Zhe Zhang’s research while affiliated with Chinese PLA General Hospital (301 Hospital) and other places

Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. To address this, we collected clinical samples from three cancer types - colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer - and generated serial tissue sections for systematic evaluation. Using these uniformly processed samples, we generated spatial transcriptomics data across five high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, Visium HD FF, CosMx 6K, and Xenium 5K. To establish ground truth datasets, we profiled proteins from adjacent tissue sections corresponding to all five platforms using CODEX and performed single-cell RNA sequencing on the same samples. Leveraging manual cell segmentation and detailed annotations, we systematically assessed each platform's performance across key metrics, including capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, and transcript-protein alignment with adjacent CODEX. The uniformly generated, processed, and annotated multi-omics dataset is valuable for advancing computational method development and biological discoveries. The dataset is accessible via SPATCH, a user-friendly web server for visualization and download (http://spatch.pku-genomics.org/).

BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. RESULTS Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Background: Despite breakthroughs in treatment, ovarian cancer (OC) remains one of the most lethal gynecological malignancies, with an increasing age-standardized mortality rate. This underscores an urgent need for novel biomarkers and therapeutic targets. Although growth factor receptor-bound protein 7 (GRB7) is implicated in cell signaling and tumorigenesis, its expression pattern and clinical implications in OC remain poorly characterized. Methods: To systematically investigate GRB7’s expression in OC, our study utilized extensive datasets from TCGA, GTEx, CCLE, and GEO. The prognostic significance of GRB7 was evaluated by means of Kaplan–Meier and Cox regression analyses. Using a correlation analysis and gene set enrichment analysis, relationships between GRB7’s expression and gene networks, immune cell infiltration and immunotherapy response were investigated. In vitro experiments were conducted to confirm GRB7’s function in the biology of OC. Results: Compared to normal tissues, OC tissues exhibited a substantial upregulation of GRB7. Reduced overall survival, disease-specific survival, and disease-free interval were all connected with high GRB7 mRNA levels. The network study demonstrated that GRB7 is involved in pathways relevant to the course of OC and has a positive connection with several key driver genes. Notably, GRB7’s expression was linked to the infiltration of M2 macrophage and altered response to immunotherapy. Data from single-cell RNA sequencing data across multiple cancer types indicated GRB7’s predominant expression in malignant cells. Moreover, OC cells with GRB7 deletion showed decreased proliferation and migration, as well as increased susceptibility to T cell-mediated cytotoxicity. Conclusion: With respect to OC, our results validated GRB7 as a viable prognostic biomarker and a promising therapeutic target, providing information about its function in tumorigenesis and immune modulation. GRB7’s preferential expression in malignant cells highlights its significance in the biology of cancer and bolsters the possibility that it could be useful in enhancing the effectiveness of immunotherapy.

Objective: Genomic alterations and potential neoantigens for cervical cancer immunotherapy were identified in a cohort of Chinese patients with cervical squamous cell carcinoma (CSCC). Methods: Whole-exome sequencing was used to identify genomic alterations and potential neoantigens for CSCC immunotherapy. RNA Sequencing was performed to analyze neoantigen expression. Results: Systematic bioinformatics analysis showed that C>T/G>A transitions/transversions were dominant in CSCCs. Missense mutations were the most frequent types of somatic mutation in the coding sequence regions. Mutational signature analysis detected signature 2, signature 6, and signature 7 in CSCC samples. PIK3CA, FBXW7, and BICRA were identified as potential driver genes, with BICRA as a newly reported gene. Genomic variation profiling identified 4,960 potential neoantigens, of which 114 were listed in two neoantigen-related databases. Conclusion: The present findings contribute to our understanding of the genomic characteristics of CSCC and provide a foundation for the development of new biotechnology methods for individualized immunotherapy in CSCC.

The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.

With the development of artificial intelligence (AI) technologies, biomedical imaging data play an important role in scientific research and clinical application, but the available resources are limited. Here we present Open Biomedical Imaging Archive (OBIA), a repository for archiving biomedical imaging and related clinical data. OBIA adopts five data objects (Collection, Individual, Study, Series, and Image) for data organization, and accepts the submission of biomedical images of multiple modalities, organs, and diseases. In order to protect personal privacy, OBIA has formulated a unified de-identification and quality control process. In addition, OBIA provides friendly and intuitive web interfaces for data submission, browsing, and retrieval, as well as image retrieval. As of September 2023, OBIA has housed data for a total of 937 individuals, 4136 studies, 24,701 series, and 1,938,309 images covering 9 modalities and 30 anatomical sites. Collectively, OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world. OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.

With the development of artificial intelligence (AI) technologies, biomedical imaging data play important role in scientific research and clinical application, but the available resources are limited. Here we present Open Biomedical Imaging Archive (OBIA), a repository for archiving biomedical imaging data and related clinical data. OBIA adopts five data objects (Collection, Individual, Study, Series, and Image) for data organization, accepts the submission of biomedical images of multiple modalities, organs, and diseases. In order to protect data privacy, OBIA has formulated a unified de-identification and quality control process. In addition, OBIA provides friendly and intuitive web interfaces for data submission, browsing, and retrieval. In particular, OBIA supports both metadata retrieval and image retrieval. As of September 2023, OBIA has housed data for a total of 937 individuals, 4136 studies, 24,701 series, and 1,938,309 images covering 9 modalities and 30 anatomical sites. Collectively, OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world. OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.

Ovarian cancer (OC) is characterised by heterogeneity that complicates the prediction of patient survival and treatment outcomes. Here, we conducted analyses to predict the prognosis of patients from the Genomic Data Commons database and validated the predictions by fivefold cross-validation and by using an independent dataset in the International Cancer Genome Consortium database. We analysed the somatic DNA mutation, mRNA expression, DNA methylation, and microRNA expression data of 1203 samples from 599 serous ovarian cancer (SOC) patients. We found that principal component transformation (PCT) improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than the decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. Our study provides perspective on building reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC. Impact statement What is already known on this subject? Recent studies have focussed on predicting cancer outcomes based on omics data. But the limitation is the performance of single-platform genomic analyses or the small numbers of genomic analyses. What do the results of this study add? We analysed multi-omics data, found that principal component transformation (PCT) significantly improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than did decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. What are the implications of these findings for clinical practice and/or further research? Our study provides perspective on how to build reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC for future studies.

Background: The mortality rate of ovarian cancer (OC) ranks first among female genital tract malignant tumors, which seriously threatens women's life and health. Because of its insidious onset and poor prognosis, it has become a thorny problem in the clinic, especially for patients with platinum-resistant recurrent ovarian cancer (PROC). In recent years, the medical treatment of OC has made gratifying results, bringing hope to the patients. Case description: A 54-year-old OC patient who has failed previous neoadjuvant chemotherapy, cytoreductive surgery, and postoperative chemotherapy was diagnosed with PROC. Then she received combination treatment of fuzuloparib (100mg PO BID), apatinib (250mg PO QD), and camrelizumab (200mg IV Q3W) for every 3-week cycle in a Phase II study for PROC patients. In the phase II study, her condition stabilized, responded well to treatment with a sharp decrease by 91.14% of target lesions and disappearances of non-target lesions, and continued to receive regular treatment with progression-free survival exceeding 15 months and no serious adverse events. Conclusion: The present case proves PROC patients might have a sustained response to triplet combination with camrelizumab, combined with fuzuloparib and apatinib.

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.