Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC

Advancements in proteogenomics for preclinical targeted cancer therapy research

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

Unravelling approaches to study macrophages: from classical to novel biophysical methodologies

Macrophages play crucial roles in immune responses and tissue homeostasis. Despite the fact that macrophages were described more than a century ago, they continue to be the cells of intensive interest. Advanced understanding of phenotypic diversity in macrophages holds great promise for development of cell-based therapeutic strategies. The introduction of innovative approaches in cell biology greatly enhances our ability to investigate the unique characteristics of macrophages. The review considers both classical methods to study macrophages and high-tech approaches, including single-cell sequencing, single-cell mass spectrometry, droplet microfluidics, scanning probe microscopy and atomic force spectroscopy. This review will be valuable both to specialists beginning their study of macrophages and to experienced scientists seeking to deepen their understanding of methods at the intersection of biological and physical sciences.

Transcriptional profiles analysis of effects of Toxoplasma gondii rhoptry protein 16 on THP-1 macrophages

Introduction

Toxoplasma gondii, an intracellular parasitic protozoan, is globally recognized for its ability to cause parasitic diseases and has developed diverse strategies to evade immune-mediated elimination. The protein ROP16 of T.gondii plays a crucial role in this evasion process by specifically targeting macrophages and mononuclear phagocytes in vivo. However, the precise mechanisms underlying the involvement of type II ROP16 proteins in infection, inflammation, and other processes remain unknown.

Methods

To investigate the mechanism of action of gonococcal ROP16 proteins in human macrophages, we constructed a lentivirus overexpressing ROP16 and established stably transfected cell lines. We then analyzed the gene transcriptional profiles of ROP16 II in THP-1 macrophages using transcriptome sequencing. Interaction networks were constructed by screening differentially expressed genes and performing gene function enrichment analysis.

Results

As a result, five differentially expressed genes were identified: AAMDC, GPR158, RAD9A, STOML1, and STRA13. Immuno-featured differential analysis showed that type 17 T helper cells were more strongly correlated with GPR158 and STRA13, while CD8 T-cell was most strongly correlated with STOML1.

Discussion

Therefore, we conclude that the ROP16 protein plays a pivotal role in THP-1 macrophage infection and these five differentially expressed genes may serve as promising molecular targets for the prevention or control of toxoplasmosis. These findings have significant implications for the diagnosis and treatment of toxoplasmosis.

The mechanism underlying metastasis in triple-negative breast cancer: focusing on the interplay between ferroptosis, epithelial-mesenchymal transition, and non-coding RNAs

Triple-negative breast cancer (TNBC) is a type of breast cancer with lack the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It is the most aggressive breast cancer and the most difficult to treat due to its poor response to treatments and extremely invasive characteristics. The typical treatment for TNBC frequently results in relapse because of the lack of particular treatment choices. It is urgent to focus on identifying a workable and effective target for the treatment of TNBC. Cancer metastasis is significantly influenced by epithelial-mesenchymal transition (EMT). Ferroptosis is an iron-dependent cell death form, and changes its key factor to affect the proliferation and metastasis of TNBC. Several reports have established associations between EMT and ferroptosis in TNBC metastasis. Furthermore, non-coding RNA (ncRNA), which has been previously described, can also control cancer cell death and metastasis. Thus, in this review, we summarize the correlation and pathways among the ferroptosis, EMT, and ncRNAs in TNBC metastasis. Also, aim to find out a novel strategy for TNBC treatment through the ncRNA-ferroptosis-EMT axis.

The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers

BACKGROUND

It is worthwhile to establish a prognostic prediction model based on microenvironment cells (MCs) infiltration and explore new treatment strategies for triple-negative breast cancer (TNBC).

METHODS

The xCell algorithm was used to quantify the cellular components of the TNBC microenvironment based on bulk RNA sequencing (bulk RNA-seq) data. The MCs index (MCI) was constructed using the least absolute shrinkage and selection operator Cox (LASSO-Cox) regression analysis. Single-cell RNA sequencing (scRNA-seq), spatially resolved transcriptomics (SRT), and multiplex immunofluorescence (mIF) staining analyses verified MCI. The mechanism of action of the MCI was investigated in tumor-bearing mice.

RESULTS

MCI consists of the six types of MCs, which can precisely predict the prognosis of the TNBC patients. scRNA-seq, SRT, and mIF analyses verified the existence and proportions of these cells. Furthermore, combined with the spatial distribution characteristics of the six types of MCs, an MCI-enhanced (MCI-e) model was constructed, which could predict the prognosis of the TNBC patients more accurately. More importantly, inhibition of the insulin signaling pathway activated in the cancer cells of the MCIhigh the TNBC patients significantly prolonged the survival time of tumor-bearing mice.

CONCLUSIONS

Overall, our results demonstrate that MCs infiltration can be exploited as a novel indicator for the prognosis and therapeutic regimen selection of the TNBC patients.

Prognostic value of tumor-infiltrating lymphocyte subtypes and microorganisms in triple-negative breast cancer

ABSTRACT

Tumor-infiltrating lymphocytes (TILs) are key components of the tumor microenvironment (TME) and serve as prognostic markers for breast cancer. Patients with high TIL infiltration generally experience better clinical outcomes and extended survival compared to those with low TIL infiltration. However, as the TME is highly complex and TIL subtypes perform distinct biological functions, TILs may only provide an approximate indication of tumor immune status, potentially leading to biased prognostic results. Therefore, we reviewed the interactions between immune-infiltrating subtypes and tumor cells throughout the entire TME. By examining the antitumor or protumor effects of each TIL subtype, we aimed to better characterize the tumor immune landscape, offering more accurate and comprehensive insights for guiding triple-negative breast cancer (TNBC) treatment. In addition, this approach could lead to the development of new therapeutic targets, enabling tailored treatment strategies and precision medicine. Accumulating evidence suggests that the intestinal microbiome and its metabolites influence antitumor responses by modulating innate and adaptive immunity, with specific bacteria potentially serving as biomarkers for predicting clinical responses. Various studies have identified microorganisms in breast tissue, previously considered sterile, revealing differences in breast microbial composition between patients with breast cancer and controls, as well as associations between specific breast microorganisms and clinicopathologic features, including immune correlations. The aim of this review was to provide a more comprehensive set of prognostic markers for TNBC and to tap into potential-specific therapeutic targets.

Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis

Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospective studies are necessary to validate the findings.

From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology

Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

Multiomics and single-cell sequencings reveal the specific biological characteristics of low Ki-67 triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) displays high heterogeneity. The majority of TNBC cases are characterized by high Ki-67 expression. TNBC with low Ki-67 expression accounts for only a small fraction of cases and has been relatively less studied.

Methods

This study analyzed a large single-center multiomics TNBC data set, combined with a single-cell data set. The clinical, genomic, and metabolic characteristics of patients with low Ki-67 TNBC were analyzed.

Results

The clinical and pathological characteristics were analyzed in 2217 TNBC patients. Low Ki-67 TNBC was associated with a higher patient age at diagnosis, a lower proportion of invasive ductal carcinoma, increased alterations in the PI3K-AKT-mTOR pathway, upregulated lipid metabolism pathways, and enhanced infiltration of M2 macrophages. High Ki-67 TNBC exhibited a higher prevalence of TP53 gene mutations, elevated nucleotide metabolism, and increased infiltration of M1 macrophages.

