Comprehensive single-cell and bulk RNA-seq analyses reveal a novel CD8+ T cell-associated prognostic signature in ovarian cancer.

CD8+ T cells play pivotal roles in combating intracellular pathogens and eliminating malignant cells in cancer. However, the prognostic role of CD8+ T cells in ovarian carcinoma is insufficiently exploited. Herein, through univariate Cox regression along with least absolute shrinkage and selection operator (LASSO) regression analyses, we developed a novel prognostic model based on CD8+ T cell markers identified by single-cell sequencing (scRNA-seq) analyses. Patient grouping by the median risk score reveals an excellent prognostic efficacy of this model in both training and validation cohorts. Of note, patients classified as low-risk group exhibit a dramatically improved prognosis. In addition, higher enrichment level of immune-related pathways and increased infiltration level of multiple immune cells are found in patients with lower risk score. Importantly, low-risk patients also exhibited higher response rate to immunotherapies. Summarily, this developed CD8+ T cell-associated prognostic model serves as an excellent predictor for clinical outcomes and aids in guiding therapeutic strategy choices for ovarian cancer patients.

Single-cell analyses reveal evolution mimicry during the specification of breast cancer subtype.

Background: The stem or progenitor antecedents confer developmental plasticity and unique cell identities to cancer cells via genetic and epigenetic programs. A comprehensive characterization and mapping of the cell-of-origin of breast cancer using novel technologies to unveil novel subtype-specific therapeutic targets is still absent. Methods: We integrated 195,144 high-quality cells from normal breast tissues and 406,501 high-quality cells from primary breast cancer samples to create a large-scale single-cell atlas of human normal and cancerous breasts. Potential heterogeneous origin of malignant cells was explored by contrasting cancer cells against reference normal epithelial cells. Multi-omics analyses and both in vitro and in vivo experiments were performed to screen and validate potential subtype-specific treatment targets. Novel biomarkers of identified immune and stromal cell subpopulations were validated by immunohistochemistry in our cohort. Results: Tumor stratification based on cancer cell-of-origin patterns correlated with clinical outcomes, genomic aberrations and diverse microenvironment constitutions. We found that the luminal progenitor (LP) subtype was robustly associated with poor prognosis, genomic instability and dysfunctional immune microenvironment. However, the LP subtype patients were sensitive to neoadjuvant chemotherapy (NAC), PARP inhibitors (PARPi) and immunotherapy. The LP subtype-specific target PLK1 was investigated by both in vitro and in vivo experiments. Besides, large-scale single-cell profiling of breast cancer inspired us to identify a range of clinically relevant immune and stromal cell subpopulations, including subsets of innate lymphoid cells (ILCs), macrophages and endothelial cells. Conclusion: The present single-cell study revealed the cellular repertoire and cell-of-origin patterns of breast cancer. Combining single-cell and bulk transcriptome data, we elucidated the evolution mimicry from normal to malignant subtypes and expounded the LP subtype with vital clinical implications. Novel immune and stromal cell subpopulations of breast cancer identified in our study could be potential therapeutic targets. Taken together, Our findings lay the foundation for the precise prognostic and therapeutic stratification of breast cancer.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. METHODS: We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. RESULTS: A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. CONCLUSION: Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Integrative multi-omics analyses unravel the immunological implication and prognostic significance of CXCL12 in breast cancer.

