Biomedical subjects

Qing Zhong

Publications and source records attributed to Qing Zhong.

3 recordsLinked to original sources

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2026

Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.

BACKGROUND: Neoadjuvant therapy (NAT) is recommended for locally advanced gastric cancer (LAGC), but some patients respond poorly. We aimed to construct a multimodal model integrating CT images, transcriptomic sequencing, and clinicopathological data to assess prognosis in LAGC patients receiving NAT. MATERIALS AND METHODS: This multicenter study included 505 LAGC patients who underwent NAT. Radiomic features were extracted from preoperative CT images of 505 patients. RNA-seq was performed on 277 post-NAT specimens, with additional data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (n = 804). Patients were divided into training (168 cases), internal validation (72 cases), and external validation cohorts. Machine learning algorithms identified key radiomic, molecular, and clinical features associated with NAT response, which were then integrated into a multimodal model to predict overall survival (OS) and disease-free survival (DFS). RESULTS: Six radiomic and three molecular features significantly associated with NAT response were selected. Radiomic risk (hazard ratio [HR]: 4.0, P < 0.001) and molecular risk (HR: 7.1, P < 0.001) were independent prognostic factors. By integrating radiomic risk, molecular risk, and clinical characteristics, a multimodal model (MuMo) was constructed.The C-index results (OS, C-index = 0.855; DFS, C-index = 0.786) demonstrated that MuMo outperformed the single-modality models and ypTNM staging.Mechanistic analysis suggested that the efficacy of neoadjuvant therapy was significantly enriched in immune-inflammatory pathways. CONCLUSIONS: MuMo can effectively predict postoperative survival risk in LAGC patients receiving NAT, serving as a powerful tool for optimizing prognostic assessment.

Humans↗

PubMed2025

Ras-MAPK pathway in patients with lupus nephritis.

BACKGROUND: Pathogenic mutations in genes encoding components of the Ras/mitogen-activated protein kinase (Ras-MAPK) pathway cause RASopathy. Here, we describe five unrelated patients with SLE carrying mutations associated with RASopathy and investigate the activity of the Ras-MAPK pathway. METHODS: Pathogenic variants were identified by whole-exome/whole-genome sequencing. The activity of the Ras-MAPK pathway in peripheral blood mononuclear cells (PBMC) and kidneys was evaluated using RNA sequencing and datasets from the nephroseq database, respectively. RESULTS: Five (likely) pathogenic variants in four Ras-MAPK genes were identified, including NRAS: c.G38A: p.G13D; ARAF: c.C1435T: p.R479C; KRAS: c.T341C: p.V114A; PTPN11: c.G455A: p.R152H and NRAS: c.G34A: p.G12S. Kidney injury is the main feature, presenting with nephrotic syndrome (2/5), proteinuria and haematuria (2/5). Acute kidney injury and rapidly progressive nephritic syndrome were noted in one patient each. Other clinical features included mucocutaneous lesions (5/5), cardiac involvement (4/5) and arthralgia (3/5). Laboratory abnormalities included hypocomplementaemia (5/5), presence of antiphospholipid antibodies (4/5), decreased regulatory T cells (3/3), pancytopenia (3/5) and persistent monocytosis (2/5). Kidney biopsy revealed lupus nephritis. Most patients responded well to standard therapy, with the exception of the patient with the NRAS p.G13D mutation who died. The Ras-MAPK pathway was activated in both PBMC and kidney of patients with LN as indicated by increased expression of NRAS, KRAS, RIT1, MRAS, PPP1CB, SHOC2, SOS2 and MAP2K1, as well as decreased expression of negative regulators of the Ras-MAPK pathway, CBL, LZTR1 and NF1. CONCLUSION: Kidney involvement may be the main feature of the clinical spectrum of RASopathy. Genetic screening should be considered for patients with early onset lupus.

Humans↗