Conclusions

We identified specific genomic and metabolic characteristics unique to low Ki-67 TNBC, which have implications for the development of precision therapies and patient stratification strategies.

Integrating Bulk and Single-cell RNA-seq to Construct a Macrophage-related Prognostic Model for Prognostic Stratification in Triple-negative Breast Cancer

Background: Triple-negative breast cancer (TNBC) is a poor prognostic subtype of breast cancer due to limited treatment. Macrophage plays a critical role in tumor growth and survival. Our study intends to explore the heterogeneity of macrophage in TNBC and establish a macrophage-related prognostic model for TNBC prognostic stratification. Materials and Methods: Seurat package was conducted to analyze the single-cell RNA expression profilers. The cell types were identified by the markers derived from public research and online database. The cell-cell interactions were calculated by the CellChat package. Monocle package was used to visualize the cell trajectory of macrophages. The prognostic model was constructed by six macrophage-related genes after a series of selections. The expression of six genes were validated in normal and TNBC tissues. And several potential agents for high-risk TNBC patients were analyzed by Connectivity Map analysis. Results: Nine cell types were identified, and the macrophages were highly enriched in TNBC samples. five distinct subgroups of macrophage were identified. Notably, SPP1+ tumor-associated macrophages exhibited a poor prognosis. The prognostic model was constructed by HSPA6, LPL, IDO1, ALDH2, TK1, and QPCT with good predictive accuracy at 3-, 5- years overall survival for TNBC patients in both training and external test cohorts. Finally, several drugs were identified for the high-risk TNBC patients decided by model. Conclusion: Our study provides a valuable source for clarifying macrophage heterogeneity in TNBC, and a promising tool for prognostic risk stratification of TNBC.

An update to experimental and clinical aspects of tumor-associated macrophages in cancer development: hopes and pitfalls

Tumor-associated macrophages (TAMs) represent one of the most abundant tumor-infiltrating stromal cells, and their normal function in tumor microenvironment (TME) is to suppress tumor cells by producing cytokines which trigger both direct cell cytotoxicity and antibody-mediated immune response. However, upon prolonged exposure to TME, the classical function of these so-called M1-type TAMs can be converted to another type, "M2-type," which are recruited by tumor cells so that they promote tumor growth and metastasis. This is the reason why the accumulation of TAMs in TME is correlated with poor prognosis in cancer patients. Both M1- and M2-types have high degree of plasticity, and M2-type cells can be reprogrammed to M1-type for therapeutic purposes. This characteristic introduces TAMs as promising target for developing novel cancer treatments. In addition, inhibition of M2-type cells and blocking their recruitment in TME, as well as their depletion by inducing apoptosis, are other approaches for effective immunotherapy of cancer. In this review, we summarize the potential of TAMs to be targeted for cancer immunotherapy and provide an up-to-date about novel strategies for targeting TAMs.

M2 macrophages promote PD-L1 expression in triple-negative breast cancer via secreting CXCL1

BACKGROUND

M2 macrophages are known to play a significant role in the progression of triple-negative breast cancer (TNBC) by creating an immunosuppressive microenvironment. The aim of this study is to investigate the impact of M2 macrophages on TNBC and their correlation with programmed death-ligand 1 (PD-L1) expression.

METHODS

We employed a co-culture system to analyze the role of the mutual regulation of M2 macrophages and TNBC cells. Employing a multifaceted approach, including bioinformatics analysis, Western blotting, flow cytometry analysis, ELISA, qRT-PCR, lentivirus infection, mouse models, and IHC, we aimed to elucidate the influence and mechanism of M2 macrophages on PD-L1 expression.

RESULTS

The results showed a substantial infiltration of M2 macrophages in TNBC tissue, which demonstrated a positive correlation with PD-L1 expression. CXCL1 exhibited abnormally high expression in M2 macrophages and enhanced the expression of PD-L1 in TNBC cells. Notably, silencing CXCL1 or its receptor CXCR2 inhibited M2 macrophages-induced expression of PD-L1. Mechanistically, CXCL1 derived from M2 macrophages binding to CXCR2 activated the PI3K/AKT/NF-κB signaling pathway, resulting in increased PD-L1 expression in TNBC.

CONCLUSION

Broadly speaking, these results provide evidence for the immunosuppressive role of M2 macrophages and CXCL1 in TNBC cells, indicating their potential as therapeutic biomarkers.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

YTHDF2 favors protumoral macrophage polarization and implies poor survival outcomes in triple negative breast cancer

Patients with triple-negative breast cancer (TNBC) frequently experience resistance to chemotherapy, leading to recurrence. The approach of optimizing anti-tumoral immunological effect is promising in overcoming such resistance, given the heterogeneity and lack of biomarkers in TNBC. In this study, we focused on YTHDF2, an N6-methyladenosine (m6A) RNA-reader protein, in macrophages, one of the most abundant intra-tumoral immune cells. Using single-cell sequencing and ex vivo experiments, we discovered that YTHDF2 significantly promotes pro-tumoral phenotype polarization of macrophages and is closely associated with down-regulated antigen-presentation signaling to other immune cells in TNBC. The in vitro deprivation of YTHDF2 favors anti-tumoral effect. Expressions of multiple transcription factors, especially SPI1, were consistently observed in YTHDF2-high macrophages, providing potential therapeutic targets for new strategies. In conclusion, YTHDF2 in macrophages appears to promote pro-tumoral effects while suppressing immune activity, indicating the treatment targeting YTHDF2 or its transcription factors could be a promising strategy for chemoresistant TNBC.

MIF as a potential diagnostic and prognostic biomarker for triple-negative breast cancer that correlates with the polarization of M2 macrophages

Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that plays a crucial role in antitumor immunity. However, the role of MIF in influencing the tumor microenvironment (TME) and prognosis of triple-negative breast cancer (TNBC) remains to be elucidated. Using R, we analyzed single-cell RNA sequencing (scRNA-seq) data of 41 567 cells from 10 TNBC tumor samples and spatial transcriptomic data from two patients. Relationships between MIF expression and immune cell infiltration, clinicopathological stage, and survival prognosis were determined using samples from The Cancer Genome Atlas (TCGA) and validated in a clinical cohort using immunohistochemistry. Analysis of scRNA-seq data revealed that MIF secreted by epithelial cells in TNBC patients could regulate the polarization of macrophages into the M2 phenotype, which plays a key role in modulating the TME. Spatial transcriptomic data also showed that epithelial cells (tumor cells) and MIF were proximally located. Analysis of TCGA samples confirmed that tumor tissues of patients with high MIF expression were enriched with M2 macrophages and showed a higher T stage. High MIF expression was significantly associated with poor patient prognosis. Immunohistochemical staining showed high MIF expression was associated with younger patients and worse clinicopathological staging. MIF secreted by epithelial cells may represent a potential biomarker for the diagnosis and prognosis of TNBC and may promote TNBC invasion by remodeling the tumor immune microenvironment.

