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Spatial Transcriptomic Analysis Reveals Associations Between Genes and Cellular Topology in Breast and Prostate Cancers

Overview
Journal Cancers (Basel)
Publisher MDPI
Specialty Oncology
Date 2022 Oct 14
PMID 36230778
Authors
Affiliations
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Abstract

Background: Cancer is the leading cause of death worldwide with breast and prostate cancer the most common among women and men, respectively. Gene expression and image features are independently prognostic of patient survival; but until the advent of spatial transcriptomics (ST), it was not possible to determine how gene expression of cells was tied to their spatial relationships (i.e., topology).

Methods: We identify topology-associated genes (TAGs) that correlate with 700 image topological features (ITFs) in breast and prostate cancer ST samples. Genes and image topological features are independently clustered and correlated with each other. Themes among genes correlated with ITFs are investigated by functional enrichment analysis.

Results: Overall, topology-associated genes (TAG) corresponding to extracellular matrix (ECM) and Collagen Type I Trimer gene ontology terms are common to both prostate and breast cancer. In breast cancer specifically, we identify the ZAG-PIP Complex as a TAG. In prostate cancer, we identify distinct TAGs that are enriched for GI dysmotility and the IgA immunoglobulin complex. We identified TAGs in every ST slide regardless of cancer type.

Conclusions: These TAGs are enriched for ontology terms, illustrating the biological relevance to our image topology features and their potential utility in diagnostic and prognostic models.

Citing Articles

Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.

Maciejewski K, Czerwinska P Cancers (Basel). 2024; 16(17).

PMID: 39272958 PMC: 11394603. DOI: 10.3390/cancers16173100.

References
1.
Liu Y, Ye X, Yu C, Shao W, Hou J, Feng W . TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery. BMC Bioinformatics. 2021; 22(Suppl 4):111. PMC: 8543836. DOI: 10.1186/s12859-021-03964-5. View

2.
Zhong Z, Nan K, Weng M, Yue Y, Zhou W, Wang Z . Pro- and Anti- Effects of Immunoglobulin A- Producing B Cell in Tumors and Its Triggers. Front Immunol. 2021; 12:765044. PMC: 8640120. DOI: 10.3389/fimmu.2021.765044. View

3.
Chelebian E, Avenel C, Kartasalo K, Marklund M, Tanoglidi A, Mirtti T . Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer. Cancers (Basel). 2021; 13(19). PMC: 8507756. DOI: 10.3390/cancers13194837. View

4.
Ash J, Darnell G, Munro D, Engelhardt B . Joint analysis of expression levels and histological images identifies genes associated with tissue morphology. Nat Commun. 2021; 12(1):1609. PMC: 7952575. DOI: 10.1038/s41467-021-21727-x. View

5.
Brassart-Pasco S, Brezillon S, Brassart B, Ramont L, Oudart J, Monboisse J . Tumor Microenvironment: Extracellular Matrix Alterations Influence Tumor Progression. Front Oncol. 2020; 10:397. PMC: 7174611. DOI: 10.3389/fonc.2020.00397. View