Immune cellular components and signaling pathways in the tumor microenvironment

Tumor-associated CD8+T cell tolerance induced by erythroid progenitor cells

Introduction

CD8+T cell tolerance plays an important role in tumor escape. Recent studies have shown that CD45+ erythroid progenitor cells (CD45+EPCs) generated through splenic extramedullary erythropoiesis suppress tumor immunity. However, the mechanism underlying how CD45+EPCs mediate CD8+T cell tolerance remains incompletely understood and requires further research.

Methods

In this study, the antigen-processing abilities of CD45+EPCs was verified through both in vitro and in vivo experiments. We have used the method of co-culture in vitro and adoptive transfer experiments in vivo to explore the effects of CD45+EPCs on CD8+T cell tolerance. RNA-sequencing analysis and blocking experiments were used to evaluate the role of ROS in the CD45+EPC mediated tolerance of CD8+T cells. Finally, we incorporated uric acid into the adoptive transfer experiments to rescue the CD45+EPC mediated tumor-promoting effect.

Results and discussion

We found that CD45+EPCs take up soluble proteins, present antigenic epitopes on their surface, and induce antigen-specific CD8+T cell anergy. In addition, we found that CD45+EPC directly nitrates tyrosine within the TCR/CD8 complex via the production of reactive oxygen species and peroxynitrite, preventing CD8+ T cells from responding to their specific peptide antigens. Furthermore, uric acid treatment effectively abolished the immunosuppressive effects of CD45+EPCs during CD8+T cell adoptive transfer, thereby enhancing the anti-tumor efficacy. These results demonstrated that CD8+T cell tolerance in tumor-bearing mice is induced by CD45+EPCs. The results of this study have direct implications for tumor immunotherapy.

Immune Features of Tumor Microenvironment: A Genetic Spotlight

A tumor represents a highly intricate tissue entity, characterized by an exceptionally complex microenvironment that starkly contrasts with the typical physiological surroundings of healthy tissues. Within this tumor microenvironment (TME), every component and factor assume paramount importance in the progression of malignancy and exerts a pivotal influence on a patient's clinical outcome. One of the remarkable aspects of the TME is its remarkable heterogeneity, not only across different types of cancers but even within the same histological category of tumors. In-depth research has illuminated the intricate interplay between specific immune cells and molecules and the dynamic characteristics of the TME. Recent investigations have yielded compelling evidence that several mutations harbored by tumor cells possess the capacity to instigate substantial alterations in the TME. These mutations, often acting as drivers of tumorigenesis, can orchestrate a cascade of events that remodel the TME, thereby influencing crucial aspects of cancer behavior, including its invasiveness, immune evasion, and response to therapies. It is within this nuanced context that the present study endeavors to provide a concise yet comprehensive summary of how specific mutations, within the genetic landscape of cancer cells, can instigate profound changes in TME features. By elucidating the intricate relationship between genetic mutations and the TME, this research aims to contribute to a deeper understanding of cancer biology. Ultimately, the knowledge gained from this study holds the potential to inform the development of more targeted and effective treatments, thereby offering new hope to patients grappling with the complexities of cancer.

Acidity-induced ITGB6 promote migration and invasion of lung cancer cells by epithelial-mesenchymal transition and focal adhesion

