Comprehensive description of the current breast cancer microenvironment advancements via single-cell analysis

The C/EBPβ antagonist peptide lucicebtide (ST101) induces macrophage polarization toward a pro-inflammatory phenotype and enhances anti-tumor immune responses

Immune-checkpoint inhibitors (ICIs) have shown unprecedented success in a subset of immunogenic tumors, however a host of patients with advanced solid tumors fail to respond well or at all to immunotherapy. Refractory tumors commonly display a tumor microenvironment (TME) rich in immunosuppressive macrophages (M2-like) that suppress adaptive immunity and promote tumor progression. The ability to reprogram macrophages in the TME into an immune-active state holds great promise for enhancing responses to ICIs. Lucicebtide (previously referred to as ST101) is a peptide antagonist of the transcription factor C/EBPβ, a key activator of the transcriptional program in immunosuppressive macrophages. Here we show that lucicebtide exposure reprograms human immunosuppressive M2-like macrophages to a pro-inflammatory M1-like phenotype, restores cytotoxic T cell activation in immunosuppressed co-culture assays in vitro, and further increases T-cell activity in M1-like/T cell co-cultures. In immunocompetent, macrophage-rich triple-negative breast and colorectal cancer models, lucicebtide induces repolarization of tumor-associated macrophages (TAMs) to a pro-inflammatory M1-like phenotype and suppresses tumor growth. Lucicebtide synergizes with anti-PD-1 therapy and overcomes resistance to checkpoint inhibition in anti-PD-1-refractory tumors, but in vivo responses are impaired by systemic macrophage depletion, indicating that macrophage reprogramming is integral to lucicebtide activity. These results identify lucicebtide as a novel immunomodulator that reprograms immunosuppressive macrophage populations to enhance anti-tumor activity and suggests its utility for combination strategies in cancers with poor response to ICIs.

CTGF (CCN2): a multifaceted mediator in breast cancer progression and therapeutic targeting

Breast cancer, with its diverse subtypes like ER-positive, HER-2-positive, and triple-negative, presents complex challenges demanding personalized treatment approaches. The intricate interplay of genetic, environmental, and lifestyle factors underscores its status as a primary contributor to cancer-related fatalities in women globally. Understanding the molecular drivers specific to each subtype is crucial for developing effective therapies. In this landscape, connective tissue growth factor (CTGF), also referred to as cellular communication network factor 2 (CCN2), emerges as a significant player. CTGF regulates critical biological activities like cell growth, invasion, and migration, impacting breast cancer development and progression. It modulates breast tumor microenvironment by promoting angiogenesis, activating cancer-associated fibroblasts (CAFs), and inducing inflammation. The activity of CTGF depends on several factors including oxygen levels, hormone signals, and growth factors and differs according to the type of breast cancer. CTGF can regulate breast cancer cells by activating various signaling pathways and modulating the transcription of other genes that are involved in tumor development and metastasis including S100A4, glucose transporter 3 (GLUT3), and vascular endothelial growth factor (VEGF). The matricellular protein can be considered a potential therapeutic target, as it can promote tumor growth and confer drug resistance in breast cancer. Numerous tactics, including neutralizing antibodies, antisense oligonucleotides, natural compounds, recombinant proteins, and short hairpin RNAs have been suggested to block its function. This review highlights the structure of CTGF, regulation of its expression, and current knowledge of its oncogenic role in breast cancer, as well as focusing on potential therapeutic strategies for targeting CTGF in breast cancer.

Liquid biopsy technologies: innovations and future directions in breast cancer biomarker detection

Globally, breast cancer is the most frequent type of cancer, and its early diagnosis and screening can significantly improve the probability of survival and quality of life of those affected. Liquid biopsy-based targets such as circulating tumor cells, circulating tumor DNA, and exosomes have been instrumental in the early discovery of cancer, and have been found to be effective in stage therapy, recurrence monitoring, and drug selection. Biosensors based on these target related biomarkers convert the tested substances into quantifiable signals such as electrical and optical signals through signal transduction, which has the advantages of high sensitivity, simple operation, and low invasiveness. This review provides an overview of the latest progress of liquid biopsy biomarkers in the diagnosis, prognosis and treatment of breast cancer, compares the application and advantages of different biosensors based on these biomarkers in the diagnosis of breast cancer, and analyzes the limitations and solutions of biosensor based methods.

