, prevalence data and epidemiology are used to estimate the impact of a tobacco tax increase on cigarette consumption, government tax revenue, lives saved, employment and revenue loss in the cigarette industry and tobacco farming.

                    Results

                    The recent Chinese tax adjustment, if passed along to the retail price, would reduce the number of smokers by 630,000 saving 210,000 lives, at a price elasticity of -0.15. A tax increase of 1 RMB (or US$0.13) per pack of cigarettes would increase the Chinese government's tax revenue by 129 billion RMB (US 17.2 billion), decrease consumption by 3.0 billion packs of cigarettes, reduce the number of smokers by 3.42 million and save 1.14 million lives.

                    Conclusion

                    The empirical economic analysis and tax simulation results clearly indicate that increasing the tobacco tax in China is the most cost-effective instrument for tobacco control.
                      Cover page: The role of taxation in tobacco control and its potential economic impact in China
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