Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy

Paracrine Orchestration of Tumor Microenvironment Remodeling Induced by GLO1 Potentiates Lymph Node Metastasis in Breast Cancer

Breast cancer is the most prevalent form of malignant tumor that frequently metastasizes to axillary lymph nodes (LNs). Nonetheless, the precise mechanisms underlying alterations in the tumor microenvironment (TME) in LN metastasis in breast cancer remain poorly understood. Single-cell RNA sequencing of 28 LN samples from 23 patients is performed, and a comprehensive landscape of the entire ecosystem is generated. Ten major cell types are identified, with the subclusters of each major cell type exhibiting diverse characteristics. Furthermore, multiple signatures are collected to evaluate the key components of the subclusters using multi-omics methodologies. This study finds that myCAFs may hasten LN metastasis, and observed a notable increase in APOE+ macrophages and a higher proportion of exhausted CD8+ T cells, contributing to the immunosuppressive TME. Moreover, cancer cells in metastatic lesions exhibited diverse expression patterns linked to proliferation, metastasis, oxidative phosphorylation, hypoxia, and interferon responses. Using multi-omics approaches and experimental validations, it determines that GLO1 can promote lymphatic angiogenesis, metastasis, and inhibit the proteasomal degradation of GSS, thereby maintaining intracellular glutathione (GSH) and reactive oxygen species (ROS) balance. Collectively, the study offers novel perspectives on the microenvironment remodeling of breast cancer LN metastases, suggesting that GLO1 may be a promising therapeutic target.

Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation

Investigating the essential function of CD300LG within the tumor microenvironment in triple-negative breast cancer (TNBC). Transcriptomic and single-cell data from TNBC were systematically collected and integrated. Four machine learning algorithms were employed to identify distinct target genes in TNBC patients. Specifically, CIBERSORT and ssGSEA algorithms were utilized to elucidate immune infiltration patterns, whereas TIDE and TCGA algorithms predicted immune-related outcomes. Moreover, single-cell sequencing data were analyzed to investigate the function of CD300LG-positive cells within the tumor microenvironment. Finally, immunofluorescence staining confirmed the significance of CD300LG in tumor phenotyping. After machine learning screening and independent dataset validation, CD300LG was identified as a unique prognostic biomarker for triple-negative breast cancer. Enrichment analysis revealed that CD300LG expression is strongly linked to immune infiltration and inflammation-related pathways, especially those associated with the cell cycle. The presence of CD8+ T cells and M1-type macrophages was elevated in the CD300LG higher group, whereas the abundance of M2-type macrophage infiltration showed a significant decrease. Immunotherapy prediction models indicated that individuals with low CD300LG expression exhibited better responses to PD-1 therapy. Additionally, single-cell RNA sequencing and immunofluorescence analyses uncovered a robust association between CD300LG and genes involved in tumor invasion. CD300LG plays a pivotal role in the tumor microenvironment of TNBC and represents a promising therapeutic target.

Molecular subtypes based on ferroptosis-related genes and tumor microenvironment infiltration characterization in small cell lung cancer

Background

Ferroptosis is an iron-dependent form of regulated cell death associated with cancer. However, the characteristics of ferroptosis in small cell lung cancer (SCLC) are still uncertain. This study aimed to explore the application value of ferroptosis-related genes (FRGs) classification in prognosis and characteristics prediction to provide clues for targeted SCLC therapy.

Method

We systematically characterized mRNA expression and genetic alterations of FRGs in SCLC, evaluating their expression pattern in 181 samples from 3 datasets. Unsupervised clustering analysis was performed to identify the molecular subtypes based on FRGs. We then conducted association analyses between FRG subtypes and various tumor microenvironment (TME) characteristics, traditional key transcript factor subtypes, clinical features, transcriptional and post-transcriptional regulation, drug response, and the efficacy of immunotherapy. Furthermore, the novel classification was validated in an independent cohort of 34 samples from Beijing.

Result

In this study, we identified three distinct ferroptosis subtypes in SCLC: S1, S2, and S3. We found that patients in S2 had the poorest prognosis. The FRG classification was correlated with the NOTCH pathway, MYC pathway, Neuroendocrine (NE), and epithelial-to-mesenchymal transition (EMT) process. Additionally, the FRG classification was strongly associated with TME 4 subtypes. To validate the classification, we employed an independent cohort. The FRG classification could also help to guide the prediction of chemical drugs. Finally, the heatmap showed the landscape of FRG subtypes, TME subtypes, NE subtypes, key transcription subtypes, age, gender, and stage.

Conclusion

Our identification of new SCLC subtypes provides novel insights into tumor biology and has potential clinical implications for the management of SCLC.

A Systematic Review of Immune Cell Roles in Breast Cancer Immunotherapy

BACKGROUND

Breast cancer (BC) is the most prevalent malignancy among women and is associated with high mortality and significant clinical challenges. Although conventional treatments such as surgery, chemotherapy, and radiotherapy have significantly improved patient survival, their efficacy remains limited by severe side effects and treatment resistance. In recent years, advances in immunotherapy have underscored the pivotal role of immune cells in treating BC.

RECENT FINDINGS

This systematic review summarizes the current knowledge on the roles of immune cells within the BC tumor microenvironment (TME), including their phenotypes, functions, and implications for immunotherapy. Following PRISMA guidelines, 71 studies published between 2010 and 2024 were analyzed. The results indicate that immune cell populations-such as tumor-associated macrophages (TAMs), tumor-infiltrating lymphocytes (TILs), natural killer (NK) cells, dendritic cells (DCs), and myeloid-derived suppressor cells (MDSCs)-are integral to tumor progression and therapeutic response. However, their functional heterogeneity and plasticity remain key obstacles to the development of effective and personalized immunotherapeutic strategies.