Non-small cell lung cancer (NSCLC) is a prevalent tumor and acidic tumor microenvironment provides an energy source driving tumor progression. We previously demonstrated significantly upregulated Integrin β6 (ITGB6) in NSCLC cells. This study was designed to investigate the role of ITGB6 in NSCLC metastasis and explore the potential mechanisms. The expression of ITGB6 was evaluated in patients with NSCLC. Migration and invasion assays were utilized to investigate the role of ITGB6, and ChIP-qPCR and dual-luciferase reporter experiments preliminarily analyzed the relationship between ETS proto-oncogene 1 (ETS1) and ITGB6. Bioinformatics analysis and rescue models were performed to explore the underlying mechanisms. The results demonstrated that ITGB6 was upregulated in NSCLC patients and the difference was even more pronounced in patients with poor prognosis. Functionally, acidity-induced ITGB6 promoted migration and invasion of NSCLC cells in vitro, and epithelial-mesenchymal transition (EMT) and focal adhesion were the important mechanisms responsible for ITGB6-involved metastasis. Mechanistically, we revealed ETS1 enriched in the ITGB6 promoter region and promoted transcription to triggered the activation of subsequent signaling pathways. Moreover, ChIP-qPCR and dual-luciferase reporter experiments demonstrated that ETS1 played an important role in directly mediating ITGB6 expression. Furthermore, we found ITGB6 was responsible for the acidic microenvironment-mediated migration and invasion processes in NSCLC by performing rescue experiments with ITGB6 knockdown. Our findings indicated acidic microenvironment directly induced ETS1 to regulate the expression of ITGB6, and then the highly expressed ITGB6 further mediate EMT and activates the downstream focal adhesion pathways, eventually promotes the invasion and migration in NSCLC progression and metastasis.

Analyzing the impact of metabolism on immune cells in tumor microenvironment to promote the development of immunotherapy

Tumor metabolism and tumor immunity are inextricably linked. Targeting the metabolism of tumors is a point worth studying in tumor immunotherapy. Recently, the influence of the metabolism of tumors and immune cells on the occurrence, proliferation, metastasis, and prognosis of tumors has attracted more attention. Tumor tissue forms a specific tumor microenvironment (TME). In addition to tumor cells, there are also immune cells, stromal cells, and other cells in TME. To adapt to the environment, tumor cells go through the metabolism reprogramming of various substances. The metabolism reprogramming of tumor cells may further affect the formation of the tumor microenvironment and the function of a variety of cells, especially immune cells, eventually promoting tumor development. Therefore, it is necessary to study the metabolism of tumor cells and its effects on immune cells to guide tumor immunotherapy. Inhibiting tumor metabolism may restore immune balance and promote the immune response in tumors. This article will describe glucose metabolism, lipid metabolism, amino acid metabolism, and immune cells in tumors. Besides, the impact of metabolism on the immune cells in TME is also discussed for analyzing and exploring tumor immunotherapy.

A Systematic Immune and Prognostic Analysis of CD48 Interaction with Tumor Microenvironment in Pan-Cancer

Background

The cluster of differentiation 48 (CD48) is a member of the signaling lymphocyte activation molecule family, constitutively expressed on most hematopoietic cells. CD48 was reported to affect immune regulation in certain tumors, thereby influencing tumor development and prognosis, but its impact on the prognosis and immune infiltration in pan-cancer remains unclear.

Material and Methods

We systematically analyzed the raw data from The Cancer Genome Atlas (TCGA), Tumor Immune Estimation Resource (TIMER), and Tumor Immune Dysfunction and Exclusion (TIDE) databases. Initially, we investigated the differences in CD48 expression between pan-cancer and adjacent normal tissues. Then, the correlation analysis of CD48 with tumor mutational burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), and immune-related genes was evaluated. Moreover, bioinformatics tools: ESTIMATE and gene set enrichment analysis (GSEA) were used for tumor immunology analysis in pan-cancer. We performed validation studies including quantitative real-time PCR (qPCR) and Western blotting.

Results

Differential analysis revealed that CD48 was significantly altered in pan-cancer as compared with normal tissues. Meanwhile, the survival analysis demonstrated that CD48 strongly correlated with overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS), indicating its crucial role in the tumor patients' prognosis. CD48 expression was also associated with TMB and MSI levels in 17 and 14 types of pan-cancers, respectively. Moreover, CD48 was linked to immune infiltrating cells and stromal components in the TME.

Conclusion

Concludingly, patients with pan-cancer may benefit from evaluating CD48 as a prognostic and immunotherapy response biomarker.