Background: CXCL12 is a vital factor in physiological and pathological processes, by inducing migration of multiple cells. We aimed to comprehensively detect the role of CXCL12 in breast cancer, and explore novel CXCL12-related biomarkers through integrative multi-omics analyses to build a powerful prognostic model for breast cancer patients. Methods: Immunohistochemistry analysis of the tissue microarray was performed to evaluate the correlation between CXCL12 expression levels and breast cancer patient outcomes. Combined single-nucleus and spatial transcriptomics data was used to uncover the expression distribution of CXCL12 in breast cancer microenvironment. CXCL12-related genes were identified by WGCNA analysis. Univariate Cox and LASSO regression analyses were then conducted to screen prognostic genes from above CXCL12-related genes, followed by the construction of the CXCL12-related prognostic signature, identification of risk groups, and external validation of the prognostic signature. Analyses of biological function, mutation landscape, immune checkpoint genes and immune cells, were performed to further reveal the differences between high/low-risk groups. Paired single-cell RNA-seq and bulk RNA-seq were analyzed to further disclose the association between the risk score and the complex tumor immune microenvironment. To screen potential therapeutic agents for breast cancer patients, analyses of gene-drug correlation and sensitivity to immunotherapy were conducted. Results: High expression of CXCL12 was linked with a prolonged survival in breast cancer. A total of 402 genes were identified by WGCNA analysis and 11 genes, covering VAT1L, TMEM92, SDC1, RORB, PCSK9, NRN1, NACAD, JPH3, GJA1, BMP8B and ADAMTS2, were screened as the candidate prognostic genes. Next, the prognostic signature was built and validated using these genes to predict the outcomes of breast cancers. The high-risk group patients exhibited significantly inferior prognoses. The combination of the risk score and tumor mutational burden (TMB) had remarkably improved performance in predicting patient outcomes. Besides, high-risk group patients showed higher infiltration of M2-like macrophages. Finally, several potential anticancer drugs were identified. The high-risk group patients were more sensitive to immunotherapy but resistant to docetaxel. Conclusions: CXCL12 has important immunological implication and prognostic significance in breast cancer. The CXCL12-related prognostic model could well predict the prognosis and treatment response of breast cancers.

Single-cell analysis of white adipose tissue reveals the tumor-promoting adipocyte subtypes.

BACKGROUND: The tumor-adipose microenvironment (TAME) is characterized by the enrichment of adipocytes, and is considered a special ecosystem that supports cancer progression. However, the heterogeneity and diversity of adipocytes in TAME remains poorly understood. METHODS: We conducted a single-cell RNA sequencing analysis of adipocytes in mouse and human white adipose tissue (WAT). We analyzed several adipocyte subtypes to evaluate their relationship and potential as prognostic factors for overall survival (OS). The potential drugs are screened by using bioinformatics methods. The tumor-promoting effects of a typical adipocyte subtype in breast cancer are validated by performing in vitro functional assays and immunohistochemistry (IHC) in clinical samples. RESULTS: We profiled a comprehensive single-cell atlas of adipocyte in mouse and human WAT and described their characteristics, origins, development, functions and interactions with immune cells. Several cancer-associated adipocyte subtypes, namely DPP4+ adipocytes in visceral adipose and ADIPOQ+ adipocytes in subcutaneous adipose, are identified. We found that high levels of these subtypes are associated with unfavorable outcomes in four typical adipose-associated cancers. Some potential drugs including Trametinib, Selumetinib and Ulixertinib are discovered. Emphatically, knockdown of adiponectin receptor 1 (AdipoR1) and AdipoR2 impaired the proliferation and invasion of breast cancer cells. Patients with AdipoR2-high breast cancer display significantly shorter relapse-free survival (RFS) than those with AdipoR2-low breast cancer. CONCLUSION: Our results provide a novel understanding of TAME at the single-cell level. Based on our findings, several adipocyte subtypes have negative impact on prognosis. These cancer-associated adipocytes may serve as key prognostic predictor and potential targets for treatment in the future.

Identification of a centrosome-related prognostic signature for breast cancer.

Background: As the major microtubule organizing center in animal cells, the centrosome is implicated with human breast tumor in multiple ways, such as promotion of tumor cell immune evasion. Here, we aimed to detect the expression of centrosome-related genes (CRGs) in normal and malignant breast tissues, and construct a novel centrosome-related prognostic model to discover new biomarkers and screen drugs for breast cancer. Methods: We collected CRGs from the public databases and literature. The differentially expressed CRGs between normal and malignant breast tissues were identified by the DESeq2. Univariate Cox and LASSO regression analyses were conducted to screen candidate prognostic CRGs and develop a centrosome-related signature (CRS) to score breast cancer patients. We further manipulated and visualized data from TCGA, GEO, IMvigor210, TCIA and TIMER to explore the correlation between CRS and patient outcomes, clinical manifestations, mutational landscapes, tumor immune microenvironments, and responses to diverse therapies. Single cell analyses were performed to investigate the difference of immune cell landscape between high- and low-risk group patients. In addition, we constructed a nomogram to guide clinicians in precise treatment. Results: A total of 726 CRGs were collected from the public databases and literature. PSME2, MAPK10, EIF4EBP1 were screened as the prognostic genes in breast cancer. Next, we constructed a centrosome-related prognostic signature and validated its efficacy based on the genes for predicting the survival of breast cancer patients. The high-risk group patients had poor prognoses, the area under the ROC curve for 1-, 3-, and 5-year overall survival (OS) was 0.77, 0.67, and 0.65, respectively. The predictive capacity of CRS was validated by other datasets from GEO dataset. In addition, high-risk group patients exhibited elevated level of mutational landscapes and decreased level of immune infiltration, especially T and B lymphocytes. In terms of treatment responses, patients in the high-risk group were found to be resistant to immunotherapy but sensitive to chemotherapy. Moreover, we screened a series of candidate anticancer drugs with high sensitivity in the high-risk group. Conclusion: Our work exploited a centrosome-related prognostic signature and developed a predictive nomogram capable of accurately predicting breast cancer OS. The above discoveries provide deeper insights into the vital roles of the centrosome and contribute to the development of personalized treatment for breast cancer.

Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer.

The heterogeneity and the complex cellular architecture have a crucial effect on breast cancer progression and response to treatment. However, deciphering the neoplastic subtypes and their spatial organization is still challenging. Here, we combine single-nucleus RNA sequencing (snRNA-seq) with a microarray-based spatial transcriptomics (ST) to identify cell populations and their spatial distribution in breast cancer tissues. Malignant cells are clustered into distinct subpopulations. These cell clusters not only have diverse features, origins and functions, but also emerge to the crosstalk within subtypes. Furthermore, we find that these subclusters are mapped in distinct tissue regions, where discrepant enrichment of stromal cell types are observed. We also inferred the abundance of these tumorous subpopulations by deconvolution of large breast cancer RNA-seq cohorts, revealing differential association with patient survival and therapeutic response. Our study provides a novel insight for the cellular architecture of breast cancer and potential therapeutic strategies.

Effects of over-expressing resistin on glucose and lipid metabolism in mice.

Resistin, a newly discovered peptide hormone mainly secreted by adipose tissues, is present at high levels in serum of obese mice and may be a potential link between obesity and insulin resistance in rodents. However, some studies of rat and mouse models have associated insulin resistance and obesity with decreased resistin expression. In humans, no relationship between resistin level and insulin resistance or adiposity was observed. This suggests that additional studies are necessary to determine the specific role of resistin in the regulation of energy metabolism and adipogenesis. In the present study, we investigated the effect of resistin in vivo on glucose and lipid metabolism by over-expressing resistin in mice by intramuscular injection of a recombinant eukaryotic expression vector pcDNA3.1-Retn encoding porcine resistin gene. After injection, serum resistin and serum glucose (GLU) levels were significantly increased in the pcDNA3.1-Retn-treated mice; there was an obvious difference in total cholesterol (TC) level between the experiment and the control groups on Day 30. In pcDNA3.1-Retn-treated mice, both free fatty acid (FFA) and high density lipoprotein (HDL) cholesterol levels were markedly lower than those of control, whereas HDL cholesterol and triglyceride (TG) levels did not differ between the two groups. Furthermore, lipase activity was expressly lower on Day 20. Our data suggest that resistin over-expressed in mice might be responsible for insulin resistance and parameters related to glucose and lipid metabolism were changed accordingly.

Three new triterpenoid saponins from Ixeris sonchifolia and their cytotoxic activity.

Bioassay-guided fractionation of an active n-BuOH extract of the whole plant of Ixeris sonchifolia, using a cytotoxicity assay, resulted in the isolation of three new triterpenoid saponins, ixeris saponins A (1), B (2), and C (3). On the basis of chemical evidence and extensive spectral studies, their structures were established as echinocystic acid 3-O-beta-D-glucopyranosyl(1-->3)-beta-D-glucopyranosyl(1-->3)-alpha- L-arabinopyranoside (1), 3-O-[bis[beta-D-glucopyranosyl(1-->2 and 1-->3)-alpha-L-arabinopyranosyl]]echinocystic acid 28-O-beta-D-glucopyranosyl ester (2), and 3- O-[beta-D-glucopyranosyl(1-->3)-beta- D-glucopyranosyl(1-->3)-alpha-L-arabinopyranosyl]-16alpha, 23-dihydroxyolean-12-ene 28-O-beta-D-glucopyranosyl ester (3). Compounds 2 and 3 showed significant cytotoxicity against cancer cell lines A375, L929, and HeLa with IC50 values ranging from 8.83 microM to 15.83 microM, while compound 1 was inactive against these three cell lines.