CONCLUSION

Further research is needed to clarify the mechanisms governing immune cell behavior within the BC TME and to advance precision immunotherapy. Such insights will lay the foundation for individualized treatment approaches, ultimately improving patient outcomes and quality of life (QoL).

Lactylation-Driven NUPR1 Promotes Immunosuppression of Tumor-Infiltrating Macrophages in Hepatocellular Carcinoma

While checkpoint immunotherapy effectively mobilizes T-cell responses against tumors, its success in hepatocellular carcinoma (HCC) is frequently undermined by immunosuppressive myeloid cells within the tumor microenvironment. This study investigates the role of nuclear protein 1 (NUPR1), a gene prominently expressed in tumor-associated macrophages (TAMs), in mediating this suppression and influencing immunotherapy outcomes. Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) datasets and functional assays in vitro and in vivo, NUPR1 is identified as a critical regulator within TAMs. The upregulation of NUPR1 is associated with enhanced M2 macrophage polarization and increased expression of immune checkpoints PD-L1 and SIRPA, resulting in CD8+ T cell exhaustion and a diminished response to immunotherapy. Mechanistically, NUPR1 inhibits the ERK and JNK signaling pathways, thereby creating an immunosuppressive milieu conducive to tumor progression. Additionally, tumor-derived lactate is shown to upregulate NUPR1 expression in macrophages via histone lactylation, perpetuating a feedback loop that intensifies immune suppression. Pharmacological targeting of NUPR1 reverses M2 polarization, curtails tumor growth, and augments the efficacy of PD-1 blockade in preclinical models, positioning NUPR1 as both a potential biomarker for immunotherapy responsiveness and a therapeutic target to boost immunotherapy efficacy in HCC.

Spatial multi-omics profiling of breast cancer oligo-recurrent lung metastasis

Primary breast cancer (BC) and metastatic tumors exhibit distinct tumor microenvironment (TME) ecosystems, and the heterogeneity of the TME of BC poses challenges to effective therapies. Evaluating the TME at the single-cell and spatial profiles offers potential for more precise treatments. However, due to the challenge of obtaining surgical specimens of both primary BC and oligo-recurrent lung metastasis simultaneously for high-resolution spatial analysis, the TME of lung-specific metastases using paired samples remains largely unexplored. In this study, we developed a comprehensive strategy using imaging mass cytometry (IMC), spatial proteomics, single-nucleus RNA-seq (snRNA-seq) and multiplex immunofluorescence to explore the spatial topology of lung-specific metastasis and the underlying biological mechanisms based on formalin-fixed paraffin-embedded (FFPE) samples from BC and paired lung metastasis. A total of 250,600 high-quality cells with spatial information revealed by IMC depicted the spatial differences in the TME between BC and lung metastasis. A significant increase in HLA-DR+ epithelial cells, endothelial cells and exhausted T cells was detected in lung metastases compared to primary sites, with this difference accentuated in the triple-negative subtype. Moreover, a distinct cellular hub comprising endothelial cells and HLA-DR+ epithelial cells implies the potential promising effect of anti-angiogenic therapy and immunotherapy in BC with lung metastasis, which was further validated by multiplex immunofluorescence analysis. Spatial proteomics further explored the underlying mechanism of TME components identified by IMC analysis. snRNA-seq validated the enrichment of endothelial cells in lung metastasis than that in BC at a whole FFPE slide level. In conclusion, this study determines the spatial multi-omics profiling of TME components at a single-cell resolution using paired samples of primary BC and lung oligo-metastasis. The comprehensive analysis may contribute to the development of therapeutic options.

Spatially segregated APOE+ macrophages restrict immunotherapy efficacy in clear cell renal cell carcinoma

Background: Immunotherapy has revolutionized cancer treatment and holds great potential for them, including metastatic clear cell renal cell carcinoma (ccRCC). However, immune resistance remains a major obstacle, limiting its efficacy and durability. Understanding the mechanisms of immune tolerance in the tumor microenvironment (TME) is pivotal for overcoming these challenges and enhancing therapeutic outcomes. Methods: Over 2000 samples, including a real-world cohort of 230 advanced ccRCC patients treated with immune checkpoint blockade (ICB) were analyzed. Single-cell RNA sequencing data from 13 tumor regions were categorized into ICB-exposed, ICB-resistant, and ICB-responsive groups. Multiple robust algorithms and multiplex immunofluorescence were used to explore TME composition and macrophage heterogeneity. Spatial communication dynamics were further investigated. In vitro experiments were performed to evaluate the impact of SPP1 on 786-O and 769-P cells. Co-culture experiments with THP-1-derived macrophages, followed by Western blot, flow cytometry, and functional assays, were performed to investigate SPP1-mediated macrophage polarization and its impact on tumor progression. Results: The results revealed an elevated presence of Apolipoprotein E (APOE)+ macrophages in ICB-resistant ccRCC. Notably, higher APOE+ macrophage proportion indicated shorter prognosis and worse response to ICB (P < 0.001). Elevated expression of CCAAT Enhancer Binding Protein Delta (CEBPD) was markedly linked to several immunosuppressive pathways, hindering T cell recruitment, promoting exhaustion, ultimately diminishing poorer prognosis and worse ICB efficacy. Meanwhile, upregulated Secreted Phosphoprotein 1 (SPP1) significantly enhances the proliferation, clonal formation, and migration of ccRCC cells. Tumor-derived SPP1. Additionally, SPP1 signaling from malignant cells appeared to recruit APOE+ macrophages to tumor margins, and promotes macrophage polarization into APOE+ M2-like macrophages. In the vicinity of the tumor, these APOE+ macrophages shape immunosuppressive TME by releasing abundant TGF-β signals, limiting anti-tumor effector T cells activity in ICB-resistant tumors, and contributing to tumor progression. Conclusion: This study reveals the critical role of APOE+ macrophages in promoting immune suppression and resistance to ICB therapy in ccRCC. By promoting T cell exhaustion and immunosuppressive signaling, particularly via localized TGF-β, these spatially segregated macrophages undermine treatment efficacy. Targeting APOE+ macrophages, especially in conjunction with ICB, presents a promising strategy to overcome immune resistance and enhance outcomes for ccRCC patients.