Selenium metabolism heterogeneity in pan-cancer: insights from bulk and single-cell RNA sequencing

BACKGROUND

Selenium, a natural microelement with both nutritional and toxicological properties, is intertwined with tumorigenesis and progression. However, it is not fully understood how selenium metabolism affects immune response and cancer biology.

METHODS

We estimated selenium metabolism by Gene Set Enrichment Analysis (GSEA) to delineate the selenium metabolism landscape using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE) and a integrated pan-cancer single-cell dataset. We systematically explored the prognostic implications of selenium metabolism and selenium-related regulatory patterns. The therapeutic value of selenium metabolism was explored through machine learning and examined in several immunotherapy cohorts. The heterogeneity and underlying mechanism of selenium metabolism were investigated by cell‒cell communication analysis at the single-cell level.

RESULTS

A GSEA analysis based on 86 genes was used to evaluate the selenium metabolism landscape. The selenium metabolism score exhibited prognostic value in predicting the lower risk of mortality, possibly due to its correlation with multiple cancer hallmarks, including a positive correlation with complement (R = 0.761, P < 0.001), inflammatory response (R = 0.663, P < 0.001), apoptosis (R = 0.626, P < 0.001), hypoxia (R = 0.587, P < 0.001), reactive oxygen species (ROS) (R = 0.558, P < 0.001), and interferon gamma response (R = 0.539, P < 0.001). We also observed heterogeneity in the relationship between selenium metabolism and immunity across different cancers. Based on selenium-related genes, we constructed a machine learning model with area under the ROC curve (AUC) of 0.82 in predicting immune checkpoint inhibitor (ICI)-based immunotherapy response. Single-cell selenium metabolism quantification revealed that adjacent and tumor tissues had higher selenium metabolism compared with normal tissues, especially in epithelial cells, fibroblasts and macrophages. The communication between high-selenium epithelium and high-selenium fibroblast was significantly higher than other cells, especially in cytokines, chemokines, collagen, Wnt, VEGF, IGF and FGF pathways.

CONCLUSION

Our study provides a comprehensive landscape of selenium metabolism levels and diverse regulatory patterns in different cancers, deepening the understanding of selenium's roles in tumorigenesis and immunity.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.

Tumor immune microenvironment in pituitary neuroendocrine tumors (PitNETs): increased M2 macrophage infiltration and PD-L1 expression in PIT1-lineage subset

PURPOSE

Tumor immune microenvironment in pituitary neuroendocrine tumors (PitNETs) and application of current immunotherapy for refractory PitNETs remains debated. We aim to evaluate the immune landscape in different lineages of PitNETs and determine the potential role of pituitary transcription factors in reshaping the tumor immune microenvironment (TIME), thus promoting the application of current immunotherapy for aggressive and metastatic PitNETs.

METHODS

Immunocyte infiltration and expression patterns of immune checkpoint molecules in different lineages of PitNETs were estimated via in silico analysis and validated using an IHC validation cohort. The correlation between varying immune components with clinicopathological features was assessed in PIT1-lineage PitNETs.

RESULTS

Transcriptome profiles from 210 PitNETs/ 8 normal pituitaries (NPs) and immunohistochemical validations of 77 PitNETs/6 NPs revealed a significant increase in M2-macrophage infiltration in PIT1-lineage PitNETs, compared with the TPIT-lineage, SF1-lineage subsets and NPs. While CD68 + macrophage, CD4 + T cells, and CD8 + T cells were not different among them. Increased M2-macrophage infiltration was associated with tumor volume (p < 0.0001, r = 0.57) in PIT1-lineage PitNETs. Meanwhile, differentially expressed immune checkpoint molecules (PD-L1, PD1, and CTLA-4) were screened and validated in IHC cohorts. The results showed that PD-L1 was highly expressed in PIT1-lineage subsets, and PD-L1 overexpression showed a positive correlation with tumor volume (p = 0.04, r = 0.29) and cavernous sinus invasion (p < 0.0001) in PIT1-lineage PitNETs.