Exploring the Potential Link between Aluminum-Containing Deodorants/Antiperspirants and Breast Cancer: A Comprehensive Review

The potential association between aluminum-containing deodorants/antiperspirants and breast cancer has been investigated and debated. This paper comprehensively analyzes existing literature to examine the evidence and provide insights into this relationship. This comprehensive review discusses aspects related to the absorption and distribution of aluminum compounds, its effects on the induction of oxidative stress, the estrogenic activity of aluminum, and potential disruption of hormonal pathways, and the potential role in breast cancer induction. Currently, available research, consisting of epidemiological studies as well as clinical trials, together with meta-analyses and previously published reviews conducted on identifying the relationship between aluminum-containing deodorants/antiperspirants and the risk of breast cancer were also analyzed and discussed. Societal factors, personal hygiene considerations, and lifestyle changes contribute to the increased usage of antiperspirants, but they do not establish a direct causal connection with breast cancer. Further research employing larger-scale studies and rigorous methodologies must validate the existing findings and explore the underlying mechanisms involved. Continued multidisciplinary research efforts and collaboration between researchers, regulatory bodies, and public health authorities are vital to developing a more definitive understanding of this complex topic.

CDK4/6 inhibitors in HR-positive breast cancer immunotherapy

Breast cancer is the most prevalent malignant tumour among women. Approximately 70 % of patients are hormone receptor (HR)-positive and undergo endocrine therapy as the main form of treatment; however, the efficacy of this type of therapy is limited by some factors, such as drug resistance and complex tumour microenvironments. Using network pharmacology and molecular docking, this study examined how CDK4/6 inhibitors enhance the effects of immunotherapy for HR-positive breast cancer, focusing on their effects on the tumour microenvironment (TME) and immune cell activity. CDK4/6 inhibitor influence crucial signalling pathways, including TP53 and EGFR, underscoring their importance in modulating immune responses and combating drug resistance. Our analysis revealed that the combination of CDK4/6 inhibitors with other immunotherapies, such as immune checkpoint inhibitors, might play an important role in improving the effectiveness of treatment in patients with cancer. This study provides new perspectives on treatment options for HR-positive breast cancers, promoting ongoing research and improvements in therapeutic approaches.

Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer

Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in a 3D matrix using a panel of three ER+ breast cancer cell lines, we showed that secreted S100A11 levels from cancer cells were associated with increased monocyte infiltration that subsequently differentiation toward macrophages. Genetic silencing of S100A11 in the S100A11-high T47D cancer cells reduced monocyte infiltration, consistent with results using a S100A11 blocking antibody in T47D cancer cells and in a clinically relevant patient-derived organoid model. Phenotypic analysis of macrophages cocultured with T47D cancer cells following S100A11 knockdown revealed lower expression of the immunosuppressive marker CD206, further underscoring the role of S100A11 as a paracrine regulator of pro-tumorigenic cancer-macrophage crosstalk. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.

Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine

Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis

Solid tumours exhibit a well-defined architecture, comprising a differentiated core and a dynamic border that interfaces with the surrounding tissue. This border, characterised by distinct cellular morphology and molecular composition, serves as a critical determinant of the tumour's invasive behaviour. Notably, the invasive border of the primary tumour represents the principal site for intravasation of metastatic cells. These cells, known as circulating tumour cells (CTCs), function as 'seeds' for distant dissemination and display remarkable heterogeneity. Advancements in spatial sequencing technology are progressively unveiling the spatial biological features of tumours. However, systematic investigations specifically targeting the characteristics of the tumour border remain scarce. In this comprehensive review, we illuminate key biological insights along the tumour body-border-haematogenous metastasis axis over the past five years. We delineate the distinctive landscape of tumour invasion boundaries and delve into the intricate heterogeneity and phenotype of CTCs, which orchestrate haematogenous metastasis. These insights have the potential to explain the basis of tumour invasion and distant metastasis, offering new perspectives for the development of more complex and precise clinical interventions and treatments.