A bibliometric and visual analysis of the impact of senescence on tumor immunotherapy

Background

Recently, many studies have focused on the relationship between senescence and immunotherapy in cancer treatment. However, relatively few studies have examined the intrinsic links between the three. Whether these studies can act synergistically in the fight against cancer and the specific links between them are still unclear.

Methods

We extracted, quantified, and visualized data from the literature (n = 2396) for the period 2004-2023 after rigorous quality control using citespace, GraphPad Prism, the R software package, and VOSviewer.

Results

Linear fit analyses were generated to predict the number of annual publications and citations as a function of the top-performing authors, journals, countries, and affiliations academically over the past two decades such as Weiwei, Aging-us, China, and the UT MD Anderson Cancer Center. Vosviewer-based hierarchical clustering further categorized study characteristics into six clusters, including two major clusters of immunotherapy research, immunosenescence-related research factors, and timeline distributions suggesting that cellular senescence and tumor progression is a relatively new research cluster that warrants further exploration and development. Study characterization bursts and linear regression analyses further confirmed these findings and revealed other important results, such as aging (a = 1.964, R² = 0.6803) and immunotherapy (a = 16.38, R² = 0.8812). Furthermore, gene frequency analysis in this study revealed the most abundant gene, APOE, and SIRT1-7 proteins.

Conclusion

The combination of aging therapies with tumor immunotherapies is currently in its preliminary stages. Although senescence has the greatest impact on ICB therapies, mechanistic investigations, and drug development for APOE and sirt1-7 (Sirtuins family) targets may be the key to combining senescence therapies with immunotherapies in the treatment of tumors.

Unveiling purine metabolism dysregulation orchestrated immunosuppression in advanced pancreatic cancer and concentrating on the central role of NT5E

Background

The dismal efficacy of immunotherapy for Pancreatic cancer (PC) can be predominantly ascribed to its distinctive cold-tumor properties. The by-products of purine metabolic reprogramming are extensively engaged in tumor immune modulation, influencing the functions and recruitment of immune cells and molding an immune microenvironment that is propitious for tumor growth.

Methods

We harnessed single-cell transcriptomics and spatial transcriptomics to concurrently analyze the purine metabolism (PM) features of the PC microenvironment. We quantitatively appraised the PM traits of diverse cell subsets via scoring algorithms such as AUCell and Ucell. Moreover, cell development and cell-cell interaction analysis elucidated the alterations in TME induced by PM dysregulation. Additionally, we defined the PM disorder characteristics of PC patients and utilized this to assess the immune phenotypes and prognoses of the patient population. Also, we identified the crucial intermediate genes that impact PM reprogramming and the establishment of an immunosuppressive environment within the TME of PC, and validated them through spatial sectioning and cell co-culture experiments.

Results

Multi - dimensional transcriptome data elucidated the unique heterogeneity of PM in the PC microenvironment, which manifested that tumor cells and fibroblasts demonstrating higher PM scores in the TME. Cellchat analysis revealed that malignant cells with elevated PM expression were concomitantly associated with frequent interactions with CAFs as well as high expression of ligand-receptor pairs and transcription factors. Spatial data further corroborated this finding. Furthermore, the newly constructed PM disorder criteria indicated that patients with high PM levels were associated with a lack of response to immunotherapy and an immunosuppressive microenvironment. Finally, this study identified the singular role of NT5E in the immunosuppression resulting from PM reprogramming in PC. CCK8 and invasion experiments following the co-culture model demonstrated that intervention targeting NT5E could reverse the augmented malignancy of PC induced by co-cultured CAFs. NT5E is potentially a key target for reversing the "stiff-cancer" characteristics of PC.

Conclusion

This study demonstrates that PM metabolic disorders could impinge upon tumor immunotherapy and exacerbate the immunosuppression engendered by the progression of PC fibrosis. Therapeutic strategies targeting PM or NT5E may offer a ray of hope for patients with advanced PDAC.

Artificial Intelligence and Breast Cancer Management: From Data to the Clinic

Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement and refinement, artificial intelligence (AI) has demonstrated exceptional capabilities in processing intricate multidimensional BC-related data. AI has proven advantageous in various facets of BC management, encompassing efficient screening and diagnosis, precise prognosis assessment, and personalized treatment planning. However, the implementation of AI into precision medicine and clinical practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, and integration of multiple clinical pathways. In this review, we provide a comprehensive overview of the current research related to AI in BC, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact. Furthermore, this article emphasizes the significance of constructing patient-oriented AI algorithms. Additionally, we explore the opportunities and potential research directions within this burgeoning field.