CONCLUSION

PIT1-lineage PitNETs exhibit a distinct immune profile with enrichment of M2 macrophage infiltration and PD-L1 expression, which may contribute to its clinical aggressiveness. Application of current immune checkpoint inhibitors and M2-targeted immunotherapy might be more beneficial to treat aggressive and metastatic PIT-lineage PitNETs.

Nanomaterials in tumor immunotherapy: new strategies and challenges

Tumor immunotherapy exerts its anti-tumor effects by stimulating and enhancing immune responses of the body. It has become another important modality of anti-tumor therapy with significant clinical efficacy and advantages compared to chemotherapy, radiotherapy and targeted therapy. Although various kinds of tumor immunotherapeutic drugs have emerged, the challenges faced in the delivery of these drugs, such as poor tumor permeability and low tumor cell uptake rate, had prevented their widespread application. Recently, nanomaterials had emerged as a means for treatment of different diseases due to their targeting properties, biocompatibility and functionalities. Moreover, nanomaterials possess various characteristics that overcome the defects of traditional tumor immunotherapy, such as large drug loading capacity, precise tumor targeting and easy modification, thus leading to their wide application in tumor immunotherapy. There are two main classes of novel nanoparticles mentioned in this review: organic (polymeric nanomaterials, liposomes and lipid nanoparticles) and inorganic (non-metallic nanomaterials and metallic nanomaterials). Besides, the fabrication method for nanoparticles, Nanoemulsions, was also introduced. In summary, this review article mainly discussed the research progress of tumor immunotherapy based on nanomaterials in the past few years and offers a theoretical basis for exploring novel tumor immunotherapy strategies in the future.

Immunotherapy Plus Radiotherapy for the Treatment of Sarcomas: Is There a Potential for Synergism?

Soft tissue sarcoma (STS) is a highly heterogeneous malignant tumor derived from mesenchymal tissue. Advanced STS has a poor response to the current anti-cancer therapeutic options, with a median overall survival of less than two years. Thus, new and more effective treatment methods for STS are needed. Increasing evidence has shown that immunotherapy and radiotherapy have synergistic therapeutic effects against malignant tumors. In addition, immunoradiotherapy has yielded positive results in clinical trials for various cancers. In this review, we discuss the synergistic mechanism of immunoradiotherapy in cancer treatment and the application of this combined regimen for the treatment of several cancers. In addition, we summarize the existing evidence on the use of immunoradiotherapy for the treatment of STS and the relevant clinical trials that are currently ongoing. Furthermore, we identify challenges in the use of immunoradiotherapy for the treatment of sarcomas and propose methods and precautions for overcoming these challenges. Lastly, we propose clinical research strategies and future research directions to help in the research and treatment of STS.

Immune-related adverse effects of checkpoint immunotherapy and implications for the treatment of patients with cancer and autoimmune diseases

During the past decade, there has been a revolution in cancer therapeutics by the emergence of antibody-based immunotherapies that modulate immune responses against tumors. These therapies have offered treatment options to patients who are no longer responding to classic anti-cancer therapies. By blocking inhibitory signals mediated by surface receptors that are naturally upregulated during activation of antigen-presenting cells (APC) and T cells, predominantly PD-1 and its ligand PD-L1, as well as CTLA-4, such blocking agents have revolutionized cancer treatment. However, breaking these inhibitory signals cannot be selectively targeted to the tumor microenvironment (TME). Since the physiologic role of these inhibitory receptors, known as immune checkpoints (IC) is to maintain peripheral tolerance by preventing the activation of autoreactive immune cells, IC inhibitors (ICI) induce multiple types of immune-related adverse effects (irAEs). These irAEs, together with the natural properties of ICs as gatekeepers of self-tolerance, have precluded the use of ICI in patients with pre-existing autoimmune diseases (ADs). However, currently accumulating data indicates that ICI might be safely administered to such patients. In this review, we discuss mechanisms of well established and newly recognized irAEs and evolving knowledge from the application of ICI therapies in patients with cancer and pre-existing ADs.