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing

We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

Single-cell tracking as a tool for studying EMT-phenotypes

This article demonstrates that label-free single-cell video tracking is a useful approach for in vitro studies of Epithelial-Mesenchymal Transition (EMT). EMT is a highly heterogeneous process, involved in wound healing, embryogenesis and cancer. The process promotes metastasis, and increased understanding can aid development of novel therapeutic strategies. The role of EMT-associated biomarkers depends on biological context, making it challenging to compare and interpret data from different studies. We demonstrate single-cell video tracking for comprehensive phenotype analysis. In this study we performed single-cell video tracking on 72-h long recordings. We quantified several behaviours at a single-cell level during induced EMT in MDA-MB-468 cells. This revealed notable variations in migration speed, with different dose-response patterns and varying distributions of speed. By registering cell morphologies during the recording, we determined preferred paths of morphological transitions. We also found a clear association between migration speed and cell morphology. We found elevated rates of cell death, diminished proliferation, and an increase in mitotic failures followed by re-fusion of sister-cells. The method allows tracking of phenotypes in cell lineages, which can be particularly useful in epigenetic studies. Sister-cells were found to have significant similarities in their speeds and morphologies, illustrating the heritability of these traits.

Advanced insights on tumor-associated macrophages revealed by single-cell RNA sequencing: The intratumor heterogeneity, functional phenotypes, and cellular interactions

Single-cell RNA sequencing (scRNA-seq) is an emerging technology used for cellular transcriptome analysis. The application of scRNA-seq has led to profoundly advanced oncology research, continuously optimizing novel therapeutic strategies. Intratumor heterogeneity extensively consists of all tumor components, contributing to different tumor behaviors and treatment responses. Tumor-associated macrophages (TAMs), the core immune cells linking innate and adaptive immunity, play significant roles in tumor progression and resistance to therapies. Moreover, dynamic changes occur in TAM phenotypes and functions subject to the regulation of the tumor microenvironment. The heterogeneity of TAMs corresponding to the state of the tumor microenvironment has been comprehensively recognized using scRNA-seq. Herein, we reviewed recent research and summarized variations in TAM phenotypes and functions from a developmental perspective to better understand the significance of TAMs in the tumor microenvironment.

Specific molecular weight of Lycium barbarum polysaccharide for robust breast cancer regression by repolarizing tumor-associated macrophages

The pro-tumorigenic M2-type tumor-associated macrophages (TAMs) in the immunosuppressive tumor microenvironment (TME) promote the progression, angiogenesis, and metastasis of breast cancer. The repolarization of TAMs from an M2-type toward an M1-type holds great potential for the inhibition of breast cancer. Here, we report that Lycium barbarum polysaccharides (LBPs) can significantly reconstruct the TME by modulating the function of TAMs. Specifically, we separated four distinct molecular weight segments of LBPs and compared their repolarization effects on TAMs in TME. The results showed that LBP segments within 50-100 kDa molecular weight range exhibited the prime effect on the macrophage repolarization, augmented phagocytosis effect of the repolarized macrophages on breast cancer cells, and regression of breast tumor in a tumor-bearing mouse model. In addition, RNA-sequencing confirms that this segment of LBP displays an enhanced anti-breast cancer effect through innate immune responses. This study highlights the therapeutic potential of LBP segments within the 50-100 kDa molecular weight range for macrophage repolarization, paving ways to offer new strategies for the treatment of breast cancer.

Biomarker screening using integrated bioinformatics for the development of "normal-impaired glucose intolerance-type 2 diabetes mellitus"

Type 2 diabetes mellitus (T2DM) is a progressive disease. We utilized bioinformatics analysis and experimental research to identify biomarkers indicative of the progression of T2DM, aiming for early detection of the disease and timely clinical intervention. Integrating Mfuzz analysis with differential expression analysis, we identified 76 genes associated with the progression of T2DM, which were primarily enriched in signaling pathways such as apoptosis, p53 signaling, and necroptosis. Subsequently, using various analytical methods, including machine learning, we further narrowed down the hub genes to STK17A and CCT5. Based on the hub genes, we calculated the risk score for samples and interestingly found that the score correlated with multiple programmed cell death (PCD) pathways. Animal experiments revealed that the diabetes model exhibited higher levels of MDA and LDH, with lower expression of SOD, accompanied by islet cell apoptosis. In conclusion, our study suggests that during the progression of diabetes, STK17A and CCT5 may contribute to the advancement of the disease by regulating oxidative stress, programmed cell death pathways, and critical signaling pathways such as p53 and MAPK, thereby promoting the death of islet cells. This provides substantial evidence in support of further disease prevention and treatment strategies.

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.

Single-cell landscape identifies the immunophenotypes and microenvironments of HBV-positive and HBV-negative liver cancer

BACKGROUND

HBV infection leads to HCC and affects immunotherapy. We are exploring the tumor ecosystem in HCC to help gain a deeper understanding and design more effective immunotherapy strategies for patients with HCC with or without HBV infection.

METHODS

Single-cell RNA sequencing series were integrated as a discovery cohort to interrogate the tumor microenvironment of HBV-positive (HBV+) HCC and HBV-negative (HBV-) HCC. We further dissect the intratumoral immune status of HBV+ HCC and HBV- HCC. An independent cohort, including samples treated with immune checkpoint blockade therapy, was used to validate the major finding and investigate the effect of HBV infection on response to immunotherapy.

RESULTS

The interrogation of tumor microenvironment indicated that regulatory T cells, exhausted CD8+ T cells, and M1-like Macrophage_MMP9 were enriched in HBV+ HCC, while mucosa-associated invariant T cells were enriched in HBV- HCC. All subclusters of T cells showed high expression of immune checkpoint genes in HBV+ HCC. Regulatory T cells enriched in HBV+ HCC also showed more robust immunosuppressive properties, which was confirmed by cross talk between immune cell subsets. The ability of antigen presentation with major histocompatibility complex-II was downregulated in HBV+ HCC and this phenomenon can be reversed by immunotherapy. Two types of HCC also present different responses to immunotherapy.

CONCLUSIONS

There is a more immunosuppressive and exhausted tumor microenvironment in HBV+ HCC than in HBV- HCC. This in-depth immunophenotyping strategy is critical to understanding the impact of HBV and the HCC immune microenvironment and helping develop more effective treatments in patients with HCC.

Breast Cancer Immunity: It is TIME for the Next Chapter

Our ability to interrogate the tumor immune microenvironment (TIME) at an ever-increasing granularity has uncovered critical determinants of disease progression. Not only do we now have a better understanding of the immune response in breast cancer, but it is becoming possible to leverage key mechanisms to effectively combat this disease. Almost every component of the immune system plays a role in enabling or inhibiting breast tumor growth. Building on early seminal work showing the involvement of T cells and macrophages in controlling breast cancer progression and metastasis, single-cell genomics and spatial proteomics approaches have recently expanded our view of the TIME. In this article, we provide a detailed description of the immune response against breast cancer and examine its heterogeneity in disease subtypes. We discuss preclinical models that enable dissecting the mechanisms responsible for tumor clearance or immune evasion and draw parallels and distinctions between human disease and murine counterparts. Last, as the cancer immunology field is moving toward the analysis of the TIME at the cellular and spatial levels, we highlight key studies that revealed previously unappreciated complexity in breast cancer using these technologies. Taken together, this article summarizes what is known in breast cancer immunology through the lens of translational research and identifies future directions to improve clinical outcomes.