Deciphering the role of ELAVL1: Insights from pan-cancer multiomics analyses with emphasis on nasopharyngeal carcinoma

Background and Objectives

Cancer continues to be a predominant cause of mortality worldwide, underscoring the critical need to identify and develop novel biomarkers to improve prognostic accuracy and therapeutic approaches. The dysregulation of ELAVL1 is linked to various diseases, including cancer. Nevertheless, its role across different cancer types remains insufficiently investigated.

Methods

We conducted a systematic investigation into the expression patterns, prognostic significance, genomic alterations, modifications, and functional implications of ELAVL1 in pan-cancer types. Besides, we performed in vitro and in vivo experiments to confirm the role of ELAVL1 in nasopharyngeal carcinoma (NPC).

Results

By utilizing multi-omics datasets, we found obvious overexpression of ELAVL1 in various cancer types at both the mRNA and protein levels, with predominant expression in malignant cells. Survival analysis revealed that increased ELAVL1 expression was linked to unfavorable outcomes in certain cancers; however, its effect difers among various cancer types. Additionally, we found that the genomic alterations and modifications of ELAVL1 were related to tumor progression. We discovered that ELAVL1 was elevated in NPC tissues. In addition, survival analysis indicated that NPC patients with higher ELAVL1 expression had worse prognoses. Functional assays demonstrated that ELAVL1 suppression led to decreased proliferation and migration in NPC cell lines. Moreover, ELAVL1 knockdown effectively inhibited NPC progression in the lymph node and lung metastasis models.

Conclusions

In summary, ELAVL1 exhibits diverse and complex involvement in tumor progression. Targeting it might inhibit tumor progression, making it a promising biomarker and therapeutic target for enhancing cancer treatment outcomes.

Pan-cancer analysis of co-inhibitory molecules revealing their potential prognostic and clinical values in immunotherapy

Background

The widespread use of immune checkpoint inhibitors (anti-CTLA4 or PD-1) has opened a new chapter in tumor immunotherapy by providing long-term remission for patients. Unfortunately, however, these agents are not universally available and only a minority of patients respond to them. Therefore, there is an urgent need to develop novel therapeutic strategies targeting other co-inhibitory molecules. However, comprehensive information on the expression and prognostic value of co-inhibitory molecules, including co-inhibitory receptors and their ligands, in different cancers is not yet available.

Methods

We investigated the expression, correlation, and prognostic value of co-inhibitory molecules in different cancer types based on TCGA, UCSC Xena, TIMER, CellMiner datasets. We also examined the associations between the expression of these molecules and the extent of immune cell infiltration. Besides, we conducted a more in-depth study of VISTA.

Result

The results of differential expression analysis, correlation analysis, and drug sensitivity analysis suggest that CTLA4, PD-1, TIGIT, LAG3, TIM3, NRP1, VISTA, CD80, CD86, PD-L1, PD-L2, PVR, PVRL2, FGL1, LGALS9, HMGB1, SEMA4A, and VEGFA are associated with tumor prognosis and immune cell infiltration. Therefore, we believe that they are hopefully to serve as prognostic biomarkers for certain cancers. In addition, our analysis indicates that VISTA plays a complex role and its expression is related to TMB, MSI, cancer cell stemness, DNA/RNA methylation, and drug sensitivity.

Conclusions

These co-inhibitory molecules have the potential to serve as prognostic biomarkers and therapeutic targets for a broad spectrum of cancers, given their strong associations with key clinical metrics. Furthermore, the analysis results indicate that VISTA may represent a promising target for cancer therapy.

Multi-omics and machine learning-driven CD8+ T cell heterogeneity score for head and neck squamous cell carcinoma

The heterogeneity of head and neck squamous cell carcinoma (HNSCC) poses a significant challenge to treatment, underscoring the urgent need for more precise and personalized therapeutic approaches. CD8+ T cells, integral components of the tumor immune microenvironment, have emerged as key targets for immunotherapy. Our research has established a correlation between a decrease in CD8+ T cell score and a poor clinical prognosis, highlighting the prognostic value of this biomarker. By analyzing the gene expression related to CD8+ T cells, we have differentiated HNSCC into cold and hot tumor subtypes, uncovering disparities in clinical prognosis and responses to immunotherapy. Utilizing eight machine learning methods, we identified the key gene OLR1. Single-cell analysis of HNSCC tissues and peripheral blood, along with spatial transcriptome analysis, revealed that OLR1 predominantly functions in macrophages, modulating the immune microenvironment of HNSCC. The expression level of OLR1 may serve as a predictive marker for immunotherapy responses. Moreover, drug sensitivity analysis and molecular docking studies have indicated that simvastatin and pazopanib are potential inhibitors of OLR1. These findings suggest that simvastatin and pazopanib could open up innovative potential therapeutic avenues for individuals with HNSCC.