Dissection of triple-negative breast cancer microenvironment and identification of potential therapeutic drugs using single-cell RNA sequencing analysis

Breast cancer remains a leading cause of mortality in women worldwide. Triple-negative breast cancer (TNBC) is a particularly aggressive subtype characterized by rapid progression, poor prognosis, and lack of clear therapeutic targets. In the clinic, delineation of tumor heterogeneity and development of effective drugs continue to pose considerable challenges. Within the scope of our study, high heterogeneity inherent to breast cancer was uncovered based on the landscape constructed from both tumor and healthy breast tissue samples. Notably, TNBC exhibited significant specificity regarding cell proliferation, differentiation, and disease progression. Significant associations between tumor grade, prognosis, and TNBC oncogenes were established via pseudotime trajectory analysis. Consequently, we further performed comprehensive characterization of the TNBC microenvironment. A crucial epithelial subcluster, E8, was identified as highly malignant and strongly associated with tumor cell proliferation in TNBC. Additionally, epithelial-mesenchymal transition (EMT)-associated fibroblast and M2 macrophage subclusters exerted an influence on E8 through cellular interactions, contributing to tumor growth. Characteristic genes in these three cluster cells could therefore serve as potential therapeutic targets for TNBC. The collective findings provided valuable insights that assisted in the screening of a series of therapeutic drugs, such as pelitinib. We further confirmed the anti-cancer effect of pelitinib in an orthotopic 4T1 tumor-bearing mouse model. Overall, our study sheds light on the unique characteristics of TNBC at single-cell resolution and the crucial cell types associated with tumor cell proliferation that may serve as potent tools in the development of effective anti-cancer drugs.

The G Protein Estrogen Receptor (GPER) is involved in the resistance to the CDK4/6 inhibitor palbociclib in breast cancer

BACKGROUND

The cyclin D1-cyclin dependent kinases (CDK)4/6 inhibitor palbociclib in combination with endocrine therapy shows remarkable efficacy in the management of estrogen receptor (ER)-positive and HER2-negative advanced breast cancer (BC). Nevertheless, resistance to palbociclib frequently arises, highlighting the need to identify new targets toward more comprehensive therapeutic strategies in BC patients.

METHODS

BC cell lines resistant to palbociclib were generated and used as a model system. Gene silencing techniques and overexpression experiments, real-time PCR, immunoblotting and chromatin immunoprecipitation studies as well as cell viability, colony and 3D spheroid formation assays served to evaluate the involvement of the G protein-coupled estrogen receptor (GPER) in the resistance to palbociclib in BC cells. Molecular docking simulations were also performed to investigate the potential interaction of palbociclib with GPER. Furthermore, BC cells co-cultured with cancer-associated fibroblasts (CAFs) isolated from mammary carcinoma, were used to investigate whether GPER signaling may contribute to functional cell interactions within the tumor microenvironment toward palbociclib resistance. Finally, by bioinformatics analyses and k-means clustering on clinical and expression data of large cohorts of BC patients, the clinical significance of novel mediators of palbociclib resistance was explored.

RESULTS

Dissecting the molecular events that characterize ER-positive BC cells resistant to palbociclib, the down-regulation of ERα along with the up-regulation of GPER were found. To evaluate the molecular events involved in the up-regulation of GPER, we determined that the epidermal growth factor receptor (EGFR) interacts with the promoter region of GPER and stimulates its expression toward BC cells resistance to palbociclib treatment. Adding further cues to these data, we ascertained that palbociclib does induce pro-inflammatory transcriptional events via GPER signaling in CAFs. Of note, by performing co-culture assays we demonstrated that GPER contributes to the reduced sensitivity to palbociclib also facilitating the functional interaction between BC cells and main components of the tumor microenvironment named CAFs.

CONCLUSIONS

Overall, our results provide novel insights on the molecular events through which GPER may contribute to palbociclib resistance in BC cells. Additional investigations are warranted in order to assess whether targeting the GPER-mediated interactions between BC cells and CAFs may be useful in more comprehensive therapeutic approaches of BC resistant to palbociclib.