Identification of the shared genes in type 2 diabetes mellitus and osteoarthritis and the role of quercetin

This study investigated the underlying comorbidity mechanism between type 2 diabetes mellitus (T2DM) and osteoarthritis (OA), while also assessing the therapeutic potential of quercetin for early intervention and treatment of these two diseases. The shared genes were obtained through GEO2R, limma and weighted gene co-expression network analysis (WGCNA), and validated using clinical databases and the area under the curves (ROC). Functional enrichment analysis was conducted to elucidate the underlying mechanisms of comorbidity between T2DM and OA. The infiltration of immune cells was analysed using the CIBERSORT algorithm in conjunction with ESTIMATE algorithm. Subsequently, transcriptional regulation analysis, potential chemical prediction, gene-disease association, relationships between the shared genes and ferroptosis as well as immunity-related genes were investigated along with molecular docking. We identified the 12 shared genes (EPHA3, RASIP1, PENK, LRRC17, CEBPB, EFEMP2, UBAP1, PPP1R15A, SPEN, MAFF, GADD45B and KLF4) across the four datasets. Our predictions suggested that targeting these shared genes could potentially serve as therapeutic interventions for both T2DM and OA. Specifically, they are involved in key signalling pathways such as p53, IL-17, NF-kB and MAPK signalling pathways. Furthermore, the regulation of ferroptosis and immunity appears to be interconnected in both diseases. Notably, in this context quercetin emerges as a promising drug candidate for treating T2DM and OA by specifically targeting the shared genes. We conducted a bioinformatics analysis to identify potential therapeutic targets, mechanisms and drugs for T2DM and OA, thereby offering novel insights into molecular therapy for these two diseases.

Small extracellular vesicles-mediated cellular interactions between tumor cells and tumor-associated macrophages: Implication for immunotherapy

The tumor microenvironment consists of cancer cells and various stromal cells, including macrophages, which exhibit diverse phenotypes with either pro-inflammatory (M1) or anti-inflammatory (M2) effects. The interaction between cancer cells and macrophages plays a crucial role in tumor progression. Small extracellular vesicles (sEVs), which facilitate intercellular communication, are known to play a vital role in this process. This review provides a comprehensive summary of how sEVs derived from cancer cells, containing miRNAs, lncRNAs, proteins, and lipids, can influence macrophage polarization. Additionally, we discuss the impact of macrophage-secreted sEVs on tumor malignant transformation, including effects on proliferation, metastasis, angiogenesis, chemoresistance, and immune escape. Furthermore, we address the therapeutic advancements and current challenges associated with macrophage-associated sEVs, along with potential solutions.

Cancer-associated fibroblast-associated gene IGFBP2 promotes glioma progression through induction of M2 macrophage polarization

We elucidated the molecular mechanism of cancer-associated fibroblast (CAF)-associated gene insulin-like growth factor binding protein-2 (IGFBP2)-induced M2 macrophage polarization in the tumor microenvironment involved in glioma progression. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) provided bulk RNA-sequencing datasets, ESTIMATE scores for glioma stromal cells, and overall survival-clinicopathological correlation analyses. TIMER provided CAF abundance in the TCGA glioma-related dataset, differential gene analysis was performed for high- and low-CAF groups, and weighted gene coexpression network analysis identified CAF-related genes. Univariate and multifactorial cyclooxygenase (COX) regression analyses created the CAF risk models single sample gene set enrichment analysis, CIBERSORT, and GSE84465. Mice were implanted with gliomas, and Western blot and RT-quantitative PCR showed IGFBP2 in tumor tissues. Adeno-associated virus (AAV) decreased IGFBP2, flow cytometry measured M1 and M2 macrophage ratios, and immunohistochemistry detected markers. TCGA and CGGA transcriptome data showed malignant gliomas had higher stromal cell scores and worse prognoses. Low- and high-CAF TCGA gliomas were detected, and differential expression, WGCNA, and multifactorial COX identified 132 CAF-related genes and seven high-risk genes (CPQ, EFEMP2, IGFBP2, RAB42, TNFRSF12A, and VASN). Neither CAF risk score, grade, nor 1p/19q affected glioma prognosis. CAF only enriched EFEMP2 and IGFBP2. Gene Expression Profiling Interactive Analysis compared EFEMP2 and IGFBP2 expression in normal brain tissue and gliomas. Low-grade glioma and malignant glioblastoma highly expressed IGFBP2 and EFEMP2. GSEA raised IGFBP2. CIBERSORT linked M2 macrophage infiltration to TCGA glioma immune cell subpopulation IGFBP2 expression. IGFBP2 knockdown stopped mouse glioma and M2 macrophage polarization. CAF plays a procarcinogenic role in glioma, and the CAF-related gene IGFBP2 could promote glioma progression by inducing M2 macrophage polarization.NEW & NOTEWORTHY The cancer-associated fibroblast (CAF)-related gene insulin-like growth factor binding protein-2 (IGFBP2) is highly expressed in gliomas and is associated with poor prognosis. CAF-related gene IGFBP2 promotes glioma progression by inducing polarization of M2 macrophages. This study provides a new basis for an in-depth investigation of the functional mechanisms of the glioma tumor microenvironment and the search for key genes involved in immune regulation in CAF.

Long noncoding RNA LINC00885 upregulates NCK1 to promote cell viability and migration of triple-negative breast cancer cells through sponging miR-654-3p

BACKGROUND

LINC00885 is a novel oncogenic long noncoding RNA (LncRNA) which is upregulated in various types of cancer, but its function in triple-negative breast cancer (TNBC) remains unknown.

OBJECTIVE

This study aimed to determine the role of LINC00885 on TNBC development.

METHODS

Clinical interrelation and survival analysis were determined using online database. The CCK-8 and Transwell assays were used to detect the proliferation and migration behaviors in TNBC cell lines. The interaction among genes was detected by RNA pull down assay.

RESULTS

LncRNA LINC00885 was highly expressed in TNBC compared to normal breast like. Low levels of LINC00885 was related to good prognosis in TNBC patients compared to TNBC patients with high LINC00885. LINC00885-downregulation inhibited, whereas LINC00885-overexpression promoted the proliferation and migration capability of TNBC cell lines. In TNBC cell lines, noncatalytic region of tyrosine kinase 1 (NCK1) expression was positively associated with LINC00885 expression, and shRNA-mediated the depletion of NCK1 significantly abolished LINC00885 upregulation-mediated pro-tumor effects. Combined with online databases, miR-654-3p was screened as the direct target gene of LINC00885, which could directly bind to 3'-untranslated regions (3'-UTR) of NCK1, resulting in the decreased expression of NCK1 in TNBC cell lines. LINC00885 overexpression-mediated the upregulation of NCK1 was abrogated by miR-654-3p mimics. MiR-654-3p mimics significantly rescued the tumor promotive role caused by LINC00885-overexpression. However, exogenous NCK1 notably eliminated the anti-tumor effects caused by miR-654-3p mimics in LINC00885-overexpressed cells.

CONCLUSIONS

LINC00885 is expressed at a high level in TNBC. LINC00885 promoted proliferation and migration by regulating the miR-654-3p/NCK1 axis in TNBC cell lines. Possibly, LINC00885 can be served as a potential therapeutic target for TNBC.

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.