Tissue macrophages: origin, heterogenity, biological functions, diseases and therapeutic targets

Macrophages are immune cells belonging to the mononuclear phagocyte system. They play crucial roles in immune defense, surveillance, and homeostasis. This review systematically discusses the types of hematopoietic progenitors that give rise to macrophages, including primitive hematopoietic progenitors, erythro-myeloid progenitors, and hematopoietic stem cells. These progenitors have distinct genetic backgrounds and developmental processes. Accordingly, macrophages exhibit complex and diverse functions in the body, including phagocytosis and clearance of cellular debris, antigen presentation, and immune response, regulation of inflammation and cytokine production, tissue remodeling and repair, and multi-level regulatory signaling pathways/crosstalk involved in homeostasis and physiology. Besides, tumor-associated macrophages are a key component of the TME, exhibiting both anti-tumor and pro-tumor properties. Furthermore, the functional status of macrophages is closely linked to the development of various diseases, including cancer, autoimmune disorders, cardiovascular disease, neurodegenerative diseases, metabolic conditions, and trauma. Targeting macrophages has emerged as a promising therapeutic strategy in these contexts. Clinical trials of macrophage-based targeted drugs, macrophage-based immunotherapies, and nanoparticle-based therapy were comprehensively summarized. Potential challenges and future directions in targeting macrophages have also been discussed. Overall, our review highlights the significance of this versatile immune cell in human health and disease, which is expected to inform future research and clinical practice.

Analysis of single-cell and spatial transcriptomics in TNBC cell-cell interactions

Triple-negative breast cancer (TNBC) is a highly malignant tumor in women, characterized by high morbidity, mortality, and recurrence rates. Although surgical treatment, radiotherapy, and chemotherapy are the mainstays of current treatment methods, the high heterogeneity of TNBC results in unsatisfactory outcomes with low 5-year survival rates. Rapid advancements in omics technology have propelled the understanding of TNBC molecular biology. The emergence of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) has significantly enhanced knowledge of tumor heterogeneity and the distribution, functionality, and intercellular interactions of various cell types within the tumor microenvironment, including tumor cells, T cells, B cells, macrophages, and fibroblasts. The present study provides an overview of the technical characteristics of scRNA-seq and ST, highlighting their applications in exploring TNBC heterogeneity, cell spatial distribution patterns, and intercellular interactions. This review aims to enhance the comprehension of TNBC at the cellular level for the development of effective therapeutic targets.

Pan-cancer analysis of the transcriptional expression of histone acetylation enzymes in solid tumors defines a new classification scheme for gliomas

Introduction

The altered expression of genes encoding histone acetyltransferases (HATs) and histone deacetylases (HDACs) has been implicated in the tumorigenesis and progression of various solid tumors. However, systematic characterization of the transcriptomic landscape and clinical relevance of HATs and HDACs in pan-cancer contexts remains lacking.

Methods

Transcriptome and clinical data of 9,483 patients across 31 tumor types from The Cancer Genome Atlas were collected for systematic pan-cancer analysis. Additional glioma-specific datasets (Chinese Glioma Genome Atlas, GlioVis, GSE43378, and GSE182109) were also collected to validate the transcriptional characteristics of HATs and HDACs in gliomas. Consensus clustering analysis was applied to identify distinct expression patterns of HATs and HDACs.

Results

Based on the transcriptomic data of 25 genes encoding 9 HATs and 16 HDACs, we identified five major subtypes across 31 cancer types (AC-I to AC-V). Notably, the AC-V subtype comprised over 95% of glioma patients, suggesting glioma patients exhibited distinct expression patterns of histone acetylation-modifying enzymes compared to patients with other solid tumors. Therefore, we re-conducted the consensus clustering analysis specifically within the context of gliomas and identified five subtypes, denoted "AC-GI" to "AC-GV", which were characterized by differences in HATs/HDACs expression patterns, biological and immune status, genetic alterations, and clinical outcomes. The AC-GII patients exhibited the best prognosis and were sensitive to temozolomide, while AC-GV patients had the poorest prognosis and the lowest sensitivity to temozolomide among all subtypes. Moreover, based on the Connectivity Map database analysis and experimental verification, we identified several pan-HDAC inhibitors that could serve as sensitizers for temozolomide therapy in AC-GV patients, such as panobinostat and scriptaid. Considering the distinctive clinical characteristics of patients with AC-GII and AC-GV, we constructed the "ACG score" model capable of effectively recognizing patients with these subtypes and predicting patient prognosis.

Conclusion

Herein, we established novel biologically and clinically relevant molecular classifications for pan-solid tumors and gliomas based on transcriptional expression profiles of HATs and HDACs. Moreover, the ACG score model, calculated by the transcriptional expression of 29 genes, was not only an independent prognostic factor for glioma patients, but can also provide valuable references for promoting more effective therapeutic strategies.

Intestinal Subtype as a Biomarker of Response to Neoadjuvant Immunochemotherapy in Locally Advanced Gastric Adenocarcinoma: Insights from a Prospective Phase II Trial

PURPOSE

Neoadjuvant immunochemotherapy (NAIC) markedly induces pathologic regression in locally advanced gastric adenocarcinoma. However, specific biomarkers are still lacking to effectively identify the beneficiary patients for NAIC.

PATIENTS AND METHODS

A prospective, single-arm, phase II study was conducted to treat locally advanced gastric adenocarcinoma with NAIC (NCT05515796). Correlation between clinicopathologic characteristics and neoadjuvant efficacy was investigated. Bulk RNA sequencing data from 104 samples (from 75 patients in two independent cohorts) and single-cell RNA sequencing data from 105 treatment-naïve gastric adenocarcinomas were comprehensively analyzed to decipher the association of epithelial and microenvironmental characteristics and clinical responses.