COL5A1 promotes triple-negative breast cancer progression by activating tumor cell-macrophage crosstalk

Triple-negative breast cancer (TNBC) is an exceptionally aggressive subtype of breast cancer. Despite the recognized interplay between tumors and tumor-associated macrophages in fostering drug resistance and disease progression, the precise mechanisms leading these interactions remain elusive. Our study revealed that the upregulation of collagen type V alpha 1 (COL5A1) in TNBC tissues, particularly in chemoresistant samples, was closely linked to an unfavorable prognosis. Functional assays unequivocally demonstrated that COL5A1 played a pivotal role in fueling cancer growth, metastasis, and resistance to doxorubicin, both in vitro and in vivo. Furthermore, we found that the cytokine IL-6, produced by COL5A1-overexpressing TNBC cells actively promoted M2 macrophage polarization. In turn, TGFβ from M2 macrophages drived TNBC doxorubicin resistance through the TGFβ/Smad3/COL5A1 signaling pathway, establishing a feedback loop between TNBC cells and macrophages. Mechanistically, COL5A1 interacted with TGM2, inhibiting its K48-linked ubiquitination-mediated degradation, thereby enhancing chemoresistance and increasing IL-6 secretion. In summary, our findings underscored the significant contribution of COL5A1 upregulation to TNBC progression and chemoresistance, highlighting its potential as a diagnostic and therapeutic biomarker for TNBC.

Integrative analysis identifies cancer cell-intrinsic RARRES1 as a predictor of prognosis and immune response in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor prognosis and limited treatment options. Although immune checkpoint inhibitors (ICIs) have been proven to improve outcomes in TNBC patients, the potential mechanisms and markers that determine the therapeutic response to ICIs remains uncertain. Revealing the relationship and interaction between cancer cells and tumor microenvironment (TME) could be helpful in predicting treatment efficacy and developing novel therapeutic agents. By analyzing single-cell RNA sequencing dataset, we comprehensively profiled cell types and subpopulations as well as identified their signatures in the TME of TNBC. We also proposed a method for quantitatively assessment of the TME immune profile and provided a framework for identifying cancer cell-intrinsic features associated with TME through integrated analysis. Using integrative analyses, RARRES1 was identified as a TME-associated gene, whose expression was positively correlated with prognosis and response to ICIs in TNBC. In conclusion, this study characterized the heterogeneity of cellular components in TME of TNBC patients, and brought new insights into the relationship between cancer cells and TME. In addition, RARRES1 was identified as a potential predictor of prognosis and response to ICIs in TNBC.

Single-cell sequencing reveals the immune landscape of breast cancer patients with brain metastasis

BACKGROUND

Breast cancer has the highest incidence rate of cancer worldwide, and brain metastases (BrM) are among the most malignant cases. While some patients have benefited from immune checkpoint inhibitors (ICIs), the complex anatomical structure of the brain and the heterogeneity of metastatic tumors have made it difficult to characterize the tumor immune microenvironment (TME) of metastatic tumors.

METHODS

To address this, we used single-cell RNA sequencing (scRNA-seq) to analyze immune cells in the cerebrospinal fluid (CSF) of BrM patients with breast cancer, thereby providing a comprehensive view of the immune microenvironment landscape of BrM.

RESULTS

Based on canonical marker genes, we identified nine cell types, and further identified their subtypes through differential expression gene (DEG) analysis. We compared the changes in cells and functions in the immune microenvironment of patients with different prognoses. Our analysis revealed a series of genes that promote tumor immune function (CCR5, LYZ, IGKC, MS4A1, etc.) and inhibit tumor immune function (SCGB2A2, CD24, etc.).

CONCLUSIONS

The scRNA-seq in CSF provides a noninvasive method to describe the TME of breast cancer patients and guide immunotherapy.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data

BACKGROUND

Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing.

METHODS

We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method.

RESULTS

The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets.

CONCLUSIONS

This work provides an approach to bridge single-cell level information toward population-level survival prediction.

Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer

Introduction

The tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.

Methods

We applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.

Results

In this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.

Conclusions

In conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.

Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging data

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction.

STATEMENT OF TRANSLATIONAL RELEVANCE

Our findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.

