Combined Single‐Cell and Spatial Transcriptomics Reveal the Metabolic Evolvement of Breast Cancer during Early Dissemination

Liu, Yiming; Ge, Jing-Yu; Chen, Yufei; Liu, Tong; Chen, Lie; Liu, Cuicui; Ma, Ding; Chen, Yiyu; Cai, Yiran; Xu, Yingying; Shao, Zhi‐Ming; Yu, Ke-Da

doi:10.1002/advs.202205395

Cited by 39 publications

(17 citation statements)

References 53 publications

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“…65 Several malignant conditions are entwined with this pathway. 64 The aforementioned modulatory role of Tregs in suppressing monocyte activity might represent one such regulatory mechanism. 60 Concurrently, our investigations unveiled a divergence in the outbound and inbound signaling dynamics within T cells compared to other cellular entities.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, a diminished communication of T cells with surrounding cells might be intricately linked with the tumor's malignancy grade. And this may be precisely related to the high activation of the MIF pathway between disseminated tumor cells and immune cells 64 …”

Section: Discussionmentioning

confidence: 99%

“…This cytokine orchestrates the assembly of CXCR4‐expressing malignant cells, concurrently enhancing the membrane display of immunosuppressive CD74+/CD204+ macrophages, thereby fueling tumor progression and metastasis 65 . Several malignant conditions are entwined with this pathway 64 . The aforementioned modulatory role of Tregs in suppressing monocyte activity might represent one such regulatory mechanism 60 .…”

Section: Discussionmentioning

confidence: 99%

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Precision unveiled: Synergistic genomic landscapes in breast cancer—Integrating single‐cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

Jiang,

Zhang,

Jiang

et al. 2024

Environmental Toxicology

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BackgroundGlobally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.MethodsTwo frameworks, T‐cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T‐cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a “Mixed” class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.ResultsUtilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.ConclusionUtilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype‐specific characteristics essential for prognostic assessment, clinical decision‐making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Precision unveiled: Synergistic genomic landscapes in breast cancer—Integrating single‐cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

Jiang,

Zhang,

Jiang

et al. 2024

Environmental Toxicology

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“…MPCs are critically involved in the regulation of various types of cells in the tumor and TME ( 23 ). New technologies such as single-cell sequencing and spatial transcriptomics have revealed the heterogeneity of MPCs ( 24 , 25 ), and the protumor proliferation and tumor drug resistance properties of MPCs have recently been identified ( 26 - 28 ). However, the details of the mechanisms which primarily influence MPCs to promote breast tumor development still need to be clarified.…”

Section: Discussionmentioning

confidence: 99%

Tumor-derived mesenchymal progenitor cell-related genes in the regulation of breast cancer proliferation

Chen,

Zhu,

Wang

et al. 2024

Gland Surg

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Background Breast cancer (BC) is one of the most common malignancies worldwide, and its development is affected in various ways by the tumor microenvironment (TME). Tumor-derived mesenchymal progenitor cells (MPCs), as the most important components of the TME, participate in the proliferation and metastasis of BC in several ways. In this study, we aimed to characterize the genes associated with tumor-derived MPCs and determine their effects on BC cells. Methods Tumor-derived MPCs and normal breast tissue-derived mesenchymal stem cells (MSCs) were isolated from tissues specimens of patients with BC. We conducted culture and passage, phenotype identification, proliferation and migration detection, inflammatory factor release detection, and other experiments on isolated MPCs from tumors and MSCs from normal breast tissues. Three paired tumor-derived MPCs and normal breast tissue-derived MSCs were then subjected to transcriptome analysis to determine the expression profiles of the relevant genes, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to further confirm gene expression. Subsequently, the overexpression plasmids were transfected into tumor-derived MPCs, and the expression of various inflammatory factors of tumor-derived MPCs and their proliferation were characterized with a cell viability test reagent (Cell Counting Kit 8). Subsequently, the transfected tumor-derived MPCs were cocultured with BC cells using a conditioned medium coculture method to clarify the role of tumor-derived MSCs in BC. Results Tumor-derived MPCs expressed stem cell characteristics including CD105, CD90, and CD73 and exhibited adipogenic and osteogenic differentiation in vitro. The proliferation of tumor-derived MPCs was significantly lower than that of normal breast tissue-derived MSCs, and the invasive metastatic ability was comparable; however, MPCs were found to release inflammatory factors such as interleukin 6 (IL-6) and transforming growth factor β (TGF-β). Transcriptome analysis showed that stomatin ( STOM ), collagen and calcium binding EGF domains 1 ( CCBE1 ), and laminin subunit alpha 5 ( LAMA5 ) were significantly upregulated in tumor-derived MPCs. Among them, STOM was highly expressed in tumor-derived MPCs, which mediated the slow proliferation of MPCs and promoted the proliferation of BC cells. Conclusions STOM , CCBE1 , and LAMA5 were highly expressed in tumor-derived MPCs, with STOM being found to retard the proliferation of MPCs but promote the proliferation of BC cells. There findings present new possibilities in targeted microenvironmental therapy for BC.

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“…Liu YM revealed a phenomenon of elevated oxidative phosphorylation distribution along the leading edge of breast cancer [ 71 ]. We unveil, for the first time, spatially defined metabolic heterogeneity in ccRCC.…”

Section: Discussionmentioning

confidence: 99%

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

Yang,

Cheng,

et al. 2024

J Transl Med

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Background Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment. Methods Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis. Results Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial–mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation. Conclusions The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

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Combined Single‐Cell and Spatial Transcriptomics Reveal the Metabolic Evolvement of Breast Cancer during Early Dissemination

Cited by 39 publications

References 53 publications

Precision unveiled: Synergistic genomic landscapes in breast cancer—Integrating single‐cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

Precision unveiled: Synergistic genomic landscapes in breast cancer—Integrating single‐cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

Tumor-derived mesenchymal progenitor cell-related genes in the regulation of breast cancer proliferation

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

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