Integration of single-cell and bulk RNA sequencing to establish a prognostic signature based on tumor-associated macrophages in colorectal cancer

Several studies have shown significant involvement of tumor-associated macrophages (TAMs) in the tumor microenvironment and cancer progression. However, no data on reliable TAM-related biomarkers are available for predicting the prognosis of patients with colorectal cancer (CRC). We analyzed the clinical data and gene expression profiles of patients with CRC from databases. The single-cell transcriptomic data was applied to identify M2-like TAM-related differentially expressed genes. Univariate Cox and least absolute shrinkage and selection operator regression analyses were used to determine the prognostic signature genes. Then, seven key genes were screened to develop the prognostic signature. In the training and external validation cohorts, the overall survival (OS) of patients in the high-risk group was significantly shorter compared to the low-risk group. Consequently, we created a nomogram that could accurately and reliably predict the prognosis of patient with CRC. A significant correlation was observed between the patient's prognosis, clinical features, sensitivity to anticancer drugs, TME, and risk scores. Moreover, risk score was strongly related to the response to immunotherapy in patients from GSE91061, GSE78220, and GSE60331 cohorts. Finally, high expression of HSPA1A, SERPINA1, CXCL1, and low expression of DNASE1L3 were found in human CRC tissue and normal tissue by using qRT-PCR. In conclusion, the M2-like TAM-related prognostic signature could predict the survival, prognosis, and response of patients with CRC to immunotherapy, which sheds light on the role of TAMs in CRCs and enhances our understanding of TAMs.

Dipeptidyl peptidase 4 inhibitor reduces tumor-associated macrophages and enhances anti-PD-L1-mediated tumor suppression in non-small cell lung cancer

PURPOSE

The efficacy of immune checkpoint inhibitors such as programmed cell death ligand 1 (PD-L1) antibodies in non-small cell lung cancer (NSCLC) is limited, and combined use with other therapies is recommended. Dipeptidyl peptidase 4 (DPP4) inhibitors, a class of small molecule inhibitors, are highly effective for treating type 2 diabetes. Emerging evidence implicates DPP4 inhibitors as immunomodulators that modify aspects of innate and adaptive immunity. We evaluated the combination of a DPP4 inhibitor (anagliptin) and PD-L1 blockade in an NSCLC mouse model.

METHODS

The effect of the combination of anti-PD-L1 and anagliptin was evaluated in subcutaneous mouse models of NSCLC. Tumor-infiltrating immune cells were analyzed by flow cytometry. Bone marrow-derived monocytes of C57BL/6 mice were isolated in vitro to examine the underlying mechanism of anagliptin on the differentiation and polarization of macrophage.

RESULTS

Anagliptin dramatically improved the efficacy of PD-L1 antibody monotherapy by inhibiting macrophage formation and M2 polarization in the tumor microenvironment. Mechanistically, anagliptin suppressed the production of reactive oxygen species in bone marrow monocytes by inhibiting NOX1 and NOX2 expression induced by macrophage colony-stimulating factor, reduced late ERK signaling pathway activation, and inhibited monocyte-macrophage differentiation. However, the inhibitory effect was reactivated by lipopolysaccharide and interferon-gamma interacting with corresponding receptors during M1 macrophage polarization, but not M2.

CONCLUSIONS

Anagliptin can enhance PD-L1 blockade efficacy in NSCLC by inhibiting macrophage differentiation and M2 macrophage polarization, and combination therapy may be a promising strategy for treating PD-L1 blockade therapy-resistant patients with NSCLC.

Liposome-encapsulated zoledronate increases inflammatory macrophage population in TNBC tumours

BACKGROUND

Tumour associated macrophages (TAMs) are important players in breast tumour progression and metastasis. Clinical and preclinical evidence suggests a role for zoledronate (ZOL) in breast cancer metastasis prevention. Further, zoledronate is able to induce inflammatory activation of monocytes and macrophages, which can be favourable in cancer treatments. The inherent bone tropism of zoledronate limits its availability in soft tissues and tumours. In this study we utilised an orthotopic murine breast cancer model to evaluate the possibility to use liposomes (EMP-LIP) to target zoledronate to tumours to modify TAM activation.

METHODS

Triple-negative breast cancer 4T1 cells were inoculated in the 4th mammary fat pad of female Balb/c mice. Animals were divided according to the treatment: vehicle, ZOL, EMP-LIP and liposome encapsulated zoledronate (ZOL-LIP). Treatment was done intravenously (with tumour resection) and intraperitoneally (without tumour resection). Tumour growth was followed by bioluminescence in vivo imaging (IVIS) and calliper measurements. Tumour-infiltrating macrophages were assessed by immunohistochemical and immunofluorescence staining. Protein and RNA expression levels of inflammatory transcription factors and cytokines were measured by Western Blotting and Taqman RT-qPCR.

RESULTS

Liposome encapsulated zoledronate (ZOL-LIP) treatment suppressed migration of 4T1 cell in vitro. Tumour growth and expression of the angiogenic marker CD34 were reduced upon both ZOL and ZOL-LIP treatment in vivo. Long-term ZOL-LIP treatment resulted in shift towards M1-type macrophage polarization, increased CD4 T cell infiltration and activation of NF-κB indicating changes in intratumoural inflammation, whereas ZOL treatment showed similar but non-significant trends. Moreover, ZOL-LIP had a lower bisphosphonate accumulation in bone compared to free ZOL.

CONCLUSION

Results show that the decreased bisphosphonate accumulation in bone promotes the systemic anti-tumour effect of ZOL-LIP by increasing inflammatory response in TNBC tumours via M1-type macrophage activation.

Subverted macrophages in the triple-negative breast cancer ecosystem

Tumor-associated macrophages (TAMs) are the most critical effector cells of innate immunity and the most abundant tumor-infiltrating immune cells. They play a key role in the clearance of apoptotic bodies, regulation of inflammation, and tissue repair to maintain homeostasis in vivo. With the progression of triple-negative breast cancer（TNBC）, TAMs are "subverted" from tumor-promoting immune cells to tumor-promoting immune suppressor cells, which play a significant role in tumor development and are considered potential targets for cancer therapy. Here, we explored how macrophages, as the most important part of the TNBC ecosystem, are "subverted" to drive cancer evolution and the uniqueness of TAMs in TNBC progression and metastasis. Similarly, we discuss the rationale and available evidence for TAMs as potential targets for TNBC therapy.

A comprehensive analysis of the role of QPRT in breast cancer

To explore the clinical role of QPRT in breast cancer. The gene expression, methylation levels and prognostic value of QPRT in breast cancer was analyzed using TCGA data. Validation was performed using the data from GEO dataset and TNMPLOT database. Meta analysis method was used to pool the survival data for QPRT. The predictive values of QPRT for different drugs were retrieved from the ROC plot. The expression differences of QPRT in acquired drug-resistant and sensitive cell lines were analyzed using GEO datasets. GO and KEGG enrichment analysis were conducted for those genes which were highly co-expressed with QPRT in tissue based on TCGA data and which changed after QPRT knockdown. Timer2.0 was utilized to explore the correlation between QPRT and immune cells infiltration, and the Human Protein Atlas was used to analyse QPRT's single-cell sequencing data across different human tissues. The expression of QPRT in different types of macrophages, and the expression of QPRT were analysed after coculturing HER2+ breast cancer cells with macrophages. Additionally, TargetScan, Comparative Toxicogenomics and the connectivity map were used to research miRNAs and drugs that could regulate QPRT expression. Cytoscape was used to map the interaction networks between QPRT and other proteins. QPRT was highly expressed in breast cancer tissue and highly expressed in HER2+ breast cancer patients (P < 0.01). High QPRT expression levels were associated with worse OS, DMFS, and RFS (P < 0.01). Two sites (cg02640602 and cg06453916) were found to be potential regulators of breast cancer (P < 0.01). QPRT might predict survival benefits in breast cancer patients who received taxane or anthracycline. QPRT was associated with tumour immunity, especially in macrophages. QPRT may influence the occurrence and progression of breast cancer through the PI3K-AKT signalling pathway, Wnt signalling pathway, and cell cycle-related molecules.