RESULTS

The prespecified primary endpoints were achieved: pathologic complete regression rate was 30%, major pathologic regression rate was 43%, and the regimen was well tolerated. Analysis of baseline clinical-pathologic parameters revealed the intestinal subtype of Lauren's classification as a key feature stratifying patients with increased sensitivity to NAIC. Mechanistically, an increased pool of DNA damage repair-active cancer cells and enrichment of CLEC9A+ dendritic cells in the tumor microenvironment were associated with enhanced responsiveness of the intestinal subtype gastric adenocarcinoma to NAIC. More importantly, an intestinal subtype-specific signature model was constructed by the machine learning algorithm NaiveBayes via integrating the transcriptomic features of both DNA damage repair-active cancer cells and CLEC9A+ dendritic cells, which accurately predicted the efficacy of NAIC in multiple independent gastric adenocarcinoma cohorts.

CONCLUSIONS

Intestinal subtype is a histologic biomarker of enhanced sensitivity of gastric adenocarcinoma to NAIC. The intestinal subtype-specific signature model is applicable to guide NAIC for patients with locally advanced gastric adenocarcinoma.

Disrupting EDEM3-induced M2-like macrophage trafficking by glucose restriction overcomes resistance to PD-1/PD-L1 blockade

BACKGROUND

Immunotherapy is beneficial for some colorectal cancer (CRC) patients, but immunosuppressive networks limit its effectiveness. Cancer-associatedfibroblasts (CAFs) are significant in immune escape and resistance toimmunotherapy, emphasizing the urgent need for new treatment strategies.

METHODS

Flow cytometric, Western blotting, proteomics analysis, analysis of public database data, genetically modified cell line models, T cell coculture, crystal violetstaining, ELISA, metabonomic and clinical tumour samples were conducted to assess the role of EDEM3 in immune escape and itsmolecular mechanisms. We evaluated theeffects of FMD plus 2-DG on antitumour immunity using multipleximmunofluorescence, flow cytometry, cytokine profiling, TUNEL assays, xenografttumours, and in vivo studies.

RESULTS

We show thatCAFs upregulate PD-L1 glycosylation and contribute to immune evasion byglycosyltransferase EDEM3. Additionally, EDEM3 plays a role in tumour immunityduring tumour progression. However, the EDEM3-mediated upregulation of PD-L1 expression underpins PD-1/PD-L1 blockade resistance in vivo. This finding contradictsthe previous trend that positive PD-L1 expression indicates a strong responseto PD-1/PD-L1 blockade. Mechanistically, high-EDEM3 expression facilitates M2-like This finding contradictsthe previous trend that positive PD-L1 expression indicates a strong responseto PD-1/PD-L1 blockade.Mechanistically, polarizationand chemotactic migration of macrophages, which are enriched in theperipheral region of tumours compared to thecore region, precluding access of CD8+ T cells to tumourfoci. Furthermore, we EDEM3 predominantly activates the recruited M2-like macrophagesvia a glucose metabolism-dependent mechanism. Manipulationof glucose utilization by a fasting-mimicking diet(FMD) plus 2-DG treatmentsynergistically with PD-1 antibody elicits potent antitumour activity byeffectively decreasing tumour glycosylated PD-L1 expression, augmenting the CD8+effector T cell infiltration and activation while concurrently reducing the infiltration.TheCAFs-EDEM3-M2-like macrophage axis plays a critical role in promotingimmunotherapy resistance. infiltration.TheCAFs-EDEM3-M2-like macrophage axis plays a critical role in promotingimmunotherapy resistance.

CONCLUSIONS

Our study suggests that blocking EDEM3-induced M2-like macro phage trafficking by FMD plus 2-DG is a promising and effective strategy to overcomeresistance to checkpoint blockade therapy offeringhope for improved treatment outcomes.

KEY POINTS

Cancer-associated fibroblasts (CAFs) can enhance PD-L1 glycosylation through the glycosyltransferase EDEM3, contributing to immune evasion during tumour progression. EDEM3 predominantly activates the recruit M2-like macrophages via a glucose metabolism-dependent mechanism. Blocking glucose utilization antagonizes recruiting and polarizing M2-like macrophages synergistically with PD-1 antibody to improve anticancer immunity.

Comprehensive analysis of single-cell and bulk RNA sequencing reveals postoperative progression markers for non-muscle invasive bladder cancer and predicts responses to immunotherapy

BACKGROUND

Non-muscle-invasive bladder cancer (NMIBC) is renowned for its high recurrence, invasiveness, and poor prognosis. Consequently, developing new biomarkers for risk assessment and investigating innovative therapeutic targets postoperative in NMIBC patients are crucial to aid in treatment planning.

APPROACHES

Differential gene expression analysis was performed using multiple Gene Expression Omnibus (GEO) datasets to identify differentially expressed genes (DEGs) between NMIBC and normal tissue, as well as between NMIBC and muscle-invasive bladder cancer (MIBC). Functional enrichment analysis was conducted based on the DEGs identified. Subsequently, prognosis-related genes were selected using Kaplan-Meier (KM) analysis and Cox regression analysis. The Boruta algorithm was utilized to further screen for core DEGs related to postoperative progression in NMIBC based on the aforementioned prognosis-related genes. Single-cell and clinical correlation studies were performed to verify their expression across various stages of bladder cancer. To investigate the link between core genes and the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA) was utilized, and Receiver Operating Characteristic (ROC) and KM analyses were performed to confirm predictive power for immune therapy outcomes. Machine learning (ML) models were constructed using the DepMap dataset to predict the efficacy of core gene inhibitors in treating bladder cancers. The prognostic performance of the core genes was evaluated using ROC curve analysis. An online prediction tool was developed based on the core genes to provide prognostic predictions. Finally, RT-qPCR, CCK-8, and Transwell assays were used to verify the pro-tumor effects of the GINS2 in bladder cancer.