The role of cancer-associated fibroblasts in breast cancer metastasis

Breast cancer deaths are primarily caused by metastasis. There are several treatment options that can be used to treat breast cancer. There are, however, a limited number of treatments that can either prevent or inhibit the spread of breast tumor metastases. Thus, novel therapeutic strategies are needed. Studies have increasingly focused on the importance of the tumor microenvironment (TME) in metastasis of breast cancer. As the most abundant cells in the TME, cancer-associated fibroblasts (CAFs) play important roles in cancer pathogenesis. They can remodel the structure of the extracellular matrix (ECM) and engage in crosstalk with cancer cells or other stroma cells by secreting growth factors, cytokines, and chemokines, as well as components of the ECM, which assist the tumor cells to invade through the TME and cause distant metastasis. Clinically, CAFs not only foster the initiation, growth, angiogenesis, invasion, and metastasis of breast cancer but also serve as biomarkers for diagnosis, therapy, and prediction of prognosis. In this review, we summarize the biological characteristics and subtypes of CAFs and their functions in breast cancer metastasis, focusing on their important roles in the diagnosis, prognosis, and treatment of breast cancer. Recent studies suggest that CAFs are vital partners of breast cancer cells that assist metastasis and may represent ideal targets for prevention and treatment of breast cancer metastasis.

Technologies and applications of single-cell DNA methylation sequencing

DNA methylation is the most stable epigenetic modification. In mammals, it usually occurs at the cytosine of CpG dinucleotides. DNA methylation is essential for many physiological and pathological processes. Aberrant DNA methylation has been observed in human diseases, particularly cancer. Notably, conventional DNA methylation profiling technologies require a large amount of DNA, often from a heterogeneous cell population, and provide an average methylation level of many cells. It is often not realistic to collect sufficient numbers of cells, such as rare cells and circulating tumor cells in peripheral blood, for bulk sequencing assays. It is therefore essential to develop sequencing technologies that can accurately profile DNA methylation using small numbers of cells or even single cells. Excitingly, many single-cell DNA methylation sequencing and single-cell omics sequencing technologies have been developed, and applications of these methods have greatly expanded our understanding of the molecular mechanism of DNA methylation. Here, we summaries single-cell DNA methylation and multi-omics sequencing methods, delineate their applications in biomedical sciences, discuss technical challenges, and present our perspective on future research directions.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Deciphering cell-cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing

A tumor's heterogeneity has important implications in terms of its clonal origin, progression, stemness, and drug resistance. Therefore, because of its significance in treatment, it is important to understand the gene expression pattern of a single cell, track gene expression or mutation in heterogeneous cells, evaluate the clonal origin of cancer cells, and determine the selective evolution of different subpopulations of cancer cells. Researchers are able to trace a cell's mutation and identify different types of tumor cells by measuring the whole transcriptome with single-cell sequencing (scRNA-seq). This technology provides a better understanding of the molecular mechanisms driving tumor growth than that offered by traditional RNA sequencing methods. In addition, it has revealed changes in the mutations and functions of somatic cells as a tumor evolves; it has also clarified immune cell infiltration and activation. Research on scRNA-seq technology has recently advanced significantly, suggesting new strategies for the treatment of cancer. In short, cancer researchers have become increasingly dependent on scRNA-seq. This paper reviews the development, detection principles, and processes of scRNA-seq technology and their application in tumor research. It also considers potential clinical applications.

Secreted Non-Coding RNAs: Functional Impact on the Tumor Microenvironment and Clinical Relevance in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a subtype of breast carcinoma characterized by poor prognosis and high rate of metastasis. Current treatment is based on chemo- and/or radiotherapy and surgery. TNBC is devoid of estrogen, progesterone and HER2 receptors. Although precision medicine has come a long way to ameliorate breast cancer disease management, targeted therapies for the treatment of TNBC patients are still limited. Mounting evidence has shown that non-coding RNAs (ncRNAs) drive many oncogenic processes at the basis of increased proliferation, invasion and angiogenesis in TNBC, strongly contributing to tumor progression and resistance to treatments. Many of these ncRNAs are secreted in the tumor microenvironment (TME) and impinge on the activity of the diverse immune and stromal cell types infiltrating the TME. Importantly, secreted ncRNAs may be detected as circulating molecules in serum/plasma from cancer patients and are emerging a promising diagnostic/therapeutic tools in TNBC. This review aims to discuss novel insights about the role of secreted circulating ncRNAs in the intercellular communication in the tumor microenvironment and their potential clinical use as diagnostic and prognostic non-invasive biomarkers in TNBC.