Non-spatial and spatial heterogeneity revealed a suppressive immune feature of Siglec-15 in lung adenocarcinomas

BACKGROUND

Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) has emerged as a novel immunotherapy candidate, which deserves a comprehensive investigation in lung adenocarcinoma (LUAD).

METHODS

Multiplex fluorescence-based immunohistochemistry was conducted to assess Siglec-15 expression and tumor-infiltrating immune cells in LUAD from Tianjin cohort, with validation cohorts Xinchao 04 and 07.

RESULTS

This study revealed that Siglec-15 was positively correlated with CD8+ T cells and tumor-associated macrophages (TAMs) infiltration, but CD8+ T cells were mostly infiltrated in the stroma area, not in the tumor area. Spatially, fewer CD8+ T cells surrounded Siglec-15+ tumor cells in PD-L1- cells, and more TAMs surrounded Siglec-15+ tumor cells in PD-L1-/+ cells. Siglec-15+ TAMs infiltrated with more CD8+ T cells, and were closer to CD8+ T cells than Siglec-15- TAMs and Siglec-15+ tumor cells. Siglec-15+ TAMs infiltrated with more Tregs and were closer to Tregs than Siglec-15+ tumor cells. Siglec-15+ tumor cells or TAMs reversed CD8+ T cells prognosis value, and enhanced the prognosis value of Tregs and TAMs. The immunotyping based on Siglec-15 and CD8A / CD8+ T cells revealed that patients with high CD8A and Siglec-15 expression exhibited immune activation. Patients with low CD8A expression / CD8+ T cells infiltration and Siglec-15 overexpression were related to the activation of immunosuppressive signature and metabolism-related pathway, and infiltrated with more TAMs.

CONCLUSIONS

We revealed the distinct characteristics between Siglec-15+ tumor cells and TAMs in relation to CD8+ T cells, and a unique relationship between Siglec-15 and immunosuppressive TIME in LUAD, which may provide potential value for anti-Siglec-15 therapy.

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.

Metabolic Alterations in Canine Mammary Tumors

Canine mammary tumors (CMTs) are among the most common diseases in female dogs and share similarities with human breast cancer, which makes these animals a model for comparative oncology studies. In these tumors, metabolic reprogramming is known as a hallmark of carcinogenesis whereby cells undergo adjustments to meet the high bioenergetic and biosynthetic demands of rapidly proliferating cells. However, such alterations are also vulnerabilities that may serve as a therapeutic strategy, which has mostly been tested in human clinical trials but is poorly explored in CMTs. In this dedicated review, we compiled the metabolic changes described for CMTs, emphasizing the metabolism of carbohydrates, amino acids, lipids, and mitochondrial functions. We observed key factors associated with the presence and aggressiveness of CMTs, such as an increase in glucose uptake followed by enhanced anaerobic glycolysis via the upregulation of glycolytic enzymes, changes in glutamine catabolism due to the overexpression of glutaminases, increased fatty acid oxidation, and distinct effects depending on lipid saturation, in addition to mitochondrial DNA, which is a hotspot for mutations. Therefore, more attention should be paid to this topic given that targeting metabolic fragilities could improve the outcome of CMTs.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

Prognostic analysis of uveal melanoma based on the characteristic genes of M2-type macrophages in the tumor microenvironment

Uveal melanoma arises from stromal melanocytes and is the most prevalent primary intraocular tumor in adults. It poses a significant diagnostic and therapeutic challenge due to its high malignancy and early onset of metastases. In recent years, there has been a growing interest in the role of diverse immune cells in tumor cell development and metastasis. Using The Cancer Genome Atlas and the gene expression omnibus databases, and the CIBERSORT method, we investigated the topography of intra-tumor immune infiltration in uveal melanoma in this research. We evaluated the prognosis of uveal melanoma patients using the M2 macrophage immune cell infiltration score in conjunction with clinical tumor patient data. We built a prognostic model based on the distinctive genes of M2 macrophages and combined it with patients' clinical data in the database; we ran a survival prognostic analysis to authenticate the model's accuracy. The functional study revealed the importance of macrophage-associated genes in the development of uveal melanoma. Moreover, the reliability of our prediction model was verified by combining tumor mutational load, immune checkpoint, and drug sensitivity, respectively. Our study provides a reference for the follow-up study of uveal melanoma.

Development and validation of a ligand-receptor pairs signature to predict outcome and provide a therapeutic strategy in gastric cancer

BACKGROUND

An important factor in tumor development and progression is the tumor microenvironment (TME), which is heterogeneous. Previous studies have mainly investigated the expression profile and prognostic values of genes in gastric cancer (GC) at the cell population level but neglected the interactions and heterogeneity between cells.

METHODS

The pattern of ligand-receptor (LR) interactions was delineated on a scRNA-seq dataset containing 44,953 cells from nine GC patients and a fourth bulk RNA-seq dataset including data from 1159 GC patients. We then constructed an LR.Score scoring model to comprehensively evaluate the influence of LR-pairs on the TME, overall survival, and immunotherapy response in GC patients from several cohorts.

RESULTS

Cell communication network among 13 cell types was constructed based on the LR-pairs. We proposed a new molecular subtyping model for GC based on the LR-pairs and revealed the differences in prognosis, pathophysiologic features, mutation characteristics, function enrichment, and immunological characteristics among the three subtypes. Finally, an LR.Score model based on LR-pairs was developed and validated on several datasets.

CONCLUSIONS

Based on the selected LR-pairs, we successfully constructed a novel prediction model and observed its well performance on molecular subtyping, target and pathway screening, prognosis judging, and immunotherapy response predicting.

Integration of bulk RNA sequencing data and single-cell RNA sequencing analysis on the heterogeneity in patients with colorectal cancer

The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway has emerged as a critical innate immune pathway that could virtually impact nearly all aspects of tumorigenesis including colorectal cancer. This work aimed to develop and validate molecular subtypes related to cGAS-STING pathways for colorectal cancer using Bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data. Bulk RNA-seq data were acquired from The Cancer Genome Atlas dataset (training dataset) and Gene Expression Omnibus dataset (validation dataset). Univariate COX survival analysis was utilized to identify prognostic differentially expressed genes (DEGs) from 6 immune pathways related to cGAS-STING. ConsensusClusterPlus package was used to classify different subtypes based on DEGs. scRNA-seq data were used to validate differences in immune status between different subtypes. Two clusters with distinct prognosis were identified based on 27 DEGs. The six cGAS-STING-related pathways had different levels of significance between the two clusters. Clust1 had most number of amplified CNVs and clust2 had the most number of loss CNVs. TP53 was the top mutated gene of which missense mutations contributed the most of single-nucleotide variants. Immune score of clust1 was higher than that in clust2, as reflected in macrophages, T cells, and natural killer cells. Three unfavorable genes and 31 protection factors were screened between the two clusters in three datasets. ScRNA-seq data analysis demonstrated that macrophages were more enriched in clust1, and tumor cells and immune cells had close interaction. We classified two distinct subtypes with different prognosis, mutation landscape, and immune characteristics.