RESULTS

A total of 70 DEGs were identified, among which 11 prognostic genes were obtained through KM analysis, and an additional 8 prognostic genes were obtained through COX analysis. The Boruta algorithm selected AURKB, GINS2, and UHRF1 as the three core DEGs. Single-cell and clinical variable correlation analyses indicated that the core genes promoted the progression of bladder cancer. The analysis of immune infiltration revealed a strong positive association between the core genes and both activated CD4 T cells and Type 2 helper T cells. Two random forest (RF) models were constructed to effectively predict the treatment effect of bladder cancer after targeted inhibition of AURKB and GINS2. In addition, an online nomogram tool was developed to effectively predict the risk of postoperative progression in NMIBC patients undergoing TURBT. Finally, RT-qPCR, CCK8, and Transwell assays showed that GINS2 promoted the growth and progression of bladder cancer.

CONCLUSION

AURKB, GINS2, and UHRF1 have the potential to enhance postoperative management of NMIBC patients undergoing transurethral resection of bladder tumor (TURBT) and can predict immunotherapy response, establishing them as promising therapeutic targets.

Immunofluorescence-Verified Sphingolipid Signatures Indicate Improved Prognosis in Liver Cancer Patients

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy, with its pathogenesis involving a complex interplay of molecular mechanisms, including cell cycle dysregulation, evasion of apoptosis, enhanced angiogenesis, and aberrant immune responses. Precision medicine approaches that target specific molecular subtypes through multi-omics integration hold promise for improving patient survival. Among the various molecular players, sphingolipids have emerged as pivotal regulators of tumor growth and apoptosis, positioning them as key targets in the search for novel anticancer therapies. Methods: To identify critical genes involved in sphingolipid metabolism (SM), we employed the AUCell algorithm and correlation analysis in conjunction with scRNA-seq data. A robust prognostic risk model was developed using Cox proportional hazards and Lasso regression, and its predictive performance was validated using an independent cohort from the International Cancer Genome Consortium (ICGC). The model's evaluation also incorporated analyses of the tumor microenvironment (TME), immunotherapy responses, mutational landscape, and pathway enrichment across different risk strata. Finally, we conducted multiplex immunofluorescence assays to investigate the functional role of ZC3HAV1 in HCC. Results: Our analysis yielded a 9-gene signature risk model with strong prognostic capabilities, effectively stratifying HCC patients into high- and low-risk groups, with significant differences in survival outcomes. Notably, the model revealed distinct variations in the immune microenvironment and responsiveness to immunotherapy between the risk groups. Further experimental validation identified ZC3HAV1 as a key gene, with multiplex immunofluorescence suggesting its involvement in promoting malignant progression in HCC through modulation of the epithelial-mesenchymal transition (EMT). Conclusion: This sphingolipid metabolism-based prognostic model is not only predictive of survival in HCC but also indicative of immunotherapy efficacy in certain patient subsets. Our findings underscore the crucial role of sphingolipid metabolism in shaping the immune microenvironment, offering new avenues for targeted therapeutic interventions.

CD8+ T cell exhaustion and its regulatory mechanisms in the tumor microenvironment: key to the success of immunotherapy

A steady dysfunctional state caused by chronic antigen stimulation in the tumor microenvironment (TME) is known as CD8+ T cell exhaustion. Exhausted-like CD8+ T cells (CD8+ Tex) displayed decreased effector and proliferative capabilities, elevated co-inhibitory receptor generation, decreased cytotoxicity, and changes in metabolism and transcription. TME induces T cell exhaustion through long-term antigen stimulation, upregulation of immune checkpoints, recruitment of immunosuppressive cells, and secretion of immunosuppressive cytokines. CD8+ Tex may be both the reflection of cancer progression and the reason for poor cancer control. The successful outcome of the current cancer immunotherapies, which include immune checkpoint blockade and adoptive cell treatment, depends on CD8+ Tex. In this review, we are interested in the intercellular signaling network of immune cells interacting with CD8+ Tex. These findings provide a unique and detailed perspective, which is helpful in changing this completely unpopular state of hypofunction and intensifying the effect of immunotherapy.

Recent progress and applications of single-cell sequencing technology in breast cancer

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Bridging Chronic Inflammation and Digestive Cancer: The Critical Role of Innate Lymphoid Cells in Tumor Microenvironments

The incidence and mortality of digestive system-related cancers have always been high and attributed to the heterogeneity and complexity of the immune microenvironment of the digestive system. Furthermore, several studies have shown that chronic inflammation in the digestive system is responsible for cancer incidence; therefore, controlling inflammation is a potential strategy to stop the development of cancer. Innate Lymphoid Cells (ILC) represent a heterogeneous group of lymphocytes that exist in contrast to T cells. They function by interacting with cytokines and immune cells in an antigen-independent manner. In the digestive system cancer, from the inflammatory phase to the development, migration, and metastasis of tumors, ILC have been found to interact with the immune microenvironment and either control or promote these processes. The conventional treatments for digestive tumors have limited efficacy, therefore, ILC-associated immunotherapies are promising strategies. This study reviews the characterization of different ILC subpopulations, how they interact with and influence the immune microenvironment as well as chronic inflammation, and their promotional or inhibitory role in four common digestive system tumors, including pancreatic, colorectal, gastric, and hepatocellular cancers. In particular, the review emphasizes the role of ILC in associating chronic inflammation with cancer and the potential for enhanced immunotherapy with cytokine therapy and adoptive immune cell therapy.

