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Hongmei Zhang

Publications and source records attributed to Hongmei Zhang.

4 recordsLinked to original sources
PubMed2026

Chinese expert consensus on precision testing and molecular diagnosis of pancreatic cancer (2025).

This consensus by the CSCO Pancreatic Cancer Expert Committee establishes evidence-based guidelines for molecular testing in pancreatic ductal adenocarcinoma. It details recommendations for biomarkers (e.g., KRAS, BRCA, MSI), liquid biopsy, and precision imaging to direct targeted therapies and immunotherapy, aiming to standardize diagnosis and optimize individualized patient care. Pancreatic ductal adenocarcinoma (PDAC) is the most common pathological type of primary pancreatic malignancy, accounting for ~95% of cases and generally referred to as pancreatic cancer [1]. Its prognosis is extremely poor and its incidence continues to rise [2]. According to the most recent global cancer statistics, the incidence of pancreatic cancer ranks 12th among all cancers, and its mortality ranks 6th, making it one of the deadliest malignancies worldwide [3]. Approximately 57% of patients have metastatic disease at diagnosis and require systemic therapy, for which chemotherapy remains the standard first-line option [1]. However, the overall response rate to currently available systemic regimens is low, and the 5-year survival rate for patients with metastatic disease remains below 5% [3]. Although most pancreatic cancers harbor canonical driver mutations, they exhibit marked heterogeneity at the molecular level. Whole-genome sequencing (WGS) and integrative genomic analyses have identified molecular subtypes of PDAC with potential clinical relevance [4-9]. With the increasing implementation of precision oncology, the Chinese Society of Clinical Oncology (CSCO) Guidelines for the Diagnosis and Treatment of Pancreatic Cancer give a level 1 recommendation to perform genetic and other molecular testing on tissue or cytologic specimens as part of the pathological diagnostic work-up, in order to guide individualized treatment, including targeted therapy and immunotherapy [10]. To further promote the use of genetic and molecular testing in the precision treatment of pancreatic cancer, the CSCO Pancreatic Cancer Expert Committee convened a multidisciplinary panel to develop the present Chinese Expert Consensus on Precision Testing and Molecular Diagnosis of Pancreatic Cancer (2025), aiming to provide clinicians with an authoritative reference for precision diagnostics and treatment decision-making.

Humans↗
PubMed2025

Assessing the causal association between celiac disease and Alzheimer disease and frontotemporal dementia: A bidirectional Mendelian randomization approach.

This study aimed to investigate the bidirectional causal relationship between celiac disease (CD) and the risk of Alzheimer disease (AD) or frontotemporal dementia (FTD) using Mendelian randomization (MR), in order to clarify prior inconsistent findings. We analyzed summary-level genome-wide association study (GWAS) data for CD, AD, and FTD. Single-nucleotide polymorphisms (SNPs) strongly associated with each condition were selected as genetic instruments. MR analysis was conducted in 2 directions: from CD to AD/FTD and from AD/FTD to CD. Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) was used to detect and correct for pleiotropy, and Cochran Q assessed heterogeneity. Leave-one-out and Mendelian Randomization-Egger (MR-Egger) regression sensitivity analyses were performed to evaluate robustness. No evidence of a causal effect was found between CD and either AD or FTD in either direction (P > .05). Similarly, genetic liability to AD or FTD did not increase the risk of CD. Sensitivity analyses supported the robustness of the results, showing no pleiotropy or heterogeneity. Our findings suggest that CD is not causally linked to the development of AD or FTD. While shared genetic factors or comorbidities may exist, the association is likely noncausal, and other mechanisms of cognitive decline in CD patients warrant further study.

Humans↗
PubMed2025

Machine learning-based analysis of the impact of 5' untranslated region on protein expression.

The 5' untranslated region (5'UTR) plays a crucial regulatory role in messenger RNA (mRNA), with modified 5'UTRs extensively utilized in vaccine production, gene therapy, etc. Nevertheless, manually optimizing 5'UTRs may encounter difficulties in balancing the effects of various cis-elements. Consequently, multiple 5'UTR libraries have been created, and machine learning models have been employed to analyze and predict translation efficiency (TE) and protein expression, providing insights into critical regulatory features. On the one hand, these screening libraries, based on TE and mean ribosome load, struggle to accurately quantify protein expression; on the other hand, a precise method for quantifying 5'UTRs necessitates a significantly costlier library. To resolve this dilemma, we constructed a library utilizing firefly luciferase as the reporter to measure accurate protein expression. In addition, we optimized the library construction method by clustering mRNA sequences to reduce redundant data and minimize the size of the dataset. This dual strategy by increasing accuracy and reducing dataset size was found to be effective in predicting the 5'UTRs from the PC3 cell line.

5' Untranslated Regions↗
PubMed2025

Deep learning-based annotation of plant abiotic stress resistance genes for crops.

The declining costs of DNA sequencing have expanded genomic data, crucial for understanding plant abiotic stress responses and crop improvement. However, accurate gene annotation remains challenging. To address this limitation, we propose the PASRGA, a deep learning approach that leverages transfer learning and contrastive learning to annotate genes related to drought, salt, cold, and UV resistance. PASRGA achieves high F1-scores, area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), and Matthews correlation coefficient (MCC) in annotating stress resistance genes, significantly outperforming the general protein annotation model CLEAN, the plant phosphatase gene annotation model PF-NET, the top-ranked model in the CAFA5 challenge NetGO 4.0, and four traditional machine learning methods. Its effectiveness was further validated with a salt stress treatment experiment in Eutrema salsugineum. To facilitate crop breeding practices, we utilized PASRGA to annotate the genomes of 17 major crops. To improve accessibility and utility, we incorporated both manually curated and PASRGA-predicted gene data, together with the PASRGA tool, into the PlantASRG database (https://bioinfor.nefu.edu.cn/PlantASRG/). This comprehensive resource aims to support crop breeding initiatives and ensure food security.

Crops, Agricultural↗