Targeting ACE2-BRD4 crosstalk in colorectal cancer and the deregulation of DNA repair and apoptosis

Zhang, Shilan; Kapoor, Sudhir; Tripathi, Chakrapani; Perez, Jorge Tovar; Mohan, Nivedhitha; Zhang, Ke; Rajendran, Praveen; Dashwood, Wan‐Mohaiza

doi:10.1038/s41698-023-00361-4

Cited by 10 publications

(12 citation statements)

References 21 publications

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“…This archetype is enriched for hallmarks of cell proliferation (Hallmark genesets G2M checkpoint, E2F targets) and growth (MYC Targets V1/2, mTORC1 signaling). The top markers of this cluster include CDC20, CDK1 and CDK4, key regulators of phase transitions during the cell cycle [47]. Concomitantly, analysis of cell cycle in the cells most strongly associated with this archetype revealed that >95% were in either the S or G2M phase (Supplementary Figure 5a-b).…”

Section: Resultsmentioning

confidence: 99%

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

Venkat,

Youlten,

Juan

et al. 2024

Preprint

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Identifying functionally important cell states and structure within a heterogeneous tumor remains a significant biological and computational challenge. Moreover, current clustering or trajectory-based computational models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. To address this, we present Archetypal Analysis network (AAnet), a neural network that learns key archetypal cell states within a phenotypic continuum of cell states in single-cell data. Applied to single-cell RNA sequencing data from pre-clinical models and a cohort of 34 clinical breast cancers, AAnet identifies archetypes that resolve distinct biological cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Notably, archetypes identified in primary tumors are recapitulated in matched liver, lung and lymph node metastases, demonstrating that a significant component of intratumoral heterogeneity is driven by cell intrinsic properties. Using spatial transcriptomics as orthogonal validation, AAnet-derived archetypes show discrete spatial organization within tumors, supporting their distinct archetypal biology. We further reveal that ligand:receptor cross-talk between cancer and adjacent stromal cells contributes to intra-archetypal biological mimicry. Finally, we use AAnet archetype identifiers to validate GLUT3 as a critical mediator of a hypoxic cell archetype harboring a cancer stem cell population, which we validate in human triple-negative breast cancer specimens. AAnet is a powerful tool to reveal functional cell states within complex samples from multimodal single-cell data.

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Section: Resultsmentioning

confidence: 99%

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

Venkat,

Youlten,

Juan

et al. 2024

Preprint

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“…In addition to the main nodes mentioned, BUB1, CCND1, CDK1, and PCNA, other studies also show the importance of NOP58, EZH2 and PPPC1A in tumorigenesis [ 44 – 46 ]. All these genes are interconnected in a complex network that ensures the correct control of cell division and genomic stability, emphasizing CDK1, as master regulator [ 39 ]. CDK1, CCND1, and PCNA play distinct roles in different phases of cell cycle and have been identified as pivotal genes in the development and progression of different cancer types, including colon, liver, and gynecological tumors [ 47 – 49 ].…”

Section: Discussionmentioning

confidence: 99%

Unraveling malignant phenotype of peritumoral tissue: transcriptomic insights into early-stage breast cancer

Morla-Barcelo,

Laguna-Macarrilla,

Cordoba

et al. 2024

Breast Cancer Res

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Background Early-stage invasive ductal carcinoma displays high survival rates due to early detection and treatments. However, there is still a chance of relapse of 3–15% after treatment. The aim of this study was to uncover the distinctive transcriptomic characteristics and monitoring prognosis potential of peritumoral tissue in early-stage cases. Methods RNA was isolated from tumoral, peritumoral, and non-tumoral breast tissue from surgical resection of 10 luminal early-stage invasive ductal carcinoma patients. Transcriptome expression profiling for differentially expressed genes (DEGs) identification was carried out through microarray analysis. Gene Ontology and KEGG pathways enrichment analysis were explored for functional characterization of identified DEGs. Protein-Protein Interactions (PPI) networks analysis was performed to identify hub nodes of peritumoral tissue alterations and correlated with Overall Survival and Relapse Free Survival. Results DEGs closely related with cell migration, extracellular matrix organization, and cell cycle were upregulated in peritumoral tissue compared to non-tumoral. Analyzing PPI networks, we observed that the proximity to tumor leads to the alteration of gene modules involved in cell proliferation and differentiation signaling pathways. In fact, in the peritumoral area were identified the top ten upregulated hub nodes including CDK1, ESR1, NOP58, PCNA, EZH2, PPP1CA, BUB1, TGFBR1, CXCR4, and CCND1. A signature performed by four of these hub nodes (CDK1, PCNA, EZH2, and BUB1) was associated with relapse events in untreated luminal breast cancer patients. Conclusions In conclusion, our study characterizes in depth breast peritumoral tissue providing clues on the changes that tumor signaling could cause in patients with early-stage breast cancer. We propose that the use of a four gene signature could help to predict local relapse. Overall, our results highlight the value of peritumoral tissue as a potential source of new biomarkers for early detection of relapse and improvement in invasive ductal carcinoma patient’s prognosis.

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“…36 The protein complex encoded by this gene participates in the cell cycle process by regulating cell cycle factors, causing cell cycle disruption, and directly contributing to cancer cell proliferation and growth. 37 Kinesin Family Member 4A (KIF4A) encodes a member of the kinesin-4 subfamily of motor proteins. The encoded protein is an ATP-dependent microtubule motor protein involved in intracellular transport of membranous organelles.…”

Section: Discussionmentioning

confidence: 99%

Identifying Biomarkers for Prognostic Treatment in Breast Cancer through Bioinformatics Analysis

Shi,

Yan

2023

Preprint

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Objective This study aims to identify biomarkers linked to breast cancer for potential treatment. Methods Three breast cancer gene microarrays were selected from the Gene Expression Omnibus (GEO) database, meeting specific criteria. Paired data analysis revealed shared Differentially Expressed Genes (DEGs) among them. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. A Protein-Protein Interaction (PPI) network was constructed using String databases and Cytoscape software to identify hub genes. These hub genes underwent analysis for differential expression, survival, and pathological presentation in various databases (UALCAN, Kaplan-Meier Plotter, and HPA(The Human Protein Atlas)). Results Integrated analysis yielded 202 shared DEGs, with 164 downregulated and 38 upregulated genes.Highlighted 10 hub genes associated with breast cancer: KIF20A, CCVB1, KIF2C, TTK, CCNA2, RRM2, TOP2A, CDK1, KIF4A and CACA8. Conclusion The study uncovers the roles of these hub genes in cancer growth and proliferation, particularly TTK's link to basal-like and triple-positive breast cancer.RRM2 exhibited significance in HER2-positive cases, while others were prominent in triple-negative breast cancer. Exploring these hub genes provides potential biomarkers and insights for breast cancer prognosis and treatment decisions.

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Targeting ACE2-BRD4 crosstalk in colorectal cancer and the deregulation of DNA repair and apoptosis

Cited by 10 publications

References 21 publications

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

Unraveling malignant phenotype of peritumoral tissue: transcriptomic insights into early-stage breast cancer

Identifying Biomarkers for Prognostic Treatment in Breast Cancer through Bioinformatics Analysis

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