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Yonggang Lu

Publications and source records attributed to Yonggang Lu.

3 recordsLinked to original sources
PubMed2026

Integrated multi-omics analysis and functional experiments reveals PPAP2C as a potential prognostic biomarker and therapeutic target in breast cancer.

BACKGROUND: This study aims to systematically elucidate the clinical significance and biological function of the phospholipid phosphatase (PLPP) family member (PPAP2C) phosphatidic acid phosphatase type 2C in breast cancer, and to evaluate its potential as a prognostic biomarker and therapeutic target. METHODS: Gene expression data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were integrated to characterize the expression profile of PLPP family members, focusing on PPAP2C in breast cancer. The prognostic value of PPAP2C, initially identified at the mRNA level (TCGA, (METABRIC) Molecular Taxonomy of Breast Cancer International Consortium, Gene Expression Omnibus (GEO)), was confirmed at the protein level by immunohistochemistry (IHC) on tissue microarrays (TMA). The oncogenic functions of PPAP2C were investigated in triple-negative breast cancer (TNBC) cells through CRISPR-Cas9-mediated knockout and ectopic overexpression, with assessment of key phenotypes including proliferation, colony formation, migration, and invasion. In vivo validation was subsequently performed using an MDA-MB-231 xenograft model. RESULTS: PPAP2C exhibits the most significant overexpression pattern across 33 cancer types (upregulated in 16 cancers, downregulated in only 3). Compared with normal tissues, PPAP2C showed specific overexpression in breast cancer tissues and was significantly associated with advanced clinical stages and aggressive subtypes (HER2+ and TNBC). Survival analysis demonstrated that high PPAP2C expression correlated with significantly shorter overall survival and disease-free survival, which was further validated in METABRIC and GEO cohorts. Tissue microarray analysis confirmed higher PPAP2C protein positivity in tumor tissues (94.7%) than in adjacent normal tissues (59.7%), with worse OS and RFS in high-expression groups. Multivariate analysis identified PPAP2C as an independent prognostic factor for OS. Functional experiments revealed that PPAP2C knockout (via 5-bp/1-bp frameshift mutations) suppressed TNBC cell proliferation, colony formation, migration, and invasion, while overexpression enhanced these phenotypes. In vivo studies further demonstrated complete tumor regression in MDA-MB-231 xenografts upon PPAP2C knockout. CONCLUSION: This study identifies PPAP2C as a key oncogenic driver and a robust independent prognostic biomarker in breast cancer. The findings provide compelling evidence that PPAP2C represents a promising therapeutic target, offering a new strategic avenue for precision therapy, particularly for aggressive breast cancer subtypes.

PLPP2↗
PubMed2006

Unified framework for anisotropic interpolation and smoothing of diffusion tensor images.

To enhance the performance of diffusion tensor imaging (DTI)-based fiber tractography, this study proposes a unified framework for anisotropic interpolation and smoothing of DTI data. The critical component of this framework is an anisotropic sigmoid interpolation kernel which is adaptively modulated by the local image intensity gradient profile. The adaptive modulation of the sigmoid kernel permits image smoothing in homogeneous regions and meanwhile guarantees preservation of structural boundaries. The unified scheme thus allows piece-wise smooth, continuous and boundary preservation interpolation of DTI data, so that smooth fiber tracts can be tracked in a continuous manner and confined within the boundaries of the targeted structure. The new interpolation method is compared with conventional interpolation methods on the basis of fiber tracking from synthetic and in vivo DTI data, which demonstrates the effectiveness of this unified framework.

Algorithms↗
PubMed2006

Improved fiber tractography with Bayesian tensor regularization.

Diffusion tensor tractography suffers from the effects of noise and partial volume averaging (PVA). For reliable reconstruction of fiber pathways, tracking algorithms that are robust to these artifacts are called for. To meet this need, the present study establishes a novel Bayesian regularization framework for fiber tracking that takes into account the effects of noise and PVA, thereby improving tracking accuracy and precision. With this framework, the propagation of a fiber path follows an optimal vector determined by Bayes decision rule; the probability functions involved are modeled on the basis of multivariate normal distributions of diffusion tensor elements, which allows the optimal solution with maximum a posteriori probability to be derived analytically. Parameters for the probability functions are estimated from the uncertainty of tensor elements and the variance among tensors within an oriented sampling volume weighted by fractional anisotropy. Experiments with Monte Carlo simulations, synthetic, and in vivo human diffusion tensor data demonstrate that this specialized scheme enhances the immunity of fiber tracking to noise and PVA, and hence enables fibers to be more faithfully reconstructed.

Adult↗