Nanomaterials: Breaking through the bottleneck of tumor immunotherapy

Immunotherapy exerts its excellent anti-tumor effects by stimulating and enhancing the immune response of the body, and has become another important class of anti-tumor therapy besides chemotherapy, targeted therapy and radiotherapy. Various types of immunotherapeutic drugs have gained their clinical values, but the in vivo delivery of drugs still faces many challenges, such as poor tumor permeability and low tumor cell uptake rate. In recent years, owing to highly targeting properties, better biocompatibility, and easy functionalization, nanomaterials have been widely applicated in tumor treatment, especially in tumor immunotherapy. Furthermore, nanomaterials have large drug loading capacity, strong tumor targeting and easy modification, which can effectively overcome the drawbacks of traditional immunotherapy. This paper reviews the progress of nanomaterial-based tumor immunotherapy in recent years and provides a theoretical basis for exploring new nanomaterial-based tumor immunotherapy strategies.

Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.

Research into the characteristic molecules significantly affecting liver cancer immunotherapy

Background

The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer.

Methods

Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and analyzed with R software.

Results

The LASSO and SVM-RFE machine learning algorithms identified 16 differentially expressed genes (DEGs) related to immunotherapy, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2. Moreover, a logistic model (CombinedScore) was established based on these DEGs, showing an excellent prediction performance for liver cancer immunotherapy. Patients with a low CombinedScore might respond better to immunotherapy. Gene Set Enrichment Analysis showed that many metabolism pathways were activated in patients with a high CombinedScore, including butanoate metabolism, bile acid metabolism, fatty acid metabolism, glycine serine and threonine metabolism, and propanoate metabolism. Our comprehensive analysis showed that the CombinedScore was negatively correlated with the levels of most tumor-infiltrating immune cells and the activities of key steps of cancer immunity cycles. Continually, the CombinedScore was negatively associated with the expression of most immune checkpoints and immunotherapy response-related pathways. Moreover, patients with a high and a low CombinedScore exhibited diverse genomic features. Furthermore, we found that CDCA7 was significantly correlated with patient survival. Further analysis showed that CDCA7 was positively associated with M0 macrophages and negatively associated with M2 macrophages, suggesting that CDCA7 could influence the progression of liver cancer cells by affecting macrophage polarization. Next, single-cell analysis showed that CDCA7 was mainly expressed in prolif T cells. Immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues.

Conclusions

Our results provide novel insights into the DEGs and factors affecting liver cancer immunotherapy. Meanwhile, CDCA7 was identified as a potential therapeutic target in this patient population.

GDPD5 Related to Lipid Metabolism Is a Potential Prognostic Biomarker in Neuroblastoma

Neuroblastoma (NB) is an extracranial solid tumor in children with poor prognosis in high-risk patients and its pathogenesis and prognostic markers urgently need to be explored. This study aimed to explore potential biomarkers related to NB from the aspect of lipid metabolism. Fifty-eight lipid metabolism-related differentially expressed genes between high-risk NB and non-high-risk NB in the GSE49710 dataset were analyzed using bioinformatics, including 45 down-regulated genes and 13 up-regulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified steroid hormone biosynthesis as an abnormal metabolic pathway in high-risk NB. Survival analysis established a three-gene prognostic model, including ACHE, GDPD5 and PIK3R1. In the test data, the AUCs of the established prognostic models used to predict patient survival at 1, 3 and 5 years were 0.84, 0.90 and 0.91, respectively. Finally, in the SH-SY5Y cell line, it was verified that overexpression of GDPD5 can inhibit cell proliferation and migration, as well as affect the lipid metabolism of SH-SY5Y, but not the sugar metabolism. hsa-miR-592 was predicted to be a potential target miRNA of GDPD5 by bioinformatics. In conclusion, this study develops a lipid-metabolism-related gene-based prognostic model for NB and demonstrates that GDPD5 inhibits SH-SY5Y proliferation and migration and may be targeted by hsa-miR-592 and inhibit SH-SY5Y fat synthesis.