Multi-omics pan-cancer analysis reveals the prognostic values and immunological functions of PPA2, with a spotlight on breast cancer

Background

Recently, the role of inorganic pyrophosphatase 2 (PPA2) has been remaining merely superficial in many tumors. Hence, the aim was to analyze the potential functions of PPA2 in pan-cancer, focusing on its role in breast cancer.

Methods

A systematic pan-cancer analysis conducted primarily utilizing various open databases such as TCGA and GTEx. We explored the clinical value of PPA2 as well as various biological functions, including expression levels and subcellular localization, multi-dimensional immune-correlation analysis, co-expression networks, and gene heterogeneity. In addition, we not only verified the function of PPA2 through cell experiments but also analyzed PPA2 at the single-cell level and its drug sensitivity.

Results

PPA2 is abnormally expressed in various tumors, and it is mainly distributed in mitochondria. Furthermore, the indicators (OS, DSS, DFI, and PFI) of analysis hint that PPA2 exhibits significant prognostic value. At the same time, the genomic heterogeneity (including TMB, MSI, MATH, and NEO) of PPA2 in pan-cancer was analyzed. Across multiple tumors, the results showed a close correlation between PPA2 expression levels and different immune signatures (such as immune cell infiltration). All of these indicate that PPA2 could potentially be applied in the guidance of immunotherapy. We also have demonstrated that PPA2 promoted the process of breast cancer. Finally, some potential therapeutic agents (such as Fulvestrant) targeting the abnormal expression of PPA2 are revealed.

Conclusion

In conclusion, the results demonstrated the great value of PPA2 in pan-cancer research, as well as its potential as a therapeutic target for breast tumors.

EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma

BACKGROUND

Osteosarcoma (OS), a bone tumor with high ability of invasion and metastasis, has seriously affected the health of children and adolescents. Many studies have suggested a connection between OS and the epithelial-mesenchymal transition (EMT). We aimed to integrate EMT-Related genes (EMT-RGs) to predict the prognosis, immune infiltration, and therapeutic response of patients with OS.

METHODS

We used consensus clustering to identify potential EMT-Related OS molecular subtypes. Somatic mutation, tumor immune microenvironment, and functional enrichment analyses were performed for each subtype. We next constructed an EMT-Related risk signature and evaluated it by Kaplan-Meier (K-M) analysis survival and receiver operating characteristic (ROC) curves. Moreover, we constructed a nomogram to more accurately predict OS patients' clinical outcomes. Response effects of immunotherapy in OS patients was analyzed by Tumor Immune Dysfunction and Exclusion (TIDE) analysis, while sensitivity for chemotherapeutic agents was analyzed using oncoPredict. Finally, the expression patterns of hub genes were investigated by single-cell RNA sequencing (scRNA-seq) data analysis.

RESULTS

A total of 53 EMT-RDGs related to prognosis were identified, separating OS samples into two separate subgroups. The EMT-high subgroup showed favourable overall survival and more active immune response. Significant correlations were found between EMT-Related DEGs and functions as well as pathways linked to the development of OS. Additionally, a risk signature was established and OS patients were divided into two categories based on the risk scores. The signature presented a good predictive performance and could be recognized as an independent predictive factor for OS. Furthermore, patients with higher risk scores exhibited better sensitivity for five drugs, while no significant difference existed in immunotherapy response between the two risk subgroups. scRNA-seq data analysis displayed different expression patterns of the hub genes.

CONCLUSION

We developed a novel EMT-Related risk signature that can be considered as an independent predictor for OS, which may help improve clinical outcome prediction and guide personalized treatments for patients with OS.

Bioinformatics analysis of the role of lysosome-related genes in breast cancer

This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy.

Identification of molecular subtypes based on chromatin regulator-related genes and experimental verification of the role of ASCL1 in conferring chemotherapy resistance to breast cancer

Objective

The aim of this study was to identify the molecular subtypes of breast cancer based on chromatin regulator-related genes.

Methods

The RNA sequencing data of The Cancer Genome Atlas-Breast Cancer cohort were obtained from the official website, while the single-cell data were downloaded from the Gene Expression Omnibus database (GSE176078). Validation was performed using the Molecular Taxonomy of Breast Cancer International Consortium dataset. Furthermore, the immune characteristics, tumor stemness, heterogeneity, and clinical characteristics of these molecular subtypes were analyzed. The correlation between chromatin regulators and chemotherapy resistance was examined in vitro using the quantitative real-time polymerase chain reaction (qRT-PCR) and Cell Counting Kit-8 (CCK8) assays.

Results

This study identified three stable molecular subtypes with different prognostic and pathological features. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction analyses revealed that the differentially expressed genes were associated with disease processes, such as mitotic nuclear division, chromosome segregation, condensed chromosome, and specific chromosome region. The T stage and subtypes were correlated with the clinical features. Tumor heterogeneity (mutant-allele tumor heterogeneity, tumor mutational burden, purity, and homologous recombination deficiency) and tumor stemness (RNA expression-based stemness score, epigenetically regulated RNA expression-based stemness score, DNA methylation-based stemness score, and epigenetically regulated DNA methylation-based stemness score) significantly varied between the three subtypes. Furthermore, Western blotting, qRT-PCR, and CCK8 assays demonstrated that the expression of ASCL1 was positively correlated with chemotherapy resistance in breast cancer.

Conclusion

This study identified the subtypes of breast cancer based on chromatin regulators and analyzed their clinical features, gene mutation status, immunophenotype, and drug sensitivity. The results of this study provide effective strategies for assessing clinical prognosis and developing personalized treatment strategies.