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Yuanmei Wang

Publications and source records attributed to Yuanmei Wang.

5 recordsLinked to original sources

Spatial-Temporal Diversity of Extrachromosomal DNA Shapes Urothelial Carcinoma Evolution and Tumor-Immune Microenvironment.

Extrachromosomal DNA (ecDNA) presents a promising target for cancer therapy; however, its spatial-temporal diversity and influence on tumor evolution and the immune microenvironment remain largely unclear. We apply computational methods to analyze ecDNA from whole-genome sequencing data of 595 urothelial carcinoma (UC) patients. We demonstrate that ecDNA drives clonal evolution through structural rearrangements during malignant transformation and recurrence of UC. This supports a model wherein tumors evolve via the selective expansion of ecDNA-bearing cells. Through multi-regional sampling of tumors, we demonstrate that ecDNA contributes to the evolution of multifocality and increased intratumoral heterogeneity. EcDNA is present in 36% of UC tumors and correlates with an immunosuppressive phenotype and poor prognosis. Single-cell RNA sequencing analyses reveal that ecDNA+ malignant cells exhibit diminished expression of major histocompatibility complex class I molecules, enabling them to evade T-cell immunity. Finally, we show that sequencing of urinary sediment-derived DNA has excellent specificity in detecting ecDNA.

Journal Article↗

Alternative splicing in ovarian cancer.

Ovarian cancer is the second leading cause of gynecologic cancer death worldwide, with only 20% of cases detected early due to its elusive nature, limiting successful treatment. Most deaths occur from the disease progressing to advanced stages. Despite advances in chemo- and immunotherapy, the 5-year survival remains below 50% due to high recurrence and chemoresistance. Therefore, leveraging new research perspectives to understand molecular signatures and identify novel therapeutic targets is crucial for improving the clinical outcomes of ovarian cancer. Alternative splicing, a fundamental mechanism of post-transcriptional gene regulation, significantly contributes to heightened genomic complexity and protein diversity. Increased awareness has emerged about the multifaceted roles of alternative splicing in ovarian cancer, including cell proliferation, metastasis, apoptosis, immune evasion, and chemoresistance. We begin with an overview of altered splicing machinery, highlighting increased expression of spliceosome components and associated splicing factors like BUD31, SF3B4, and CTNNBL1, and their relationships to ovarian cancer. Next, we summarize the impact of specific variants of CD44, ECM1, and KAI1 on tumorigenesis and drug resistance through diverse mechanisms. Recent genomic and bioinformatics advances have enhanced our understanding. By incorporating data from The Cancer Genome Atlas RNA-seq, along with clinical information, a series of prognostic models have been developed, which provided deeper insights into how the splicing influences prognosis, overall survival, the immune microenvironment, and drug sensitivity and resistance in ovarian cancer patients. Notably, novel splicing events, such as PIGV|1299|AP and FLT3LG|50,941|AP, have been identified in multiple prognostic models and are associated with poorer and improved prognosis, respectively. These novel splicing variants warrant further functional characterization to unlock the underlying molecular mechanisms. Additionally, experimental evidence has underscored the potential therapeutic utility of targeting alternative splicing events, exemplified by the observation that knockdown of splicing factor BUD31 or antisense oligonucleotide-induced BCL2L12 exon skipping promotes apoptosis of ovarian cancer cells. In clinical settings, bevacizumab, a humanized monoclonal antibody that specifically targets the VEGF-A isoform, has demonstrated beneficial effects in the treatment of patients with advanced epithelial ovarian cancer. In conclusion, this review constitutes the first comprehensive and detailed exposition of the intricate interplay between alternative splicing and ovarian cancer, underscoring the significance of alternative splicing events as pivotal determinants in cancer biology and as promising avenues for future diagnostic and therapeutic intervention.

Humans↗

A comparison of truncated total least squares with Tikhonov regularization in imaging by ultrasound inverse scattering.

For good image quality using ultrasound inverse scattering, one alternately solves the well-posed forward scattering equation for an estimated total field and the ill-posed inverse scattering equation for the desired object property function. In estimating the total field, error or noise contaminates the coefficients of both matrix and data of the inverse scattering equation. Previous work on ill-posed inverse ultrasonic scattering commonly used Tikhonov regularization, which considers error only in the data. The solution so obtained is not precise enough to reconstruct the quantitative internal structure of a large or high-contrast object. This paper adopts the truncated total least squares method, simultaneously considering error and noise on both sides of the inverse scattering equation, and compares it with the classical Tikhonov regularization. We show that it can substantially improve reconstruction fit and image quality when the inverse scattering equation system is strongly ill-posed.

Algorithms↗

[Application of weighted minimum-norm estimation with Tikhonov regularization for neuromagnetic source imaging].

In magnetoencepholography(MEG) inverse research, according to the point source model and distributed source model, the neuromagnetic source reconstruction methods are classified as parametric current dipole localization and nonparametric source imaging (or current density reconstruction). MEG source imaging technique can be formulated as an inherent ill-posed and highly underdetermined linear inverse problem. In order to yield a robust and plausible neural current distribution image, various approaches have been proposed. Among those, the weighted minimum-norm estimation with Tikhonov regularization is a popular technique. The authors present a relatively overall theoretical framework Followed by a discussion of the development, several regularized minimum-norm algorithms have been described in detail, including the depth normalization, low resolution electromagnetic tomography(LORETA), focal underdetermined system solver(FOCUSS), selective minimum-norm(SMN). In addition, some other imaging methods, e.g., maximum entropy method(MEM), the method incorporating other brain functional information such as fMRI data and maximum a posteriori(MAP) method using Markov random field model, are explained as well. From the generalized point of view based on minimum-norm estimation with Tikhonov regularization, all these algorithms are aiming to resolve the tradeoff between fidelity to the measured data and the constraints assumptions about the neural source configuration such as anatomical and physiological information. In conclusion, almost all the source imaging approaches can be consistent with the regularized minimum-norm estimation to some extent.

Algorithms↗

Vector entropy imaging theory with application to computerized tomography.

Medical imaging theory for x-ray CT and PET is based on image reconstruction from projections. In this paper a novel vector entropy imaging theory under the framework of multiple criteria decision making is presented. We also study the most frequently used image reconstruction methods, namely, least square, maximum entropy, and filtered back-projection methods under the framework, of the single performance criterion optimization. Finally, we introduce some of the results obtained by various reconstruction algorithms using computer-generated noisy projection data from the Hoffman phantom and real CT scanner data. Comparison of the reconstructed images indicates that the vector entropy method gives the best in error (difference between the original phantom data and reconstruction), smoothness (suppression of noise), grey value resolution and is free of ghost images.

Algorithms↗