In Silico Analysis Predicts Nuclear Factors NR2F6 and YAP1 as Mesenchymal Subtype-Specific Therapeutic Targets for Ovarian Cancer Patients

BACKGROUND

Tumour heterogeneity in high-grade serous ovarian cancer (HGSOC) is a proposed cause of acquired resistance to treatment and high rates of relapse. Among the four distinct molecular subtypes of HGSOC, the mesenchymal subtype (MES) has been observed with high frequency in several study cohorts. Moreover, it exhibits aggressive characteristics with poor prognosis. The failure to adequately exploit such subtypes for treatment results in high mortality rates, highlighting the need for effective targeted therapeutic strategies that follow the idea of personalized medicine (PM).

METHODS

As a proof-of-concept, bulk and single-cell RNA data were used to characterize the distinct composition of the tumour microenvironment (TME), as well as the cell-cell communication and its effects on downstream transcription of MES. Moreover, transcription factor activity contextualized with causal inference analysis identified novel therapeutic targets with potential causal impact on transcription factor dysregulation promoting the malignant phenotype.

FINDINGS

Fibroblast and macrophage phenotypes are of utmost importance for the complex intercellular crosstalk of MES. Specifically, tumour-associated macrophages were identified as the source of interleukin 1 beta (IL1B), a signalling molecule with significant impact on downstream transcription in tumour cells. Likewise, signalling molecules tumour necrosis factor (TNF), transforming growth factor beta (TGFB1), and C-X-C motif chemokine 12 (CXCL12) were prominent drivers of downstream gene expression associated with multiple cancer hallmarks. Furthermore, several consistently hyperactivated transcription factors were identified as potential sources for treatment opportunities. Finally, causal inference analysis identified Yes-associated protein 1 (YAP1) and Nuclear Receptor Subfamily 2 Group F Member 6 (NR2F6) as novel therapeutic targets in MES, verified in an independent dataset.

INTERPRETATION

By utilizing a sophisticated bioinformatics approach, several candidates for treatment opportunities, including YAP1 and NR2F6 were identified. These candidates represent signalling regulators within the cellular network of the MES. Hence, further studies to confirm these candidates as potential targeted therapies in PM are warranted.

WISP2 downregulation inhibits the osteogenic differentiation of BMSCs in congenital scoliosis by regulating Wnt/β-catenin pathway

OBJECTIVES

Bone marrow mesenchymal stem cells (BMSCs) are instrumental in bone development, metabolism, and marrow microenvironment homeostasis. Despite this, the relevant effects and mechanisms of BMSCs on congenital scoliosis (CS) remain undefined. Herein, it becomes our focus to reveal the corresponding effects and mechanisms implicated.

METHODS

BMSCs from CS patients (hereafter referred as CS-BMSCs) and healthy donors (NC-BMSCs) were observed and identified. Differentially expressed genes in BMSCs were analyzed utilizing scRNA-seq and RNA-seq profiles. The multi-differentiation potential of BMSCs following the transfection or infection was evaluated. The expression levels of factors related to osteogenic differentiation and Wnt/β-catenin pathway were further determined as appropriate.

RESULTS

A decreased osteogenic differentiation ability was shown in CS-BMSCs. Both the proportion of LEPR⁺ BMSCs and the expression level of WNT1-inducible-signaling pathway protein 2 (WISP2) were decreased in CS-BMSCs. WISP2 knockdown suppressed the osteogenic differentiation of NC-BMSCs, while WISP2 overexpression facilitated the osteogenesis of CS-BMSCs via acting on the Wnt/β-catenin pathway.

CONCLUSIONS

Our study collectively indicates WISP2 knockdown blocks the osteogenic differentiation of BMSCs in CS by regulating Wnt/β-catenin signaling, thus providing new insights into the aetiology of CS.

Pro-inflammatory polarization and colorectal cancer modulate alternative and intronic polyadenylation in primary human macrophages

Introduction

Macrophages are essential cells of the immune system that alter their inflammatory profile depending on their microenvironment. Alternative polyadenylation in the 3'UTR (3'UTR-APA) and intronic polyadenylation (IPA) are mechanisms that modulate gene expression, particularly in cancer and activated immune cells. Yet, how polarization and colorectal cancer (CRC) cells affect 3'UTR-APA and IPA in primary human macrophages was unclear.

Methods

In this study, we isolated primary human monocytes from healthy donors, differentiated and polarized them into a pro-inflammatory state and performed indirect co-cultures with CRC cells. ChrRNA-Seq and 3'RNA-Seq was performed to quantify gene expression and characterize new 3'UTR-APA and IPA mRNA isoforms.

Results

Our results show that polarization of human macrophages from naïve to a pro-inflammatory state causes a marked increase of proximal polyA site selection in the 3'UTR and IPA events in genes relevant to macrophage functions. Additionally, we found a negative correlation between differential gene expression and IPA during pro-inflammatory polarization of primary human macrophages. As macrophages are abundant immune cells in the CRC microenvironment that either promote or abrogate cancer progression, we investigated how indirect exposure to CRC cells affects macrophage gene expression and 3'UTR-APA and IPA events. Co-culture with CRC cells alters the inflammatory phenotype of macrophages, increases the expression of pro-tumoral genes and induces 3'UTR-APA alterations. Notably, some of these gene expression differences were also found in tumor-associated macrophages of CRC patients, indicating that they are physiologically relevant. Upon macrophage pro-inflammatory polarization, SRSF12 is the pre-mRNA processing gene that is most upregulated. After SRSF12 knockdown in M1 macrophages there is a global downregulation of gene expression, in particular in genes involved in gene expression regulation and in immune responses.

Discussion

Our results reveal new 3'UTR-APA and IPA mRNA isoforms produced during pro-inflammatory polarization of primary human macrophages and CRC co-culture that may be used in the future as diagnostic or therapeutic tools. Furthermore, our results highlight a function for SRSF12 in pro-inflammatory macrophages, key cells in the tumor response.

AXL antibody and AXL-ADC mediate antitumor efficacy via targeting AXL in tumor-intrinsic epithelial-mesenchymal transition and tumor-associated M2-like macrophage

The receptor tyrosine kinase AXL is an emerging driver of cancer recurrence, while its molecular mechanism remains unclear. In this study we investigated how AXL regulated the disease progression and poor prognosis in non-small cell lung cancer (NSCLC) and triple negative breast cancer (TNBC). We performed AXL transcriptome analysis from TCGA datasets, and found that AXL expression was significantly elevated in NSCLC and TNBC correlating with poor prognosis, epithelial-mesenchymal transition (EMT) and immune-tolerant tumor microenvironment (TME). Knockdown of AXL or treatment with two independent AXL antibodies (named anti-AXL and AXL02) all diminished cell migration and EMT in AXL-high expressing NSCLC and TNBC cell lines. In a mouse model of 4T1 TNBC, administration of anti-AXL antibody substantially inhibited lung metastases formation and growth, accompanied by reduced downstream signaling activation, EMT and proliferation index, as well as an increased apoptosis and activated anti-tumor immunity. We found that AXL was abundantly activated in tumor nodule-infiltrated M2-macrophages. A specific anti-AXL antibody blocked bone marrow-derived macrophage (BMDM) M2-polarization in vitro. Targeting of AXL in M2-macrophage in addition to tumor cell substantially suppressed CSF-1 production and eliminated M2-macrophage in TME, leading to a coordinated enhancement in both the innate and adaptive immunity reflecting M1-like macrophages, mature dendritic cells, cytotoxic T cells and B cells. We generated a novel and humanized AXL-ADC (AXL02-MMAE) employing a site-specific conjugation platform. AXL02-MMAE exerted potent cytotoxicity against a panel of AXL-high expressing tumor cell lines (IC50 < 0.1 nmol/L) and suppressed in vivo growth of multiple NSCLC and glioma tumors (a minimum efficacy dose<1 mg/kg). Compared to chemotherapy, AXL02-MMAE achieved a superior efficacy in regressing large sized tumors, eliminated AXL-H tumor cell-dependent M2-macrophage infiltration with a robust accumulation of inflammatory macrophages and mature dendritic cells. Our results support AXL-targeted therapy for treatment of advanced NSCLC and TNBC.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer

BACKGROUND

Triple negative breast cancer (TNBC) is an aggressive and fatal malignancy. The current success of tumor immunotherapy has focused attention on intermediate T-cell subsets and the tumor microenvironment, which are essential for activation of the anti-tumor response. Therefore, both areas require further research to accelerate progress in developing tailored immunotherapeutic approaches for patients with TNBC.

METHODS

We obtained scRNA-seq data of TNBC from the GEO database. A multiplex strategy was used to analyze and identify the T-cell heterogeneity of TNBC. By combining the METABRIC and GEO databases, a prognostic risk model for T-cell marker genes was constructed and validated. In addition, the immune-infiltrating cells of TNBC was analyzed using CIBERSORT, and the association between the risk model and response to immunotherapy was investigated.

RESULTS

Based on scRNA-seq data, 25,932 cells were identified for multiple analyzes. T cells were studied with a focus on 2 subtypes, including CD8⁺ and CD4⁺. There were also communication relationships between T cells and multiple cell types. The results of the enrichment analysis showed that the T-cell marker genes were focused in pathways related to the immune system. In addition, OPTN, TMEM176A, PKM and HES1 deserve attention as prognostic markers in TNBC. The immune infiltration results showed that the high-risk group had significant immune cell infiltration and immunosuppression status.

CONCLUSION

This study provides a resource for understanding T-cell heterogeneity and the associated prognostic risk model for TNBC. The results show that the model helps predict prognosis and response to treatment in breast cancer.

Identification and analysis of type 2 diabetes-mellitus-associated autophagy-related genes

Introduction

Autophagy, an innate safeguard mechanism for protecting the organism against harmful agents, is implicated in the survival of pancreatic â cells and the development of type 2 diabetes mellitus (T2DM). Potential autophagy-related genes (ARGs) may serve as potential biomarkers for T2DM treatment.

Methods

The GSE25724 dataset was downloaded from the Gene Expression Omnibus (GEO) database, and ARGs were obtained from the Human Autophagy Database. The differentially expressed autophagy-related genes (DEARGs) were screened at the intersection of ARGs and differentially expressed genes (DEGs) between T2DM and non-diabetic islet samples, which were subjected to functional enrichment analyses. A protein-protein interaction (PPI) network was constructed to identify hub DEARGs. Expressions of top 10 DEARGs were validated in human pancreatic â-cell line NES2Y and rat pancreatic INS-1 cells using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Cell viability and insulin secretion were measured after cell transfection with lentiviral vector EIF2AK3 or RB1CC1 into islet cells.

Results

In total, we discovered 1,270 DEGs (266 upregulated and 1,004 downregulated genes) and 30 DEARGs enriched in autophagy- and mitophagy-related pathways. In addition, we identified GAPDH, ITPR1, EIF2AK3, FOXO3, HSPA5, RB1CC1, LAMP2, GABARAPL2, RAB7A, and WIPI1 genes as the hub ARGs. Next, qRT-PCR analysis revealed that expressions of hub DEARGs were consistent with findings from bioinformatics analysis. EIF2AK3, GABARAPL2, HSPA5, LAMP2, and RB1CC1 were both differentially expressed in the two cell types. Overexpression of EIF2AK3 or RB1CC1 promoted cell viability of islet cells and increased the insulin secretion.

Discussion

This study provides potential biomarkers as therapeutic targets for T2DM.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

ZIM3 activation of CCL25 expression in pulmonary metastatic nodules of osteosarcoma recruits M2 macrophages to promote metastatic growth

Tumor-associated macrophages (TAMs) play an important role in tumor growth and metastasis. However, the involvement of TAMs infiltration in pulmonary osteosarcoma (OS) metastasis remains poorly understood. Therefore, the effect of OS cells on macrophages migration was investigated by in vivo and in vitro experiments to evaluate the infiltration and mechanism of TAMs in pulmonary OS metastases. The results showed that the zinc finger protein ZIM3 was upregulated in OS cells than in osteoblasts and activated the expression of CCL25, which subsequently promoted the migration of M2 macrophages. CCL25 or ZIM3 silencing in OS cells inhibited the infiltration of M2 macrophages and the formation of pulmonary metastatic nodules in a mouse model of pulmonary OS metastasis and prolonged the survival of the mice. Furthermore, bioinformatics analyses revealed that CCL25 and ZIM3 expressions are negatively correlated with the prognosis of OS patients. In conclusion, this study found that a large number of M2 TAMs were recruited into pulmonary metastatic nodules of OS through the activation of the ZIM3-CCL25 axis in OS cells, thereby facilitating OS metastasis. Therefore, the suppression of ZIM3-CCL25-induced recruitment of M2 TAMs to the metastatic sites might be considered as a therapeutic approach to inhibit the growth of pulmonary OS metastases.

M1 polarization enhances the antitumor activity of chimeric antigen receptor macrophages in solid tumors

BACKGROUND

Chimeric antigen receptor macrophage (CAR-M) therapy is a novel cancer immunotherapy approach that integrates CAR structure and macrophage functions. CAR-M therapy has shown unique and impressive antitumor effects in immunotherapy for solid tumors. However, the polarization state of macrophages can affect the antitumor effect of CAR-M. We hypothesized that the antitumor activity of CAR-Ms may be further improved after inducing M1-type polarization.

METHODS

In this report, we constructed a novel HER2-targeting CAR-M, which was composed of humanized anti-HER2 scFv, CD28 hinge region and FcγRI transmembrane domain and intracellular domain. Phagocytosis, tumor-killing capacities, and cytokine release of CAR-Ms were detected with or without M1-polarization pretreatment. Several syngeneic tumor models were used to monitor the in vivo antitumor activity of M1-polarized CAR-Ms.

RESULTS

After polarization with LPS combined with interferon-γ in vitro, we found that the phagocytic and tumor-killing capacities of CAR-Ms against target cells were significantly enhanced. The expression of costimulatory molecules and proinflammatory cytokines was also significantly increased after polarization. By establishing several syngeneic tumor models in vivo, we also demonstrated that infusing polarized M1-type CAR-Ms could effectively suppress tumor progression and prolong the survival of tumor-bearing mice with enhanced cytotoxicity.

CONCLUSIONS

We demonstrated that our novel CAR-M can effectively eliminate HER2-positive tumor cells both in vitro and in vivo, and M1 polarization significantly enhanced the antitumor ability of CAR-M, resulting in a stronger therapeutic effect in solid cancer immunotherapy.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome

Introduction

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza.

Methods

Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized.

Results

We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79.

Discussion

The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.