The CXCL Family Contributes to Immunosuppressive Microenvironment in Gliomas and Assists in Gliomas Chemotherapy

Gliomas are a type of malignant central nervous system tumor with poor prognosis. Molecular biomarkers of gliomas can predict glioma patient's clinical outcome, but their limitations are also emerging. C-X-C motif chemokine ligand family plays a critical role in shaping tumor immune landscape and modulating tumor progression, but its role in gliomas is elusive. In this work, samples of TCGA were treated as the training cohort, and as for validation cohort, two CGGA datasets, four datasets from GEO database, and our own clinical samples were enrolled. Consensus clustering analysis was first introduced to classify samples based on CXCL expression profile, and the support vector machine was applied to construct the cluster model in validation cohort based on training cohort. Next, the elastic net analysis was applied to calculate the risk score of each sample based on CXCL expression. High-risk samples associated with more malignant clinical features, worse survival outcome, and more complicated immune landscape than low-risk samples. Besides, higher immune checkpoint gene expression was also noticed in high-risk samples, suggesting CXCL may participate in tumor evasion from immune surveillance. Notably, high-risk samples also manifested higher chemotherapy resistance than low-risk samples. Therefore, we predicted potential compounds that target high-risk samples. Two novel drugs, LCL-161 and ADZ5582, were firstly identified as gliomas' potential compounds, and five compounds from PubChem database were filtered out. Taken together, we constructed a prognostic model based on CXCL expression, and predicted that CXCL may affect tumor progression by modulating tumor immune landscape and tumor immune escape. Novel potential compounds were also proposed, which may improve malignant glioma prognosis.

BC-N102 suppress breast cancer tumorigenesis by interfering with cell cycle regulatory proteins and hormonal signaling, and induction of time-course arrest of cell cycle at G1/G0 phase

Mechanisms of breast cancer progression and invasion, often involve alteration of hormonal signaling, and upregulation and/or activation of signal transduction pathways that input to cell cycle regulation. Herein, we describe a rationally designed first-in-class novel small molecule inhibitor for targeting oncogenic and hormonal signaling in ER-positive breast cancer. BC-N102 treatment exhibits dose-dependent cytotoxic effects against ER+ breast cancer cell lines. BC-N102 exhibited time course- and dose-dependent cell cycle arrest via downregulation of the estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR), phosphatidylinositol 3-kinase (PI3K), phosphorylated (p)-extracellular signal-regulated kinase (ERK), p-Akt, CDK2, and CDK4 while increasing p38 mitogen-activated protein kinase (MAPK), and mineralocorticoid receptor (MR) signaling in breast cancer cell line. In addition, we found that BC-N102 suppressed breast cancer tumorigenesis in vivo and prolonged the survival of animals. Our results suggest that the proper application of BC-N102 may be a beneficial chemotherapeutic strategy for ER+ breast cancer patients.

A Schistosoma japonicum MicroRNA Exerts Antitumor Effects Through Inhibition of Both Cell Migration and Angiogenesis by Targeting PGAM1

MicroRNA (miRNA) is an important regulator for gene expression. Recent studies showed that some heterogenous miRNAs derived from both parasite and plant can regulate expression of mammalian gene in a cross-species or even a cross-kingdom manner. Here, we identified a Schistosoma japonicum miRNA (designated as sja-miR-61) that is present in the hepatocyte of mice infected with the parasite. The sja-miR-61 mimics significantly inhibited the migration of both mouse and human hepatoma cells in vitro. In a xenograft animal model, significant reductions of the tumor volume and weight were observed in mice inoculated with hepatoma cells transfected with sja-miR-61 mimics compared to the controls. We found that the in vivo inhibition of tumor growth was through its anti-angiogenesis activity. Mechanically, we identified the phosphoglycerate mutase 1 (PGAM1) gene as a target of sja-miR-61 and found that the sja-miR-61-mediated suppression of cell migration and anti-angiogenesis by cross-species down-regulation of PGAM1 expression. These data indicated that sja-miR-61 is a tumor suppressor miRNA that may have therapeutic potential for human cancers.