CMB_2025v15n4

Computational Molecular Biology 2025, Vol.15, No.4, 183-192 http://bioscipublisher.com/index.php/cmb 191 Janesick A., Shelansky R., Gottscho A., Wagner F., Rouault M., Beliakoff G., De Oliveira M., Kohlway A., Abousoud J., Morrison C., Drennon T., Mohabbat S., Williams S., and Taylor S., 2022, High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue, bioRxiv, 6: 510405. https://doi.org/10.1101/2022.10.06.510405 Kim I., Choi S., Yoo S., Lee M., and Kim I., 2022, Cancer-associated fibroblasts in the hypoxic tumor microenvironment, Cancers, 14(14): 3321. https://doi.org/10.3390/cancers14143321 Lei L., Han K., Wang Z., Shi C., Wang Z., Dai R., Zhang Z., Wang M., and Guo Q., 2024, Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics, Briefings in Bioinformatics, 25(3): bbae173. https://doi.org/10.1093/bib/bbae173 Li Q., Zhang X., and Ke R., 2022, Spatial transcriptomics for tumor heterogeneity analysis, Frontiers in Genetics, 13: 906158. https://doi.org/10.3389/fgene.2022.906158 Li X., Huang W., Xu X., Zhang H., and Shi Q., 2023, Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network, Frontiers in Genetics, 14: 1202409. https://doi.org/10.3389/fgene.2023.1202409 Li Z., Hu Y., He Z., Xu H., Wang H., and He Y., 2025, Comparative transcriptomic and genomic analysis of tumor cells in the marginal and center regions of tumor nests in human hepatocellular carcinoma, Frontiers in Cell and Developmental Biology, 13: 1611951. https://doi.org/10.3389/fcell.2025.1611951 Liang Q., Soto L., Haymaker C., and Chen K., 2024, Interpretable spatial gradient analysis for spatial transcriptomics data, bioRxiv, 19: 585725. https://doi.org/10.1101/2024.03.19.585725 Lin Y., Wang Y., Liang Y., Yu Y., Li J., Ma Q., He F., and Xu D., 2022, Sampling and ranking spatial transcriptomics data embeddings to identify tissue architecture, Frontiers in Genetics, 13: 912813. https://doi.org/10.3389/fgene.2022.912813 Liu J., Manabe H., Qian W., Wang Y., Gu Y., Chu A., Gadhvi G., Song Y., Ono N., and Welch J., 2024, CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data, bioRxiv, 8: 584153. https://doi.org/10.1101/2024.03.08.584153 Liu J., Tran V., Vemuri V., Byrne A., Borja M., Kim Y., Agarwal S., Wang R., Awayan K., Murti A., Taychameekiatchai A., Wang B., Emanuel G., He J., Haliburton J., Pisco A., and Neff N., 2022, Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing, Life Science Alliance, 6(1): e202201701. https://doi.org/10.1101/2022.03.04.483068 Long Y., Ang K., Li M., Chong K., Sethi R., Zhong C., Xu H., Ong Z., Sachaphibulkij K., Chen A., Li Z., Fu H., Wu M., Lim H., Liu L., and Chen J., 2023, Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST, Nature Communications, 14(1): 1155. https://doi.org/10.1038/s41467-023-36796-3 Lu T., Zhang Y., Li M., Kang Q., Fang S., Zhang Y., Brix S., and Xu X., 2024, EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics, GigaScience, 13: giad097. https://doi.org/10.1093/gigascience/giad097 Mao X., Xu J., Wang W., Liang C., Hua J., Liu J., Zhang B., Meng Q., Yu X., and Shi S., 2021, Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives, Molecular Cancer, 20(1): 131. https://doi.org/10.1186/s12943-021-01428-1 Rademacher A., Huseynov A., Bortolomeazzi M., Wille S., Schumacher S., Sant P., Keitel D., Okonechnikov K., Ghasemi D., Pajtler K., Mallm J., and Rippe K., 2024, Comparison of spatial transcriptomics technologies using tumor cryosections, Genome Biology, 26(1): 176. https://doi.org/10.1101/2024.04.03.586404 Saqib J., Park B., Jin Y., Seo J., Mo J., and Kim J., 2023, Identification of niche-specific gene signatures between malignant tumor microenvironments by integrating single cell and spatial transcriptomics data, Genes, 14(11): 2033. https://doi.org/10.3390/genes14112033 Shan Y., Zhang Q., Guo W., Wu Y., Miao Y., Xin H., Lian Q., and Gu J., 2022, TIST: transcriptome and histopathological image integrative analysis for spatial transcriptomics, Genomics, Proteomics & Bioinformatics, 20(5): 974-988. https://doi.org/10.1101/2022.07.23.501220 Tian T., Zhang J., Lin X., Wei Z., and Hakonarson H., 2024, Dependency-aware deep generative models for multitasking analysis of spatial omics data, Nature Methods, 21(8): 1501-1513. https://doi.org/10.1038/s41592-024-02257-y Wang T., Tian L., Wei B., Li J., Zhang C., Long R., Zhu X., Zhang Y., Wang B., Tang G., Yang J., and Guo Y., 2024, Effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer, Scientific Reports, 14(1): 26617. https://doi.org/10.1038/s41598-024-77630-0 Wang Y., Song B., Wang S., Chen M., Xie Y., Xiao G., Wang L., and Wang T., 2022, Sprod for de-noising spatially resolved transcriptomics data based on position and image information, Nature Methods, 19(8): 950-958. https://doi.org/10.1038/s41592-022-01560-w Wu Y., Shi Y., Luo Z., Zhou X., Chen Y., Song X., and Liu S., 2025, Spatial multi-omics analysis of tumor–stroma boundary cell features for predicting breast cancer progression and therapy response, Frontiers in Cell and Developmental Biology, 13: 1570696. https://doi.org/10.3389/fcell.2025.1570696

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